Sedimentation Dynamics and Seismic Vulnerability Using Integrated VSM-HVSR Analysis in Lake Limboto for Disaster Mitigation

Sedimentation Dynamics and Seismic Vulnerability Using Integrated VSM-HVSR Analysis in Lake Limboto for Disaster Mitigation | 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 Sedimentation Dynamics and Seismic Vulnerability Using Integrated VSM-HVSR Analysis in Lake Limboto for Disaster Mitigation Raghel Yunginger, Muhammad Zulkifli, Nurfitri Abdul Gafur, Indriati Martha Patuti, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5716869/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Lake Limboto, located in the Gorontalo basin, is experiencing significant degradation due to sedimentation exacerbated by deforestation and unsustainable agricultural practices. Sedimentation dynamics in Lake Limboto are shaped by a combination of natural and anthropogenic processes, including erosion from the catchment area and waste generated by human activities. These processes contribute to the accumulation of thick and soft sediment layers, which in turn increases seismic vulnerability. This study aims to analyze the sedimentation dynamics and seismic vulnerability of Lake Limboto using an integrative approach that combines Vibrating Sample Magnetometer (VSM) and Horizontal-to-Vertical Spectral Ratio (HVSR) methods. This dual methodology approach provides a comprehensive understanding of the linkages between sedimentation processes and seismic hazard risk. Sediment dynamics seismic vulnerability. VSM HVSR Limboto Lake Disaster Mitigation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Limboto Lake is part of the Gorontalo Depression, formed through the complex interaction of tectonic structures in the North Arm of Sulawesi, particularly the North Sulawesi Fault and the Sangihe Fault (Katili, 1978 ; Hamilton, 1979 ): Past and present geotectonic position of Sulawesi, Indonesia, Tectonophysics, 45, 289–322). The dynamic interplay between these faults generates compressional forces that cause subsidence, creating the Gorontalo Depression basin where Limboto Lake is located. This tectonic activity also influences the lithological patterns in the lake area (Hall et al, 2002; Pholbud, et al, 2012 ). According to Apandi and Bachri ( 1997 ), the bedrock around the Limboto basin consists of lake sediment formations, indicating a prolonged and consistent sedimentation process. Cottam et al, 2011 ; Nugraha et al, 2022 further noted that during the Holocene, the Limboto basin underwent significant changes due to tectonic activity and sea level fluctuations, resulting in distinctive lithologies around the lake, including reef limestone, clastic limestone, and other sedimentary units. These lake deposits, as the primary bedrock of Limboto Lake, reflect a complex interplay of tectonic activity, sedimentation processes, and environmental changes over millions of years. The natural process of sedimentation is an integral part of a lake's environmental dynamics (Putra et al 2013 ). However, human activities such as deforestation, unsustainable agricultural practices, and uncontrolled development have significantly accelerated sedimentation and contributed to the shrinkage of Lake Limboto's area Limboto Lake serves as the estuary for 23 rivers that transport sediment, yet it has only one outlet, the Tapodu River. This imbalance in the hydrological system reduces the lake's capacity to regulate the flow of incoming and outgoing water and sediment effectively. Consequently, Lake Limboto, which covered approximately 7,000 hectares with an average depth of 30 meters in 1923, has drastically diminished to around 3,000 hectares with an average depth of just 3 meters today (Kimijima et al., 2022; Yunginger et al., 2024 ). According to Sarson et al (2020), significant portions of the shallow Lake Limboto area and its buffer zones have been converted into dense residential areas (1,272 hectares), public infrastructure developments (3,594 hectares), and agricultural land (966 hectares). This land ownership lacks proper administrative documentation in accordance with regulations. The transformation of these areas, which are part of the lake's sediment basin, has heightened vulnerability to flooding. Yan and Long ( 2024 ) highlighted that lake sediments typically have low density and an unconsolidated composition, which can amplify seismic waves. This characteristic increases the risk of damage to residential settlements located on lake areas dominated by sedimentary layers or deposits. Despite this, the sediment dynamics and characteristics of Lake Limboto have not been comprehensively mapped. Furthermore, no clear framework exists to link these sediment dynamics with potential seismic vulnerabilities. This gap in knowledge poses challenges for organizing safer settlement zones and ensuring the sustainable conservation of Lake Limboto, allowing it to function optimally over the long term. Sediment dynamics have been analyzed using various methods, including radionuclide dating to establish sediment chronology (Goharrokhi et al., 2021 ; Begi et al., 2023); geostatistical techniques (Yan and Long, 2024 ); Universal Soil Loss Equation (USLE) and Sediment Delivery Ratio (SDR) modeling (Hendratta et al., 2024 ); bathymetric surveys and satellite imaging (Samiev et al., 2023 ); and geotechnical methods combining sediment characterization with hydrological modeling (Kabir et al., 2009; Hartono and Yoshimura, 2020 ). These approaches predominantly focus on the bulk properties of sediments and often overlook the specific magnetic characteristics of sediments. Such magnetic properties can provide valuable insights into sediment transport dynamics, deposition rates, and sediment sources, which are critical for understanding environmental changes that impact lake health (Lascu and Planck, 2013). According to Liu et al ( 2012 ) and Hapsoro et al. ( 2023 ), the sedimentation environment influences the magnetic characteristics of sediments, providing insights into sediment control and transport processes. Additionally, Evan and Heller (2003) and Wang et al. ( 2020 ) noted that environmental magnetic approaches, such as Vibrating Sample Magnetometer (VSM) tests, can effectively record environmental changes by detecting magnetic characteristics of sediments, even at low concentrations of magnetic minerals. Minor magnetic concentrations, often undetectable by methods focusing solely on bulk properties, are captured by VSM tests, which emphasize specific magnetic characteristics in sediment dynamics. Hatfield and Maher et al, 2009 and Yang et al, 2023 further confirmed that VSM tests can analyze sediment structure and composition, trace sediment sources, and identify mineralogical characteristics, sediment transport processes, and the causes of environmental changes in lake systems. Variations in sediment magnetic mineral characteristics—such as concentration, grain size, and magnetic domain—are closely linked to transportation processes and sediment sources. These variations serve as critical indicators for understanding the dynamics and stability of sedimentation in lakes (Maher et al., 2009 ; Wang et al., 2020 ). Lake sedimentation dynamics exhibit characteristics specific to the lake environment, influencing both sediment consolidation levels and seismic amplification potential (Verma et al., 2014 ). Seismic vulnerability reflects an area's susceptibility to earthquake impacts and is analyzed using the Horizontal-to-Vertical Spectral Ratio (HVSR), which evaluates the spectral ratio between the horizontal and vertical components of seismic waves (Nakamura, 1989 ; Anbazhagan et al., 2019 ). The ability of Vibrating Sample Magnetometer (VSM) analysis to uncover lake sedimentation dynamics could be further enhanced by examining its relationship with seismic vulnerability in the Limboto Lake area using HVSR analysis. Combining VSM and HVSR approaches offers a novel and comprehensive model, supported by sediment drill log data, to evaluate sedimentation dynamics and the seismic susceptibility index in the Limboto Lake area. This study aims to integrate VSM and HVSR analyses to better understand the sedimentation dynamics and seismic vulnerability of the Lake Limboto region. By combining these methodologies, the research not only showcases cutting-edge analytical techniques but also provides innovative solutions for addressing seismic risks and supporting conservation efforts in the lake area. The findings are expected to serve as a foundation for designing effective seismic disaster mitigation strategies for residential zones and ensuring the sustainable conservation of Lake Limboto, optimizing its capacity and functionality for future generations. Theory and Methodology Geology of the Study Area Limboto Lake is part of the Gorontalo Low Depression, which is believed to have been uplifted during the Plio-Pleistocene due to the Sangihe subduction. This is evidenced by the presence of coral growth that formed elevated limestone deposits around the lake area. Additionally, the Gorontalo Low is influenced by the Gorontalo Fault, a horizontal fault oriented perpendicular to the forces generated by the Sangihe thrust (Pholbud et al, 2012 ; Sidarto and Bachri, 2013 ). Today, the Gorontalo Low extends into the Paguyaman Valley, which is thought to have originally been an ancient lake deposit. The tectonic activity in the North Arm of Sulawesi, spanning from the Eocene to the Pleistocene and Holocene, has resulted in the lithological units surrounding Limboto Lake being predominantly composed of limestone formations, including lake sediment units, reef limestone, and clastic limestone. In contrast, the areas to the northwest and north of Limboto Lake are characterized by the presence of Bilungala volcanic rock formations and the Bone Diorite formation (Fig. 1 ) (Apandi and Bachri, 1997 ; Bachri et al., 2012). The lithology of the study area is primarily dominated by the Lake Sediment Unit (Qpl), estimated to have formed during the Early Pleistocene. This unit consists of brownish-gray clay, sandstone, and gravel. These sediments are predominantly distributed across the Paguyaman Valley and the area surrounding Lake Limboto, underlain by diorite rocks (Apandi and Bachri, 1997 ; Bachri et al., 2012). VSM (Vibrating Sample Magnetometer) The magnetic properties of materials primarily arise from orbital and spin movements, along with electron interactions, which are highly influenced by factors such as magnetic mineral content, grain size, temperature, and pressure. These magnetic properties are useful for studying environmental changes because magnetic minerals are highly sensitive to such changes (Hunt et al., 1995 ; Evans and Heller, 2003 ; Demory et al., 2004 ; Wise, 2010). One environmental parameter that can reveal the nature of magnetic minerals is the Vibrating Sample Magnetometer (VSM) test, which provides information about the quantity, grain size, magnetic domain, and mineralogy in sediments. The VSM is a tool known for its ability to measure magnetic moments with high precision. It allows the analysis of the magnetic properties of rock and sediment samples, both in their remanent states and when influenced by external magnetic fields (Dunlop dan Ozdemir, 1997; Liu et al, 2012 ). VSM is used to determine and study the magnetic properties of materials, particularly in relation to changes in external magnetic fields, which are depicted in the form of hysteresis curves. A hysteresis curve represents the relationship between magnetic intensity (H) and the magnetic field (B), showing the magnetic flux retained after an increase or decrease in magnetization force. This curve illustrates parameters such as saturation magnetization (Ms), coercivity (Hc), and magnetic remanence (Mr) (Dunlop and Ozdemir, 1997; Maher et al., 2009 ). The Ms value indicates the saturation state, where the magnetic field (B) remains constant even with continuous increases in the external field. It also reflects the ability of particles to maintain the alignment of their magnetic domains under an external magnetic field, or the amount of external field required to achieve magnetization. The Mr value represents the residual magnetic field (M) remaining after the external magnetic field (Hc) is removed or reduced to zero. The coercivity (Hc) is the amount of external field needed to eliminate the material’s magnetic properties or to return the magnetization to zero, reflecting the magnetic properties of the material. Microtremor Microtremor, also known as ambient noise, refers to ground vibrations with microtremor amplitudes caused by both natural and human factors. Natural events include wind, ground shaking, land movement, earthquakes, and ocean waves, while human activities encompass industrial operations, buildings, vehicles, and other sources (Kang et al., 2020 ; Siburian et al., 2024 ). Microtremors can also be described as natural harmonic vibrations in the soil, occurring continuously and trapped in the surface sediment layer. These vibrations are reflected by the presence of a layer boundary plane with a fixed frequency, resulting from micro-vibrations below the surface and other natural activities (Nakamura, 2000 ). Research on microtremors can help determine the characteristics of soil layers based on their dominant frequency parameters and wave amplification factors. The recorded microtremor data is analyzed using the HVSR (Horizontal-to-Vertical Spectral Ratio) method, which produces the HVSR spectrum (Nakamura, 1989 ). The HVSR method is a non-invasive technique used to understand the nature of subsurface structures without disturbing them. This method provides key parameters, such as dominant frequency and amplification values, which are linked to the physical properties of the subsurface. Additionally, the HVSR method helps explain the phenomenon of resonant frequency in the surface layer by taking measurements at a given location and producing three components: vertical, horizontal north-south, and horizontal east-west. According to Nakamura ( 1989 , 2000 ), the amplitude and frequency of the HVSR peak represent the amplification and frequency characteristics of the local site. Site effects arise due to the presence of sedimentary layers that fill half of the basin above the bedrock. In this context, four motion components are involved: horizontal and vertical motion in the bedrock, and horizontal and vertical motion at the surface. The HVSR method is effectively used to determine the dynamics of soil layers over large areas, as it is considered a highly efficient technique. Nakamura introduced the HVSR method to characterize the dynamics of the surface soil layer caused by local site effects during earthquakes (Nakamura, 1997 ). This method relies on comparing the horizontal and vertical components, assuming that the ratio of the horizontal and vertical spectra of surface vibrations is a function of displacement. Figure 2 illustrates seismic motion on the ground surface, where the amplification factor, or amplitude, of horizontal and vertical motion is in direct contact with the bedrock in the basin area. The amplification factors of horizontal and vertical motion in the sedimentary layer are denoted as Th and Tv (Nakamura, 2000 ; Rivera et al, 2024 ). The amplification factor of horizontal motion in the sediment layer is denoted by (TH), while the amplification factor of vertical motion is denoted by (TV) (Nakamura, 2000 ). The site effect on the sediment layer's surface is usually described by comparing the amplification factors of horizontal and vertical motion at the sediment surface. Natural Frequency ( fo ) The natural frequency, or dominant frequency, is the frequency that occurs most frequently. Research by Kang et al, ( 2020 ) and Siburian et al. ( 2024 ) shows that peak frequency values change with geological variations. The natural frequency of the soil is related to the dominant period of the soil, meaning that the natural frequency measured at the surface can reflect the characteristics of the underlying rock. Nakamura, 1989 ; Capobianco, et al ( 2024 ) stated that the natural frequency of an area is influenced by the thickness of the weathered layer (h) and the average subsurface velocity (vs), which can be formulated as shown in Eq. 1. f_o = Vs/4h 1) Period Dominant The dominant period value is the time it takes for microseismic waves to travel through the soil layer or experience one reflection from the reflection plane to the surface. This value indicates the characteristics of the rock layers in an area. According to Nakamura ( 1989 , 2000 ), a high dominant period suggests thick soft sediments, while a low dominant period indicates thin soft sediments. Areas with a high dominant period are generally more vulnerable to damage during an earthquake. The dominant period value is calculated using Eq. 2. T〗_o = 1/f_o (2) Amplification Factor According to Nakamura et al. (2000), amplification occurs when seismic waves are magnified due to a significant difference between layers. Specifically, seismic waves are amplified when they pass through a softer medium compared to the initial medium they traveled through. The greater the difference between the layers, the greater the wave amplification. Soil amplification values are related to the impedance contrast between the surface layer and the underlying layer. A high impedance contrast between the two layers results in a higher amplification factor, and vice versa. The relationship between the impedance contrast and soil amplification value is calculated using Eq. 3. A_o=(ρ_b V_b)/(ρ_s V_s ) 3) Seismic Vulnerability Index ( Kg) Seismic vulnerability index values are used to estimate an area's susceptibility to landslides, which are derived from the relationship between natural frequency and amplification or also derived from surface measurements and changes due to earthquake effects. The vulnerability of the soil also needs to consider the value of the strain shift (γ) in the soil layer during an earthquake (Nakamura, 1997 ). In determining the seismic vulnerability index, it is necessary to consider the shear strain on the ground surface. Figure 3 represents the shear strain deformation of the ground surface, the value of γ is shown in equations 4 and 5. γ = A_(m ) x d/H 4) d = a_b/(2πf_0 )^2 5) The Kg value is the most easily identifiable vulnerability index from the measurement location. The ground velocity, Vb, represents the shear wave velocity at the bedrock, and this value is assumed to be constant. The Kg value indicates the measurement used to determine the level of resilience. The seismic vulnerability index is calculated by squaring the peak value of the microtremor spectrum and dividing it by the resonance frequency. Mathematically, the relationship between the seismic susceptibility index (Kg), natural frequency (fo), and amplification factor (Ao) is expressed in Eq. 6 (Nakamura, 2008). Kg=〖A_o〗^2/f_0 6) High Kg values are generally found in soils with soft sedimentary rock lithologies. This high value illustrates that the area is vulnerable to earthquakes and in the event of an earthquake can experience strong shaking. Lake Limboto vertical sediment sampling and VSM test Vertical sediment samples from Lake Limboto were collected at two drilling points, each with a depth of 20 meters, as shown on the map in Fig. 4 . Point BH01 is located near residential areas and geothermal water manifestations, which are also tourist attractions. Point BH02 is situated near a residential area and agricultural land. For VSM testing, samples were chosen based on the sediment characteristics represented in the drill log data. Wet sediment samples were sieved using a 325 mesh sieve to achieve a homogeneous sediment size, then dried at room temperature. The dried sediments were pulverized into a bulk form and prepared for testing using an OXFORD VSM 1,2H Vibrating Sample Magnetometer (VSM). The VSM testing was conducted at the Testing Laboratory of the Isotope and Radiation Technology Application Center of BATAN, Indonesia. Acquisition of microtremor data The microseismic measurements were conducted using a Taide TDL-303S digital seismograph. Figure 1 shows the location map of 24 microtremor measurement points across Gorontalo Regency, including Telaga, Telaga Biru, Limboto, West Limboto, Batudaa, and Gorontalo City, with a focus on Lekobalo Village in West City. The microtremor measurement tool recorded three types of waves: one vertical seismic wave and two horizontal seismic waves, all in CDM format. The recorded data was then processed using Geopsy software, which applies Fourier transformation to generate the HVSR curve. The data was validated using the standard deviation based on the Site Effects Assessment Using Ambient Excitations (SESAME 2004) guidelines. From the HVSR curve, the values of fo, Ao, and To were determined to calculate the seismic vulnerability index (Kg) using Eq. 6. Results and Discussions VSM Data on Hysteresis Curve Ten sediment samples were tested using VSM at depths of 1m, 6m, 10m, 15m, and 20m at both BH01 and BH02. These samples were selected based on sediment type as indicated in the sediment lithology data core from the drill log shown in Fig. 5 B. The VSM test results were analyzed using hysteresis curves to examine the magnetic mineral characteristics that reflect the sediment dynamics in Lake Limboto. The relationship between the magnetic field (H) and magnetization (M), shown in Fig. 6 , reveals that the magnetization process of magnetic minerals in the samples from BH01 (at depths of 1m, 14m, and 20m) increases sharply as the external magnetic field intensifies, eventually saturating around 300 mT. The saturation magnetization value (Ms) at these depths is notably high, with values of 48.00 emu/gr at 1m, 30.61 emu/gr at 15m, and 47.70 emu/gr at 20m. When the external magnetic field is reduced to zero, the curve does not return to its initial position but instead forms a distinct curve with a very small hysteresis loop area. The remanent magnetization (Mr) values range from 1.25 to 2.16 emu/gr, and the coercivity (Hcr) values range from 20 to 26 mT. This indicates that the magnetic minerals at these depths respond easily to external magnetic fields, suggesting that the dipole moments of the minerals quickly align with the field. Additionally, the magnetic materials in the sediments tend to retain very low residual magnetization once the external magnetic field is removed. This behavior reflects the presence of soft ferromagnetic minerals in the sediments. Interestingly, at depths of 6m and 10m, the magnetic material undergoes magnetization, although the saturation magnetization (Ms) values are much lower compared to the depths of 1m, 14m, and 20m. At 6m, the Ms value is approximately 12.60 emu/gr, and at 10m, it is only about 2.77 emu/gr, with both depths reaching saturation at induced fields above 500 mT. The magnetic minerals at these depths also easily lose their magnetization, as evidenced by the remanent magnetization (Mr) values of about 0.23 emu/gr at Hcr of 19.36 mT at 6m, and 0.019 emu/gr at Hcr of 6.17 mT at 10m. Overall, these data suggest that the magnetic minerals in the BH01 sediments are easily magnetized by external magnetic fields and also lose their magnetization properties quickly. This behavior is characteristic of soft ferromagnetic minerals such as magnetite (Fe3O4) or hematite (Fe2O3) (Jordanova et al, 2004 ; Hatfield dan Maher, 2008,; Mariyanto et al, 2021). The magnetic mineral behavior at BH01 is similar to that observed in sediments at BH02, although the lowest Ms values are found at depths of 6m, 14m, and 20m. HVSR Data for Seismic Susceptibility Microtremor measurement data consists of seismic wave signal data in the time domain for the study area. Figure 7 shows an example of the HVSR curve from the measurement results, which have been validated using standard deviation based on the SESAME 2004 standard, ensuring reliability and clear peak criteria for the H/V curve. Based on the HVSR curves, the values of fo, To, Ao, and Kg are presented in Table 1 . The dominant frequency, which is related to the depth of the wave reflection field at the research site, ranges from 0.211 Hz (at point A2) to 2.556 Hz (at point D1). The highest amplification factor (Ao) value is 5.246 at point A5, while the lowest is 1.649 at point B12. The highest dominant period (To) value is 4.738 at point A2, and the lowest is 0.391 at point D1. The highest seismic vulnerability index (Kg) is 89.907 cm/s² at point A2, and the lowest is 0.488 at point D1. Table 1 Data fo, To, Ao and Kg Based on HVSR Analysis at the Research Site Titik fo (Hz) To (sekon) A0 Kg (cm/s2) A1 0,227 4,400 3,194 61,834 A2 0,211 4,738 4,005 89,907 A3 0,841 1,189 2,909 4,110 A4 1,722 0,581 2,725 0,920 A5 1,809 0,553 5,246 1,604 A6 0,567 1,765 2,173 6,767 A7 1,522 0,657 2,228 0,962 A8 1,485 0,674 2,402 1,090 A9 0,610 1,639 2,979 8,000 A10 0,821 1,218 2,158 3,203 B1 1,448 0,690 2,171 1,035 B2 1,025 0,976 3,898 3,710 B3 1,189 0,841 3,231 2,287 B4 1,000 1,000 3,489 3,489 B5 0,762 1,312 2,496 4,296 B6 0,821 1,218 2,269 3,369 B7 0,841 1,189 2,528 3,572 B8 0,884 1,131 2,388 3,057 B9 0,567 1,765 4,457 13,877 B10 1,379 0,725 3,277 1,725 Discussions Analysis of the interrelated relationship between sedimentation dynamics and seismic vulnerability based on hysteresis and HVSR curve data. Environmental changes in Lake Limboto are closely linked to sediment dynamics, which are influenced by both natural conditions and anthropogenic activities from upstream to downstream of the lake. These environmental changes can be recorded based on the magnetic properties of minerals in rocks and sediments, making it valuable to analyze sediment dynamics, including sediment sources, transport processes, and deposition processes, all of which contribute to the degradation of Lake Limboto. According to the magnetic mineral analysis using the hysteresis curve in Fig. 6 , the sediment layers in BH01 at depths of 1m, 14m, and 20m exhibit rapid magnetization and have higher saturation magnetization values compared to the 6m and 10m depths. These magnetic mineral properties were found in relatively unconsolidated fine sand and silty sand sedimentary layers at depths of 1–5 meters, and then at depths of 15–20 meters, as shown in the drill log data in Fig. 5 A. Low magnetization values are observed at depths of 6m and 10m, corresponding to sedimentary layers containing clay and silt at depths of 5–14 meters. The magnetic properties of minerals in the sandy sediments of BH01 align with those found at point BH02 at depths of 1–4 meters (Fig. 5 B). Similarly, the magnetic properties with low magnetization in silt loam sediments at BH01 correspond to the silt loam layers at depths of 5–7 meters and 12–20 meters at point BH02 (Fig. 5 B). This indicates that magnetic mineral facies tend to exhibit higher saturation magnetization and a better ability to retain magnetization in fine sand layers compared to silt loam layers. According to Tamuntuan et al. ( 2015 ) and Wang et al. ( 2020 ), magnetic minerals that are quickly magnetized and demagnetized exhibit the characteristics of soft ferrimagnetic minerals, such as magnetite (Fe₃O₄), with larger grain sizes (multi-domain, MD) or medium sizes (pseudo-single domain, PSD). Habsporo et al. (2023) and Yang et al. ( 2023 ) observed that soft ferrimagnetic minerals that magnetize rapidly under fields below 300 mT, such as magnetite, often originate from rocks in catchment areas eroded by physical processes like water currents. These minerals are then mixed with newer sediments and deposited along lake shores. Mariyanto et al. ( 2019 ) noted that ferromagnetic minerals with finer grains (single domain, SD) and PSD, such as those found in the Brantas River, tend to form due to geological or anthropogenic activities, including industrial and agricultural waste. Similarly, Yunginger et al. ( 2018 ) and Bijaksana et al. ( 2019 ) found that surface sediments in Lake Limboto, particularly near BH01, are dominated by magnetic minerals originating from the Bionga and Talumelito sub-watersheds. These regions are rich in magnetite minerals, which are derived not only from natural erosion processes but also from anthropogenic activities such as agricultural and residential waste. This suggests that sediment dynamics in Lake Limboto at depths of 0–5 meters and 15–20 meters in BH01, and at depths of 1–4 meters in BH02, reflect periods of high-energy sediment input. Events such as significant floods likely carried material rich in magnetic minerals (PSD and MD) from the catchment areas, influenced by both natural processes and anthropogenic contributions. Interestingly, the sediment lithology shown in Fig. 5 B reveals that the sediments at depths of 0–5 meters and 15–20 meters in BH01 are unconsolidated (loose sand) and relatively soft, as the particles are weakly bound. According to Santamarina and Cho ( 2004 ), fine sand has the potential to move or shift under pressure, which gives it an appearance of structural weakness. The magnetic minerals in silty clay sediments located at depths of 5–14 meters in BH01 and depths of 5–7 meters and 12–20 meters in BH02 exhibit low saturation magnetization (Ms) and low saturation field values. These characteristics align with the behavior of soft ferromagnetic minerals such as hematite (Fe₂O₃) (Dunlop and Özdemir 1997; Evans and Heller 2003 ). According to Yang et al. (2024), magnetic minerals with low saturation values, such as hematite, are typically fine-grained and deposited in calm environments less influenced by flood events or high-energy sediment transport. Sediment sources in such environments are often dominated by non-magnetic materials from lake sediment formations. Evans and Heller ( 2003 ), Boar and Harper ( 2002 ), and Yang et al. (2024) further explained that the low magnetization and low saturation behavior of magnetic minerals can result from reduction processes caused by high organic matter content in lake water, leading to low oxygen levels. These conditions can transform magnetic minerals such as magnetite (Fe₃O₄) into maghemite (γ-Fe₂O₃) or even hematite (Fe₂O₃) (Jordanova et al, 2004 ; Hatfield and Maher, 2008 ). This indicates that the magnetic minerals in the silty clay sediments of both BH01 and BH02 originated from deposition areas distant from erosion sources. These sediments comprise fine particles enriched with organic matter, contributing to their low magnetic properties. According to Santamarina and Cho, 2004 and Udanjargal et al, 2022., small particles such as clays and silts increase the plasticity of sediments. Under water-saturated conditions, these fine particles exhibit weak intergranular forces, contributing to the soft and easily deformable nature of the sediments under stress. Additionally, the weak inter-grain bonds in the wet state make silty clay sediments soft and susceptible to deformation. This analysis confirms that the sedimentary structure of Lake Limboto consists of a surface layer of loose fine sand overlying a finer, softer, and thicker layer of silty clay sediments in both BH01 and BH02. Relationship between lake sediment dynamics and HVSR Sediment dynamics in the Limboto Lake area, as indicated by the magnetic mineral properties at depths of 1–20 meters in both BH01 and BH02, reflect the processes of sediment deposition driven by sediment transport energy from the catchment area with reduced land cover. Additionally, sediment sources originate from the surrounding lake environment, comprising fine-grained silt loam influenced by organic matter transported into the lake and associated with magnetic minerals in the sediments. These ongoing sediment dynamics have resulted in a thick sediment layer, contributing to the shallowing of Lake Limboto and a reduction in its area (Kimijima, 2020; Yunginger et al., 2024 ). This observation is supported by the HVSR analysis, which shows that the study area is dominated by dominant frequency values (fo) of less than 2.5 Hz (Table 1 ), indicating thick sedimentary layers with a thickness h > 30h > 30 meters (Kanai 1983, Konno dan Ohmachi, 1995, Percindira et al, 2023 ). Notably, areas A1, A2, and A3 exhibit fo values of less than 0.6 Hz, as illustrated in Fig. 8 a. Furthermore, the dominant period (To) values in Table 1 reveal that most of the study area (95.83%) has To values greater than 0.4 seconds, suggesting the prevalence of Type C sedimentary layers, characterized as soft sediment layers (Zhao et al, 2004 , Percindira et al, 2023 ). These areas are marked in pink on the map in Fig. 8 b. The HVSR analysis findings align with the magnetic mineral analysis, which is dominated by magnetite with SD and PSD domain properties found in silt loam sediment types. This consistency underscores the relationship between sedimentary characteristics and sediment dynamics in the Limboto Lake area. An intriguing finding from the HVSR analysis is that the thick and soft sediments in the study area exhibit amplification factor (Ao) values exceeding 3 (Table 1 ). This amplification is closely associated with the density and wave propagation speed of the bedrock. A high impedance contrast between the sediment layer and the bedrock indicates a high amplification factor, and conversely, a low impedance contrast results in a lower amplification factor (Zhao et al., 2004 ; Percindira et al., 2023 ). The amplification value indicates whether seismic waves are magnified as they propagate between the surface and underlying layers. Amplification increases with the length of wave propagation, a condition typically observed in soft sedimentary layers. According to Nakamura ( 1989 , 2000 ), an amplification value greater than 3 poses a significant hazard, as it signifies greater wave magnification compared to the initial wave. This implies that areas with high amplification values (> 3) and soft sediment layers are more likely to experience stronger earthquake shaking, categorized as moderate in severity. Figure 9 highlights areas with amplification values in the range of 3 < Ao < 6. These include the Pentadio Resort area (points A1, A2, A5) in Pilohayanga Village, Telaga Subdistrict (B2), Lupoyo Village, Telaga Biru Subdistrict (B3), Bulota Village, Telaga Jaya Subdistrict (B4), the Limboto Lake Outlet (B9), Tabongo Village, Batudaa Subdistrict (B10, B11), and Lekobalo Village, West City (D1). Previous studies (Nakamura, 2000 ; Zhao et al., 2004 ; Lotti et al., 2018 ; Capobianco et al., 2024 ) have shown that areas characterized by low fo and high Ao values—or areas with thick, soft sediment layers—are associated with a high seismic vulnerability index (Kg), increasing the risk of earthquake damage. The Kg values presented in Table 1 indicate that Kg > 9 cm/s2K_g > 9 \, \text{cm/s}^2 (Nakamura, 2000 ; and Akkaya, 2020 ), signifying a high vulnerability to earthquake-induced damage. Areas with Kg > 9K_g > 9 values are highlighted in red on the map in Fig. 10 . Notably, the Pentadio Resort tourist area (A1 and A2) exhibits exceptionally high KgK_g values of 61.834 cm/s261.834 \, \text{cm/s}^2 and 89.907 cm/s289.907 \, \text{cm/s}^2, respectively. This area, which is adjacent to Lake Limboto and features a geothermal water source, is likely affected by low sediment consolidation. Consequently, the Pentadio Resort area is particularly susceptible to significant ground shaking during earthquakes. Additionally, area B9, which is the outlet area of Lake Limboto, has a Kg value of approximately 13,877 cm/s², making it highly vulnerable to earthquake events. Areas with a Kg value between 5 and 10 cm/s², which are also highly seismic, include Kayubulan Village (A6) and the Alopohu River area in Hutabohu Village, West Limboto Sub-district (A9). Meanwhile, areas with a lower risk of earthquake damage, classified as having Kg values ≤ 3 cm/s², are located in the blue-colored zones. These include Dutulanaa and Bolihuangga villages in Limboto Sub-district, Tabongo and Iluta villages in Batudaa Sub-district, and Lekobalo Village in North Town. Areas with a high seismic vulnerability index around Lake Limboto, particularly the Pentadio Resort area, Lake Limboto Outlet, Kayubulan Village, and the Alopohu River area, are attributed to the thick and soft sedimentary characteristics of these regions. This conclusion is supported by vertical sediment profiles drilled to a depth of 20 meters in Lake Limboto (Fig. 5 ). These findings indicate that the soft soil or unconsolidated rock in residential areas near Lake Limboto exhibits a long vibration period, leading to significant wave amplification during earthquake events. Sediment dynamics, based on mineral properties, confirm that sediment supply to Lake Limboto originates from the catchment area, influenced by both natural processes and anthropogenic activities. These factors result in thick layers of fine sand, clay, and silt, transported by varying energy levels. The surrounding area has become densely populated, with activities such as agriculture and trade contributing pollutants to the lake's water (Yunginger et al., 2018 ; Yunginger et al., 2024 ). Additionally, the soft, thick sediments in the Limboto Lake area are influenced by the lake's own sediments, which contain low levels of magnetic minerals and are further impacted by organic matter during deposition, increasing sediment softness. Therefore, conserving Lake Limboto is crucial, particularly by reducing sediment supply from the catchment area, which is causing the lake's sediment to thicken as the surrounding area becomes more developed. Furthermore, it is essential to minimize anthropogenic activities that add organic matter to the sediments, contributing to their softness. These conservation efforts align with measures to mitigate the risk of seismic disasters for the local community. Moreover, to reduce vulnerability to seismic vibrations or earthquakes in residential areas near Lake Limboto, it is important to design building infrastructure that can withstand stress and ensure safety from earthquake impacts. Conclusion Sediment dynamics, as inferred from mineral properties, confirm that the sediment supply originates from the catchment area and is influenced by high-energy transport processes. These processes carry sediments derived from both natural and anthropogenic activities, particularly in the upper layers of lake sediments (1–5 m and 15–20 m) at BH01 and the 0–4 m layer at BH02. This transport results in the deposition of thick sediment layers composed of unconsolidated silty fine sand. These sediments overlay a thicker, finer, and softer silty clay layer, which is influenced by local sediment dynamics. These dynamics include non-magnetic sediments associated with organic matter resulting from anthropogenic activities, leading to reduced magnetic mineral properties at depths of 6 m and 10 m at BH01 and 6 m, 14 m, and 20 m at BH02. This reduction contributes to the increased softness of the sediment layers. The sediment dynamics contribute to the accumulation of thick, yet soft, sediment layers. VSM analysis reveals a sediment thickness exceeding 30 meters with varying magnetic mineral characteristics, correlating with HVSR analysis results. The HVSR data indicate dominant frequency values (fo 0.4 s), which suggest the potential for seismic wave amplification, as reflected by high amplification factor values (Ao > 3). Areas with low fo but high Ao values are particularly susceptible, resulting in elevated seismic vulnerability indices (Kg). These high Kg values are especially pronounced in the Pentadio Resort area (A1, A2, A3), the Tapodu River Lake outlet area (B9), Kayubulan Village (A6), and the Alopohu River area (A9). The VSM data further indicate that sediment dynamics are significantly influenced by anthropogenic activities, such as agricultural runoff and catchment area degradation, which exacerbate sediment accumulation and reduce consolidation. These factors contribute to HVSR findings that highlight areas of significant seismic amplification and susceptibility. The unconsolidated fine sand layer in the upper sediments is particularly prone to particle movement under stress, while the underlying silty clay layer, despite its high plasticity, remains soft and susceptible to deformation under external pressure. This combination worsens the overall stability of the sediment layers. The connection between VSM and HVSR data demonstrates that the magnetic mineral properties of sediments, particularly in soft layers, have a direct impact on seismic vulnerability characteristics. These findings emphasize the urgent need for seismic disaster risk mitigation in the Lake Limboto area. Key strategies include effective management of the upstream-to-downstream regions to reduce sediment supply, curbing anthropogenic activities, and designing earthquake-resistant infrastructure in vulnerable zones. Furthermore, to mitigate seismic vibration risks in residential areas near Lake Limboto, reducing building density and limiting heavy structures that exert substantial pressure on the soft sediment mass is critical. Declarations Acknowledgements The authors extend their heartfelt gratitude to the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia for providing research funding to Raghel Yunginger under the regular fundamental research scheme. The authors appreciation also goes to the Rector and Head of LPPM at Gorontalo State University for their continuous support in facilitating this research. Also, the authors grateful to the Sulawesi II River Basin Authority and the Gorontalo Provincial Government for granting permission to conduct research in Lake Limboto. Special thanks to the Head of the Physics and Civil Engineering Laboratory at Gorontalo State University for assisting with sediment sample preparation. The authors acknowledge the facilities, scientific and technical support form Advanced Nuclear Materials Laboratories – Nuclear Energy Research Organization, National Research and Innovation Agency through E- Layanan Sains-BRIN. Author Contributions R.Y., M.Z., N.A.G., drafted the manuscript and performed the data analysis. I.M.P., M.K., I.S., A.B., A.B., S.S., A.W., conducted field investigations, provide critical feedback and concluded the initiation mechanism. All authors reviewed and approved the final manuscript. Funding This research was financially by the Ministry of Education, Culture, Research and Technology of the Republic of Indonesia, fiscal year 2024 with contract number 063/E5/PG.02.00.PL/2024, and contract number 914/UN47.D1.1/PT.01.03/2024 with the Decree of the State University Rector Number 733/UN47/HK.02/2024. Author Information 1 Physics Department, Faculty of Mathematic and Natural Sciences, Universitas Negeri Gorontalo, 96128, Indonesia 2 Agency for Meteorology, Climatology, and Geophysics, Manado, Indonesia 3 Research Center for Environment and Clean Technology National Research and Innovation Agency, South Tangerang City, Banten 1534, Indonesia 4 Civil Engineering Department, Faculty of Engineering, Universitas Negeri Gorontalo, 96128, Indonesia 5 Geology Engineering Department, Faculty of Mathematic and Natural Sciences, Universitas Negeri Gorontalo, 96128, Indonesia 6 Faculty of Mathematics and Natural Sciences, Universitas Lambung Mangkurat, Banjarmasin, Indonesia 7 Mine Engineering Department, Universitas Muhammadiyah Mataram, Indonesia Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare no competing interests. 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14:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5716869/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5716869/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72589584,"identity":"3667c3e4-ee93-49b7-962b-b2bd9a228b24","added_by":"auto","created_at":"2024-12-30 06:56:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4200665,"visible":true,"origin":"","legend":"\u003cp\u003eLithology map around Lake Limboto\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5716869/v1/42ab5702e1c168bbd699be00.png"},{"id":72588526,"identity":"e0143da9-1138-4e86-bffd-713b6af234d1","added_by":"auto","created_at":"2024-12-30 06:48:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64314,"visible":true,"origin":"","legend":"\u003cp\u003eModel of a Basin Containing Fine Material (Nakamura, 2000)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5716869/v1/61565ff5564db93a50bbeef8.png"},{"id":72588527,"identity":"1370be6b-fd7a-43b3-b378-33eb70c4100b","added_by":"auto","created_at":"2024-12-30 06:48:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10774,"visible":true,"origin":"","legend":"\u003cp\u003eSurface Ground Deformation (Nakamura, 2000)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5716869/v1/02ba82169443772117767eff.png"},{"id":72589829,"identity":"44e6cc9f-2367-4101-bf07-5e62f11726dc","added_by":"auto","created_at":"2024-12-30 07:04:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":400934,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5716869/v1/dab84d2b6a145f9d70e78d98.png"},{"id":72589830,"identity":"04dab6e7-5fde-4e99-ab19-5fbea063f0de","added_by":"auto","created_at":"2024-12-30 07:04:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":415734,"visible":true,"origin":"","legend":"\u003cp\u003eA) Appearance of Sediment Core at Depth 1-20 meters in BH01 and BH02, B) Lithology of Sediment Core at Depth 1-20 in BH01 and BH02.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5716869/v1/125840ee75816ca0402228b5.png"},{"id":72589590,"identity":"b5d9c018-795d-47c0-a9f2-11826c7f86eb","added_by":"auto","created_at":"2024-12-30 06:56:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":416945,"visible":true,"origin":"","legend":"\u003cp\u003eTypical Hysteresis Curve Typical Hysteresis Curve (after slope correction) at depths of 1m, 6m, 10m, 14m and 20m in cores BH-02 and BH-03.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5716869/v1/79514d7b9054b3698cd18337.png"},{"id":72588546,"identity":"9a13564f-69ab-42a6-8909-b895563b8097","added_by":"auto","created_at":"2024-12-30 06:48:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":314844,"visible":true,"origin":"","legend":"\u003cp\u003eExample of Typical HVSR Curves\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5716869/v1/adefb111f73e9000687a8823.png"},{"id":72588543,"identity":"8c1b8b9d-a9e9-412c-a5cd-7da503522410","added_by":"auto","created_at":"2024-12-30 06:48:24","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1133218,"visible":true,"origin":"","legend":"\u003cp\u003eMap of measurement locations based on, a) Fo value, b) To value\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5716869/v1/3b5a41306aa97f5a45ee68f1.png"},{"id":72588545,"identity":"4bb1f87f-8afc-4f73-924c-fcd7f6c552ec","added_by":"auto","created_at":"2024-12-30 06:48:24","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":736500,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Ao Value in the Research Area\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5716869/v1/211f0a11f055ef43133d4520.png"},{"id":72588541,"identity":"d2c56328-600a-4d1d-b2e5-051df2ca35de","added_by":"auto","created_at":"2024-12-30 06:48:24","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":671402,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Kg Values in the Study Area\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5716869/v1/134e2f07079b742ba5ff2227.png"},{"id":74113474,"identity":"e4b5e4bc-ac30-4749-8686-b93acbbd0d68","added_by":"auto","created_at":"2025-01-18 02:01:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8108187,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5716869/v1/129fd8aa-3691-4cb8-a4d5-d65e4239a990.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sedimentation Dynamics and Seismic Vulnerability Using Integrated VSM-HVSR Analysis in Lake Limboto for Disaster Mitigation","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eLimboto Lake is part of the Gorontalo Depression, formed through the complex interaction of tectonic structures in the North Arm of Sulawesi, particularly the North Sulawesi Fault and the Sangihe Fault (Katili, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Hamilton, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1979\u003c/span\u003e): Past and present geotectonic position of Sulawesi, Indonesia, Tectonophysics, 45, 289\u0026ndash;322). The dynamic interplay between these faults generates compressional forces that cause subsidence, creating the Gorontalo Depression basin where Limboto Lake is located. This tectonic activity also influences the lithological patterns in the lake area (Hall et al, 2002; Pholbud, et al, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). According to Apandi and Bachri (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), the bedrock around the Limboto basin consists of lake sediment formations, indicating a prolonged and consistent sedimentation process. Cottam et al, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Nugraha et al, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e further noted that during the Holocene, the Limboto basin underwent significant changes due to tectonic activity and sea level fluctuations, resulting in distinctive lithologies around the lake, including reef limestone, clastic limestone, and other sedimentary units. These lake deposits, as the primary bedrock of Limboto Lake, reflect a complex interplay of tectonic activity, sedimentation processes, and environmental changes over millions of years.\u003c/p\u003e \u003cp\u003eThe natural process of sedimentation is an integral part of a lake's environmental dynamics (Putra et al \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, human activities such as deforestation, unsustainable agricultural practices, and uncontrolled development have significantly accelerated sedimentation and contributed to the shrinkage of Lake Limboto's area Limboto Lake serves as the estuary for 23 rivers that transport sediment, yet it has only one outlet, the Tapodu River. This imbalance in the hydrological system reduces the lake's capacity to regulate the flow of incoming and outgoing water and sediment effectively. Consequently, Lake Limboto, which covered approximately 7,000 hectares with an average depth of 30 meters in 1923, has drastically diminished to around 3,000 hectares with an average depth of just 3 meters today (Kimijima et al., 2022; Yunginger et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to Sarson et al (2020), significant portions of the shallow Lake Limboto area and its buffer zones have been converted into dense residential areas (1,272 hectares), public infrastructure developments (3,594 hectares), and agricultural land (966 hectares). This land ownership lacks proper administrative documentation in accordance with regulations. The transformation of these areas, which are part of the lake's sediment basin, has heightened vulnerability to flooding. Yan and Long (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlighted that lake sediments typically have low density and an unconsolidated composition, which can amplify seismic waves. This characteristic increases the risk of damage to residential settlements located on lake areas dominated by sedimentary layers or deposits. Despite this, the sediment dynamics and characteristics of Lake Limboto have not been comprehensively mapped. Furthermore, no clear framework exists to link these sediment dynamics with potential seismic vulnerabilities. This gap in knowledge poses challenges for organizing safer settlement zones and ensuring the sustainable conservation of Lake Limboto, allowing it to function optimally over the long term.\u003c/p\u003e \u003cp\u003eSediment dynamics have been analyzed using various methods, including radionuclide dating to establish sediment chronology (Goharrokhi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Begi et al., 2023); geostatistical techniques (Yan and Long, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Universal Soil Loss Equation (USLE) and Sediment Delivery Ratio (SDR) modeling (Hendratta et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); bathymetric surveys and satellite imaging (Samiev et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); and geotechnical methods combining sediment characterization with hydrological modeling (Kabir et al., 2009; Hartono and Yoshimura, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These approaches predominantly focus on the bulk properties of sediments and often overlook the specific magnetic characteristics of sediments. Such magnetic properties can provide valuable insights into sediment transport dynamics, deposition rates, and sediment sources, which are critical for understanding environmental changes that impact lake health (Lascu and Planck, 2013).\u003c/p\u003e \u003cp\u003eAccording to Liu et al (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Hapsoro et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the sedimentation environment influences the magnetic characteristics of sediments, providing insights into sediment control and transport processes. Additionally, Evan and Heller (2003) and Wang et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) noted that environmental magnetic approaches, such as Vibrating Sample Magnetometer (VSM) tests, can effectively record environmental changes by detecting magnetic characteristics of sediments, even at low concentrations of magnetic minerals. Minor magnetic concentrations, often undetectable by methods focusing solely on bulk properties, are captured by VSM tests, which emphasize specific magnetic characteristics in sediment dynamics. Hatfield and Maher et al, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e and Yang et al, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e further confirmed that VSM tests can analyze sediment structure and composition, trace sediment sources, and identify mineralogical characteristics, sediment transport processes, and the causes of environmental changes in lake systems. Variations in sediment magnetic mineral characteristics\u0026mdash;such as concentration, grain size, and magnetic domain\u0026mdash;are closely linked to transportation processes and sediment sources. These variations serve as critical indicators for understanding the dynamics and stability of sedimentation in lakes (Maher et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLake sedimentation dynamics exhibit characteristics specific to the lake environment, influencing both sediment consolidation levels and seismic amplification potential (Verma et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Seismic vulnerability reflects an area's susceptibility to earthquake impacts and is analyzed using the Horizontal-to-Vertical Spectral Ratio (HVSR), which evaluates the spectral ratio between the horizontal and vertical components of seismic waves (Nakamura, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Anbazhagan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The ability of Vibrating Sample Magnetometer (VSM) analysis to uncover lake sedimentation dynamics could be further enhanced by examining its relationship with seismic vulnerability in the Limboto Lake area using HVSR analysis. Combining VSM and HVSR approaches offers a novel and comprehensive model, supported by sediment drill log data, to evaluate sedimentation dynamics and the seismic susceptibility index in the Limboto Lake area. This study aims to integrate VSM and HVSR analyses to better understand the sedimentation dynamics and seismic vulnerability of the Lake Limboto region. By combining these methodologies, the research not only showcases cutting-edge analytical techniques but also provides innovative solutions for addressing seismic risks and supporting conservation efforts in the lake area. The findings are expected to serve as a foundation for designing effective seismic disaster mitigation strategies for residential zones and ensuring the sustainable conservation of Lake Limboto, optimizing its capacity and functionality for future generations.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Theory and Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGeology of the Study Area\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eLimboto Lake is part of the Gorontalo Low Depression, which is believed to have been uplifted during the Plio-Pleistocene due to the Sangihe subduction. This is evidenced by the presence of coral growth that formed elevated limestone deposits around the lake area. Additionally, the Gorontalo Low is influenced by the Gorontalo Fault, a horizontal fault oriented perpendicular to the forces generated by the Sangihe thrust (Pholbud et al, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sidarto and Bachri, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Today, the Gorontalo Low extends into the Paguyaman Valley, which is thought to have originally been an ancient lake deposit.\u003c/p\u003e \u003cp\u003eThe tectonic activity in the North Arm of Sulawesi, spanning from the Eocene to the Pleistocene and Holocene, has resulted in the lithological units surrounding Limboto Lake being predominantly composed of limestone formations, including lake sediment units, reef limestone, and clastic limestone. In contrast, the areas to the northwest and north of Limboto Lake are characterized by the presence of Bilungala volcanic rock formations and the Bone Diorite formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Apandi and Bachri, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Bachri et al., 2012).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe lithology of the study area is primarily dominated by the Lake Sediment Unit (Qpl), estimated to have formed during the Early Pleistocene. This unit consists of brownish-gray clay, sandstone, and gravel. These sediments are predominantly distributed across the Paguyaman Valley and the area surrounding Lake Limboto, underlain by diorite rocks (Apandi and Bachri, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Bachri et al., 2012).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVSM (Vibrating Sample Magnetometer)\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe magnetic properties of materials primarily arise from orbital and spin movements, along with electron interactions, which are highly influenced by factors such as magnetic mineral content, grain size, temperature, and pressure. These magnetic properties are useful for studying environmental changes because magnetic minerals are highly sensitive to such changes (Hunt et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Evans and Heller, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Demory et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Wise, 2010). One environmental parameter that can reveal the nature of magnetic minerals is the Vibrating Sample Magnetometer (VSM) test, which provides information about the quantity, grain size, magnetic domain, and mineralogy in sediments.\u003c/p\u003e \u003cp\u003eThe VSM is a tool known for its ability to measure magnetic moments with high precision. It allows the analysis of the magnetic properties of rock and sediment samples, both in their remanent states and when influenced by external magnetic fields (Dunlop dan Ozdemir, 1997; Liu et al, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). VSM is used to determine and study the magnetic properties of materials, particularly in relation to changes in external magnetic fields, which are depicted in the form of hysteresis curves. A hysteresis curve represents the relationship between magnetic intensity (H) and the magnetic field (B), showing the magnetic flux retained after an increase or decrease in magnetization force. This curve illustrates parameters such as saturation magnetization (Ms), coercivity (Hc), and magnetic remanence (Mr) (Dunlop and Ozdemir, 1997; Maher et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The Ms value indicates the saturation state, where the magnetic field (B) remains constant even with continuous increases in the external field. It also reflects the ability of particles to maintain the alignment of their magnetic domains under an external magnetic field, or the amount of external field required to achieve magnetization. The Mr value represents the residual magnetic field (M) remaining after the external magnetic field (Hc) is removed or reduced to zero. The coercivity (Hc) is the amount of external field needed to eliminate the material\u0026rsquo;s magnetic properties or to return the magnetization to zero, reflecting the magnetic properties of the material.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMicrotremor\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMicrotremor, also known as ambient noise, refers to ground vibrations with microtremor amplitudes caused by both natural and human factors. Natural events include wind, ground shaking, land movement, earthquakes, and ocean waves, while human activities encompass industrial operations, buildings, vehicles, and other sources (Kang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Siburian et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Microtremors can also be described as natural harmonic vibrations in the soil, occurring continuously and trapped in the surface sediment layer. These vibrations are reflected by the presence of a layer boundary plane with a fixed frequency, resulting from micro-vibrations below the surface and other natural activities (Nakamura, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Research on microtremors can help determine the characteristics of soil layers based on their dominant frequency parameters and wave amplification factors. The recorded microtremor data is analyzed using the HVSR (Horizontal-to-Vertical Spectral Ratio) method, which produces the HVSR spectrum (Nakamura, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe HVSR method is a non-invasive technique used to understand the nature of subsurface structures without disturbing them. This method provides key parameters, such as dominant frequency and amplification values, which are linked to the physical properties of the subsurface. Additionally, the HVSR method helps explain the phenomenon of resonant frequency in the surface layer by taking measurements at a given location and producing three components: vertical, horizontal north-south, and horizontal east-west. According to Nakamura (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1989\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), the amplitude and frequency of the HVSR peak represent the amplification and frequency characteristics of the local site. Site effects arise due to the presence of sedimentary layers that fill half of the basin above the bedrock. In this context, four motion components are involved: horizontal and vertical motion in the bedrock, and horizontal and vertical motion at the surface.\u003c/p\u003e \u003cp\u003eThe HVSR method is effectively used to determine the dynamics of soil layers over large areas, as it is considered a highly efficient technique. Nakamura introduced the HVSR method to characterize the dynamics of the surface soil layer caused by local site effects during earthquakes (Nakamura, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). This method relies on comparing the horizontal and vertical components, assuming that the ratio of the horizontal and vertical spectra of surface vibrations is a function of displacement. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates seismic motion on the ground surface, where the amplification factor, or amplitude, of horizontal and vertical motion is in direct contact with the bedrock in the basin area. The amplification factors of horizontal and vertical motion in the sedimentary layer are denoted as Th and Tv (Nakamura, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Rivera et al, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The amplification factor of horizontal motion in the sediment layer is denoted by (TH), while the amplification factor of vertical motion is denoted by (TV) (Nakamura, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The site effect on the sediment layer's surface is usually described by comparing the amplification factors of horizontal and vertical motion at the sediment surface.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eNatural Frequency (\u003c/b\u003e \u003cb\u003efo\u003c/b\u003e \u003cb\u003e)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe natural frequency, or dominant frequency, is the frequency that occurs most frequently. Research by Kang et al, (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Siburian et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) shows that peak frequency values change with geological variations. The natural frequency of the soil is related to the dominant period of the soil, meaning that the natural frequency measured at the surface can reflect the characteristics of the underlying rock. Nakamura, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Capobianco, et al (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) stated that the natural frequency of an area is influenced by the thickness of the weathered layer (h) and the average subsurface velocity (vs), which can be formulated as shown in Eq.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003ef_o\u0026thinsp;=\u0026thinsp;Vs/4h 1)\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePeriod Dominant\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe dominant period value is the time it takes for microseismic waves to travel through the soil layer or experience one reflection from the reflection plane to the surface. This value indicates the characteristics of the rock layers in an area. According to Nakamura (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1989\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), a high dominant period suggests thick soft sediments, while a low dominant period indicates thin soft sediments. Areas with a high dominant period are generally more vulnerable to damage during an earthquake. The dominant period value is calculated using Eq.\u0026nbsp;2.\u003c/p\u003e \u003cp\u003eT〗_o\u0026thinsp;=\u0026thinsp;1/f_o (2)\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eAmplification Factor\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAccording to Nakamura et al. (2000), amplification occurs when seismic waves are magnified due to a significant difference between layers. Specifically, seismic waves are amplified when they pass through a softer medium compared to the initial medium they traveled through. The greater the difference between the layers, the greater the wave amplification. Soil amplification values are related to the impedance contrast between the surface layer and the underlying layer. A high impedance contrast between the two layers results in a higher amplification factor, and vice versa. The relationship between the impedance contrast and soil amplification value is calculated using Eq.\u0026nbsp;3.\u003c/p\u003e \u003cp\u003eA_o=(ρ_b V_b)/(ρ_s V_s ) 3)\u003c/p\u003e \u003cp\u003e \u003cb\u003eSeismic Vulnerability Index (\u003c/b\u003e \u003cb\u003eKg)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSeismic vulnerability index values are used to estimate an area's susceptibility to landslides, which are derived from the relationship between natural frequency and amplification or also derived from surface measurements and changes due to earthquake effects.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe vulnerability of the soil also needs to consider the value of the strain shift (γ) in the soil layer during an earthquake (Nakamura, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In determining the seismic vulnerability index, it is necessary to consider the shear strain on the ground surface. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e represents the shear strain deformation of the ground surface, the value of γ is shown in equations 4 and 5.\u003c/p\u003e \u003cp\u003eγ\u0026thinsp;=\u0026thinsp;A_(m ) x d/H 4)\u003c/p\u003e \u003cp\u003ed\u0026thinsp;=\u0026thinsp;a_b/(2πf_0 )^2 5)\u003c/p\u003e \u003cp\u003eThe Kg value is the most easily identifiable vulnerability index from the measurement location. The ground velocity, Vb, represents the shear wave velocity at the bedrock, and this value is assumed to be constant. The Kg value indicates the measurement used to determine the level of resilience. The seismic vulnerability index is calculated by squaring the peak value of the microtremor spectrum and dividing it by the resonance frequency. Mathematically, the relationship between the seismic susceptibility index (Kg), natural frequency (fo), and amplification factor (Ao) is expressed in Eq.\u0026nbsp;6 (Nakamura, 2008).\u003c/p\u003e \u003cp\u003eKg=〖A_o〗^2/f_0 6)\u003c/p\u003e \u003cp\u003eHigh Kg values are generally found in soils with soft sedimentary rock lithologies. This high value illustrates that the area is vulnerable to earthquakes and in the event of an earthquake can experience strong shaking.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLake Limboto vertical sediment sampling and VSM test\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eVertical sediment samples from Lake Limboto were collected at two drilling points, each with a depth of 20 meters, as shown on the map in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Point BH01 is located near residential areas and geothermal water manifestations, which are also tourist attractions. Point BH02 is situated near a residential area and agricultural land.\u003c/p\u003e \u003cp\u003eFor VSM testing, samples were chosen based on the sediment characteristics represented in the drill log data. Wet sediment samples were sieved using a 325 mesh sieve to achieve a homogeneous sediment size, then dried at room temperature. The dried sediments were pulverized into a bulk form and prepared for testing using an OXFORD VSM 1,2H Vibrating Sample Magnetometer (VSM). The VSM testing was conducted at the Testing Laboratory of the Isotope and Radiation Technology Application Center of BATAN, Indonesia.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAcquisition of microtremor data\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe microseismic measurements were conducted using a Taide TDL-303S digital seismograph. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the location map of 24 microtremor measurement points across Gorontalo Regency, including Telaga, Telaga Biru, Limboto, West Limboto, Batudaa, and Gorontalo City, with a focus on Lekobalo Village in West City. The microtremor measurement tool recorded three types of waves: one vertical seismic wave and two horizontal seismic waves, all in CDM format. The recorded data was then processed using Geopsy software, which applies Fourier transformation to generate the HVSR curve. The data was validated using the standard deviation based on the Site Effects Assessment Using Ambient Excitations (SESAME 2004) guidelines. From the HVSR curve, the values of fo, Ao, and To were determined to calculate the seismic vulnerability index (Kg) using Eq.\u0026nbsp;6.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Results and Discussions","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVSM Data on Hysteresis Curve\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTen sediment samples were tested using VSM at depths of 1m, 6m, 10m, 15m, and 20m at both BH01 and BH02. These samples were selected based on sediment type as indicated in the sediment lithology data core from the drill log shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB. The VSM test results were analyzed using hysteresis curves to examine the magnetic mineral characteristics that reflect the sediment dynamics in Lake Limboto. The relationship between the magnetic field (H) and magnetization (M), shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, reveals that the magnetization process of magnetic minerals in the samples from BH01 (at depths of 1m, 14m, and 20m) increases sharply as the external magnetic field intensifies, eventually saturating around 300 mT. The saturation magnetization value (Ms) at these depths is notably high, with values of 48.00 emu/gr at 1m, 30.61 emu/gr at 15m, and 47.70 emu/gr at 20m. When the external magnetic field is reduced to zero, the curve does not return to its initial position but instead forms a distinct curve with a very small hysteresis loop area. The remanent magnetization (Mr) values range from 1.25 to 2.16 emu/gr, and the coercivity (Hcr) values range from 20 to 26 mT. This indicates that the magnetic minerals at these depths respond easily to external magnetic fields, suggesting that the dipole moments of the minerals quickly align with the field. Additionally, the magnetic materials in the sediments tend to retain very low residual magnetization once the external magnetic field is removed. This behavior reflects the presence of soft ferromagnetic minerals in the sediments.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eInterestingly, at depths of 6m and 10m, the magnetic material undergoes magnetization, although the saturation magnetization (Ms) values are much lower compared to the depths of 1m, 14m, and 20m. At 6m, the Ms value is approximately 12.60 emu/gr, and at 10m, it is only about 2.77 emu/gr, with both depths reaching saturation at induced fields above 500 mT. The magnetic minerals at these depths also easily lose their magnetization, as evidenced by the remanent magnetization (Mr) values of about 0.23 emu/gr at Hcr of 19.36 mT at 6m, and 0.019 emu/gr at Hcr of 6.17 mT at 10m. Overall, these data suggest that the magnetic minerals in the BH01 sediments are easily magnetized by external magnetic fields and also lose their magnetization properties quickly. This behavior is characteristic of soft ferromagnetic minerals such as magnetite (Fe3O4) or hematite (Fe2O3) (Jordanova et al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Hatfield dan Maher, 2008,; Mariyanto et al, 2021). The magnetic mineral behavior at BH01 is similar to that observed in sediments at BH02, although the lowest Ms values are found at depths of 6m, 14m, and 20m.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHVSR Data for Seismic Susceptibility\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMicrotremor measurement data consists of seismic wave signal data in the time domain for the study area. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows an example of the HVSR curve from the measurement results, which have been validated using standard deviation based on the SESAME 2004 standard, ensuring reliability and clear peak criteria for the H/V curve.\u003c/p\u003e \u003cp\u003eBased on the HVSR curves, the values of fo, To, Ao, and Kg are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The dominant frequency, which is related to the depth of the wave reflection field at the research site, ranges from 0.211 Hz (at point A2) to 2.556 Hz (at point D1). The highest amplification factor (Ao) value is 5.246 at point A5, while the lowest is 1.649 at point B12. The highest dominant period (To) value is 4.738 at point A2, and the lowest is 0.391 at point D1. The highest seismic vulnerability index (Kg) is 89.907 cm/s\u0026sup2; at point A2, and the lowest is 0.488 at point D1.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003c/h2\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\u003eData fo, To, Ao and Kg Based on HVSR Analysis at the Research Site\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitik\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003efo\u003c/p\u003e \u003cp\u003e(Hz)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo (sekon)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKg\u003c/p\u003e \u003cp\u003e(cm/s2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61,834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89,907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,725\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"},{"header":"Discussions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eAnalysis of the interrelated relationship between sedimentation dynamics and seismic vulnerability based on hysteresis and HVSR curve data.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEnvironmental changes in Lake Limboto are closely linked to sediment dynamics, which are influenced by both natural conditions and anthropogenic activities from upstream to downstream of the lake. These environmental changes can be recorded based on the magnetic properties of minerals in rocks and sediments, making it valuable to analyze sediment dynamics, including sediment sources, transport processes, and deposition processes, all of which contribute to the degradation of Lake Limboto. According to the magnetic mineral analysis using the hysteresis curve in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the sediment layers in BH01 at depths of 1m, 14m, and 20m exhibit rapid magnetization and have higher saturation magnetization values compared to the 6m and 10m depths. These magnetic mineral properties were found in relatively unconsolidated fine sand and silty sand sedimentary layers at depths of 1\u0026ndash;5 meters, and then at depths of 15\u0026ndash;20 meters, as shown in the drill log data in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA.\u003c/p\u003e \u003cp\u003eLow magnetization values are observed at depths of 6m and 10m, corresponding to sedimentary layers containing clay and silt at depths of 5\u0026ndash;14 meters. The magnetic properties of minerals in the sandy sediments of BH01 align with those found at point BH02 at depths of 1\u0026ndash;4 meters (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Similarly, the magnetic properties with low magnetization in silt loam sediments at BH01 correspond to the silt loam layers at depths of 5\u0026ndash;7 meters and 12\u0026ndash;20 meters at point BH02 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). This indicates that magnetic mineral facies tend to exhibit higher saturation magnetization and a better ability to retain magnetization in fine sand layers compared to silt loam layers.\u003c/p\u003e \u003cp\u003eAccording to Tamuntuan et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Wang et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), magnetic minerals that are quickly magnetized and demagnetized exhibit the characteristics of soft ferrimagnetic minerals, such as magnetite (Fe₃O₄), with larger grain sizes (multi-domain, MD) or medium sizes (pseudo-single domain, PSD). Habsporo et al. (2023) and Yang et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) observed that soft ferrimagnetic minerals that magnetize rapidly under fields below 300 mT, such as magnetite, often originate from rocks in catchment areas eroded by physical processes like water currents. These minerals are then mixed with newer sediments and deposited along lake shores. Mariyanto et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) noted that ferromagnetic minerals with finer grains (single domain, SD) and PSD, such as those found in the Brantas River, tend to form due to geological or anthropogenic activities, including industrial and agricultural waste. Similarly, Yunginger et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Bijaksana et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that surface sediments in Lake Limboto, particularly near BH01, are dominated by magnetic minerals originating from the Bionga and Talumelito sub-watersheds. These regions are rich in magnetite minerals, which are derived not only from natural erosion processes but also from anthropogenic activities such as agricultural and residential waste. This suggests that sediment dynamics in Lake Limboto at depths of 0\u0026ndash;5 meters and 15\u0026ndash;20 meters in BH01, and at depths of 1\u0026ndash;4 meters in BH02, reflect periods of high-energy sediment input. Events such as significant floods likely carried material rich in magnetic minerals (PSD and MD) from the catchment areas, influenced by both natural processes and anthropogenic contributions. Interestingly, the sediment lithology shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB reveals that the sediments at depths of 0\u0026ndash;5 meters and 15\u0026ndash;20 meters in BH01 are unconsolidated (loose sand) and relatively soft, as the particles are weakly bound. According to Santamarina and Cho (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), fine sand has the potential to move or shift under pressure, which gives it an appearance of structural weakness.\u003c/p\u003e \u003cp\u003eThe magnetic minerals in silty clay sediments located at depths of 5\u0026ndash;14 meters in BH01 and depths of 5\u0026ndash;7 meters and 12\u0026ndash;20 meters in BH02 exhibit low saturation magnetization (Ms) and low saturation field values. These characteristics align with the behavior of soft ferromagnetic minerals such as hematite (Fe₂O₃) (Dunlop and \u0026Ouml;zdemir 1997; Evans and Heller \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). According to Yang et al. (2024), magnetic minerals with low saturation values, such as hematite, are typically fine-grained and deposited in calm environments less influenced by flood events or high-energy sediment transport. Sediment sources in such environments are often dominated by non-magnetic materials from lake sediment formations. Evans and Heller (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), Boar and Harper (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and Yang et al. (2024) further explained that the low magnetization and low saturation behavior of magnetic minerals can result from reduction processes caused by high organic matter content in lake water, leading to low oxygen levels. These conditions can transform magnetic minerals such as magnetite (Fe₃O₄) into maghemite (γ-Fe₂O₃) or even hematite (Fe₂O₃) (Jordanova et al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Hatfield and Maher, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This indicates that the magnetic minerals in the silty clay sediments of both BH01 and BH02 originated from deposition areas distant from erosion sources. These sediments comprise fine particles enriched with organic matter, contributing to their low magnetic properties. According to Santamarina and Cho, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2004\u003c/span\u003e and Udanjargal et al, 2022., small particles such as clays and silts increase the plasticity of sediments. Under water-saturated conditions, these fine particles exhibit weak intergranular forces, contributing to the soft and easily deformable nature of the sediments under stress. Additionally, the weak inter-grain bonds in the wet state make silty clay sediments soft and susceptible to deformation. This analysis confirms that the sedimentary structure of Lake Limboto consists of a surface layer of loose fine sand overlying a finer, softer, and thicker layer of silty clay sediments in both BH01 and BH02.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between lake sediment dynamics and HVSR\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSediment dynamics in the Limboto Lake area, as indicated by the magnetic mineral properties at depths of 1\u0026ndash;20 meters in both BH01 and BH02, reflect the processes of sediment deposition driven by sediment transport energy from the catchment area with reduced land cover. Additionally, sediment sources originate from the surrounding lake environment, comprising fine-grained silt loam influenced by organic matter transported into the lake and associated with magnetic minerals in the sediments. These ongoing sediment dynamics have resulted in a thick sediment layer, contributing to the shallowing of Lake Limboto and a reduction in its area (Kimijima, 2020; Yunginger et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis observation is supported by the HVSR analysis, which shows that the study area is dominated by dominant frequency values (fo) of less than 2.5 Hz (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), indicating thick sedimentary layers with a thickness h\u0026thinsp;\u0026gt;\u0026thinsp;30h\u0026thinsp;\u0026gt;\u0026thinsp;30 meters (Kanai 1983, Konno dan Ohmachi, 1995, Percindira et al, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, areas A1, A2, and A3 exhibit fo values of less than 0.6 Hz, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea. Furthermore, the dominant period (To) values in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reveal that most of the study area (95.83%) has To values greater than 0.4 seconds, suggesting the prevalence of Type C sedimentary layers, characterized as soft sediment layers (Zhao et al, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Percindira et al, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These areas are marked in pink on the map in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb. The HVSR analysis findings align with the magnetic mineral analysis, which is dominated by magnetite with SD and PSD domain properties found in silt loam sediment types. This consistency underscores the relationship between sedimentary characteristics and sediment dynamics in the Limboto Lake area.\u003c/p\u003e \u003cp\u003eAn intriguing finding from the HVSR analysis is that the thick and soft sediments in the study area exhibit amplification factor (Ao) values exceeding 3 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This amplification is closely associated with the density and wave propagation speed of the bedrock. A high impedance contrast between the sediment layer and the bedrock indicates a high amplification factor, and conversely, a low impedance contrast results in a lower amplification factor (Zhao et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Percindira et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe amplification value indicates whether seismic waves are magnified as they propagate between the surface and underlying layers. Amplification increases with the length of wave propagation, a condition typically observed in soft sedimentary layers. According to Nakamura (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1989\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), an amplification value greater than 3 poses a significant hazard, as it signifies greater wave magnification compared to the initial wave. This implies that areas with high amplification values (\u0026gt;\u0026thinsp;3) and soft sediment layers are more likely to experience stronger earthquake shaking, categorized as moderate in severity.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e highlights areas with amplification values in the range of 3\u0026thinsp;\u0026lt;\u0026thinsp;Ao\u0026thinsp;\u0026lt;\u0026thinsp;6. These include the Pentadio Resort area (points A1, A2, A5) in Pilohayanga Village, Telaga Subdistrict (B2), Lupoyo Village, Telaga Biru Subdistrict (B3), Bulota Village, Telaga Jaya Subdistrict (B4), the Limboto Lake Outlet (B9), Tabongo Village, Batudaa Subdistrict (B10, B11), and Lekobalo Village, West City (D1). Previous studies (Nakamura, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Lotti et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Capobianco et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have shown that areas characterized by low fo and high Ao values\u0026mdash;or areas with thick, soft sediment layers\u0026mdash;are associated with a high seismic vulnerability index (Kg), increasing the risk of earthquake damage.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Kg values presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicate that Kg\u0026thinsp;\u0026gt;\u0026thinsp;9 cm/s2K_g\u0026thinsp;\u0026gt;\u0026thinsp;9 \\, \\text{cm/s}^2 (Nakamura, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; and Akkaya, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), signifying a high vulnerability to earthquake-induced damage. Areas with Kg\u0026thinsp;\u0026gt;\u0026thinsp;9K_g\u0026thinsp;\u0026gt;\u0026thinsp;9 values are highlighted in red on the map in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Notably, the Pentadio Resort tourist area (A1 and A2) exhibits exceptionally high KgK_g values of 61.834 cm/s261.834 \\, \\text{cm/s}^2 and 89.907 cm/s289.907 \\, \\text{cm/s}^2, respectively. This area, which is adjacent to Lake Limboto and features a geothermal water source, is likely affected by low sediment consolidation. Consequently, the Pentadio Resort area is particularly susceptible to significant ground shaking during earthquakes.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAdditionally, area B9, which is the outlet area of Lake Limboto, has a Kg value of approximately 13,877 cm/s\u0026sup2;, making it highly vulnerable to earthquake events. Areas with a Kg value between 5 and 10 cm/s\u0026sup2;, which are also highly seismic, include Kayubulan Village (A6) and the Alopohu River area in Hutabohu Village, West Limboto Sub-district (A9). Meanwhile, areas with a lower risk of earthquake damage, classified as having Kg values\u0026thinsp;\u0026le;\u0026thinsp;3 cm/s\u0026sup2;, are located in the blue-colored zones. These include Dutulanaa and Bolihuangga villages in Limboto Sub-district, Tabongo and Iluta villages in Batudaa Sub-district, and Lekobalo Village in North Town.\u003c/p\u003e \u003cp\u003eAreas with a high seismic vulnerability index around Lake Limboto, particularly the Pentadio Resort area, Lake Limboto Outlet, Kayubulan Village, and the Alopohu River area, are attributed to the thick and soft sedimentary characteristics of these regions. This conclusion is supported by vertical sediment profiles drilled to a depth of 20 meters in Lake Limboto (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These findings indicate that the soft soil or unconsolidated rock in residential areas near Lake Limboto exhibits a long vibration period, leading to significant wave amplification during earthquake events.\u003c/p\u003e \u003cp\u003eSediment dynamics, based on mineral properties, confirm that sediment supply to Lake Limboto originates from the catchment area, influenced by both natural processes and anthropogenic activities. These factors result in thick layers of fine sand, clay, and silt, transported by varying energy levels. The surrounding area has become densely populated, with activities such as agriculture and trade contributing pollutants to the lake's water (Yunginger et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yunginger et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, the soft, thick sediments in the Limboto Lake area are influenced by the lake's own sediments, which contain low levels of magnetic minerals and are further impacted by organic matter during deposition, increasing sediment softness. Therefore, conserving Lake Limboto is crucial, particularly by reducing sediment supply from the catchment area, which is causing the lake's sediment to thicken as the surrounding area becomes more developed. Furthermore, it is essential to minimize anthropogenic activities that add organic matter to the sediments, contributing to their softness. These conservation efforts align with measures to mitigate the risk of seismic disasters for the local community. Moreover, to reduce vulnerability to seismic vibrations or earthquakes in residential areas near Lake Limboto, it is important to design building infrastructure that can withstand stress and ensure safety from earthquake impacts.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSediment dynamics, as inferred from mineral properties, confirm that the sediment supply originates from the catchment area and is influenced by high-energy transport processes. These processes carry sediments derived from both natural and anthropogenic activities, particularly in the upper layers of lake sediments (1\u0026ndash;5 m and 15\u0026ndash;20 m) at BH01 and the 0\u0026ndash;4 m layer at BH02. This transport results in the deposition of thick sediment layers composed of unconsolidated silty fine sand. These sediments overlay a thicker, finer, and softer silty clay layer, which is influenced by local sediment dynamics. These dynamics include non-magnetic sediments associated with organic matter resulting from anthropogenic activities, leading to reduced magnetic mineral properties at depths of 6 m and 10 m at BH01 and 6 m, 14 m, and 20 m at BH02. This reduction contributes to the increased softness of the sediment layers.\u003c/p\u003e \u003cp\u003eThe sediment dynamics contribute to the accumulation of thick, yet soft, sediment layers. VSM analysis reveals a sediment thickness exceeding 30 meters with varying magnetic mineral characteristics, correlating with HVSR analysis results. The HVSR data indicate dominant frequency values (fo\u0026thinsp;\u0026lt;\u0026thinsp;2.5 Hz) and dominant period values (To \u0026gt;\u0026thinsp;0.4 s), which suggest the potential for seismic wave amplification, as reflected by high amplification factor values (Ao\u0026thinsp;\u0026gt;\u0026thinsp;3). Areas with low fo but high Ao values are particularly susceptible, resulting in elevated seismic vulnerability indices (Kg). These high Kg values are especially pronounced in the Pentadio Resort area (A1, A2, A3), the Tapodu River Lake outlet area (B9), Kayubulan Village (A6), and the Alopohu River area (A9).\u003c/p\u003e \u003cp\u003eThe VSM data further indicate that sediment dynamics are significantly influenced by anthropogenic activities, such as agricultural runoff and catchment area degradation, which exacerbate sediment accumulation and reduce consolidation. These factors contribute to HVSR findings that highlight areas of significant seismic amplification and susceptibility. The unconsolidated fine sand layer in the upper sediments is particularly prone to particle movement under stress, while the underlying silty clay layer, despite its high plasticity, remains soft and susceptible to deformation under external pressure. This combination worsens the overall stability of the sediment layers.\u003c/p\u003e \u003cp\u003eThe connection between VSM and HVSR data demonstrates that the magnetic mineral properties of sediments, particularly in soft layers, have a direct impact on seismic vulnerability characteristics. These findings emphasize the urgent need for seismic disaster risk mitigation in the Lake Limboto area. Key strategies include effective management of the upstream-to-downstream regions to reduce sediment supply, curbing anthropogenic activities, and designing earthquake-resistant infrastructure in vulnerable zones. Furthermore, to mitigate seismic vibration risks in residential areas near Lake Limboto, reducing building density and limiting heavy structures that exert substantial pressure on the soft sediment mass is critical.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their heartfelt gratitude to the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia for providing research funding to Raghel Yunginger under the regular fundamental research scheme. The authors appreciation also goes to the Rector and Head of LPPM at Gorontalo State University for their continuous support in facilitating this research. Also, the authors grateful to the Sulawesi II River Basin Authority and the Gorontalo Provincial Government for granting permission to conduct research in Lake Limboto. Special thanks to the Head of the Physics and Civil Engineering Laboratory at Gorontalo State University for assisting with sediment sample preparation. The authors acknowledge the facilities, scientific and technical support form Advanced Nuclear Materials Laboratories \u0026ndash; Nuclear Energy Research Organization, National Research and Innovation Agency through E- Layanan Sains-BRIN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.Y., M.Z., N.A.G., drafted the manuscript and performed the data analysis. I.M.P., M.K., I.S., A.B., A.B., S.S., A.W., conducted field investigations, provide critical feedback and concluded the initiation mechanism. All authors reviewed and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was financially by the Ministry of Education, Culture, Research and Technology of the Republic of Indonesia, fiscal year 2024 with contract number 063/E5/PG.02.00.PL/2024, and contract number 914/UN47.D1.1/PT.01.03/2024 with the Decree of the State University Rector Number 733/UN47/HK.02/2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003ePhysics Department, Faculty of Mathematic and Natural Sciences, Universitas Negeri Gorontalo, 96128, Indonesia\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eAgency for Meteorology, Climatology, and Geophysics, Manado, Indonesia\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eResearch Center for Environment and Clean Technology National Research and Innovation Agency, South Tangerang City, Banten 1534, Indonesia\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eCivil Engineering Department, Faculty of Engineering, Universitas Negeri Gorontalo, 96128, Indonesia\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eGeology Engineering Department, Faculty of Mathematic and Natural Sciences, Universitas Negeri Gorontalo, 96128, Indonesia\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e6\u003c/sup\u003eFaculty of Mathematics and Natural Sciences, Universitas Lambung Mangkurat, Banjarmasin, Indonesia\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e7\u003c/sup\u003eMine Engineering Department, Universitas Muhammadiyah Mataram, Indonesia\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkkaya, I.: Availability of seismic vulnerability index (Kg) in the assessment of building damage in Van, Eastern Turkey. 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Jurnal Natural, 24(2), 99-106 (2024).\u003c/li\u003e\n\u003cli\u003eZhao, J.X., Irikura, K., Zhang, J., Fukushima, Y., Somerville, P.G., Asano, A., Saiki, T., Okada, H., Takahashi, T.: Site classification for strong-motion stations in Japan using H/V response spectral ratio. 13th World Conference on Earthquake Engineering, Vancouver, B.C., Canada (2004).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sediment dynamics, seismic vulnerability. VSM, HVSR, Limboto Lake, Disaster Mitigation","lastPublishedDoi":"10.21203/rs.3.rs-5716869/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5716869/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLake Limboto, located in the Gorontalo basin, is experiencing significant degradation due to sedimentation exacerbated by deforestation and unsustainable agricultural practices. Sedimentation dynamics in Lake Limboto are shaped by a combination of natural and anthropogenic processes, including erosion from the catchment area and waste generated by human activities. These processes contribute to the accumulation of thick and soft sediment layers, which in turn increases seismic vulnerability. This study aims to analyze the sedimentation dynamics and seismic vulnerability of Lake Limboto using an integrative approach that combines Vibrating Sample Magnetometer (VSM) and Horizontal-to-Vertical Spectral Ratio (HVSR) methods. This dual methodology approach provides a comprehensive understanding of the linkages between sedimentation processes and seismic hazard risk.\u003c/p\u003e","manuscriptTitle":"Sedimentation Dynamics and Seismic Vulnerability Using Integrated VSM-HVSR Analysis in Lake Limboto for Disaster Mitigation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-30 06:48:19","doi":"10.21203/rs.3.rs-5716869/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"42fd4fa6-ea73-4039-b477-b621f01d5d56","owner":[],"postedDate":"December 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-18T01:53:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-30 06:48:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5716869","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5716869","identity":"rs-5716869","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}