Evaluating Earthquake Impacts in Oslo, Norway: A Multi-Method Approach

Evaluating Earthquake Impacts in Oslo, Norway: A Multi-Method Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating Earthquake Impacts in Oslo, Norway: A Multi-Method Approach Federica Ghione, Sergio Molina-Palacios, Anita Torabi, Volker Oye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5804859/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Aug, 2025 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract Effectively presenting seismic risk results is crucial for ensuring that stakeholders, including policymakers and first-responders can understand and utilize the information for decision-making and risk mitigation. In this study, we conducted a comprehensive seismic risk assessment for Oslo (capital of Norway), integrating both deterministic and probabilistic approaches to capture a holistic view of potential earthquake impacts. Based on the field data from exposed major faults in the Oslo rift margin and historical earthquakes in this region, the deterministic analysis examined three scenarios using SELENA software: i) an Mw 5.4 earthquake that occurred in 1904 in the Oslo rift zone; ii) a hypothetical Mw 6.0 event on the east side of the rift zone, and iii) a hypothetical Mw 6.0 event along an exposed fault zone in the central rift zone. These scenarios were selected to reflect a few possible seismic events with varying likelihoods and severities. For both, deterministic and probabilistic approaches, one specific neighbourhood in the Oslo city region emerged as the most affected area due to its dense population and older building stock, highlighting the critical interplay between physical hazard and community-specific vulnerability. The combination of deterministic and probabilistic approaches offers a detailed and nuanced understanding of seismic risks in Oslo. The findings underscore the need for targeted mitigation efforts and preparedness strategies, particularly in neighbourhoods that are more susceptible to seismic risks. By integrating these comprehensive risk assessments, the study provides valuable insights into the uneven distribution of seismic risk across Oslo. The results aim to inform local authorities and policymakers, aiding in the development of effective strategies to enhance the resilience of the city's infrastructure and population against future seismic events. Seismic risk SELENA deterministic scenario probabilistic approach Oslo (Norway). Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Highlights • Seismic risk assessment combines deterministic and probabilistic methods in Oslo. • Three deterministic scenarios analyze earthquake impacts, focusing on the building stock and population. • Sensitivity analysis shows fault type significantly affect seismic damage outcomes. • Results guide targeted mitigation and policy strategies for enhancing Oslo's resilience. 1. Introduction Seismic risk assessment is a critical component in urban planning and disaster mitigation, especially for cities located in seismically active regions. Although the city of Oslo, Norway, is located in a region with relatively low seismic activity, several earthquakes have occurred in the past (Bungum et al., 2009 ; Molina and Lindholm, 2005 ; Taylor, 2017 ) that underscore the necessity for comprehensive seismic risk evaluations. One study though (Swensson, 1990 ) highlights the potential for significant seismic activity in the Oslo region due to its historical seismicity patterns (Fig. 1 ). Molina and Lindholm ( 2005 ) conducted the first seismic risk assessment for Oslo, laying the groundwork for understanding the city's vulnerability to earthquakes. Their research primarily focused on evaluating seismic risk scenarios with two earthquake sources, accounting for soil amplification using a basic microzonation map and characterising the building behaviour using predefined vulnerability functions from HAZUS (FEMA, 2004 ). The present study expands upon their work by incorporating a more detailed exposure model (in terms of building inventory and population) and using both deterministic and probabilistic approaches, including the most recent vulnerability functions database, leveraging new and updated data to deliver a more comprehensive assessment of seismic risk for Oslo. Deterministic scenarios allow for the evaluation of specific earthquake scenarios, providing insights into potential impacts from significant seismic events. For this analysis, we have chosen three distinct deterministic scenarios based on historical and plausible earthquake events of varying magnitudes, epicentral locations and source mechanisms. These scenarios help in understanding the possible range of seismic impacts on the city’s buildings and population. Complementing the deterministic scenarios, a probabilistic approach was conducted to estimate the likelihood of different levels of seismic shaking over a specified time period, accounting for the inherent uncertainties in earthquake occurrence, size, and location. Therefore, offering a comprehensive risk profile by integrating the probability of different seismic events with their potential impacts. This dual approach not only enhances our understanding of the seismic risks facing Oslo but also serves as a model for other cities in similar tectonic settings and similar building stock. To perform the analysis, we have used SELENA software (SEismic Loss EstimatioN using a logic tree Approach), that is a tool designed for seismic risk assessment and loss estimation in urban environments by integrating seismic hazard with detailed information about buildings and infrastructure (Molina et al., 2010 ) and as previously mentioned, an earlier version of that software was used in the first seismic risk study for Oslo (Molina and Lindholm, 2005 ). The results presented in this paper are crucial for informing urban planning, emergency preparedness, and mitigation strategies for Oslo. In addition, the study provides a solid foundation for future research and policy-making aimed at enhancing the resilience of urban areas against seismic threats. 2. Study area Our study focuses on the city of Oslo, Norway and its municipality covers an area of 480 km 2 divided in 18 urban districts. The associated boroughs are called Alna, Bjerke, Frogner, Gamle Oslo, Grorud, Grünerløkka, Nordmarka, Nordre Aker, Nordstrand, Østmarka, Østensjø, Sagene, Sentrum, Søndre Nordstrand, St. Hanshaugen, Stovner, Ullern, Vestre Aker (Fig. 1 and Table 1 ). This division facilitates the compilation of a comprehensive inventory of the current building stock and the incorporation of demographic data that will be discussed in Section 3. This section presents a geological introduction of the area, with additional information related to its seismicity and the largest historical earthquakes. 2.1 The Oslo Rift Zone The Caledonian Orogeny, the largest tectonic event in Norway, resulted in significant E-W shortening and the development of N-S weakness zones across the entire Norwegian continent and its shelf regions. During this orogeny, which occurred between the Silurian and Devonian periods, the Precambrian basement rocks underwent deformation and later erosion, forming a peneplain (Neumann et al., 1992 ; Ramberg and Larsen, 1978 ). Subsequent to the orogenic collapse of the Caledonian mountain belt, the Oslofjord and adjacent areas were exposed to stretching and rifting of the crust during Carboniferous and Permian time (Nielsen and Nielsen, 2007). This rifting activity led to the development of extensional structures, such as normal faults and their associated horst and grabens. By the late Triassic-Cretaceous, the rifting stopped, leaving behind these extensional features as a record of the earlier tectonic processes. Among these is a 400 km long graben system between Denmark and Norway reaching from the Sorgenfrei-Tornquist Zone to the Skagerrak Graben. The northern part of the Oslo Rift Zone is called Oslo Graben and it is exposed onshore striking N-S. The continuation of the Oslo Graben offshore is called Skagerrak Graben striking NE-SW (Neumann et al., 1992 ; Ro et al., 1990 ). The Oslo Graben is divided into three onshore segments: the Vestfold Graben Segment in the south, the Akershus Graben Segment in the middle and the Rendalen Graben Segment in the north (Ramberg et al., 2008 ). These graben segments have opposite polarities: the main faults are at their east boundary for the Vestfold and Rendalen Graben, and at the west boundary for the Akershus Graben. The Oslofjorden Fault (OF) is the eastern boundary fault of the Vestfold Graben and it is the major fault within the Oslo Rift Zone. The northern part of the fault, known as Nesodden Fault Segment (NFS), has a well exposed deformation zone with more than 40 m thick cataclastic rocks (Swensson, 1990 ). This fault is quite relevant in the area due to its potential to generate seismic activity. 2.2 Seismicity and largest earthquakes Although the seismicity level can be described as low-medium, Norway shows a higher level of seismic hazard compared to the other Northern European countries, with the exception of Iceland (Danciu et al., 2021 ). From the seismotectonic point of view, the Oslo Rift Zone is considered to be relatively active compared to other parts of Scandinavia and for this reason Oslo shows an intermediate seismic hazard level compared to national scale (Bungum et al., 2010 ). The faults delimiting the boundaries of the Oslo Graben are believed to be long-lived structures, already active in the Neoproterozoic. The Oslo Rift Zone overprints basement faults, where accumulated stress over time, surpasses the stress level of the geological structures. Although tectonic activity has long ceased, the region still experiences earthquakes. Presently, there is no geological evidence indicating any recent significant fault displacement within the Oslo Rift Zone. Nevertheless, the Nesodden Fault, like other faults within the Oslo Rift Zone, is capable of generating earthquakes due to ongoing tectonic stresses and the release of accumulated strain along the fault (Swensson, 1990 ). While large earthquakes are rare in the region, there have been historical instances of moderate seismic activity. The last major earthquake in the Oslo region is the Oslofjord event that occurred on the 23rd of October 1904, with a magnitude estimated to be around 5.4 and epicentre located 115 km south of Oslo at about 30 km depth (Fig. 1 ). The vertical rupture plane generated ground motions that propagated along the Oslo fjord, moreover this event was felt all over northern Europe, from Namsos in the North to Poland and Helsinki in the East and all across southern Norway (Bungum et al., 2009 ). However, the 1904 Oslofjord earthquake did not cause any casualties nor major destructions, only some minor damage to a few wooden and unreinforced masonry buildings were reported in the Oslo city area. Understanding the geological and seismotectonic characteristics of the Oslo Rift Zone, including the Nesodden Fault, is hence crucial for assessing seismic hazard and implementing measures to mitigate risks to infrastructure and populations in the Oslo region and surrounding areas. 3. Material and Methods To perform a comprehensive seismic risk assessment, a wide range of information is required to accurately characterize the risk and its potential impacts. In the following subchapters, we describe the main components related to hazard, exposure and vulnerability that were investigated and developed for this study. 3.1 Seismic hazard in Oslo The hazard component in seismic risk assessment refers to the characterization and quantification of the likelihood and severity of seismic events that can affect a given area in a certain period of time. It encompasses the analysis of earthquake occurrence and the intensity of ground shaking that can be expected over a specified time period. Understanding the hazard component is crucial as it is the key component (combined together with the vulnerability of the exposed buildings, populations and infrastructures) to provide seismic risk assessments. The input data for the seismicity used in this study stems from a Probabilistic Seismic Haza r d Analysis (PSHA) conducted for Norway and Svalbard (Lindholm et al., 2024 ). The earthquake catalogue includes data for Norway from 1497 until 2018. The hazard model uses two different and equally weighted models, both based on area sources: 1) the zones are defined by the seismicity patterns and 2) the zones are defined through geologically mapped structural features (see Figure SM1 in the Online Resource). Fault source modelling is not included due to the lack of knowledge for fault parameters. For the hazard calculations, a maximum magnitude (Mmax) of 6 ± 0.3 was considered for all zones, based on historical earthquakes in the region. Due to the relatively low seismicity in the region, the b-value of the Gutenberg-Richter relationship was derived from a larger area and applied uniformly to all zones, to achieve a statistically representative b-value (equal to 0.927, with 0.10 coefficient of variation). The a-value, however, was calculated for each individual zone to account for differences in seismicity rates (Table SM1 in the Online Resource). To assess the seismic hazard, it is essential to select suitable Ground Motion Prediction Equations (GMPEs) that are both robust and representative of the region’s characteristic, especially considering the lack of sufficient local strong-motion records in the investigated area. While three of the chosen GMPEs (Akkar et al., 2014 ; Bindi et al., 2017 ; Cauzzi et al., 2015 ) are primarily calibrated for active tectonic regions, the fourth one (Yenier and Atkinson, 2015 ) is specifically suited for stable continental regions, aligning more closely with the seismotectonic setting of Oslo. The four GMPEs were selected based on their suitability across various parameters, including magnitude and distance ranges, distance metrics and site conditions (specifically Vs30 values). Akkar et al. ( 2014 ) covers a magnitude range of 4.0–7.6 and Vs30 of 150–1200 m/s. Bindi et al. ( 2017 ) spans magnitudes 3.0–7.9 and Vs30 of 360–1500 m/s. Cauzzi et al. ( 2015 ) is suitable for magnitudes 4.5–7.9 and Vs30 of 150–1200 m/s, and Yenier and Atkinson ( 2015 ) applies to magnitudes 3.8–8.0 and Vs30 of 150–1500 m/s. In both seismic hazard and seismic risk computations, a logic tree computation scheme is adopted to handle epistemic uncertainty, with equal weights assigned to all these GMPEs, ensuring a balanced consideration of each model's contributions to the calculation. A key output of the PSHA is the seismic hazard map, which illustrates the expected levels of ground shaking for a specific likelihood of exceedance. Figure 2 presents the Peak Ground Acceleration (PGA) hazard map for Oslo for a 475-year return period, corresponding to a 10% probability of exceedance in 50 years. The results are computed at bedrock condition (using Vs30 equal to 1200 m/s) and the PGA values in Oslo range from 0.0240 g to 0.0248 g (in the southeastern part of the study area), indicating moderate hazard levels compared to other regions in Norway. 3.1.1 Geological field data The seismic risk scenarios are based on specific events that can occur with a certain magnitude and distance to the area of interest. Earthquakes predominantly occur on pre-existing faults. In our study, we have tested different earthquake sources based on historical events and geological evidence. We conducted geological fieldwork in the area of Alværn brygge (Nesodden) to focus on the Nesodden Fault Segment (northern part of the Oslofjord Fault), with focus on three areas (see Fig. 3 ): In area a, we observed the fault core of the main segment of the Nesodden Fault striking NE-SW. In area b, we identified two sets of faults striking NE-SW with similar dips towards E and W. Area c shows fault plane measurements striking N-S to NE-SW and dipping towards E. The data collected during the fieldwork were used as input parameters for the deterministic scenarios in this study. 3.1.2 Soil characterization Bellalem et al. ( 2024 ), Dobry et al. ( 2000 ) and Holzer et al. ( 2005 ) emphasize that the shear wave velocity within the uppermost 30 m (Vs30) is a key parameter to effectively predict local site amplification. To characterize the local site conditions of each geounit, we have used a combined method using existing well databases, topographic slope derived from Digital Elevation Models and near-surface Quaternary geological maps. More information on the methodology adopted is discussed by Ghione et al. ( 2023 ). In this study, average Vs30 values as well as minimum and maximum values are extracted for each geounit to capture variability and investigate their impact on seismic risk results (see Table 1 ). The dataset includes average Vs30 values ranging from 446 m/s to 1140 m/s, with standard deviations varying between 122 m/s and 330 m/s. The minimum and maximum Vs30 values reflect the range of variability within the study area, spanning from 100 m/s (which corresponds to areas known or suspected to have quick clay) to 1200 m/s (representing bedrock conditions), depending on the geounit. This comprehensive approach provides a robust basis for assessing the impact of soil amplification on risk calculations, particularly in urban districts with significant geological heterogeneity. Table 1 List of the 18 geounits with their corresponding names, coordinates of the centroids of each polygon, average Vs30 values with ± standard deviation, minimum and maximum Vs30 values for each geounit, depth to bedrock, number of buildings and population. Geounit Name Longitude Latitude Average Vs30 ± Std Deviation [m/s] Minimum-Maximum Vs30 [m/s] Depth to Bedrock [m] No. buildings Population 1 Alna 10.87 59.93 706 ± 330 100–1200 19.28 7702 49373 2 Bjerke 10.82 59.94 683 ± 245 100–1200 14.97 5709 35117 3 Frogner 10.69 59.91 545 ± 201 140–760 12.41 6010 59026 4 Gamle Oslo 10.74 59.90 521 ± 278 100–1200 22.23 4630 60209 5 Grorud 10.88 59.96 897 ± 294 100–1200 10.83 4534 27457 6 Grünerløkka 10.78 59.93 603 ± 128 180–760 17.1 3874 63891 7 Nordmarka 10.69 60.04 1086 ± 257 180–1200 4.85 3077 4272 8 Nordre Aker 10.76 59.95 740 ± 267 100–1200 15.12 15895 53109 9 Nordstrand 10.78 59.87 726 ± 316 140–1200 17.5 18994 52595 10 Østmarka 10.89 59.87 1140 ± 188 180–1200 5.38 275 475 11 Østensjø 10.83 59.89 758 ± 286 100–1200 13.28 11285 50837 12 Sagene 10.76 59.94 583 ± 157 100–760 15.34 2271 46424 13 Sentrum 10.74 59.91 446 ± 196 140–760 22.03 809 0 14 Søndre Nordstrand 10.82 59.83 978 ± 289 140–1200 12.56 11368 39037 15 St Hanshaugen 10.74 59.93 619 ± 122 180–760 13.48 2769 39066 16 Stovner 10.92 59.96 704 ± 216 100–1200 11.96 7422 33259 17 Ullern 10.66 59.93 631 ± 146 100–760 13.69 9833 34896 18 Vestre Aker 10.67 59.95 725 ± 218 100–1200 10.1 17975 50784 3.2 Exposure model An exposure model, in the context of seismic risk assessment, refers to a quantitative representation of the elements at risk within a given geographical area that could be affected by an earthquake. It provides a detailed description of the distribution, characteristics, and attributes of these elements, which can include buildings, infrastructure, population, land use, and economic assets. The primary purpose of an exposure model is to provide a comprehensive understanding of what is potentially vulnerable to seismic hazards. By analysing the characteristics and spatial distribution of the elements at risk, potential losses and damages can be estimated in the event of an earthquake. This information is crucial for emergency planning, disaster response coordination, and the development of effective mitigation strategies. Exposure models typically incorporate various datasets, such as building inventories, land use maps, economic information and population data. These datasets are often integrated using Geographic Information Systems (GIS) technology to create a spatially explicit representation of the elements at risk. 3.2.1 Building inventory For the Oslo case study, a comprehensive building inventory was created using different sources for the different components. Building typologies were defined following a few steps: a first overview of the typologies was obtained from Google Street View, detailed fieldwork in specific areas of the city, survey questionnaire regarding seismic vulnerability assessment was sent to experts (mainly engineers working in Norway) and finally the definition of the five main Model Building Typologies (MBT) for Oslo: Timber (T), Reinforced Concrete (CR), Unreinforced Masonry (MUR), Steel (S) and Steel-Reinforced Concrete (SRC). The combination of Google Street View images and machine learning allowed us to automatically recognise the typologies for most of the city. More detailed information is provided by Ghione et al. ( 2022 ). Number of storeys and total floor area are provided by Karteverket ( https://www.kartverket.no/ , the Norwegian mapping authority, responsible for mapping, property registration and geographical data). Year of construction and occupancy are provided by Statistics Norway ( https://www.ssb.no/ , the national statistical institute of Norway, responsible for collecting and producing official statistics). In some cases (about 45% of the total number of buildings for the MBT, 16% for the number of storeys, 2.5% for the occupancy and 1.7% for the year of construction), not all information was available due to incompleteness in the different databases. We then compiled the database as follows: in case the MBT is missing, we have defined the typology based on a combination of occupancy and year of construction (when available): garage with T; public toilet with CR; industrial/warehouse with S; church, prison, museum and school with T for buildings built before 1985, MUR between 1985 and 1995, CR after 1995 (and SRC after 2010 only for museum and school). For all other occupancy, for buildings built before 2010 we have attributed T typology, after 2010 SRC. When the number of storeys is missing, we have attributed the corresponding number to the different occupancy: based on a combination of occupancy and year of construction (when available): garage, public toilet, sports facility, entertainment building with 1 storey; industrial/warehouse, police, restaurant, transport with 2; school, prison and museum with 3; office with 4; dwelling, university and hospital with 5; hotel and business building with 8. In the case neither occupancy or MBT is available, we have checked the location of those buildings that are mainly located around the forest and the islands. For this reason, we have attributed the T typology, and when the number storeys are missing, we have attributed 2 storeys. At the end, when we have information on the different MBT but missing occupancy and number storeys, we have decided to use Google Earth to recognise the occupancy. In most of cases, the buildings are garage or small structures, hence we have decided to attribute 1 as number of storeys. All the elements were combined and were integrated in a single file and finally used for advanced risk analysis. The most represented MBT in Oslo is Timber which constitutes 82% of all typologies (see Fig. 3 ). The residential occupancy is the most significant in terms of building counts, due to the large number of apartment buildings and single-family dwellings, which constitute around 59% of all buildings. The economic information related to new construction cost (in terms of Norwegian Crown per meter square) are taken from estimates made by Statsbygg ( https://www.statsbygg.no/,the Norwegian government agency responsible for public building management and construction) for late 2022/beginning 2023. An informative table of the values used in our study is provided in Table 2 . Table 2 Construction cost values, expressed in terms of NOK/m 2 , for the different building materials. In the third column, the values used in our study are presented. Building Material Construction Cost [NOK/m²] Used value [NOK/m 2 ] Timber (T) 18,000–25,000 21,500 Reinforced Concrete (CR) 22,000–28,000 25,000 Unreinforced Masonry (MUR) 14,000–19,000 16,500 Steel (S) 24,000–29,000 26,500 Steel-Reinforced Concrete (SRC) 23,000–29,000 26,000 3.2.2 Population In addition to structural information, the population distribution through the different neighbourhoods is taken from the Oslo municipality webpage ( https://bydelsfakta.oslo.kommune.no/bydel/alle/befolkningsutvikling/ ). The total number of 699.827 inhabitants were officially registered as Oslo residents at the beginning of 2023. 3.3 Vulnerability model In seismic risk, vulnerability refers to the degree of damage or loss that a structure or system is likely to suffer during an earthquake. It is a measure of how susceptible a building or infrastructure is to damage from ground shaking, fault rupture, soil liquefaction, and other seismic hazards. Vulnerability is affected by a range of factors, including the design and construction of the structure, its age and condition, the soil and geologic conditions at the site, and the intensity and duration of the earthquake. By identifying the structures that are most vulnerable to seismic hazards, engineers and policymakers can prioritize investments in retrofitting and other mitigation measures to reduce the risk of damage and loss during earthquakes. Capacity and fragility curves are two important tools used to assess the vulnerability of structures to earthquake damage when the spectral displacement method is chosen to obtain the damage probability. They are often used together to assess the seismic performance of structures by computing the performance point in terms of spectral displacement which is later used to interrogate the fragility curve providing the corresponding damage probability, and to inform decisions about risk mitigation and management strategies. Other approaches compute the damage probability using the ground motion in the region in terms of spectral acceleration Sa (spectral acceleration method). As we can see from Figure SM2, for the same earthquake using spectral displacement fragility functions or spectral acceleration fragility functions may lead to different damage probabilities values so it is important to characterize this uncertainty by modelling the damage using both methodologies as we will see later. In our study, capacity and fragility curves are taken from Martins and Silva ( 2020 ) database for the different building typologies and building heights (Table 3 ). In order to perform the correct selection of vulnerability functions, it is important to define (in addition to the building material) the building ductility and frame structure. Ductility denotes an ability of a building’s structure to undergo significant deformations before the failure occurs in structural members or their connections. It is one of the most important factors affecting building performance in an earthquake. In general, a building can be classified as ductile or non-ductile (DNO), depending on its expected seismic performance before an earthquake. In the context of structural vulnerability functions, three ductility levels are critical for understanding how different buildings will respond to various hazards, influencing design choices and vulnerability assessments: DUL (low ductility): the building or material has a low capacity to undergo deformation without failing. It is commonly used in low seismic hazard regions and in older buildings, often constructed before modern seismic design principles were fully understood and widely adopted (pre-1970s). DUM (moderate ductility): this represents a moderate capacity to deform before failure. It is suitable for moderate seismic hazard areas, and it is commonly used in buildings constructed between the late 1970s and early 1990s. It is also applicable to more recent buildings in low seismic hazard regions. DUH (high ductility): it indicates a high capacity for deformation, and it is required for high seismic hazard zones and widely used for modern buildings constructed after the late 1990s. Regarding the frame structure, in our selection we have used moment frame (LFM - a frame consisting of beams and columns, with strong and rigid beam-to-column connections), wall (LWAL - a vertical planar building element which usually resists gravity loads, horizontal forces and provides stability) and dual frame-wall system (LDUAL - here the lateral load-resisting structure consists of moment frames and shear walls acting together in the same direction). More information can be found in the glossary for GEM taxonomy ( https://taxonomy.openquake.org/ ). Table 3 List of the vulnerability functions taken from Martins and Silva ( 2020 ) used in this analysis. The functions are selected based on the five building typologies and the different building heights (low, mid or high rise). *IM: Intensity measure used in the spectral acceleration (Sa) fragility curves. Building typology Heights Vulnerability function used from Martins and Silva ( 2020 ) IM* Timber low W_LFM-DUL_H1 Sa(0.3 s) mid W_LFM-DUL_H4 Sa(0.6 s) high W_LFM-DUL_H6 Sa(0.6 s) Reinforced Concrete low CR_LFM-DUL_H2 Sa(0.6 s) mid CR_LFM-DUL_H5 Sa(1.0 s) high CR_LFM-DUL_H10 Sa(1.0 s) Unreinforced Masonry low MUR_LWAL-DNO_H1 Sa(0.3 s) mid MUR_LWAL-DNO_H3 Sa(0.3 s) high MUR_LWAL-DNO_H5 Sa(0.6 s) Steel low S_LFM-DUL_H2 Sa(0.3 s) mid S_LFM-DUL_H5 Sa(0.6 s) high S_LFM-DUL_H10 Sa(1.0 s) Steel-Reinforced Concrete low SRC_LDUAL-DUM_H2 Sa(0.3 s) mid SRC_LDUAL-DUM_H5 Sa(0.6 s) high SRC_LDUAL-DUM_H10 Sa(1.0 s) 3.4 Damage and Loss computation methods Two approaches have been adopted to calculate the damage probability of a given building typology due to spectral demand: the spectral displacement (Sd) and the spectral acceleration (Sa) methods. The Sd method utilizes the N2 method proposed by Fajfar ( 2000 ). Using the inelastic response spectrum and the capacity curve, this method determines the performance point in terms of displacement, which can later be used to interrogate the spectral displacement fragility curves, providing the damage probability for each damage state. On the other hand, the Sa method directly combines the intensity measure (IM) from the spectral demand to interrogate the IM-fragility function and derive the damage probability. For example, for the Timber typology with low height, the IM is chosen as the spectral acceleration at 0.3s (Sa0.3s as shown in Table 3 ) and the corresponding Sa0.3s fragility function is used to compute the damage probability. To quantify the damage to buildings caused by earthquakes is inherently complex due to the varying levels of detail in damage estimates across different scenarios and building types. The Mean Damage Ratio (MDR) is an effective metric for addressing this issue, as it allows for standardized comparisons of risk estimates across different geographic areas within a city. MDR is calculated as the cost associated with each damage state, expressed as a proportion of the cost for a new construction (Bellalem et al., 2024 ; Lang et al., 2008 ). According to Molina et al. ( 2010 ), the MDR for a city is calculated as: $$\:MDR=\frac{\sum\:_{i=1}^{geounit}\sum\:_{j=1}^{mbt}{{\sum\:}_{k}{DR}_{k}^{j}N}_{ki}^{j}}{{N}_{T}}$$ \(\:{DR}_{k}^{j}\) is the economic damage ratio of model building type j corresponding to damage state k, where k = S for slight, M for moderate, E for extensive and C for complete damage, that is the price of repair for a given damage state divided by the total price of the model building type. \(\:{N}_{ki}^{j}\) is the damaged built area corresponding to damage state k (S, M, E, C) the model building type j in geounit i. \(\:{N}_{T}\) is the total built area of all building types j and all geounits i. This ratio is particularly useful because it remains consistent and is not influenced by factors such as inflation, exchange rates, or other variables that affect repair and reconstruction costs (Molina et al., 2010 ). Despite its benefits, MDRs do not replace the need for detailed damage estimates, which are crucial for assessing social impacts like casualties. Severe damage states, especially building collapses, are the primary contributors to casualties and fatalities, highlighting the importance of detailed damage assessments. The here selected and presented scenarios are considered the most relevant for the area under investigation. We have computed different scenarios using both spectral displacement Sd (using capacity curves and spectral displacement fragility curves) and spectral acceleration Sa (adopting Sa fragility curves) methods. Both methods can be used to compute deterministic seismic risk scenarios, where the impact of specific events in cities is in focus or probabilistic seismic risk scenarios where the investigation aims more at the annual rate of exceedance of a given risk metric, for example the Mean Damage Ratio. 4. Results For computing the results, it is essential to select suitable Ground Motion Prediction Equations (GMPEs). As previously described in Section 3.1 , a logic tree computation scheme is adopted to handle epistemic uncertainty, with equal weights (0.25) assigned to all four selected GMPEs (Akkar et al., 2014 ; Bindi et al., 2017 ; Cauzzi et al., 2015 ; Yenier and Atkinson, 2015 ), ensuring a balanced consideration of each model's contributions to the calculation. 4.1 Deterministic scenarios The disaggregation results from the PSHA at different return periods have allowed us to select relevant deterministic earthquakes. Therefore, the selected earthquake scenarios (the 1904 event, the Østmarka event, and the Nesodden event, see Table 4 ) represent a diverse range of seismic activities in the region. The return period for a magnitude 6 event is approximately 120 years for a region covering southern Norway within a 300 km radius centered on Oslo, underscoring the importance of considering these scenarios. The 1904 event, with a moderate magnitude (Mw 5.4) and a deeper focal depth (29 km), serves as a historical benchmark for understanding the potential impact of deeper earthquakes. The Østmarka event (Mw 6.0) represents a significant reverse faulting scenario at shallow depth (5 km), which is crucial for assessing the risk of surface-level seismic hazards in highly populated areas. The Nesodden event (Mw 6.0) is chosen to evaluate the risk posed by normal faulting mechanisms, with similar depth but a different fault orientation. Together, these scenarios provide a comprehensive overview of varying earthquake mechanisms and depths, essential for a thorough seismic risk assessment. Besides, these events were assessed using the average Vs30 values, but also by incorporating the minimum and maximum Vs30 values for each geounit to capture the variability of local soil conditions. The results are provided in terms of ground motion distributions, Mean Damage Ratio (MDR), and estimates of human and economic losses. Table 4 Input parameters for the three earthquake scenarios (1904, Østmarka, and Nesodden), with information on geographic coordinates, depth in km, magnitude, orientation (strike) and angle (dip) of the fault plane, and fault type (“all” means a reverse or normal fault with strike-slip component). ID Name Latitude Longitude Depth [km] Mw Strike [°] Dip [°] Fault type 1 1904 event 58.87 10.77 29 5.4 210 88 all 2 Østmarka event 59.90 10.90 5 6.0 180 45 reverse 3 Nesodden event 59.82 10.62 5 6.0 215 47 normal 4.1.1 Sensitivity analysis A sensitivity analysis is performed to assess the influence of the different fault type, strike and dip angles using Østmarka scenario at 5 km depth with the Sd method. Three values of dip angle (5, 45 and 85 degrees) and of strike direction (135, 180 and 225 degrees) are used (see Figure SM3). In the sensitivity analysis conducted using the Østmarka scenario with the Sd method, the impact of varying fault type, strike and dip angles on the model outcomes was evaluated (see Figure SM3). The results indicate that variations in strike and dip angles had minimal impact on the outcomes. In contrast, fault type and depth demonstrated a significant influence, particularly affecting the results. Specifically, reverse fault mechanisms consistently resulted in higher MDR compared to normal fault mechanisms. This is because reverse faults involve compression, which tends to produce larger ground movements and structural damage compared to the extensional forces associated with normal faults. As a result, the reverse fault scenarios led to more severe impacts and losses. Additionally, a comparison was made using the same Østmarka scenario as in the study by Molina and Lindholm ( 2005 ), which modeled a magnitude 6.0 event at 20 km depth. In their study, it was found that 45% of all buildings sustained slight damage, while 1.8% suffered complete damage. In contrast, our Østmarka scenario (using the Sd method) for the same magnitude 6.0 event at 20 km depth yielded significantly lower damage estimates, with only 5% of buildings experiencing slight damage and 0.02% experiencing complete damage. However, when the event depth was reduced to 5 km in our scenario, the results were more similar to those reported by Molina and Lindholm ( 2005 ), with 32% of buildings experiencing slight damage and 1.2% complete damage. This discrepancy could be attributed to several factors: first, the 2005 study used only one GMPE, Ambraseys et al. ( 1996 ), which might have led to less accurate estimations of ground motion, particularly regarding depth-dependent effects. The current study uses updated GMPEs (see Section 3.1 ) which may represent better the ground motion scenarios and its uncertainties. Second, their study did not involve a proper recognition of building typologies, instead relying on a broad estimate, which could have affected the accuracy of the damage predictions. Additionally, a different selection of vulnerability functions was used sinceMolina and Lindholm ( 2005 ) used vulnerability functions developed for the USA in HAZUS (FEMA, 2004 ), while our study has improved the building behavior characterization by usingMartins and Silva ( 2020 ) vulnerability functions, which would influence the damage estimates. In our study, we have chosen to present the scenario at 5 km depth rather than 20 km. Although the deeper scenario aligns more realistically with the reverse faulting mechanism of the region, the shallower depth provides a more conservative estimate of potential impacts, aligning better with observed modelled damage patterns and highlighting the importance of depth in damage assessment. This approach ensures that the presented scenario reflects towards a worst-case condition that is critical for urban planning and risk mitigation strategies. 4.1.2 Deterministic ShakeMaps The spatial distribution of the ground motion is evaluated for the three scenarios using four GMPEs, previously described in Section 3.1 . The analyses are conducted for all geounits, considering average, minimum and maximum Vs30 values to account for site amplification effects across the study area (see Table SM2 in the Online Resource). Please note that the ground motion results are the same for both Sd and Sa methods. In Fig. 5 , the spatial distribution of Peak Ground Acceleration (PGA) for the deterministic scenarios (1904, Østmarka and Nesodden events) using the average Vs30 shows significant variation across the Oslo region depending on the ground motion model and the location of the seismic event. The high PGA values can be attributed to a combination of two primary factors: low average Vs30 values and proximity to the event. For example, areas located closer to the Østmarka and Nesodden events experience higher ground motion intensities due to their reduced epicentral distances. Districts in the southeast of Oslo show the highest values in the Østmarka scenario, while areas in the southwest of are most affected in the Nesodden scenario. The comparison across the four GMPEs further highlights the variability in PGA predictions, underscoring the influence of model-specific attenuation relationships and local soil conditions. 4.1.3 Mean Damage Ratio (MDR) Figure 6 provides an overview of the Mean Damage Ratio (MDR) across 18 geounits for the three deterministic scenarios (1904 labeled as 1, Østmarka as 2, and Nesodden as 3) using both spectral acceleration (on the left) and spectral displacement (on the right) methods. The MDR is presented for three Vs30 cases: average Vs30 (blue line), minimum Vs30 (orange), and maximum Vs30 (grey). Scenario 1 exhibits almost negligible MDR values for both the Sa and Sd methods, indicating minimal impact for this scenario. Higher MDR values are observed when using the minimum Vs30 because lower Vs30 values represent softer soils, which amplify ground motion and result in greater structural damage. In contrast, MDR values are lowest for maximum Vs30, as higher Vs30 corresponds to stiffer soils, leading to reduced amplification and subsequently lower damage ratios. Scenario 2 and Scenario 3 demonstrate noticeable damage ratios, particularly for the Sd method. Here, MDR values reach up to 30–35% in some districts when considering the lowest Vs30 values, highlighting the influence of local site effects on damage estimates. The results reveal varying levels of damage across geounits depending on the scenario and the method used. When using the Sd method and the average Vs30, the Nesodden event tends to produce higher MDR percentages in certain geounits (e.g., geounits 3, 13, and 15), indicating significant damage potential. The Østmarka event exhibits a consistent pattern of moderate damage across most geounits, with peaks in geounits 10, 11 and 13, highlighting its potential to cause widespread but moderate damage. The variation in damage ratios between the Sa and Sd methods underscores the importance of considering both approaches in seismic risk assessments, as they capture different aspects of structural response to ground shaking. A key difference between these methods lies in how damage is computed: the Sd method requires determining a performance point using the ground motion, which is then used to obtain damage from a fragility curve. This capacity curve is typically derived through numerical modeling, which can introduce higher uncertainty, particularly for buildings that are not made of reinforced concrete or steel. In contrast, the Sa method directly uses the ground motion to estimate damage from the fragility curve. This distinction often leads to the greatest discrepancies in results between the two methods in geounits with a significant difference in the number of Reinforced and Unreinforced buildings. Generally, Sa shows higher Mean Damage Ratio results when there is a prevalence of Reinforced Concrete buildings, whereas Sd tends to show higher MDR results in areas with a higher concentration of Unreinforced Masonry buildings. This analysis helps to identify the geounits most vulnerable to different types of seismic activity, providing valuable insights for targeted risk mitigation strategies. If we only focus on the Sd method using the average Vs30 values, the most affected geounit with the highest MDR are geounit 13 (Sentrum) for both 1904 and Østmarka events (with 0.018% and 8.5% MDR) and geounit 3 (Frogner) for the Nesodden scenario with 9.6% MDR. In geounit 3 the most common building typology is Timber represented by 54% of the total buildings in the neighborhood, and for geounit 13 is Unreinforced Masonry (MUR) with 47%. 4.1.4 Economic and human losses Figure 7 a provides an overview of the economic losses, expressed in Norwegian Krone (NOK), Mean Damage Ratio (MDR) for the three scenarios using both spectral acceleration and spectral displacement methods, and three Vs30 cases (average, minimum and maximum Vs30). Scenario 1, similar as shown in Fig. 6 , shows almost negligible economic losses for both the Sa and Sd methods, indicating minimal impact for this scenario. Higher MDR values are observed when using the minimum Vs30 because lower Vs30 values represent softer soils, which amplify ground motion and result in greater structural damage. As seen in Fig. 6 , the losses are lowest for maximum Vs30, and highest for minimum Vs30. Scenario 2 and Scenario 3 show noticeable losses, particularly for the Sd method. The losses can reach up to 210 billion NOK in the case of minimum Vs30 for the Østmarka scenario. Figure 7 b presents result for human losses associated with the three scenarios, using both methods and average Vs30 values. The human losses are categorized into low, mid, and high injury levels, as well as fatalities, during nighttime (2 am) where we assume that 90% of the total population stay indoors. Considering the average Vs30 case, the Østmarka scenario stands out with the highest economic losses, especially when analyzed using the Sd method, with an estimated loss of approximately 44.7 billion NOK. This scenario also exhibits the highest cumulative human losses (2058), where of 94 are causalities. The Nesodden scenario, while less severe than Østmarka, still results in substantial economic losses, particularly when using the Sd method (approximately 29.2 billion NOK), and notable human losses (1402), including 68 fatalities. In contrast, the 1904 event results in minimal human losses, with only low injury reported when analyzed using the Sd method and no fatalities. The economic impact is also significantly lower compared to the other scenarios, with the highest loss being around 40.1 million NOK under the Sd method. This analysis highlights the critical differences in impact between the scenarios, with the Østmarka and Nesodden events representing the greatest risk in terms of both economic damage and human casualties, particularly during night. The comparison between the Sa and Sd methods further emphasizes the importance of considering different analytical approaches to fully understand the potential consequences of seismic events. Additionally, the choice of Vs30 values (whether average, minimum, or maximum) has a significant influence on the results, underlining the need for careful selection and sensitivity analysis when evaluating seismic risk. 4.2 Probabilistic approach Scozzese et al. ( 2020 ) proposed two different alternatives to compute probabilistic seismic risk scenarios. The first one, named unconditional method, uses direct information to compute the seismic risk, for example using the Monte Carlo approach (Musson, 2000 ). The added value of this method is its simplicity from a conceptual viewpoint and easy to implement in computational algorithms such as EqHaz (Assatourians and Atkinson, 2013 ). This method is considered the most robust approach; however, it requires high computational power and is therefore less widely used in practice (Rudman et al., 2024 ). The second one, named conditional approach, requires the definition of a parameter to describe the ground motion intensity at the site of interest (IM). Here the seismic hazard is used to describe the exceedance frequency of different IMs during a given period. Then, structural analysis is used to compute the conditional probability of exceeding a given engineering demand parameter (EDP) at certain IM values (Scozzese et al., 2020 ; Vamvatsikos and Allin Cornell, 2002 ). Finally, convolving both results the seismic risk is estimated (Rudman et al., 2024 ). Here we use the unconditional method due to the mentioned conceptual simplicity and since it has been implemented in the last version of SELENA (Molina et al., 2010 ). To avoid a long time of computation, the comparison with the deterministic approach is done only for two districts. Following Assatourians and Atkinson ( 2013 ), a synthetic earthquake catalogue for the study area has been computed composed of 100 subcatalogues of 25,000 years each. This will ensure the stability and reliability of the mean risk curves and the fractiles up to 1000 years return period. Then, for each earthquake of the obtained synthetic catalogue, SELENA is computing the rate of exceedance of each Mean Damage Ratio, which is then used to calculate probabilities, assuming a Poisson process (mean curve and fractiles). A detailed explanation of the mathematical process is explained by Assatourians and Atkinson ( 2013 ). 4.2.1 Probabilistic seismic risk curve in terms of MDR Figure 8 presents the seismic risk curve (mean and corresponding fractiles) for two districts (3 and 13), using the average Vs30 values. We have used Yenier and Atkinson ( 2015 ) since it is the one which produces the highest values (so we present the most conservative results). Even though, we can see that for a return period of 475 years all the MDR values are very low. Geounit 3 (Frogner) has 0.007% and 0.12% for spectral acceleration and spectral displacement methods, respectively, while district 13 has 0.005% and 0.088%. The 97.5 percentile shows the highest MDR which can reach approximately 2% for geounit 3 when the spectral displacement approach is used pointing out the large uncertainties associated to these computations. In any case, these results allow us to point out that from a probabilistic viewpoint, Oslo is not a city with high seismic risk (in terms of annual exceedance probabilities of a given MDR) but the possible occurrence of large earthquake with a very high return period, as demonstrated by the deterministic scenarios, may cause an important impact in the city. 5. Discussion and conclusions The analysis presented in this study provides a comprehensive evaluation of seismic risk in Oslo through both deterministic and probabilistic approaches, highlighting the variability in damage estimates based on ground motion characteristics and building typologies. The results emphasize the importance of utilizing both spectral acceleration (Sa) and spectral displacement (Sd) methods, as they capture different aspects of structural response to ground shaking, providing valuable insights for targeted risk mitigation strategies in the region. The deterministic scenarios, including the 1904, Østmarka, and Nesodden events, provide valuable insights into the variability of potential earthquake impacts based on differences in earthquake magnitude, depth, and focal mechanisms. Considering the average Vs30 values, the Østmarka and Nesodden events, representing reverse and normal fault mechanisms respectively, exhibit the greatest risk in terms of economic damage and human casualties. The Østmarka scenario demonstrates the highest potential economic losses (approximately 44.7 billion NOK, corresponding to roughly 4 billion € based on an exchange rate of 1 € = 11 NOK) and human casualties, particularly during nighttime, when the population is most vulnerable. The severity of damage is influenced by the reverse fault mechanism, which generates larger ground movements and greater structural damage compared to normal faults. It is important to note that the analysis does not include damage estimates for Nesodden, despite its proximity to Oslo and significant population. This exclusion was intentional, as the study focused solely on losses and impacts within Oslo municipality. In contrast, the 1904 scenario, characterized by a deeper earthquake with lower magnitude, exhibits very low Mean Damage Ratios (MDRs), with the highest value being only 0.018% for geounit 13 (Sentrum). Historically, very limited damage was reported in this area, indicating the lower impact of such events compared to shallower, more intense scenarios. The study also emphasizes the importance of employing both spectral acceleration (Sa) and spectral displacement (Sd) methods in seismic risk assessments to characterize the uncertainty when modelling the performance of a building subjected to a given ground motion. For instance, the Østmarka and Nesodden scenarios produced higher damage ratios and economic losses using the Sd method, which highlights the impact of different analytical approaches on risk estimation. This underscores the need for a comprehensive evaluation of seismic risk using multiple methods to capture the full spectrum of potential earthquake impacts. The probabilistic scenarios, using the unconditional method with Monte Carlo simulations, further contribute to understanding seismic risk by incorporating long-term variability and uncertainty. The results suggest that Oslo is not a city with high seismic risk when considering annual exceedance probabilities of damage ratios; however, the potential occurrence of rare but large earthquakes (the return period of a magnitude 6 event in the southern part of Norway is around 120 years), as demonstrated in the deterministic scenarios, could result in significant impacts. This dual perspective from deterministic and probabilistic analyses provides a nuanced view of seismic risk, informing more effective risk mitigation and preparedness strategies. The analysis reveals that specific geounits, such as Sentrum (geounit 13) and Frogner (geounit 3), are particularly vulnerable to seismic events, with high Mean Damage Ratios (MDR) observed in both deterministic and probabilistic approaches (Fig. 9 ) using the average Vs30. Table 5 Economic losses in Norwegian Krone (NOK) and affected population for the three deterministic scenarios and the 475 years probabilistic scenario. The results are calculated using the average Vs30 values. The exchange rate of 1 EURO = 11 NOK is provided for reference. ID Scenarios Economic losses in NOK Affected Population 1 1904 0.040 Billion 1 2 Østmarka 44.7 Billion 2058 3 Nesodden 29.2 Billion 1402 475 yr Return Period 0.490 Billion 403 The analysis revealed that economic losses and affected populations vary significantly across the deterministic and probabilistic scenarios, as summarized in Table 5 . These calculations were performed using average Vs30 values. The study identifies that the losses are a result of a combination of ground motion characteristics and building typology distributions. Geounits with high MDRs often have a high concentration of Unreinforced Masonry and Timber building types. The study indicates that while strike, and dip angles show minimal influence on damage outcomes, fault type plays a significant role, as demonstrated in the sensitivity analysis of the Østmarka scenario. Reverse fault mechanisms consistently result in higher MDR compared to normal faults, due to the compressional forces that generate larger ground movements and structural damage. However, it is important to note that the minimal influence of strike and dip angles could be related to the assumptions used in the software, which may not fully reflect geological conditions. It is important to consider that some of the sensitivity analysis results may reflect the software's limitations in simulating the geological response of the faults. Furthermore, the choice of Vs30 values (average, minimum, or maximum) has a substantial impact on the results. This highlights the necessity of incorporating a range of Vs30 values in sensitivity analyses to better account for variability in local soil conditions. We consider using the average Vs30 as more realistic since the standard deviation is not too significant from the average. To enhance the accuracy of future seismic risk assessments, several areas for improvement are highlighted: Soil amplification data: improved soil amplification data with higher resolution and accuracy would provide more reliable ground motion estimates, thereby refining damage predictions. Construction cost estimates: adjusting construction cost estimates to account for inflation and updated economic conditions would yield more accurate loss estimates. Current scenario models are based on costs from late 2022 to early 2023. Vulnerability functions: accurate and updated vulnerability functions that reflect the evolving building stock in Oslo are crucial for reliable damage assessments. Use of advanced tools: justification for the use of SELENA, a seismic risk assessment tool, lies in its ability to integrate updated Ground Motion Prediction Equations (GMPEs), detailed building typologies, and vulnerability functions, making it suitable for modern seismic risk assessments. To conclude, the study provides a detailed evaluation of seismic risk in Oslo, highlighting the variability of earthquake impacts across different scenarios and analytical approaches. The findings underscore the complexity of quantifying earthquake damage in urban areas and the necessity of employing both deterministic and probabilistic methods to capture the full spectrum of potential impacts. The significant variability observed based on the choice of Vs30 values underscores the critical need for future studies to better account for variability in local soil conditions and to focus on direct measurements of Vs30 to achieve a more accurate estimation. The Østmarka scenario, with its high economic and human loss estimates, exemplifies the severe consequences of shallow, reverse faulting earthquakes, while the 1904 scenario illustrates the reduced damage potential of deeper events. The probabilistic assessment offers a more nuanced perspective of seismic risk, suggesting that, while Oslo may not face high annual probabilities of severe damage, rare but significant events pose substantial risks. This dual approach offers more effective risk mitigation and preparedness strategies, serving as a valuable resource for policymakers and urban planners in developing targeted risk mitigation strategies and emphasizing the need for ongoing improvements in seismic risk assessments. It is recommended that future studies incorporate refined Vs30 data or models, to improve soil amplification data, and regularly updated economic parameters to further enhance the accuracy and applicability of seismic risk evaluations for Oslo. As Oslo continues to evolve and grow, the adoption of more sophisticated and up-to-date assessment methods (incorporating suitable GMPEs, precise building typology classifications, and accurate vulnerability functions) remains critical for enhancing the resilience of the city to future seismic events. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The results and the research showed in this article are connected to the GEObyIT project, funded by the Research Council of Norway (grant number 311596). The research connected to this publication was mainly conducted during a research stay at the University of Alicante (Spain) and it was funded by the Research Council of Norway through Funding for Research Stays Abroad for Doctoral and Postdoctoral Fellows. Author contributions FG is the main author and contributed to the data compilation, methodology, results, discussion and conclusions. She wrote the first draft of the paper and prepared all the figures. SM, AT and VO contributed to the results, discussion and conclusions and reviewing the manuscript. Acknowledgments The authors are grateful for the economic support provided by the Research Council of Norway, and to the University of Alicante (Spain) for contributing to this research. Data and Resources The building database used in this study contains restricted information, and therefore access is limited. Interested parties may request access by contacting the corresponding author. 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Bulletin of the Seismological Society of America 105, 1989–2009. https://doi.org/10.1785/0120140332 Supplementary Files supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 19 Aug, 2025 Read the published version in Natural Hazards → Version 1 posted Reviewers agreed at journal 23 Jan, 2025 Reviewers invited by journal 22 Jan, 2025 Editor invited by journal 21 Jan, 2025 Editor assigned by journal 14 Jan, 2025 First submitted to journal 10 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5804859","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405566074,"identity":"6896d1c8-819f-45aa-9471-98de8bf85550","order_by":0,"name":"Federica Ghione","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYBAC9gYGAwYQYmDgYTgAJOUIauE5gKbFmEgtECaYTGwgqIW9eePjigI7eQb+tQcPfNxhk76dvffxh597GPL4cWnhOVZseMYg2bBB4l3CwZln0nJ39hw3k+x5xlAsicM+e4kcM8kGgwOMDRJnDA7zth3O3XAjjQ3k3sQNB3DYIv/G/CdQiz1Uy/90gxtpzB//4NMiwWPGCNSS2MDfA9JyIAGohUEary08aUBHGyQnt0nwGByc2ZZsuOHMMTZpmQMSiTNx+IWH/fDGjw1/7Gz7+c8Yf/jYZidvcLyN+eObAzaJ/ThCDA7YJBJQ+BKENAABPw6nj4JRMApGwSgAAFD8XZddyk2TAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-0994-1790","institution":"NORSAR","correspondingAuthor":true,"prefix":"","firstName":"Federica","middleName":"","lastName":"Ghione","suffix":""},{"id":405566075,"identity":"33345bfc-3db9-42f4-9063-ef29d2dc482e","order_by":1,"name":"Sergio Molina-Palacios","email":"","orcid":"","institution":"University of Alicante: Universitat d'Alacant","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"Molina-Palacios","suffix":""},{"id":405566076,"identity":"5b7e4ef4-c0c5-4e02-bc0d-39c3bab30904","order_by":2,"name":"Anita Torabi","email":"","orcid":"","institution":"University of Oslo: Universitetet i Oslo","correspondingAuthor":false,"prefix":"","firstName":"Anita","middleName":"","lastName":"Torabi","suffix":""},{"id":405566077,"identity":"e90987dc-56d9-4c95-a9a0-2b615d309892","order_by":3,"name":"Volker Oye","email":"","orcid":"","institution":"NORSAR","correspondingAuthor":false,"prefix":"","firstName":"Volker","middleName":"","lastName":"Oye","suffix":""}],"badges":[],"createdAt":"2025-01-10 15:27:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5804859/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5804859/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11069-025-07588-3","type":"published","date":"2025-08-19T16:29:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74639388,"identity":"eb26afe7-d89a-44df-b615-b7a617a11431","added_by":"auto","created_at":"2025-01-24 08:54:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":243146,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e) Seismicity distribution around the city of Oslo, highlighted as grey polygon. Red stars show the locations of the events used for the deterministic scenarios representing: 1) the 1904 Mw 5.4 event; 2) the hypothetical Østmarka M 6.0 scenario on the east side of the rift zone; 3) the hypothetical Nesodden M 6.0 scenario on the Nesodden Fault. \u003cstrong\u003eb\u003c/strong\u003e) Oslo geounit locations with corresponding numbering.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5804859/v1/9ec6ea6ef34b213cda3fad07.jpg"},{"id":74638160,"identity":"1810cea9-d0a2-41b5-b68c-6396a1c7d5ae","added_by":"auto","created_at":"2025-01-24 08:46:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99840,"visible":true,"origin":"","legend":"\u003cp\u003eSeismic Hazard Map for Oslo illustrating Peak Ground Acceleration (PGA) values in g for 475 years return period, put in perspective to the national scale.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5804859/v1/1e6c8c851d5d4ca9030f53b1.jpg"},{"id":74638159,"identity":"b36fedfb-b52f-462a-8c28-e41f09ba0c89","added_by":"auto","created_at":"2025-01-24 08:46:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":161150,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eleft\u003c/strong\u003e: location of the three (a, b and c) measurements located in Alværnbrygge (Nesodden). In the same figure, the red star indicates the location of the Nesodden scenario, and the red lines show the Oslofjord main faults in the study region. On the bottom, picture taken during the fieldwork related to area a (fault core of the main segment of Nesodden Fault, picture taken NE-SW). \u003cstrong\u003eRight\u003c/strong\u003e: stereonet showing the orientation of the fault’s plane in area a (in blue), b (in black) and c (in red). The table provides strike and dip for the different measurements.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5804859/v1/e08254bda6e3d06cfd4c5bc5.jpg"},{"id":74638175,"identity":"e7f59f27-49f9-4cd2-b9f7-c4e22fb69fb3","added_by":"auto","created_at":"2025-01-24 08:46:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":207399,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e) Building typologies distribution in the 18 city sectors. The percentage values represent the number of buildings of the respective building typology relative to the total building stock of the sector. \u003cstrong\u003eb\u003c/strong\u003e) Distribution of the five MBTs in the different geounits in terms of number of buildings (in logarithmic scale). \u003cstrong\u003ec\u003c/strong\u003e) Distribution of the five MBTs in all Oslo in terms of number of buildings (in percentage).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5804859/v1/bfb75a6ccb0e1abdc39e6a8f.jpg"},{"id":74638161,"identity":"91ab351a-0e20-45f1-b0fd-a58b6cd76689","added_by":"auto","created_at":"2025-01-24 08:46:18","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":187168,"visible":true,"origin":"","legend":"\u003cp\u003ePGA values across Oslo for the earthquake scenarios (1904 (1), Østmarka (2) and Nesodden (3) events), calculated using four GMPEs (Akkar et al. (2014), Bindi et al. (2017), Cauzzi et al. (2015), and Yenier and Atkinson (2015)). The colour scale reflects increasing PGA values, from blue (0–0.1 g) to red (0.6–0.7 g).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5804859/v1/a351066193814b8a50d6e046.jpg"},{"id":74639385,"identity":"3840df7e-1478-4721-a535-463ae77c910c","added_by":"auto","created_at":"2025-01-24 08:54:18","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":141080,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the Mean Damage Ratio (MDR) (in percentage) for the three scenarios (1, 2, and 3) using Sa method (left column) and Sd method (right column). The radar plots display the percentage distribution of MDR using the Average Vs30 (blue), Minimum Vs30 (orange), and Maximum Vs30 (gray) across 18 geounits. Each subplot highlights the differences between the two methods in terms of MDR variability. Note the different scales for % values in the subplots.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5804859/v1/c80c66116990e0fe4287495f.jpg"},{"id":74639884,"identity":"28c8accd-c3d1-4f02-b5d3-521010d4a099","added_by":"auto","created_at":"2025-01-24 09:02:19","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":119268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e) Economic losses expressed in Norwegian Krone (NOK) using both Sa and Sd methods for the three earthquake scenarios (1904, Østmarka and Nesodden) and average, minimum and maximum Vs30 values. \u003cstrong\u003eb\u003c/strong\u003e) Human losses for the three scenarios using both methods: third column shows the cumulative numbers of human losses (from slightly injured to dead) during nighttime (2:00 am) in each earthquake scenario. In the following columns, human losses considering low, mid, high injuries and dead people.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5804859/v1/0164eed1c5c1625892815154.jpg"},{"id":74639888,"identity":"a380722b-68b8-4e04-ad64-7c35808f7c7f","added_by":"auto","created_at":"2025-01-24 09:02:19","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":636928,"visible":true,"origin":"","legend":"\u003cp\u003eResults for geounit 3 (Frogner) and 13 (Sentrum) plotting the Mean Damage Ratio probabilistic curves in percentages using the spectral acceleration (\u003cstrong\u003ea\u003c/strong\u003e) and spectral displacement (\u003cstrong\u003eb\u003c/strong\u003e) methods. For all cases, Yenier and Atkinson (2015) as GMPE and average Vs30 are used.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5804859/v1/f494ae6f2be1e5f083df1bc8.jpg"},{"id":74638176,"identity":"918a848b-2b3a-46e3-bcce-9461130a69f5","added_by":"auto","created_at":"2025-01-24 08:46:19","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":178228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e) median MDR in percentage for the Østmarka scenario using the spectral displacement method and average Vs30. \u003cstrong\u003eb\u003c/strong\u003e) median MDR in percentage for 475 years return period probabilistic model using the spectral displacement method and average Vs30. Please note that the scale for the two figures is different.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5804859/v1/a652bf1133be57f28d5921f3.jpg"},{"id":89847270,"identity":"412e0bc6-2268-4574-b247-452fb5553449","added_by":"auto","created_at":"2025-08-25 16:42:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3085916,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5804859/v1/ecbc17fa-c0bf-444c-96be-d9ada9617f1c.pdf"},{"id":74638170,"identity":"9a2fe21c-630c-4259-9189-dc657863de92","added_by":"auto","created_at":"2025-01-24 08:46:18","extension":"docx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":858686,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5804859/v1/e46babc78afef658df0055b6.docx"}],"financialInterests":"","formattedTitle":"Evaluating Earthquake Impacts in Oslo, Norway: A Multi-Method Approach","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Seismic risk assessment combines deterministic and probabilistic methods in Oslo.\u003c/p\u003e\u003cp\u003e\u0026bull; Three deterministic scenarios analyze earthquake impacts, focusing on the building stock and population.\u003c/p\u003e\u003cp\u003e\u0026bull; Sensitivity analysis shows fault type significantly affect seismic damage outcomes.\u003c/p\u003e\u003cp\u003e\u0026bull; Results guide targeted mitigation and policy strategies for enhancing Oslo's resilience.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eSeismic risk assessment is a critical component in urban planning and disaster mitigation, especially for cities located in seismically active regions. Although the city of Oslo, Norway, is located in a region with relatively low seismic activity, several earthquakes have occurred in the past (Bungum et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Molina and Lindholm, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Taylor, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) that underscore the necessity for comprehensive seismic risk evaluations. One study though (Swensson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) highlights the potential for significant seismic activity in the Oslo region due to its historical seismicity patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMolina and Lindholm (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) conducted the first seismic risk assessment for Oslo, laying the groundwork for understanding the city's vulnerability to earthquakes. Their research primarily focused on evaluating seismic risk scenarios with two earthquake sources, accounting for soil amplification using a basic microzonation map and characterising the building behaviour using predefined vulnerability functions from HAZUS (FEMA, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The present study expands upon their work by incorporating a more detailed exposure model (in terms of building inventory and population) and using both deterministic and probabilistic approaches, including the most recent vulnerability functions database, leveraging new and updated data to deliver a more comprehensive assessment of seismic risk for Oslo.\u003c/p\u003e \u003cp\u003eDeterministic scenarios allow for the evaluation of specific earthquake scenarios, providing insights into potential impacts from significant seismic events. For this analysis, we have chosen three distinct deterministic scenarios based on historical and plausible earthquake events of varying magnitudes, epicentral locations and source mechanisms. These scenarios help in understanding the possible range of seismic impacts on the city\u0026rsquo;s buildings and population.\u003c/p\u003e \u003cp\u003eComplementing the deterministic scenarios, a probabilistic approach was conducted to estimate the likelihood of different levels of seismic shaking over a specified time period, accounting for the inherent uncertainties in earthquake occurrence, size, and location. Therefore, offering a comprehensive risk profile by integrating the probability of different seismic events with their potential impacts.\u003c/p\u003e \u003cp\u003eThis dual approach not only enhances our understanding of the seismic risks facing Oslo but also serves as a model for other cities in similar tectonic settings and similar building stock. To perform the analysis, we have used SELENA software (SEismic Loss EstimatioN using a logic tree Approach), that is a tool designed for seismic risk assessment and loss estimation in urban environments by integrating seismic hazard with detailed information about buildings and infrastructure (Molina et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and as previously mentioned, an earlier version of that software was used in the first seismic risk study for Oslo (Molina and Lindholm, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results presented in this paper are crucial for informing urban planning, emergency preparedness, and mitigation strategies for Oslo. In addition, the study provides a solid foundation for future research and policy-making aimed at enhancing the resilience of urban areas against seismic threats.\u003c/p\u003e"},{"header":"2. Study area","content":"\u003cp\u003eOur study focuses on the city of Oslo, Norway and its municipality covers an area of 480 km\u003csup\u003e2\u003c/sup\u003e divided in 18 urban districts. The associated boroughs are called Alna, Bjerke, Frogner, Gamle Oslo, Grorud, Gr\u0026uuml;nerl\u0026oslash;kka, Nordmarka, Nordre Aker, Nordstrand, \u0026Oslash;stmarka, \u0026Oslash;stensj\u0026oslash;, Sagene, Sentrum, S\u0026oslash;ndre Nordstrand, St. Hanshaugen, Stovner, Ullern, Vestre Aker (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This division facilitates the compilation of a comprehensive inventory of the current building stock and the incorporation of demographic data that will be discussed in Section 3.\u003c/p\u003e \u003cp\u003eThis section presents a geological introduction of the area, with additional information related to its seismicity and the largest historical earthquakes.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The Oslo Rift Zone\u003c/h2\u003e \u003cp\u003eThe Caledonian Orogeny, the largest tectonic event in Norway, resulted in significant E-W shortening and the development of N-S weakness zones across the entire Norwegian continent and its shelf regions. During this orogeny, which occurred between the Silurian and Devonian periods, the Precambrian basement rocks underwent deformation and later erosion, forming a peneplain (Neumann et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Ramberg and Larsen, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1978\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubsequent to the orogenic collapse of the Caledonian mountain belt, the Oslofjord and adjacent areas were exposed to stretching and rifting of the crust during Carboniferous and Permian time (Nielsen and Nielsen, 2007). This rifting activity led to the development of extensional structures, such as normal faults and their associated horst and grabens. By the late Triassic-Cretaceous, the rifting stopped, leaving behind these extensional features as a record of the earlier tectonic processes.\u003c/p\u003e \u003cp\u003eAmong these is a 400 km long graben system between Denmark and Norway reaching from the Sorgenfrei-Tornquist Zone to the Skagerrak Graben. The northern part of the Oslo Rift Zone is called Oslo Graben and it is exposed onshore striking N-S. The continuation of the Oslo Graben offshore is called Skagerrak Graben striking NE-SW (Neumann et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Ro et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Oslo Graben is divided into three onshore segments: the Vestfold Graben Segment in the south, the Akershus Graben Segment in the middle and the Rendalen Graben Segment in the north (Ramberg et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These graben segments have opposite polarities: the main faults are at their east boundary for the Vestfold and Rendalen Graben, and at the west boundary for the Akershus Graben.\u003c/p\u003e \u003cp\u003eThe Oslofjorden Fault (OF) is the eastern boundary fault of the Vestfold Graben and it is the major fault within the Oslo Rift Zone. The northern part of the fault, known as Nesodden Fault Segment (NFS), has a well exposed deformation zone with more than 40 m thick cataclastic rocks (Swensson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). This fault is quite relevant in the area due to its potential to generate seismic activity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Seismicity and largest earthquakes\u003c/h2\u003e \u003cp\u003eAlthough the seismicity level can be described as low-medium, Norway shows a higher level of seismic hazard compared to the other Northern European countries, with the exception of Iceland (Danciu et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). From the seismotectonic point of view, the Oslo Rift Zone is considered to be relatively active compared to other parts of Scandinavia and for this reason Oslo shows an intermediate seismic hazard level compared to national scale (Bungum et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe faults delimiting the boundaries of the Oslo Graben are believed to be long-lived structures, already active in the Neoproterozoic. The Oslo Rift Zone overprints basement faults, where accumulated stress over time, surpasses the stress level of the geological structures. Although tectonic activity has long ceased, the region still experiences earthquakes. Presently, there is no geological evidence indicating any recent significant fault displacement within the Oslo Rift Zone. Nevertheless, the Nesodden Fault, like other faults within the Oslo Rift Zone, is capable of generating earthquakes due to ongoing tectonic stresses and the release of accumulated strain along the fault (Swensson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile large earthquakes are rare in the region, there have been historical instances of moderate seismic activity. The last major earthquake in the Oslo region is the Oslofjord event that occurred on the 23rd of October 1904, with a magnitude estimated to be around 5.4 and epicentre located 115 km south of Oslo at about 30 km depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The vertical rupture plane generated ground motions that propagated along the Oslo fjord, moreover this event was felt all over northern Europe, from Namsos in the North to Poland and Helsinki in the East and all across southern Norway (Bungum et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, the 1904 Oslofjord earthquake did not cause any casualties nor major destructions, only some minor damage to a few wooden and unreinforced masonry buildings were reported in the Oslo city area.\u003c/p\u003e \u003cp\u003eUnderstanding the geological and seismotectonic characteristics of the Oslo Rift Zone, including the Nesodden Fault, is hence crucial for assessing seismic hazard and implementing measures to mitigate risks to infrastructure and populations in the Oslo region and surrounding areas.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Material and Methods","content":"\u003cp\u003eTo perform a comprehensive seismic risk assessment, a wide range of information is required to accurately characterize the risk and its potential impacts. In the following subchapters, we describe the main components related to hazard, exposure and vulnerability that were investigated and developed for this study.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Seismic hazard in Oslo\u003c/h2\u003e \u003cp\u003eThe hazard component in seismic risk assessment refers to the characterization and quantification of the likelihood and severity of seismic events that can affect a given area in a certain period of time. It encompasses the analysis of earthquake occurrence and the intensity of ground shaking that can be expected over a specified time period. Understanding the hazard component is crucial as it is the key component (combined together with the vulnerability of the exposed buildings, populations and infrastructures) to provide seismic risk assessments.\u003c/p\u003e \u003cp\u003eThe input data for the seismicity used in this study stems from a Probabilistic Seismic Haza\u003cb\u003er\u003c/b\u003ed Analysis (PSHA) conducted for Norway and Svalbard (Lindholm et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The earthquake catalogue includes data for Norway from 1497 until 2018. The hazard model uses two different and equally weighted models, both based on area sources: 1) the zones are defined by the seismicity patterns and 2) the zones are defined through geologically mapped structural features (see Figure SM1 in the Online Resource). Fault source modelling is not included due to the lack of knowledge for fault parameters. For the hazard calculations, a maximum magnitude (Mmax) of 6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 was considered for all zones, based on historical earthquakes in the region. Due to the relatively low seismicity in the region, the b-value of the Gutenberg-Richter relationship was derived from a larger area and applied uniformly to all zones, to achieve a statistically representative b-value (equal to 0.927, with 0.10 coefficient of variation). The a-value, however, was calculated for each individual zone to account for differences in seismicity rates (Table SM1 in the Online Resource).\u003c/p\u003e \u003cp\u003eTo assess the seismic hazard, it is essential to select suitable Ground Motion Prediction Equations (GMPEs) that are both robust and representative of the region\u0026rsquo;s characteristic, especially considering the lack of sufficient local strong-motion records in the investigated area. While three of the chosen GMPEs (Akkar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bindi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cauzzi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) are primarily calibrated for active tectonic regions, the fourth one (Yenier and Atkinson, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) is specifically suited for stable continental regions, aligning more closely with the seismotectonic setting of Oslo. The four GMPEs were selected based on their suitability across various parameters, including magnitude and distance ranges, distance metrics and site conditions (specifically Vs30 values). Akkar et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) covers a magnitude range of 4.0\u0026ndash;7.6 and Vs30 of 150\u0026ndash;1200 m/s. Bindi et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) spans magnitudes 3.0\u0026ndash;7.9 and Vs30 of 360\u0026ndash;1500 m/s. Cauzzi et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) is suitable for magnitudes 4.5\u0026ndash;7.9 and Vs30 of 150\u0026ndash;1200 m/s, and Yenier and Atkinson (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) applies to magnitudes 3.8\u0026ndash;8.0 and Vs30 of 150\u0026ndash;1500 m/s. In both seismic hazard and seismic risk computations, a logic tree computation scheme is adopted to handle epistemic uncertainty, with equal weights assigned to all these GMPEs, ensuring a balanced consideration of each model's contributions to the calculation.\u003c/p\u003e \u003cp\u003eA key output of the PSHA is the seismic hazard map, which illustrates the expected levels of ground shaking for a specific likelihood of exceedance. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the Peak Ground Acceleration (PGA) hazard map for Oslo for a 475-year return period, corresponding to a 10% probability of exceedance in 50 years. The results are computed at bedrock condition (using Vs30 equal to 1200 m/s) and the PGA values in Oslo range from 0.0240 g to 0.0248 g (in the southeastern part of the study area), indicating moderate hazard levels compared to other regions in Norway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Geological field data\u003c/h2\u003e \u003cp\u003eThe seismic risk scenarios are based on specific events that can occur with a certain magnitude and distance to the area of interest. Earthquakes predominantly occur on pre-existing faults. In our study, we have tested different earthquake sources based on historical events and geological evidence.\u003c/p\u003e \u003cp\u003eWe conducted geological fieldwork in the area of Alv\u0026aelig;rn brygge (Nesodden) to focus on the Nesodden Fault Segment (northern part of the Oslofjord Fault), with focus on three areas (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn area a, we observed the fault core of the main segment of the Nesodden Fault striking NE-SW.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn area b, we identified two sets of faults striking NE-SW with similar dips towards E and W.\u003c/p\u003e \u003c/li\u003e\u003cli\u003e\u003cp\u003eArea c shows fault plane measurements striking N-S to NE-SW and dipping towards E.\u003c/p\u003e\u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003cp\u003eThe data collected during the fieldwork were used as input parameters for the deterministic scenarios in this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1.2 Soil characterization\u003c/h2\u003e \u003cp\u003eBellalem et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Dobry et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and Holzer et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) emphasize that the shear wave velocity within the uppermost 30 m (Vs30) is a key parameter to effectively predict local site amplification. To characterize the local site conditions of each geounit, we have used a combined method using existing well databases, topographic slope derived from Digital Elevation Models and near-surface Quaternary geological maps. More information on the methodology adopted is discussed by Ghione et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, average Vs30 values as well as minimum and maximum values are extracted for each geounit to capture variability and investigate their impact on seismic risk results (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The dataset includes average Vs30 values ranging from 446 m/s to 1140 m/s, with standard deviations varying between 122 m/s and 330 m/s. The minimum and maximum Vs30 values reflect the range of variability within the study area, spanning from 100 m/s (which corresponds to areas known or suspected to have quick clay) to 1200 m/s (representing bedrock conditions), depending on the geounit. This comprehensive approach provides a robust basis for assessing the impact of soil amplification on risk calculations, particularly in urban districts with significant geological heterogeneity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of the 18 geounits with their corresponding names, coordinates of the centroids of each polygon, average Vs30 values with \u0026plusmn;\u0026thinsp;standard deviation, minimum and maximum Vs30 values for each geounit, depth to bedrock, number of buildings and population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeounit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage Vs30\u0026thinsp;\u0026plusmn;\u0026thinsp;Std Deviation [m/s]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMinimum-Maximum Vs30 [m/s]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDepth to Bedrock [m]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo. buildings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e706\u0026thinsp;\u0026plusmn;\u0026thinsp;330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e49373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBjerke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e683\u0026thinsp;\u0026plusmn;\u0026thinsp;245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e35117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrogner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e545\u0026thinsp;\u0026plusmn;\u0026thinsp;201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140\u0026ndash;760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e59026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGamle Oslo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e521\u0026thinsp;\u0026plusmn;\u0026thinsp;278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e60209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrorud\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e897\u0026thinsp;\u0026plusmn;\u0026thinsp;294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e27457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGr\u0026uuml;nerl\u0026oslash;kka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e603\u0026thinsp;\u0026plusmn;\u0026thinsp;128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e180\u0026ndash;760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e63891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNordmarka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1086\u0026thinsp;\u0026plusmn;\u0026thinsp;257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e180\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNordre Aker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e740\u0026thinsp;\u0026plusmn;\u0026thinsp;267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e53109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNordstrand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e726\u0026thinsp;\u0026plusmn;\u0026thinsp;316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e52595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026Oslash;stmarka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1140\u0026thinsp;\u0026plusmn;\u0026thinsp;188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e180\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026Oslash;stensj\u0026oslash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e758\u0026thinsp;\u0026plusmn;\u0026thinsp;286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e50837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSagene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e583\u0026thinsp;\u0026plusmn;\u0026thinsp;157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u0026ndash;760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e46424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e446\u0026thinsp;\u0026plusmn;\u0026thinsp;196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140\u0026ndash;760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u0026oslash;ndre Nordstrand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e978\u0026thinsp;\u0026plusmn;\u0026thinsp;289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e39037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSt Hanshaugen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e619\u0026thinsp;\u0026plusmn;\u0026thinsp;122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e180\u0026ndash;760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e39066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStovner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e704\u0026thinsp;\u0026plusmn;\u0026thinsp;216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e33259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUllern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e631\u0026thinsp;\u0026plusmn;\u0026thinsp;146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u0026ndash;760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e34896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVestre Aker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e725\u0026thinsp;\u0026plusmn;\u0026thinsp;218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u0026ndash;1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e50784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Exposure model\u003c/h2\u003e \u003cp\u003eAn exposure model, in the context of seismic risk assessment, refers to a quantitative representation of the elements at risk within a given geographical area that could be affected by an earthquake. It provides a detailed description of the distribution, characteristics, and attributes of these elements, which can include buildings, infrastructure, population, land use, and economic assets.\u003c/p\u003e \u003cp\u003eThe primary purpose of an exposure model is to provide a comprehensive understanding of what is potentially vulnerable to seismic hazards. By analysing the characteristics and spatial distribution of the elements at risk, potential losses and damages can be estimated in the event of an earthquake. This information is crucial for emergency planning, disaster response coordination, and the development of effective mitigation strategies. Exposure models typically incorporate various datasets, such as building inventories, land use maps, economic information and population data. These datasets are often integrated using Geographic Information Systems (GIS) technology to create a spatially explicit representation of the elements at risk.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Building inventory\u003c/h2\u003e \u003cp\u003eFor the Oslo case study, a comprehensive building inventory was created using different sources for the different components.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBuilding typologies were defined following a few steps: a first overview of the typologies was obtained from Google Street View, detailed fieldwork in specific areas of the city, survey questionnaire regarding seismic vulnerability assessment was sent to experts (mainly engineers working in Norway) and finally the definition of the five main Model Building Typologies (MBT) for Oslo: Timber (T), Reinforced Concrete (CR), Unreinforced Masonry (MUR), Steel (S) and Steel-Reinforced Concrete (SRC). The combination of Google Street View images and machine learning allowed us to automatically recognise the typologies for most of the city. More detailed information is provided by Ghione et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNumber of storeys and total floor area are provided by Karteverket (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kartverket.no/\u003c/span\u003e\u003cspan address=\"https://www.kartverket.no/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, the Norwegian mapping authority, responsible for mapping, property registration and geographical data).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eYear of construction and occupancy are provided by Statistics Norway (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ssb.no/\u003c/span\u003e\u003cspan address=\"https://www.ssb.no/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, the national statistical institute of Norway, responsible for collecting and producing official statistics).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIn some cases (about 45% of the total number of buildings for the MBT, 16% for the number of storeys, 2.5% for the occupancy and 1.7% for the year of construction), not all information was available due to incompleteness in the different databases. We then compiled the database as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ein case the MBT is missing, we have defined the typology based on a combination of occupancy and year of construction (when available): garage with T; public toilet with CR; industrial/warehouse with S; church, prison, museum and school with T for buildings built before 1985, MUR between 1985 and 1995, CR after 1995 (and SRC after 2010 only for museum and school). For all other occupancy, for buildings built before 2010 we have attributed T typology, after 2010 SRC.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhen the number of storeys is missing, we have attributed the corresponding number to the different occupancy: based on a combination of occupancy and year of construction (when available): garage, public toilet, sports facility, entertainment building with 1 storey; industrial/warehouse, police, restaurant, transport with 2; school, prison and museum with 3; office with 4; dwelling, university and hospital with 5; hotel and business building with 8.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn the case neither occupancy or MBT is available, we have checked the location of those buildings that are mainly located around the forest and the islands. For this reason, we have attributed the T typology, and when the number storeys are missing, we have attributed 2 storeys.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAt the end, when we have information on the different MBT but missing occupancy and number storeys, we have decided to use Google Earth to recognise the occupancy. In most of cases, the buildings are garage or small structures, hence we have decided to attribute 1 as number of storeys.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAll the elements were combined and were integrated in a single file and finally used for advanced risk analysis. The most represented MBT in Oslo is Timber which constitutes 82% of all typologies (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The residential occupancy is the most significant in terms of building counts, due to the large number of apartment buildings and single-family dwellings, which constitute around 59% of all buildings.\u003c/p\u003e \u003cp\u003eThe economic information related to new construction cost (in terms of Norwegian Crown per meter square) are taken from estimates made by Statsbygg (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.statsbygg.no/,the\u003c/span\u003e\u003cspan address=\"https://www.statsbygg.no/,the\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Norwegian government agency responsible for public building management and construction) for late 2022/beginning 2023. An informative table of the values used in our study is provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConstruction cost values, expressed in terms of NOK/m\u003csup\u003e2\u003c/sup\u003e, for the different building materials. In the third column, the values used in our study are presented.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding Material\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstruction Cost [NOK/m\u0026sup2;]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUsed value [NOK/m\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTimber (T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,000\u0026ndash;25,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21,500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReinforced Concrete (CR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22,000\u0026ndash;28,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnreinforced Masonry (MUR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,000\u0026ndash;19,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16,500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteel (S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24,000\u0026ndash;29,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26,500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteel-Reinforced Concrete (SRC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23,000\u0026ndash;29,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Population\u003c/h2\u003e \u003cp\u003eIn addition to structural information, the population distribution through the different neighbourhoods is taken from the Oslo municipality webpage (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bydelsfakta.oslo.kommune.no/bydel/alle/befolkningsutvikling/\u003c/span\u003e\u003cspan address=\"https://bydelsfakta.oslo.kommune.no/bydel/alle/befolkningsutvikling/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The total number of 699.827 inhabitants were officially registered as Oslo residents at the beginning of 2023.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Vulnerability model\u003c/h2\u003e \u003cp\u003eIn seismic risk, vulnerability refers to the degree of damage or loss that a structure or system is likely to suffer during an earthquake. It is a measure of how susceptible a building or infrastructure is to damage from ground shaking, fault rupture, soil liquefaction, and other seismic hazards. Vulnerability is affected by a range of factors, including the design and construction of the structure, its age and condition, the soil and geologic conditions at the site, and the intensity and duration of the earthquake. By identifying the structures that are most vulnerable to seismic hazards, engineers and policymakers can prioritize investments in retrofitting and other mitigation measures to reduce the risk of damage and loss during earthquakes.\u003c/p\u003e \u003cp\u003eCapacity and fragility curves are two important tools used to assess the vulnerability of structures to earthquake damage when the spectral displacement method is chosen to obtain the damage probability. They are often used together to assess the seismic performance of structures by computing the performance point in terms of spectral displacement which is later used to interrogate the fragility curve providing the corresponding damage probability, and to inform decisions about risk mitigation and management strategies. Other approaches compute the damage probability using the ground motion in the region in terms of spectral acceleration Sa (spectral acceleration method).\u003c/p\u003e \u003cp\u003eAs we can see from Figure SM2, for the same earthquake using spectral displacement fragility functions or spectral acceleration fragility functions may lead to different damage probabilities values so it is important to characterize this uncertainty by modelling the damage using both methodologies as we will see later.\u003c/p\u003e \u003cp\u003eIn our study, capacity and fragility curves are taken from Martins and Silva (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) database for the different building typologies and building heights (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In order to perform the correct selection of vulnerability functions, it is important to define (in addition to the building material) the building ductility and frame structure.\u003c/p\u003e \u003cp\u003eDuctility denotes an ability of a building\u0026rsquo;s structure to undergo significant deformations before the failure occurs in structural members or their connections. It is one of the most important factors affecting building performance in an earthquake. In general, a building can be classified as ductile or non-ductile (DNO), depending on its expected seismic performance before an earthquake. In the context of structural vulnerability functions, three ductility levels are critical for understanding how different buildings will respond to various hazards, influencing design choices and vulnerability assessments:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDUL (low ductility): the building or material has a low capacity to undergo deformation without failing. It is commonly used in low seismic hazard regions and in older buildings, often constructed before modern seismic design principles were fully understood and widely adopted (pre-1970s).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDUM (moderate ductility): this represents a moderate capacity to deform before failure. It is suitable for moderate seismic hazard areas, and it is commonly used in buildings constructed between the late 1970s and early 1990s. It is also applicable to more recent buildings in low seismic hazard regions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDUH (high ductility): it indicates a high capacity for deformation, and it is required for high seismic hazard zones and widely used for modern buildings constructed after the late 1990s.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eRegarding the frame structure, in our selection we have used moment frame (LFM - a frame consisting of beams and columns, with strong and rigid beam-to-column connections), wall (LWAL - a vertical planar building element which usually resists gravity loads, horizontal forces and provides stability) and dual frame-wall system (LDUAL - here the lateral load-resisting structure consists of moment frames and shear walls acting together in the same direction). More information can be found in the glossary for GEM taxonomy (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://taxonomy.openquake.org/\u003c/span\u003e\u003cspan address=\"https://taxonomy.openquake.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of the vulnerability functions taken from Martins and Silva (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) used in this analysis. The functions are selected based on the five building typologies and the different building heights (low, mid or high rise). *IM: Intensity measure used in the spectral acceleration (Sa) fragility curves.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding typology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeights\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVulnerability function used from Martins and Silva (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIM*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTimber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW_LFM-DUL_H1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(0.3 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW_LFM-DUL_H4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(0.6 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW_LFM-DUL_H6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(0.6 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eReinforced Concrete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCR_LFM-DUL_H2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(0.6 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCR_LFM-DUL_H5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(1.0 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCR_LFM-DUL_H10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(1.0 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUnreinforced Masonry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMUR_LWAL-DNO_H1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(0.3 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMUR_LWAL-DNO_H3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(0.3 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMUR_LWAL-DNO_H5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(0.6 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSteel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS_LFM-DUL_H2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(0.3 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS_LFM-DUL_H5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(0.6 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS_LFM-DUL_H10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(1.0 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSteel-Reinforced Concrete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRC_LDUAL-DUM_H2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(0.3 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRC_LDUAL-DUM_H5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(0.6 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRC_LDUAL-DUM_H10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSa(1.0 s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Damage and Loss computation methods\u003c/h2\u003e \u003cp\u003eTwo approaches have been adopted to calculate the damage probability of a given building typology due to spectral demand: the spectral displacement (Sd) and the spectral acceleration (Sa) methods. The Sd method utilizes the N2 method proposed by Fajfar (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Using the inelastic response spectrum and the capacity curve, this method determines the performance point in terms of displacement, which can later be used to interrogate the spectral displacement fragility curves, providing the damage probability for each damage state. On the other hand, the Sa method directly combines the intensity measure (IM) from the spectral demand to interrogate the IM-fragility function and derive the damage probability. For example, for the Timber typology with low height, the IM is chosen as the spectral acceleration at 0.3s (Sa0.3s as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and the corresponding Sa0.3s fragility function is used to compute the damage probability.\u003c/p\u003e \u003cp\u003eTo quantify the damage to buildings caused by earthquakes is inherently complex due to the varying levels of detail in damage estimates across different scenarios and building types. The Mean Damage Ratio (MDR) is an effective metric for addressing this issue, as it allows for standardized comparisons of risk estimates across different geographic areas within a city. MDR is calculated as the cost associated with each damage state, expressed as a proportion of the cost for a new construction (Bellalem et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to Molina et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), the MDR for a city is calculated as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:MDR=\\frac{\\sum\\:_{i=1}^{geounit}\\sum\\:_{j=1}^{mbt}{{\\sum\\:}_{k}{DR}_{k}^{j}N}_{ki}^{j}}{{N}_{T}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{DR}_{k}^{j}\\)\u003c/span\u003e \u003c/span\u003e is the economic damage ratio of model building type j corresponding to damage state k, where k\u0026thinsp;=\u0026thinsp;S for slight, M for moderate, E for extensive and C for complete damage, that is the price of repair for a given damage state divided by the total price of the model building type.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{ki}^{j}\\)\u003c/span\u003e \u003c/span\u003e is the damaged built area corresponding to damage state k (S, M, E, C) the model building type j in geounit i.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{T}\\)\u003c/span\u003e \u003c/span\u003e is the total built area of all building types j and all geounits i.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis ratio is particularly useful because it remains consistent and is not influenced by factors such as inflation, exchange rates, or other variables that affect repair and reconstruction costs (Molina et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Despite its benefits, MDRs do not replace the need for detailed damage estimates, which are crucial for assessing social impacts like casualties. Severe damage states, especially building collapses, are the primary contributors to casualties and fatalities, highlighting the importance of detailed damage assessments.\u003c/p\u003e \u003cp\u003eThe here selected and presented scenarios are considered the most relevant for the area under investigation. We have computed different scenarios using both spectral displacement Sd (using capacity curves and spectral displacement fragility curves) and spectral acceleration Sa (adopting Sa fragility curves) methods. Both methods can be used to compute deterministic seismic risk scenarios, where the impact of specific events in cities is in focus or probabilistic seismic risk scenarios where the investigation aims more at the annual rate of exceedance of a given risk metric, for example the Mean Damage Ratio.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eFor computing the results, it is essential to select suitable Ground Motion Prediction Equations (GMPEs). As previously described in Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e, a logic tree computation scheme is adopted to handle epistemic uncertainty, with equal weights (0.25) assigned to all four selected GMPEs (Akkar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bindi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cauzzi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yenier and Atkinson, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), ensuring a balanced consideration of each model's contributions to the calculation.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Deterministic scenarios\u003c/h2\u003e \u003cp\u003eThe disaggregation results from the PSHA at different return periods have allowed us to select relevant deterministic earthquakes. Therefore, the selected earthquake scenarios (the 1904 event, the \u0026Oslash;stmarka event, and the Nesodden event, see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) represent a diverse range of seismic activities in the region. The return period for a magnitude 6 event is approximately 120 years for a region covering southern Norway within a 300 km radius centered on Oslo, underscoring the importance of considering these scenarios. The 1904 event, with a moderate magnitude (Mw 5.4) and a deeper focal depth (29 km), serves as a historical benchmark for understanding the potential impact of deeper earthquakes. The \u0026Oslash;stmarka event (Mw 6.0) represents a significant reverse faulting scenario at shallow depth (5 km), which is crucial for assessing the risk of surface-level seismic hazards in highly populated areas. The Nesodden event (Mw 6.0) is chosen to evaluate the risk posed by normal faulting mechanisms, with similar depth but a different fault orientation. Together, these scenarios provide a comprehensive overview of varying earthquake mechanisms and depths, essential for a thorough seismic risk assessment.\u003c/p\u003e \u003cp\u003eBesides, these events were assessed using the average Vs30 values, but also by incorporating the minimum and maximum Vs30 values for each geounit to capture the variability of local soil conditions. The results are provided in terms of ground motion distributions, Mean Damage Ratio (MDR), and estimates of human and economic losses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInput parameters for the three earthquake scenarios (1904, \u0026Oslash;stmarka, and Nesodden), with information on geographic coordinates, depth in km, magnitude, orientation (strike) and angle (dip) of the fault plane, and fault type (\u0026ldquo;all\u0026rdquo; means a reverse or normal fault with strike-slip component).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDepth [km]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMw\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStrike [\u0026deg;]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDip [\u0026deg;]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFault type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1904 event\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eall\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026Oslash;stmarka event\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ereverse\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNesodden event\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eA sensitivity analysis is performed to assess the influence of the different fault type, strike and dip angles using \u0026Oslash;stmarka scenario at 5 km depth with the Sd method. Three values of dip angle (5, 45 and 85 degrees) and of strike direction (135, 180 and 225 degrees) are used (see Figure SM3).\u003c/p\u003e \u003cp\u003eIn the sensitivity analysis conducted using the \u0026Oslash;stmarka scenario with the Sd method, the impact of varying fault type, strike and dip angles on the model outcomes was evaluated (see Figure SM3). The results indicate that variations in strike and dip angles had minimal impact on the outcomes. In contrast, fault type and depth demonstrated a significant influence, particularly affecting the results. Specifically, reverse fault mechanisms consistently resulted in higher MDR compared to normal fault mechanisms. This is because reverse faults involve compression, which tends to produce larger ground movements and structural damage compared to the extensional forces associated with normal faults. As a result, the reverse fault scenarios led to more severe impacts and losses.\u003c/p\u003e \u003cp\u003eAdditionally, a comparison was made using the same \u0026Oslash;stmarka scenario as in the study by Molina and Lindholm (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), which modeled a magnitude 6.0 event at 20 km depth. In their study, it was found that 45% of all buildings sustained slight damage, while 1.8% suffered complete damage. In contrast, our \u0026Oslash;stmarka scenario (using the Sd method) for the same magnitude 6.0 event at 20 km depth yielded significantly lower damage estimates, with only 5% of buildings experiencing slight damage and 0.02% experiencing complete damage. However, when the event depth was reduced to 5 km in our scenario, the results were more similar to those reported by Molina and Lindholm (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), with 32% of buildings experiencing slight damage and 1.2% complete damage.\u003c/p\u003e \u003cp\u003eThis discrepancy could be attributed to several factors: first, the 2005 study used only one GMPE, Ambraseys et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), which might have led to less accurate estimations of ground motion, particularly regarding depth-dependent effects. The current study uses updated GMPEs (see Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e) which may represent better the ground motion scenarios and its uncertainties. Second, their study did not involve a proper recognition of building typologies, instead relying on a broad estimate, which could have affected the accuracy of the damage predictions. Additionally, a different selection of vulnerability functions was used sinceMolina and Lindholm (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) used vulnerability functions developed for the USA in HAZUS (FEMA, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), while our study has improved the building behavior characterization by usingMartins and Silva (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) vulnerability functions, which would influence the damage estimates.\u003c/p\u003e \u003cp\u003eIn our study, we have chosen to present the scenario at 5 km depth rather than 20 km. Although the deeper scenario aligns more realistically with the reverse faulting mechanism of the region, the shallower depth provides a more conservative estimate of potential impacts, aligning better with observed modelled damage patterns and highlighting the importance of depth in damage assessment. This approach ensures that the presented scenario reflects towards a worst-case condition that is critical for urban planning and risk mitigation strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Deterministic ShakeMaps\u003c/h2\u003e \u003cp\u003eThe spatial distribution of the ground motion is evaluated for the three scenarios using four GMPEs, previously described in Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e. The analyses are conducted for all geounits, considering average, minimum and maximum Vs30 values to account for site amplification effects across the study area (see Table SM2 in the Online Resource). Please note that the ground motion results are the same for both Sd and Sa methods.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the spatial distribution of Peak Ground Acceleration (PGA) for the deterministic scenarios (1904, \u0026Oslash;stmarka and Nesodden events) using the average Vs30 shows significant variation across the Oslo region depending on the ground motion model and the location of the seismic event. The high PGA values can be attributed to a combination of two primary factors: low average Vs30 values and proximity to the event. For example, areas located closer to the \u0026Oslash;stmarka and Nesodden events experience higher ground motion intensities due to their reduced epicentral distances. Districts in the southeast of Oslo show the highest values in the \u0026Oslash;stmarka scenario, while areas in the southwest of are most affected in the Nesodden scenario.\u003c/p\u003e \u003cp\u003eThe comparison across the four GMPEs further highlights the variability in PGA predictions, underscoring the influence of model-specific attenuation relationships and local soil conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Mean Damage Ratio (MDR)\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e provides an overview of the Mean Damage Ratio (MDR) across 18 geounits for the three deterministic scenarios (1904 labeled as 1, \u0026Oslash;stmarka as 2, and Nesodden as 3) using both spectral acceleration (on the left) and spectral displacement (on the right) methods. The MDR is presented for three Vs30 cases: average Vs30 (blue line), minimum Vs30 (orange), and maximum Vs30 (grey). Scenario 1 exhibits almost negligible MDR values for both the Sa and Sd methods, indicating minimal impact for this scenario. Higher MDR values are observed when using the minimum Vs30 because lower Vs30 values represent softer soils, which amplify ground motion and result in greater structural damage. In contrast, MDR values are lowest for maximum Vs30, as higher Vs30 corresponds to stiffer soils, leading to reduced amplification and subsequently lower damage ratios. Scenario 2 and Scenario 3 demonstrate noticeable damage ratios, particularly for the Sd method. Here, MDR values reach up to 30\u0026ndash;35% in some districts when considering the lowest Vs30 values, highlighting the influence of local site effects on damage estimates.\u003c/p\u003e \u003cp\u003eThe results reveal varying levels of damage across geounits depending on the scenario and the method used. When using the Sd method and the average Vs30, the Nesodden event tends to produce higher MDR percentages in certain geounits (e.g., geounits 3, 13, and 15), indicating significant damage potential. The \u0026Oslash;stmarka event exhibits a consistent pattern of moderate damage across most geounits, with peaks in geounits 10, 11 and 13, highlighting its potential to cause widespread but moderate damage.\u003c/p\u003e \u003cp\u003eThe variation in damage ratios between the Sa and Sd methods underscores the importance of considering both approaches in seismic risk assessments, as they capture different aspects of structural response to ground shaking. A key difference between these methods lies in how damage is computed: the Sd method requires determining a performance point using the ground motion, which is then used to obtain damage from a fragility curve. This capacity curve is typically derived through numerical modeling, which can introduce higher uncertainty, particularly for buildings that are not made of reinforced concrete or steel. In contrast, the Sa method directly uses the ground motion to estimate damage from the fragility curve. This distinction often leads to the greatest discrepancies in results between the two methods in geounits with a significant difference in the number of Reinforced and Unreinforced buildings. Generally, Sa shows higher Mean Damage Ratio results when there is a prevalence of Reinforced Concrete buildings, whereas Sd tends to show higher MDR results in areas with a higher concentration of Unreinforced Masonry buildings.\u003c/p\u003e \u003cp\u003eThis analysis helps to identify the geounits most vulnerable to different types of seismic activity, providing valuable insights for targeted risk mitigation strategies. If we only focus on the Sd method using the average Vs30 values, the most affected geounit with the highest MDR are geounit 13 (Sentrum) for both 1904 and \u0026Oslash;stmarka events (with 0.018% and 8.5% MDR) and geounit 3 (Frogner) for the Nesodden scenario with 9.6% MDR. In geounit 3 the most common building typology is Timber represented by 54% of the total buildings in the neighborhood, and for geounit 13 is Unreinforced Masonry (MUR) with 47%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.1.4 Economic and human losses\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea provides an overview of the economic losses, expressed in Norwegian Krone (NOK), Mean Damage Ratio (MDR) for the three scenarios using both spectral acceleration and spectral displacement methods, and three Vs30 cases (average, minimum and maximum Vs30). Scenario 1, similar as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, shows almost negligible economic losses for both the Sa and Sd methods, indicating minimal impact for this scenario. Higher MDR values are observed when using the minimum Vs30 because lower Vs30 values represent softer soils, which amplify ground motion and result in greater structural damage. As seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the losses are lowest for maximum Vs30, and highest for minimum Vs30. Scenario 2 and Scenario 3 show noticeable losses, particularly for the Sd method. The losses can reach up to 210\u0026nbsp;billion NOK in the case of minimum Vs30 for the \u0026Oslash;stmarka scenario.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb presents result for human losses associated with the three scenarios, using both methods and average Vs30 values. The human losses are categorized into low, mid, and high injury levels, as well as fatalities, during nighttime (2 am) where we assume that 90% of the total population stay indoors.\u003c/p\u003e \u003cp\u003eConsidering the average Vs30 case, the \u0026Oslash;stmarka scenario stands out with the highest economic losses, especially when analyzed using the Sd method, with an estimated loss of approximately 44.7\u0026nbsp;billion NOK. This scenario also exhibits the highest cumulative human losses (2058), where of 94 are causalities. The Nesodden scenario, while less severe than \u0026Oslash;stmarka, still results in substantial economic losses, particularly when using the Sd method (approximately 29.2\u0026nbsp;billion NOK), and notable human losses (1402), including 68 fatalities. In contrast, the 1904 event results in minimal human losses, with only low injury reported when analyzed using the Sd method and no fatalities. The economic impact is also significantly lower compared to the other scenarios, with the highest loss being around 40.1\u0026nbsp;million NOK under the Sd method.\u003c/p\u003e \u003cp\u003eThis analysis highlights the critical differences in impact between the scenarios, with the \u0026Oslash;stmarka and Nesodden events representing the greatest risk in terms of both economic damage and human casualties, particularly during night. The comparison between the Sa and Sd methods further emphasizes the importance of considering different analytical approaches to fully understand the potential consequences of seismic events. Additionally, the choice of Vs30 values (whether average, minimum, or maximum) has a significant influence on the results, underlining the need for careful selection and sensitivity analysis when evaluating seismic risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Probabilistic approach\u003c/h2\u003e \u003cp\u003eScozzese et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) proposed two different alternatives to compute probabilistic seismic risk scenarios. The first one, named unconditional method, uses direct information to compute the seismic risk, for example using the Monte Carlo approach (Musson, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The added value of this method is its simplicity from a conceptual viewpoint and easy to implement in computational algorithms such as EqHaz (Assatourians and Atkinson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This method is considered the most robust approach; however, it requires high computational power and is therefore less widely used in practice (Rudman et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The second one, named conditional approach, requires the definition of a parameter to describe the ground motion intensity at the site of interest (IM). Here the seismic hazard is used to describe the exceedance frequency of different IMs during a given period. Then, structural analysis is used to compute the conditional probability of exceeding a given engineering demand parameter (EDP) at certain IM values (Scozzese et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vamvatsikos and Allin Cornell, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Finally, convolving both results the seismic risk is estimated (Rudman et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere we use the unconditional method due to the mentioned conceptual simplicity and since it has been implemented in the last version of SELENA (Molina et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). To avoid a long time of computation, the comparison with the deterministic approach is done only for two districts. Following Assatourians and Atkinson (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), a synthetic earthquake catalogue for the study area has been computed composed of 100 subcatalogues of 25,000 years each. This will ensure the stability and reliability of the mean risk curves and the fractiles up to 1000 years return period. Then, for each earthquake of the obtained synthetic catalogue, SELENA is computing the rate of exceedance of each Mean Damage Ratio, which is then used to calculate probabilities, assuming a Poisson process (mean curve and fractiles). A detailed explanation of the mathematical process is explained by Assatourians and Atkinson (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Probabilistic seismic risk curve in terms of MDR\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the seismic risk curve (mean and corresponding fractiles) for two districts (3 and 13), using the average Vs30 values. We have used Yenier and Atkinson (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) since it is the one which produces the highest values (so we present the most conservative results). Even though, we can see that for a return period of 475 years all the MDR values are very low. Geounit 3 (Frogner) has 0.007% and 0.12% for spectral acceleration and spectral displacement methods, respectively, while district 13 has 0.005% and 0.088%. The 97.5 percentile shows the highest MDR which can reach approximately 2% for geounit 3 when the spectral displacement approach is used pointing out the large uncertainties associated to these computations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn any case, these results allow us to point out that from a probabilistic viewpoint, Oslo is not a city with high seismic risk (in terms of annual exceedance probabilities of a given MDR) but the possible occurrence of large earthquake with a very high return period, as demonstrated by the deterministic scenarios, may cause an important impact in the city.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Discussion and conclusions","content":"\u003cp\u003eThe analysis presented in this study provides a comprehensive evaluation of seismic risk in Oslo through both deterministic and probabilistic approaches, highlighting the variability in damage estimates based on ground motion characteristics and building typologies. The results emphasize the importance of utilizing both spectral acceleration (Sa) and spectral displacement (Sd) methods, as they capture different aspects of structural response to ground shaking, providing valuable insights for targeted risk mitigation strategies in the region.\u003c/p\u003e \u003cp\u003eThe deterministic scenarios, including the 1904, \u0026Oslash;stmarka, and Nesodden events, provide valuable insights into the variability of potential earthquake impacts based on differences in earthquake magnitude, depth, and focal mechanisms.\u003c/p\u003e \u003cp\u003eConsidering the average Vs30 values, the \u0026Oslash;stmarka and Nesodden events, representing reverse and normal fault mechanisms respectively, exhibit the greatest risk in terms of economic damage and human casualties. The \u0026Oslash;stmarka scenario demonstrates the highest potential economic losses (approximately 44.7\u0026nbsp;billion NOK, corresponding to roughly 4\u0026nbsp;billion \u0026euro; based on an exchange rate of 1 \u0026euro; = 11 NOK) and human casualties, particularly during nighttime, when the population is most vulnerable. The severity of damage is influenced by the reverse fault mechanism, which generates larger ground movements and greater structural damage compared to normal faults. It is important to note that the analysis does not include damage estimates for Nesodden, despite its proximity to Oslo and significant population. This exclusion was intentional, as the study focused solely on losses and impacts within Oslo municipality. In contrast, the 1904 scenario, characterized by a deeper earthquake with lower magnitude, exhibits very low Mean Damage Ratios (MDRs), with the highest value being only 0.018% for geounit 13 (Sentrum). Historically, very limited damage was reported in this area, indicating the lower impact of such events compared to shallower, more intense scenarios.\u003c/p\u003e \u003cp\u003eThe study also emphasizes the importance of employing both spectral acceleration (Sa) and spectral displacement (Sd) methods in seismic risk assessments to characterize the uncertainty when modelling the performance of a building subjected to a given ground motion. For instance, the \u0026Oslash;stmarka and Nesodden scenarios produced higher damage ratios and economic losses using the Sd method, which highlights the impact of different analytical approaches on risk estimation. This underscores the need for a comprehensive evaluation of seismic risk using multiple methods to capture the full spectrum of potential earthquake impacts.\u003c/p\u003e \u003cp\u003eThe probabilistic scenarios, using the unconditional method with Monte Carlo simulations, further contribute to understanding seismic risk by incorporating long-term variability and uncertainty. The results suggest that Oslo is not a city with high seismic risk when considering annual exceedance probabilities of damage ratios; however, the potential occurrence of rare but large earthquakes (the return period of a magnitude 6 event in the southern part of Norway is around 120 years), as demonstrated in the deterministic scenarios, could result in significant impacts. This dual perspective from deterministic and probabilistic analyses provides a nuanced view of seismic risk, informing more effective risk mitigation and preparedness strategies.\u003c/p\u003e \u003cp\u003eThe analysis reveals that specific geounits, such as Sentrum (geounit 13) and Frogner (geounit 3), are particularly vulnerable to seismic events, with high Mean Damage Ratios (MDR) observed in both deterministic and probabilistic approaches (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) using the average Vs30.\u003c/p\u003e \n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e Economic losses in Norwegian Krone (NOK) and affected population for the three deterministic scenarios and the 475 years probabilistic scenario. The results are calculated using the average Vs30 values. The exchange rate of 1 EURO = 11 NOK is provided for reference.\u003c/p\u003e\n\u003cdiv style='margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;'\u003e\n \u003ctable style=\"border: none;width:312.8pt;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:24.1pt;border:solid black 1.0pt;padding:.15pt .15pt 0in .15pt;height:26.85pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003eID\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:111.85pt;border:solid black 1.0pt;border-left: none;padding:.15pt .15pt 0in .15pt;height:26.85pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003eScenarios\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:79.25pt;border:solid black 1.0pt;border-left:none;padding:.15pt .15pt 0in .15pt;height:26.85pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003eEconomic losses in NOK\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:97.6pt;border:solid black 1.0pt;border-left:none;padding:.15pt .15pt 0in .15pt;height:26.85pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003eAffected Population\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:24.1pt;border:solid black 1.0pt;border-top:none;padding:.15pt .15pt 0in .15pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cspan style='font-family: \"Times New Roman\",serif;color:black;'\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:111.85pt;border:none;border-right:solid black 1.0pt;background:#0070C0;padding:.15pt .15pt 0in .15pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003e1904\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:79.25pt;border-top:none;border-left:none;border-bottom:solid black 1.0pt;border-right:solid black 1.0pt;padding:.15pt .15pt 0in .15pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cspan style='font-family: \"Times New Roman\",serif;color:black;'\u003e0.040 Billion\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:97.6pt;border-top:none;border-left:none;border-bottom:solid black 1.0pt;border-right:solid black 1.0pt;padding:.15pt .15pt 0in .15pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cspan style='font-family: \"Times New Roman\",serif;color:black;'\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:24.1pt;border:solid black 1.0pt;border-top:none;padding:.15pt .15pt 0in .15pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cspan style='font-family: \"Times New Roman\",serif;color:black;'\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:111.85pt;border-top:none;border-left:none;border-bottom:solid black 1.0pt;border-right:solid black 1.0pt;background:#ED7D31;padding:.15pt .15pt 0in .15pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003e\u0026Oslash;stmarka\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:79.25pt;border-top:none;border-left:none;border-bottom:solid black 1.0pt;border-right:solid black 1.0pt;padding:.15pt .15pt 0in .15pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cspan style='font-family: \"Times New Roman\",serif;color:black;'\u003e44.7 Billion\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:97.6pt;border-top:none;border-left:none;border-bottom:solid black 1.0pt;border-right:solid black 1.0pt;padding:.15pt .15pt 0in .15pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cspan style='font-family: \"Times New Roman\",serif;color:black;'\u003e2058\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:24.1pt;border:solid black 1.0pt;border-top:none;padding:.15pt .15pt 0in .15pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cspan style='font-family: \"Times New Roman\",serif;color:black;'\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:111.85pt;border-top:none;border-left:none;border-bottom:solid black 1.0pt;border-right:solid black 1.0pt;background:#00B050;padding:.15pt .15pt 0in .15pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003eNesodden\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:79.25pt;border-top:none;border-left:none;border-bottom:solid black 1.0pt;border-right:solid black 1.0pt;padding:.15pt .15pt 0in .15pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cspan style='font-family: \"Times New Roman\",serif;color:black;'\u003e29.2 Billion\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:97.6pt;border-top:none;border-left:none;border-bottom:solid black 1.0pt;border-right:solid black 1.0pt;padding:.15pt .15pt 0in .15pt;height:15.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cspan style='font-family: \"Times New Roman\",serif;color:black;'\u003e1402\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width:135.95pt;border:solid black 1.0pt;border-top:none;padding:.15pt .15pt 0in .15pt;height:13.85pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cstrong\u003e\u003cspan style='font-family:\"Times New Roman\",serif;color:black;'\u003e475 yr Return Period\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:79.25pt;border-top:none;border-left:none;border-bottom:solid black 1.0pt;border-right:solid black 1.0pt;padding:.15pt .15pt 0in .15pt;height:13.85pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cspan style='font-family: \"Times New Roman\",serif;color:black;'\u003e0.490 Billion\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:97.6pt;border-top:none;border-left:none;border-bottom:solid black 1.0pt;border-right:solid black 1.0pt;padding:.15pt .15pt 0in .15pt;height:13.85pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;text-align:center;vertical-align:middle;'\u003e\u003cspan style='font-family: \"Times New Roman\",serif;color:black;'\u003e403\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n \u003cp\u003eThe analysis revealed that economic losses and affected populations vary significantly across the deterministic and probabilistic scenarios, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. These calculations were performed using average Vs30 values.\u003c/p\u003e \u003cp\u003eThe study identifies that the losses are a result of a combination of ground motion characteristics and building typology distributions. Geounits with high MDRs often have a high concentration of Unreinforced Masonry and Timber building types. The study indicates that while strike, and dip angles show minimal influence on damage outcomes, fault type plays a significant role, as demonstrated in the sensitivity analysis of the \u0026Oslash;stmarka scenario. Reverse fault mechanisms consistently result in higher MDR compared to normal faults, due to the compressional forces that generate larger ground movements and structural damage. However, it is important to note that the minimal influence of strike and dip angles could be related to the assumptions used in the software, which may not fully reflect geological conditions. It is important to consider that some of the sensitivity analysis results may reflect the software's limitations in simulating the geological response of the faults. Furthermore, the choice of Vs30 values (average, minimum, or maximum) has a substantial impact on the results. This highlights the necessity of incorporating a range of Vs30 values in sensitivity analyses to better account for variability in local soil conditions. We consider using the average Vs30 as more realistic since the standard deviation is not too significant from the average.\u003c/p\u003e \u003cp\u003eTo enhance the accuracy of future seismic risk assessments, several areas for improvement are highlighted:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSoil amplification data: improved soil amplification data with higher resolution and accuracy would provide more reliable ground motion estimates, thereby refining damage predictions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eConstruction cost estimates: adjusting construction cost estimates to account for inflation and updated economic conditions would yield more accurate loss estimates. Current scenario models are based on costs from late 2022 to early 2023.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eVulnerability functions: accurate and updated vulnerability functions that reflect the evolving building stock in Oslo are crucial for reliable damage assessments.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUse of advanced tools: justification for the use of SELENA, a seismic risk assessment tool, lies in its ability to integrate updated Ground Motion Prediction Equations (GMPEs), detailed building typologies, and vulnerability functions, making it suitable for modern seismic risk assessments.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo conclude, the study provides a detailed evaluation of seismic risk in Oslo, highlighting the variability of earthquake impacts across different scenarios and analytical approaches. The findings underscore the complexity of quantifying earthquake damage in urban areas and the necessity of employing both deterministic and probabilistic methods to capture the full spectrum of potential impacts. The significant variability observed based on the choice of Vs30 values underscores the critical need for future studies to better account for variability in local soil conditions and to focus on direct measurements of Vs30 to achieve a more accurate estimation.\u003c/p\u003e \u003cp\u003eThe \u0026Oslash;stmarka scenario, with its high economic and human loss estimates, exemplifies the severe consequences of shallow, reverse faulting earthquakes, while the 1904 scenario illustrates the reduced damage potential of deeper events. The probabilistic assessment offers a more nuanced perspective of seismic risk, suggesting that, while Oslo may not face high annual probabilities of severe damage, rare but significant events pose substantial risks.\u003c/p\u003e \u003cp\u003eThis dual approach offers more effective risk mitigation and preparedness strategies, serving as a valuable resource for policymakers and urban planners in developing targeted risk mitigation strategies and emphasizing the need for ongoing improvements in seismic risk assessments. It is recommended that future studies incorporate refined Vs30 data or models, to improve soil amplification data, and regularly updated economic parameters to further enhance the accuracy and applicability of seismic risk evaluations for Oslo. As Oslo continues to evolve and grow, the adoption of more sophisticated and up-to-date assessment methods (incorporating suitable GMPEs, precise building typology classifications, and accurate vulnerability functions) remains critical for enhancing the resilience of the city to future seismic events.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe results and the research showed in this article are connected to the GEObyIT project, funded by the Research Council of Norway (grant number 311596). The research connected to this publication was mainly conducted during a research stay at the University of Alicante (Spain) and it was funded by the Research Council of Norway through Funding for Research Stays Abroad for Doctoral and Postdoctoral Fellows.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eFG is the main author and contributed to the data compilation, methodology, results, discussion and conclusions. She wrote the first draft of the paper and prepared all the figures. SM, AT and VO contributed to the results, discussion and conclusions and reviewing the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors are grateful for the economic support provided by the Research Council of Norway, and to the University of Alicante (Spain) for contributing to this research.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData and Resources\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe building database used in this study contains restricted information, and therefore access is limited. Interested parties may request access by contacting the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkkar, S., Sandıkkaya, M.A., Bommer, J.J., 2014. Empirical ground-motion models for point- and extended-source crustal earthquake scenarios in Europe and the Middle East. 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Regionally adjustable generic ground-motion prediction equation based on equivalent point-source simulations: Application to central and eastern North America. Bulletin of the Seismological Society of America 105, 1989\u0026ndash;2009. https://doi.org/10.1785/0120140332\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Seismic risk, SELENA, deterministic scenario, probabilistic approach, Oslo (Norway).","lastPublishedDoi":"10.21203/rs.3.rs-5804859/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5804859/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEffectively presenting seismic risk results is crucial for ensuring that stakeholders, including policymakers and first-responders can understand and utilize the information for decision-making and risk mitigation. In this study, we conducted a comprehensive seismic risk assessment for Oslo (capital of Norway), integrating both deterministic and probabilistic approaches to capture a holistic view of potential earthquake impacts.\u003c/p\u003e \u003cp\u003eBased on the field data from exposed major faults in the Oslo rift margin and historical earthquakes in this region, the deterministic analysis examined three scenarios using SELENA software: i) an Mw 5.4 earthquake that occurred in 1904 in the Oslo rift zone; ii) a hypothetical Mw 6.0 event on the east side of the rift zone, and iii) a hypothetical Mw 6.0 event along an exposed fault zone in the central rift zone. These scenarios were selected to reflect a few possible seismic events with varying likelihoods and severities. For both, deterministic and probabilistic approaches, one specific neighbourhood in the Oslo city region emerged as the most affected area due to its dense population and older building stock, highlighting the critical interplay between physical hazard and community-specific vulnerability.\u003c/p\u003e \u003cp\u003eThe combination of deterministic and probabilistic approaches offers a detailed and nuanced understanding of seismic risks in Oslo. The findings underscore the need for targeted mitigation efforts and preparedness strategies, particularly in neighbourhoods that are more susceptible to seismic risks. By integrating these comprehensive risk assessments, the study provides valuable insights into the uneven distribution of seismic risk across Oslo. The results aim to inform local authorities and policymakers, aiding in the development of effective strategies to enhance the resilience of the city's infrastructure and population against future seismic events.\u003c/p\u003e","manuscriptTitle":"Evaluating Earthquake Impacts in Oslo, Norway: A Multi-Method Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-24 08:46:13","doi":"10.21203/rs.3.rs-5804859/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-01-23T13:44:13+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-22T11:07:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Natural Hazards","date":"2025-01-21T08:26:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-15T03:36:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2025-01-10T10:26:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0426c4c0-fd9a-4f14-afec-4bd3e5a8d360","owner":[],"postedDate":"January 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T16:35:20+00:00","versionOfRecord":{"articleIdentity":"rs-5804859","link":"https://doi.org/10.1007/s11069-025-07588-3","journal":{"identity":"natural-hazards","isVorOnly":false,"title":"Natural Hazards"},"publishedOn":"2025-08-19 16:29:25","publishedOnDateReadable":"August 19th, 2025"},"versionCreatedAt":"2025-01-24 08:46:13","video":"","vorDoi":"10.1007/s11069-025-07588-3","vorDoiUrl":"https://doi.org/10.1007/s11069-025-07588-3","workflowStages":[]},"version":"v1","identity":"rs-5804859","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5804859","identity":"rs-5804859","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}