Characterization of typological rocks using the geomagnetic method for mapping geological disaster susceptibility areas in Malang Regency, Indonesia

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Based on field observations, one of the villages that frequently experience landslides and subsidence almost every year is Srimulyo Village, Dampit District. This condition requires research on the subsurface to analyze the trigger factors for geological disasters to increase disaster mitigation and awareness. This study aims to analyze the typological characteristics of the rocks in the study area using the geomagnetic method as a geological disaster mitigation strategy. The method is geomagnetic; measurement designs regularly cover the entire study area with a distance of 300 meters between measurement points, while research is presented in 2D models and the analysis is based on the measured magnetic anomaly values on the reduce to pole (RTP) map. The results showed that the correlation between the RTP maps, regional geology, and field observations gave mutually correlated results. Based on the interpretation result of the RTP map, we create a Disaster Risk Zone map marked with the highest magnetic anomaly values in the northwest and northeast areas. The types of disasters in this zone vary, such as landslides, ground movements, and earthquake damage. The meeting between compact rock layers with sandy clay layers and faults causes rocks that were originally stable to become unstable, so the potential for disaster is large. The results of this study contribute to the local government in carrying out disaster mitigation and development planning. Disaster Susceptibility Areas Geological Disaster Geomagnetic Malang Regency Rock Typology RTP Map Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Indonesia's geographical position, traversed by the ring of fire, causes Indonesia to have many faults and volcanoes (Hamilton, 1979 ), causing various geological disasters throughout the region, such as earthquakes, volcanic eruptions, subsidence, and landslides (Masum & Akbar, 2019 ). These geological disaster events are spread throughout Indonesia, and the number of disaster events will increase if the area is traversed by faults or volcanoes (Handayani et al, 2021 ). Malang Regency has very complex geological conditions; two active faults traverse this area, the Blitar and Turen faults (Lestari et al, 2019 ). In addition, this area is surrounded by active volcanoes such as Mount Semeru, Bromo, Arjuno, and Welirang, which can erupt at any time. These conditions cause Malang Regency a high threat of geological disasters such as earthquakes (Susilo et al, 2023 ) and landslides (Sunaryo et al, 2019 ) with large potential losses. So far, earthquakes with a magnitude of more than 6 mW and a distance of less than 10 km can trigger various soil damage and building structures (Chasanah et al, 2022 ). Meanwhile, high rainfall will trigger landslides; besides that, the condition of the mountain topography generally has varying slopes, thus accelerating the occurrence of landslides (Faris & Fathani, 2013 ). Based on data from the Regional Disaster Management Agency (BPBD) of Malang Regency, every year, there are more than 100 geological disasters, such as earthquakes (Prasetyo et al, 2023 ), landslides, volcanic eruptions, and subsidence, with losses reaching 3 Billion (BPBD Malang Regency, 2022 ). Based on these data, the most frequent geological disasters are landslides, earthquakes, and subsidence. These various geological disasters have occurred in almost every village in Malang Regency, one of which is Srimulyo Village, Dampit District (Adi Susilo et al, 2018 ); based on field observations, the village experiences landslides and ground movement almost every year. Geographically, the condition of Srimulyo Village has undulating land contours that are prone to ground movement (Hasan et al, 2022 ); besides that, based on the geological map, Srimulyo Village is traversed by local faults stretching east to west. These conditions are very vulnerable to various geological disasters (Sukiyah et al, 2016 ), thus requiring research about the structure of the subsurface layer to analyze the typology and rock structure regionally. Typological characteristics of rocks in an area can be the basis for analyzing geological conditions; then, it can be developed into mapping geological disaster-prone areas as a disaster mitigation strategy (Wang et al, 2021 ). The geophysical method commonly used to map geological structures is the geomagnetic method (Adebo et al, 2019 ). The magnetic method is effective for near-surface studies, such as mapping the geological conditions below the ground surface. The principle of the geomagnetic method is to measure variations in the magnetic field caused by variations in the distribution of magnetized objects beneath the earth's surface (Cárdenas et al, 2022 ). Several previous studies on geological disasters using the geomagnetic method have been carried out to study underground structures to identify geological disasters. Research conducted by Alemayo and Aletro in 2021 used magnetic and geoelectrical methods to identify landslides in Ethiopia. The results showed that the main causes of landslides were caused by a combination of several factors, such as topography, high levels of weathering, regional hydrology, and human activities (Alemayo & Eritro, 2021 ). Yusupov, in 2018, also used geomagnetic data to create an earthquake prediction computational model. Research results in the Charvak reservoir area can model the process of earthquake preparation and forecasting and monitor seismic activity near the Karzhantau fault zone and the city of Tashkent (Yusupov, 2018 ). Based on several previous studies, the magnetic method is effective for studying geological structures (Holden et al, 2016 ) as a basis for planning geological disaster mitigation. In this study, the authors relate the characteristics and typology of rocks with the potential for geological disasters. So, this study aims to analyze the typological characteristics of the rocks in the study area using the geomagnetic method as a geological disaster mitigation strategy. The research results will contribute directly and indirectly to the local government and the community in anticipating signs of a geological disaster. This is important to do to increase public awareness as one of the geological disaster mitigation strategies. Materials and Methods Hybrid Materials The research location is in the Srimulyo Village, Dampit District, Malang Regency; the research used the geomagnetic method. Magnetic data collection was carried out directly at the research location according to the survey design using the PPM G856 magnetometer. The data acquisition process was completed for 2 days, from 1 August to 2 August 2023, with 104 measurement point data. The measurement points are designed as a grid with an area of 3 x 3 km, and the distance between data points is around 300 meters (Fig. 1 ). The grid design aims to cover the entire study area. The principle of the geomagnetic method is based on the difference in magnetization value in rocks due to the earth's magnetic induction and permanent magnetization. The intensity of magnetic induction depends on the susceptibility of the rock, the magnetic force, and the permanent intensity based on the geological history of the rock (Parasnis, 2012 ). Magnetic susceptibility is the ability of a rock to be magnetically influenced, which is determined by the susceptibility value of a material. Lithology, mineral content, iron oxide, and temperature affect a material's susceptibility value (de Mello et al, 2020 ). Variation in measured magnetic anomaly values is influenced by differences in the distribution of ferromagnetic, paramagnetic, and diamagnetic rocks, which respond differently to magnetic field values measured in the field (Syukri et al, 2017 ). The higher the rock with ferromagnetic properties, the higher the susceptibility value (Fajri et al, 2019 ). The determination of the value of rock susceptibility (x) is used by the equation: $$\overrightarrow{\mathbf{I} }=\mathbf{x} \overrightarrow{\mathbf{H} }$$ 1 Where I is magnetic intensity (A/m), H magnetic field strength (A/m), and x magnetic susceptibility value, from this equation, it can be seen that the magnetic intensity (I) is affected by the value of rock susceptibility ( x) (Telford et al, 1990 ). The data obtained needs several corrections, such as Drift correction, Diurnal Correction, and IGRF Correction (Sutasoma et al, 2021 ). Drift correction is performed for differences in reading values due to time differences, while Diurnal Corrections is caused by extraterrestrial activities such as ionosphere or flares. IGRF correction is used to eliminate the influence of the earth's main magnetic field. Earth's magnetic field values are obtained from the International Geomagnetic Reference Field (IGRF) (Ganguli et al, 2020 ). The data measured in the field needs to be Drift Corrected between the base point and the rover point, where the calculation process is carried out for the measurement time. This causes the observation time at the rover point to be adjusted to the time at the base point. The result required in the data processing is the value of the total magnetic field. The total magnetic field value is obtained by subtracting the total magnetic field value that has been Drift Corrected with the earth's magnetic field value and then adding the results of the Diurnal Correction. This reduction is intended so that the total magnetic field value obtained is the result of rock anomalies in the area. Based on the total magnetic field value, modelling can be carried out using the Oasis Montaj software with the griding minimum curvature method to obtain a map of the distribution of magnetic anomalies or Total Magnetic Intensity (TMI) (Asubiojo et al, 2022 ). The minimum curvature griding method was chosen to create Total Magnetic Intensity (TMI) maps because it can detect the edges of gravitational anomaly sources shaped like balls, horizontal, vertical, and faults (Kafadar, 2017 ). The concept of using this method is to use the analogy of a thin, linear plate that passes through each data with a minimum indentation. This causes the surface to be smooth without changing the original data too much. This method produces a repeating grid that aims to refine the grid that passes through the data. The TMI map obtained is a dipole magnetic anomaly, so reducing to pole (RTP) is necessary (Yatini et al, 2021 ). Reduction to the poles is carried out to turn the dipole into a monopole by making the inclination angle 90° and 0° declination so that it can cause a magnetic anomaly to point to the object immediately below (Subasinghe et al, 2014 ). During the reduce to pole (RTP) process, it is necessary to carry out the Fast Fourier Transform (FFT) process; this transformation aims to get the spatial domain into a frequency domain. Then, the Reduce to Pole (RTP) filtering can be carried out for the reduction process toward the north pole (Ganiyu et al, 2013 ). After obtaining the RTP map can be modelled into a 2D model to see the subsurface structure. Magnetic anomaly modelling uses ZondGM2D software by making an incision on the RTP map, and then the data obtained is input into the ZondGM2D software. The data that has been input into the ZondGM2D software begins with the line settings and mesh constructor configuration, where the line settings are used to set the slicing lines and the mesh constructor to set the number of meshes used in modeling. After the setting process is complete, it can be continued with the inversion process using Occam inversion at a predetermined depth. (Lase et al, 2022 ). The interpretation process can be carried out based on the distribution map of the reduced to pole (RTP) magnetic anomaly and the 2D map. Interpretation is based on measured magnetic anomaly values, which are interpreted based on information from regional geology. Determination of the rock type or constituent lithology is based on the value of rock susceptibility, where high susceptibility values usually consist of igneous rocks that contain lots of aluminium, iron, magnesium, calcium, potassium, and sodium. Meanwhile, sedimentary rocks generally have a smaller susceptibility value because they comprise weathered fragments from the sedimentation process (Reynolds, 2011 ). Results and Discussions Srimulyo Village, Dampit District, Malang Regency, is included in the regional geology of the Quadrangle. Morphologically, the study area is dominated by hills and valleys with varying slopes. Based on information from the regional geology of the study area, it is included in the Wonosari Formation (Twml), Nampol Formation (Tmn), and Wuni Formation (Tmw). Based on the regional geological map of the Turen Quadrangle ( Fig. 2 ) , the early Miocene Wonosari Formation consists of limestone, sandy marl, and claystone intercalations. The Nampol Formation is Late Miocene and consists of tuffaceous or calcareous sandstone, black claystone, sandy marl, and calcareous sandstone. While the Wuni formation has a late Miocene, and consists of andesitic-basaltic breccia and lava, tuff breccia, laharic breccia, and sandy tuff. In the Wonosari Formation and the Nampol Formation, a fault is located in the northwest area, marked by a dotted line. This fault stretches in a northwest-southeast direction and traverses several villages in the Dampit sub-district. The Total Magnetic Intensity (TMI) map is obtained based on the data processing results. Figure 3 (a) shows the distribution of magnetic values in several zones with magnetic anomaly values ranging from − 569.9 nT to 1026.6 nT. High anomaly values (red-pink) are in the northwest and northeast areas, while low anomaly values are scattered in the southwest area with values less than − 112 nT, marked in blue. High magnetic anomaly values are scattered in the northwest and northeast; this area is included in the Wonosari and Nampol formations; then, the low magnetic value in the southwest area enters the Wuni formation. Meanwhile, based on the elevation map in Fig. 3 (b) , it can be seen that the southeast and northwest areas are mountains, and in the north are valleys with elevation values ranging from 522.2 m to 728.8 m. The condition of the area, which is dominated by hills and valleys, will produce varying slopes, while steep slopes have a large potential for disasters, such as landslides and subsidence. Figure 3 (a). Total Magnetic Intensity (TMI) (b). Elevation map in the study area Data interpretation is carried out based on the reduce to pole (RTP) map due to the results of the reduction filter to the poles making the magnetic properties that were originally a dipole become a monopole (Rusman et al, 2029 ). Magnetic anomalies that are dipoles have the characteristics of 2 poles, so the magnetic anomaly is incorrect in the object or rock. The monopole anomaly resulting from a reduction filter to the pole can facilitate interpretation because the magnetic anomaly is right above the object. This filter is necessary because the dipole magnetic anomaly is very difficult to interpret, especially in determining the location and type of lithology. The reduced to pole (RTP) map in Fig. 4 shows that the reduced magnetic anomaly values have changed position compared to the Total Magnetic Intensity (TMI) map. The magnetic anomaly distribution value on the RTP map consists of -682.6 nT to 1022 nT; compared to the TMI map's anomaly value, there is a change. The change in the scale of the magnetic anomaly between RTP and TMI shows a more specific distribution compared to the map before the reduction to the poles. However, the distribution of magnetic anomaly values on the RTP map is still the same; namely, high anomaly values are in the northwestern and northeastern areas, and low anomaly values are scattered in the southwest area. The distribution of magnetic anomaly values is influenced by its constituent rocks, where the high magnetic anomaly values are mostly scattered in the Nampol and Wonosari formations, while the low anomaly values are mostly spread in the Wuni formations. Based on field observations, there are many types of limestone (Fig. 5(a)) , calcite minerals, and sandy clay intercalations in areas with high magnetic anomaly values. Limestone can produce high measured magnetic anomaly values, whereas, in the Wuni formation, there are many breccias and sandy tuffs but in weathered conditions, as shown in Fig. 5 (b) . Breccia and sandy tuff rocks with weathered conditions make the magnetic anomaly values in this area smaller than the dominant limestone formations. Theoretically, the value of the susceptibility of igneous rocks is greater than that of limestone, which is around 160 x 10 − 3 , while limestone is only about 0.1–0.3 x 10 − 3 (Reynolds, 2011 ). However, based on the RTP map, in the igneous area, several points have low values; this is probably due to the condition of the igneous rocks that are weathered and easily destroyed. Based on the RTP map of the southwest area, there is a high magnetic value that enters the Wuni formation; based on field observations, the high magnetic anomaly is caused by a large number of andesite rocks in that location. Figure 5 (a). Limestone and black clay, (b). Weathered igneous rock Correlation between field geological observations with reduce to pole (RTP) maps can be identified as the causes of high and low magnetic anomalies and their constituent lithology. The value of the magnetic anomaly on the RTP map needs clarification, so a 2D incision model is created to present the shape and source of the magnetic anomaly measured laterally and vertically. 2D modeling uses ZondGM2D software inversion by entering IGRF parameter values 45035.9 nT, inclination − 32.5099°, and declination 0.7550°. The RTP map ( Fig. 6 ) comprises 4 models of incisions; these incisions are made to obtain X, Y, and Z values and magnetic values, which are then inverted into a 2D model. The 4 incisions consist of A-A' with a length of 1800 m, B-B' with a length of 2800 m, C-C' with a length of 3200 m, and D-D' with a length of 2400 m. The depth of the incision obtained varies from 750 m to 1200 m. In 2D model inversion, depth weighting is used to produce a smooth model with better vertical resolution. The selection of incisions is made based on consideration of regional geological data, the distribution of anomalies on the RTP map, the presence of faults, and the location of geological disasters. Based on the regional geological map ( Fig. 2 ) , it is known that in the northeastern area, there is a fault stretching from southeast-northwest in the Wonosari formation (Twml), which consists of intercalated limestone, sand marl and mudstone. Then, based on the distribution of anomalies on the RTP map ( Fig. 6 ) , high anomalies are dominated in the northeast direction, so the incision was selected from southwest to northeast. This incision was chosen because it is perpendicular to the fault, where low to high anomalies meet, and there are many points where geological disasters occur in the research area. The results of the 2D model in Fig. 7 can show magnetic anomalies that are spread vertically and laterally. The A-A' incision has values ranging from − 14 x 10 − 5 SI to 18 x 10 − 5 SI, B-B’ incision − 50 x 10 − 5 SI to 90 x 10 − 5 SI, C-C’ incision − 40 x 10 − 5 SI to 90 x 10 − 5 SI, and D-D’ incision − 30 x 10 − 5 SI to 30 x 10 − 5 SI. High magnetic anomaly values are between 20 x 10 − 5 SI to 90 x 10 − 5 SI, marked in yellow-red, while low magnetic anomaly values have values of 0 to -40 x 10 − 5 SI, marked in green-dark blue. In the 2D incision model, paramagnetic and diamagnetic rocks are characterized by low anomalous values. Based on theory and field observations, the low anomalous areas are breccias weakened by weathering. Meanwhile, rocks with ferromagnetic properties are estimated to be limestone (Reynolds, 2011 ). Figure 7 The result of the incision on the Reduce to Pole (RTP) map Based on the results of observations and mapping in the field, the disaster points that occurred mostly occurred in the zones marked in red. Disaster observations consist of landslides, subsidence, and damage caused by earthquakes. Figure 8 shows several examples of damage from disasters, such as damaged roads, cracked houses, and even collapsed bridges. Geological disasters that occur in the red zone can disrupt the activities of residents and can cause economic, physical, and fatalities (Imani et al, 2021 ; Jahangiri et al, 2022 ; Kamiński et al, 2021 ; Priyono et al, 2020 ; Shanmugam & Wang, 2015 ; Uhlemann et al, 2017 ). The red area in the northeastern part has topography dominated by fields and valleys and there are rivers in each valley (Fig. 3b). The varied topography causes the slopes to become steep and steep. Steep slopes with an average slope of 40⁰-60⁰ can increase the risk of landslides and the addition of rivers at the base of the slope, which can cause soil erosion (Çellek, 2020 ). The areas with the highest levels of disaster occurrence are shown in Fig. 8 , then studied geologically based on the observations shown in Fig. 9 . Figure 8 shows disaster risk zones or areas that experience the most geological natural disasters, such as landslides, earthquake damage, and ground movements. Based on the magnetic anomaly values on the RTP map in Fig. 9 (a) , areas with high disaster risk have anomaly values that tend to be high; the constituent lithology may consist of rock types that have ferromagnetic properties. Igneous rock is one of the rocks with ferromagnetic properties, so it can produce high magnetic anomalies. Still, in the areas marked in red, there are no igneous rocks but limestone with calcite mineral inserts (Reynolds, 2011 ); it can be concluded that the measured high magnetic value is due to the presence of this mineral. Theoretically, rocks with a high density level can conduct seismic waves well; if an earthquake occurs, the area will easily experience vibration or receive greater shocks so that the resulting impact and damage will also be greater. Based on the regional geological map information shown in Fig. 9 (b) , disaster risk areas are found in 2 formations, the Wonosari and Nampol formations, which are dominated by limestone, sand, and clay (Sujanto et al, 1992 ). This is following the RTP map reading that this area has a high anomaly value and is possibly caused by limestone. The existence of faults and contact of solid rock layers with sandy clay layers causes rocks stable to become unstable. In addition, the area has a thick layer of clay on the surface, followed by limestone in the next layer; this can become a slip plane that triggers landslides and ground movements (Marino et al, 2020 ). Based on the geological map (Fig. 2 ), it is known that in the northeastern part, there are faults that enter the research area so this can also confirm the cause of the many geological disasters that occur. The presence of faults in an area can be an indication that the area is in a disaster-prone area. When an earthquake occurs in an area where there is a local fault due to the movement of the subduction zone, the earthquake vibration waves can cause the local fault to resonate. When a fault resonates due to an earthquake, it can cause other geological disasters such as landslides (Liu et al, 2020 ), ground movement, and liquefaction (Kusumawardani et al, 2021 ). The large amount of damage caused by earthquakes proves that this area has a high level of soil vulnerability. This research is a preliminary study, so it is necessary to carry out further research with other geophysical methods to determine the subsurface layer in detail. Figure 9 (a). Reduce to Pole (RTP) map of disaster risk, (b). Geological map of disaster risk Conclusions The reduced to pole (RTP) map provides information on the distribution of magnetic anomalies with values of -682.6 nT to 1022 nT. Correlation between RTP maps, regional geology, and field observations provide mutually correlated results. Theoretically, igneous rock's susceptibility value is more significant than limestone's. However, based on the RTP map, the igneous rock area several points have low values due to the condition of the igneous rocks that are weathered and easily destroyed. Information from the RTP map is obtained from the Disaster Risk Zone, where disaster points are marked in the red area. The types of disasters in this zone vary, such as landslides, ground movements, and earthquake damage. The Disaster Risk Zone on the RTP map has a high magnetic anomaly; based on the geological map, the magnetic value is interpreted as limestone and igneous rock in the bedrock layer. The areas with the highest levels of disaster occurrence are shown in Fig. 8 , then studied geologically based on the observations shown in Fig. 9. Figure 8 shows disaster risk zones or areas that experience the most geological natural disasters, such as landslides, earthquake damage, and ground movements. Based on the magnetic anomaly values on the RTP map in Fig. 9 (a) , areas with high disaster risk have anomaly values that tend to be high; the constituent lithology may consist of rock types that have ferromagnetic properties. While, the meeting between compact rock layers with sandy clay layers and faults causes rocks that were originally stable to become unstable, so the potential for disaster is large. Declarations Acknowledgements: The writers gratefully acknowledge the support of the Indonesian Collaborative Research Grant (RKI) 2023 by Universitas Negeri Malang, Brawijaya University, and Andalas University. The author also thanks the Srimulyo Village, Dampit District, Malang Regency residents for their support during the data acquisition process. Materials availability Research data can be obtained by request to the corresponding author. Author contributions Siti Zulaikah: Conceptualisation, Supervision, writing-original draft preparation, investigation, and methodology. Adi Susilo: Validation, Conceptualisation, Critical Review Article, and Resources. Ahmad Fauzi Pohan: Validation and Resources. Muhammad Fathur Rouf Hasan: Writing-original draft preparation, investigation, and methodology. Mohammad Habiby Idmi: Writing-original draft preparation, investigation, and methodology. Mochamad Aryono Adhi: Validation and Critical Review Article. Daeng Achmad Suaidi: Analysis, Validation and Critical Review Article. Nordiana Mohd. Muztaza: Analysis, Validation and Critical Review Article. Funding This research received funding from the 2023 Indonesian Collaborative Research Grant (RKI) with Number: 10.5.59/UN32.20.1/LT/2023 from Universitas Negeri Malang, Number: 801.14/UN10.C10/TU/2023 from Brawijaya University, and Number: 5/UN16.19/PT.01.03/IS-RKI+Skema A(Mitra)/2023 from Andalas University. Ethics approval: All ethical standards have been followed during this research. Consent to participate: Not applicable. Consent to publish: Not applicable. Conflict of interest: The authors declare no competing interest. References Adebo BA, Layade GO, Ilugbo SO, Hamzat AA, Otobrise, HK (2019) Mapping of subsurface geological structures using ground magnetic and electrical resistivity methods within lead City University, Southwestern Nigeria. Kada Journal of Physics, 2(2): 64–73. 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DIOP Conference Series: Earth and Environmental Science, 249: 012020. https://doi.org/10.1088/1755-1315/249/1/012020 Parasnis DS (2012) Principles of Applied Geophysics. Springer Science Business Media. Prasetyo WE, Irawan LY, Hartono R, Santosa EB (2023) Seismic hazard analysis using Deterministic Seismic Hazard Analysis (DSHA) Method: A case study in Southern Malang District. Jurnal Pendidikan Geografi: Kajian, Teori, Dan Praktek Dalam Bidang Pendidikan Dan Ilmu Geografi, 28(2): 209–227. https://doi.org/10.17977/um017v28i22023p209-227 Priyono KD, Jumadi, Saputra S, Fikriyah VN (2020) Risk Analysis of Landslide Impacts on Settlements in Karanganyar, Central Java, Indonesia. International Journal of GEOMATE, 19(73): 100–1007. https://doi.org/10.21660/2020.73.34128 Reynolds JM (2011) An Introduction to Applied and Environmental Geophysics (Second Edition). New York: John Wiley Sons, Ltd. Rusman MN, Alawiyah S, Gunawan I (2029) Study on the Significance of Reduction to the Equator (RTE): Reduction to the Pole (RTP): and Pseudogravity in Magnetic Data Interpretation. Jurnal Penelitian Pendidikan IPA, 9(8): 6197–6205. https://doi.org/10.29303/jppipa.v9i8.4705 Shanmugam G, Wang Y (2015) The landslide problem. Journal of Palaeogeography, 4(2): 109–166. https://doi.org/doi.org/10.3724/SP.J.1261.2015.00071 Subasinghe ND, Charles WKDGDR, De Silva SN (2014) Analytical signal and reduction to pole interpretation of total magnetic field data at eppawala phosphate deposit. Journal of Geoscience and Environment Protection, 2(3): 181–189. https://doi.org/10.4236/gep.2014.23023 Sujanto, Hadisantono R, Kusnama, Chaniago R, Baharuddin R (1992) Geological Map of The Turen Quadrangle, Java. Bandung: Geological Research and Development Centre. https://geologi.esdm.go.id/geomap/pages/preview/peta-geologi-lembar-blitar-jawa accessed at 28 July 2023 Sukiyah E, Syafri I, Winarto JB, Susilo MRB, Saputra A, Nurfadli E (2016) Active faults and their implications for regional development at the southern part of West Java, Indonesia. Proceeding of the FIG Working Week: 1–13. Sunaryo, Susilo, A, Yuwono AM, Wiyono (2019) Slope Stability Analysis for Landslides Natural Disaster Mitigation by Means of Geoelectrical Resistivity Data in Gedangan of South Malang, East Java, Indonesia. IOP Conference Series: Materials Science and Engineering, 546: 022030. https://doi.org/10.1088/1757-899X/546/2/022030 Susilo A, Juwono AM, Aprilia F, Hisyam F, Rohmah S, Hasan, MFR (2023) Subsurface Analysis Using Microtremor and Resistivity to Determine Soil Vulnerability and Discovery of New Local Fault. Civil Engineering Journal, 9(9): 2286–2299. https://doi.org/10.28991/CEJ-2023-09-09-014 Susilo A, Suryo EA, Fitriah F, Sutasoma M, Bahtiar (2018) Preliminary Study Of Landslide In Sri Mulyo, Malang, Indonesia Using Resistivity Method And Drilling Core Data. International Journal of GEOMATE, 15(48): 161–168. https://doi.org/10.21660/2018.48.59471 Sutasoma M, Susilo A, Sunaryo, Sarjiyana, Cahyo RHD, Suryo EA (2021) Identification of Rock Layer Contacts in the Surrounding of the Sutami DAM Using Geomagnetic Methods. International Journal of GEOMATE, 21(84): 188–193. https://doi.org/10.21660/2021.84.j2179 Syukri M, Safitri R, Fadhli Z, Andika F, Saad, R (2017) Groundmagnetic survey used to identify the weathered zone, in Blang Bintang, Aceh, Indonesia. IOP Conference Series: Earth and Environmental Science, 56: 012017. https://doi.org/10.1088/1755-1315/56/1/012017 Telford WM, Geldart LP, Sheriff R. E (1990) Applied Geophysics. United Kingdom: Cambridge University Press. Uhlemann S, Chambers J, Wilkinson P, Maurer H, Merritt A, Meldrum P, Kuras O, Gunn D, Smith A, Dijkstra T (2017) Four-dimensional imaging of moisture dynamics during landslide reactivation. Journal of Geophysical Research: Earth Surface, 122(1): 398–418. https://doi.org/10.1002/2016JF003983 Wang X, Zhang C, Wang C, Liu G, Wang H (2021) GIS-based for prediction and prevention of environmental geological disaster susceptibility: From a perspective of sustainable development. Ecotoxicology and Environmental Safety, 226: 112881. https://doi.org/10.1016/j.ecoenv.2021.112881 Yatini Y, Zakaria MF, Suyanto I (2021) Identification of Gold Mineralization Zones Using Magnetic Data at Gunung Gupit Area, Magelang, Central Java. RSF Conference Series: Engineering and Technology: 305–312. https://doi.org/10.31098/cset.v1i1.384 Yusupov V (2018) Anomalies of geomagnetic field related to natural and technogenic events in Charvak area. Geodesy and Geodynamics, 9(5): 367–371. https://doi.org/10.1016/j.geog.2018.05.002 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3608588","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264379436,"identity":"aff384a6-a0e2-49d8-b4af-ce8611a845ca","order_by":0,"name":"Siti Zulaikah","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-2419-3586","institution":"Universitas Negeri Malang Fakultas Matematika dan Ilmu Pengetahuan Alam: Universitas Negeri Malang FMIPA","correspondingAuthor":true,"prefix":"","firstName":"Siti","middleName":"","lastName":"Zulaikah","suffix":""},{"id":264379437,"identity":"eae7df62-b7f4-4799-a324-3fef0d90d203","order_by":1,"name":"Adi Susilo","email":"","orcid":"","institution":"Brawijaya University Faculty of Mathematics and Natural Sciences: Universitas Brawijaya Fakultas Matematika dan Ilmu Pengetahuan Alam","correspondingAuthor":false,"prefix":"","firstName":"Adi","middleName":"","lastName":"Susilo","suffix":""},{"id":264379438,"identity":"6e6237f9-425b-4543-9887-d2bddc0a01f5","order_by":2,"name":"Ahmad Fauzi Pohan","email":"","orcid":"","institution":"Andalas University Faculty of Mathematics and Natural Sciences: Universitas Andalas Fakultas Matematika dan Ilmu Pengetahuan Alam","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"Fauzi","lastName":"Pohan","suffix":""},{"id":264379439,"identity":"a1069860-8fcd-4691-8243-c477c85ca923","order_by":3,"name":"Muhammad Fathur Rouf Hasan","email":"","orcid":"","institution":"Politeknik Negeri Jakarta","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Fathur Rouf","lastName":"Hasan","suffix":""},{"id":264379440,"identity":"5d1dabff-31ed-42a3-aa44-6e3b6a7ba6fd","order_by":4,"name":"Mohammad Habiby Idmi","email":"","orcid":"","institution":"Brawijaya University Faculty of Mathematics and Natural Sciences: Universitas Brawijaya Fakultas Matematika dan Ilmu Pengetahuan Alam","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Habiby","lastName":"Idmi","suffix":""},{"id":264379441,"identity":"7db68636-d645-423a-a51a-b8325f10476a","order_by":5,"name":"Mochamad Aryono Adhi","email":"","orcid":"","institution":"Universitas Negeri Semarang Fakultas Matematika dan Ilmu Pengetahuan Alam","correspondingAuthor":false,"prefix":"","firstName":"Mochamad","middleName":"Aryono","lastName":"Adhi","suffix":""},{"id":264379442,"identity":"cae1a399-ae4c-4ebd-bf1d-b01707212307","order_by":6,"name":"Daeng Achmad Suaidi","email":"","orcid":"","institution":"Universitas Negeri Malang Fakultas Matematika dan Ilmu Pengetahuan Alam: Universitas Negeri Malang FMIPA","correspondingAuthor":false,"prefix":"","firstName":"Daeng","middleName":"Achmad","lastName":"Suaidi","suffix":""},{"id":264379443,"identity":"69b3e9c2-bd30-4715-b86c-ec741cb9f7b2","order_by":7,"name":"Nordiana Mohd Muztaza","email":"","orcid":"","institution":"Universiti Sains Malaysia Pusat Pengajian Sains Fizik","correspondingAuthor":false,"prefix":"","firstName":"Nordiana","middleName":"Mohd","lastName":"Muztaza","suffix":""}],"badges":[],"createdAt":"2023-11-14 06:33:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3608588/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3608588/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49045480,"identity":"990eb3c3-5089-4032-aec6-f42097db32d4","added_by":"auto","created_at":"2024-01-02 07:08:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":962292,"visible":true,"origin":"","legend":"\u003cp\u003eResearch survey design\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3608588/v1/e36c9f74185c6d3c95ec9e78.png"},{"id":49045481,"identity":"e9c29090-db56-4231-939a-678f9b772a67","added_by":"auto","created_at":"2024-01-02 07:08:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1569275,"visible":true,"origin":"","legend":"\u003cp\u003eGeological map of Turen Quadrangle (Sujanto et al, 1992)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3608588/v1/8f7600ae8e0cfe9819733429.png"},{"id":49045373,"identity":"39b550d6-cab0-4fa5-b0ac-dc870717a17b","added_by":"auto","created_at":"2024-01-02 07:00:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":449391,"visible":true,"origin":"","legend":"\u003cp\u003e(a). Total Magnetic Intensity (TMI) (b). Elevation map in the study area\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3608588/v1/fea6139d28a23c1fe03b52bb.png"},{"id":49045368,"identity":"09c37057-975e-43a3-b963-46a0eccde958","added_by":"auto","created_at":"2024-01-02 07:00:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74657,"visible":true,"origin":"","legend":"\u003cp\u003eReduce to Pole (RTP) Map\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3608588/v1/6e9ecb5b8034dcc9823b5b88.png"},{"id":49045371,"identity":"affad822-b18b-4264-80ff-92917d6cead6","added_by":"auto","created_at":"2024-01-02 07:00:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1471080,"visible":true,"origin":"","legend":"\u003cp\u003e(a). Limestone and black clay, (b). Weathered igneous rock\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3608588/v1/51ab8b0b86898a6a3b1f008e.png"},{"id":49045479,"identity":"719d65f1-7ed4-4b43-be4b-4b60209aacbc","added_by":"auto","created_at":"2024-01-02 07:08:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":84884,"visible":true,"origin":"","legend":"\u003cp\u003eIncision points on the RTP map\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3608588/v1/2107358f9f67dce12e4bc2ac.png"},{"id":49045370,"identity":"235302e2-b07c-43ec-a725-bcd848a78ea5","added_by":"auto","created_at":"2024-01-02 07:00:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2311387,"visible":true,"origin":"","legend":"\u003cp\u003eThe result of the incision on the Reduce to Pole (RTP) map\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a). The results of the incision in the A-A’ field\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b). The results of the incision in the B-B’ field\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(c). The results of the incision in the C-C’ field\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(d). The results of the incision in the D-D’ field\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3608588/v1/32e4834284157ba00e5b39de.png"},{"id":49045376,"identity":"e02f84a3-1db0-459f-ab40-078eb994effb","added_by":"auto","created_at":"2024-01-02 07:00:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":919013,"visible":true,"origin":"","legend":"\u003cp\u003eDisaster risk zone\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3608588/v1/2f6db966456c0b50a3bc4d68.png"},{"id":49045375,"identity":"3a35bbc5-784d-410d-a7a5-59d851273c5e","added_by":"auto","created_at":"2024-01-02 07:00:12","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":929157,"visible":true,"origin":"","legend":"\u003cp\u003e(a). Reduce to Pole (RTP) map of disaster risk, (b). Geological map of disaster risk\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3608588/v1/53d22c9f5e39ebc5b2a87f84.png"},{"id":66560760,"identity":"0b4c84d7-994e-4aa7-9491-34b2c7ff09fd","added_by":"auto","created_at":"2024-10-14 10:11:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12212925,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3608588/v1/7aee34fb-fb1f-4fd1-9933-3618629c94dd.pdf"}],"financialInterests":"","formattedTitle":"Characterization of typological rocks using the geomagnetic method for mapping geological disaster susceptibility areas in Malang Regency, Indonesia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIndonesia's geographical position, traversed by the ring of fire, causes Indonesia to have many faults and volcanoes (Hamilton, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1979\u003c/span\u003e), causing various geological disasters throughout the region, such as earthquakes, volcanic eruptions, subsidence, and landslides (Masum \u0026amp; Akbar, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These geological disaster events are spread throughout Indonesia, and the number of disaster events will increase if the area is traversed by faults or volcanoes (Handayani et al, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Malang Regency has very complex geological conditions; two active faults traverse this area, the Blitar and Turen faults (Lestari et al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, this area is surrounded by active volcanoes such as Mount Semeru, Bromo, Arjuno, and Welirang, which can erupt at any time. These conditions cause Malang Regency a high threat of geological disasters such as earthquakes (Susilo et al, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and landslides (Sunaryo et al, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) with large potential losses. So far, earthquakes with a magnitude of more than 6 mW and a distance of less than 10 km can trigger various soil damage and building structures (Chasanah et al, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Meanwhile, high rainfall will trigger landslides; besides that, the condition of the mountain topography generally has varying slopes, thus accelerating the occurrence of landslides (Faris \u0026amp; Fathani, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on data from the Regional Disaster Management Agency (BPBD) of Malang Regency, every year, there are more than 100 geological disasters, such as earthquakes (Prasetyo et al, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), landslides, volcanic eruptions, and subsidence, with losses reaching 3\u0026nbsp;Billion (BPBD Malang Regency, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Based on these data, the most frequent geological disasters are landslides, earthquakes, and subsidence. These various geological disasters have occurred in almost every village in Malang Regency, one of which is Srimulyo Village, Dampit District (Adi Susilo et al, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); based on field observations, the village experiences landslides and ground movement almost every year. Geographically, the condition of Srimulyo Village has undulating land contours that are prone to ground movement (Hasan et al, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); besides that, based on the geological map, Srimulyo Village is traversed by local faults stretching east to west. These conditions are very vulnerable to various geological disasters (Sukiyah et al, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), thus requiring research about the structure of the subsurface layer to analyze the typology and rock structure regionally. Typological characteristics of rocks in an area can be the basis for analyzing geological conditions; then, it can be developed into mapping geological disaster-prone areas as a disaster mitigation strategy (Wang et al, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe geophysical method commonly used to map geological structures is the geomagnetic method (Adebo et al, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The magnetic method is effective for near-surface studies, such as mapping the geological conditions below the ground surface. The principle of the geomagnetic method is to measure variations in the magnetic field caused by variations in the distribution of magnetized objects beneath the earth's surface (C\u0026aacute;rdenas et al, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Several previous studies on geological disasters using the geomagnetic method have been carried out to study underground structures to identify geological disasters. Research conducted by Alemayo and Aletro in 2021 used magnetic and geoelectrical methods to identify landslides in Ethiopia. The results showed that the main causes of landslides were caused by a combination of several factors, such as topography, high levels of weathering, regional hydrology, and human activities (Alemayo \u0026amp; Eritro, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Yusupov, in 2018, also used geomagnetic data to create an earthquake prediction computational model. Research results in the Charvak reservoir area can model the process of earthquake preparation and forecasting and monitor seismic activity near the Karzhantau fault zone and the city of Tashkent (Yusupov, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on several previous studies, the magnetic method is effective for studying geological structures (Holden et al, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) as a basis for planning geological disaster mitigation. In this study, the authors relate the characteristics and typology of rocks with the potential for geological disasters. So, this study aims to analyze the typological characteristics of the rocks in the study area using the geomagnetic method as a geological disaster mitigation strategy. The research results will contribute directly and indirectly to the local government and the community in anticipating signs of a geological disaster. This is important to do to increase public awareness as one of the geological disaster mitigation strategies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHybrid Materials\u003c/h2\u003e \u003cp\u003eThe research location is in the Srimulyo Village, Dampit District, Malang Regency; the research used the geomagnetic method. Magnetic data collection was carried out directly at the research location according to the survey design using the PPM G856 magnetometer. The data acquisition process was completed for 2 days, from 1 August to 2 August 2023, with 104 measurement point data. The measurement points are designed as a grid with an area of 3 x 3 km, and the distance between data points is around 300 meters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The grid design aims to cover the entire study area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe principle of the geomagnetic method is based on the difference in magnetization value in rocks due to the earth's magnetic induction and permanent magnetization. The intensity of magnetic induction depends on the susceptibility of the rock, the magnetic force, and the permanent intensity based on the geological history of the rock (Parasnis, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Magnetic susceptibility is the ability of a rock to be magnetically influenced, which is determined by the susceptibility value of a material. Lithology, mineral content, iron oxide, and temperature affect a material's susceptibility value (de Mello et al, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Variation in measured magnetic anomaly values is influenced by differences in the distribution of ferromagnetic, paramagnetic, and diamagnetic rocks, which respond differently to magnetic field values measured in the field (Syukri et al, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The higher the rock with ferromagnetic properties, the higher the susceptibility value (Fajri et al, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The determination of the value of rock susceptibility (x) is used by the equation:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\overrightarrow{\\mathbf{I} }=\\mathbf{x} \\overrightarrow{\\mathbf{H} }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cb\u003eI\u003c/b\u003e is magnetic intensity (A/m), \u003cb\u003eH\u003c/b\u003e magnetic field strength (A/m), and \u003cb\u003ex\u003c/b\u003e magnetic susceptibility value, from this equation, it can be seen that the magnetic intensity \u003cb\u003e(I)\u003c/b\u003e is affected by the value of rock susceptibility (\u003cb\u003ex)\u003c/b\u003e (Telford et al, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). The data obtained needs several corrections, such as Drift correction, Diurnal Correction, and IGRF Correction (Sutasoma et al, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Drift correction is performed for differences in reading values due to time differences, while Diurnal Corrections is caused by extraterrestrial activities such as ionosphere or flares. IGRF correction is used to eliminate the influence of the earth's main magnetic field. Earth's magnetic field values are obtained from the International Geomagnetic Reference Field (IGRF) (Ganguli et al, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The data measured in the field needs to be Drift Corrected between the base point and the rover point, where the calculation process is carried out for the measurement time. This causes the observation time at the rover point to be adjusted to the time at the base point. The result required in the data processing is the value of the total magnetic field. The total magnetic field value is obtained by subtracting the total magnetic field value that has been Drift Corrected with the earth's magnetic field value and then adding the results of the Diurnal Correction. This reduction is intended so that the total magnetic field value obtained is the result of rock anomalies in the area.\u003c/p\u003e \u003cp\u003eBased on the total magnetic field value, modelling can be carried out using the Oasis Montaj software with the griding minimum curvature method to obtain a map of the distribution of magnetic anomalies or Total Magnetic Intensity (TMI) (Asubiojo et al, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The minimum curvature griding method was chosen to create Total Magnetic Intensity (TMI) maps because it can detect the edges of gravitational anomaly sources shaped like balls, horizontal, vertical, and faults (Kafadar, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The concept of using this method is to use the analogy of a thin, linear plate that passes through each data with a minimum indentation. This causes the surface to be smooth without changing the original data too much. This method produces a repeating grid that aims to refine the grid that passes through the data. The TMI map obtained is a dipole magnetic anomaly, so reducing to pole (RTP) is necessary (Yatini et al, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Reduction to the poles is carried out to turn the dipole into a monopole by making the inclination angle 90\u0026deg; and 0\u0026deg; declination so that it can cause a magnetic anomaly to point to the object immediately below (Subasinghe et al, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). During the reduce to pole (RTP) process, it is necessary to carry out the Fast Fourier Transform (FFT) process; this transformation aims to get the spatial domain into a frequency domain. Then, the Reduce to Pole (RTP) filtering can be carried out for the reduction process toward the north pole (Ganiyu et al, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). After obtaining the RTP map can be modelled into a 2D model to see the subsurface structure. Magnetic anomaly modelling uses ZondGM2D software by making an incision on the RTP map, and then the data obtained is input into the ZondGM2D software. The data that has been input into the ZondGM2D software begins with the line settings and mesh constructor configuration, where the line settings are used to set the slicing lines and the mesh constructor to set the number of meshes used in modeling. After the setting process is complete, it can be continued with the inversion process using Occam inversion at a predetermined depth. (Lase et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe interpretation process can be carried out based on the distribution map of the reduced to pole (RTP) magnetic anomaly and the 2D map. Interpretation is based on measured magnetic anomaly values, which are interpreted based on information from regional geology. Determination of the rock type or constituent lithology is based on the value of rock susceptibility, where high susceptibility values usually consist of igneous rocks that contain lots of aluminium, iron, magnesium, calcium, potassium, and sodium. Meanwhile, sedimentary rocks generally have a smaller susceptibility value because they comprise weathered fragments from the sedimentation process (Reynolds, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussions","content":"\u003cp\u003eSrimulyo Village, Dampit District, Malang Regency, is included in the regional geology of the Quadrangle. Morphologically, the study area is dominated by hills and valleys with varying slopes. Based on information from the regional geology of the study area, it is included in the Wonosari Formation (Twml), Nampol Formation (Tmn), and Wuni Formation (Tmw). Based on the regional geological map of the Turen Quadrangle \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan\u003e2\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e, the early Miocene Wonosari Formation consists of limestone, sandy marl, and claystone intercalations. The Nampol Formation is Late Miocene and consists of tuffaceous or calcareous sandstone, black claystone, sandy marl, and calcareous sandstone. While the Wuni formation has a late Miocene, and consists of andesitic-basaltic breccia and lava, tuff breccia, laharic breccia, and sandy tuff. In the Wonosari Formation and the Nampol Formation, a fault is located in the northwest area, marked by a dotted line. This fault stretches in a northwest-southeast direction and traverses several villages in the Dampit sub-district.\u003c/p\u003e\n\u003cp\u003eThe Total Magnetic Intensity (TMI) map is obtained based on the data processing results. \u003cem\u003eFigure\u0026nbsp;3\u003c/em\u003e \u003cstrong\u003e(a)\u003c/strong\u003e shows the distribution of magnetic values in several zones with magnetic anomaly values ranging from \u0026minus;\u0026thinsp;569.9 nT to 1026.6 nT. High anomaly values (red-pink) are in the northwest and northeast areas, while low anomaly values are scattered in the southwest area with values less than \u0026minus;\u0026thinsp;112 nT, marked in blue. High magnetic anomaly values are scattered in the northwest and northeast; this area is included in the Wonosari and Nampol formations; then, the low magnetic value in the southwest area enters the Wuni formation. Meanwhile, based on the elevation map in \u003cstrong\u003eFig.\u0026nbsp;3 (b)\u003c/strong\u003e, it can be seen that the southeast and northwest areas are mountains, and in the north are valleys with elevation values ranging from 522.2 m to 728.8 m. The condition of the area, which is dominated by hills and valleys, will produce varying slopes, while steep slopes have a large potential for disasters, such as landslides and subsidence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;3\u003c/strong\u003e (a). Total Magnetic Intensity (TMI) (b). Elevation map in the study area\u003c/p\u003e\n\u003cp\u003eData interpretation is carried out based on the reduce to pole (RTP) map due to the results of the reduction filter to the poles making the magnetic properties that were originally a dipole become a monopole (Rusman et al, \u003cspan\u003e2029\u003c/span\u003e). Magnetic anomalies that are dipoles have the characteristics of 2 poles, so the magnetic anomaly is incorrect in the object or rock. The monopole anomaly resulting from a reduction filter to the pole can facilitate interpretation because the magnetic anomaly is right above the object. This filter is necessary because the dipole magnetic anomaly is very difficult to interpret, especially in determining the location and type of lithology.\u003c/p\u003e\n\u003cp\u003eThe reduced to pole (RTP) map in Fig. \u003cspan\u003e4\u003c/span\u003e shows that the reduced magnetic anomaly values have changed position compared to the Total Magnetic Intensity (TMI) map. The magnetic anomaly distribution value on the RTP map consists of -682.6 nT to 1022 nT; compared to the TMI map\u0026apos;s anomaly value, there is a change. The change in the scale of the magnetic anomaly between RTP and TMI shows a more specific distribution compared to the map before the reduction to the poles. However, the distribution of magnetic anomaly values on the RTP map is still the same; namely, high anomaly values are in the northwestern and northeastern areas, and low anomaly values are scattered in the southwest area. The distribution of magnetic anomaly values is influenced by its constituent rocks, where the high magnetic anomaly values are mostly scattered in the Nampol and Wonosari formations, while the low anomaly values are mostly spread in the Wuni formations.\u003c/p\u003e\n\u003cp\u003eBased on field observations, there are many types of limestone \u003cstrong\u003e(Fig.\u0026nbsp;5(a))\u003c/strong\u003e, calcite minerals, and sandy clay intercalations in areas with high magnetic anomaly values. Limestone can produce high measured magnetic anomaly values, whereas, in the Wuni formation, there are many breccias and sandy tuffs but in weathered conditions, as shown in \u003cstrong\u003eFig.\u0026nbsp;5 (b)\u003c/strong\u003e. Breccia and sandy tuff rocks with weathered conditions make the magnetic anomaly values in this area smaller than the dominant limestone formations. Theoretically, the value of the susceptibility of igneous rocks is greater than that of limestone, which is around 160 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, while limestone is only about 0.1\u0026ndash;0.3 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (Reynolds, \u003cspan\u003e2011\u003c/span\u003e). However, based on the RTP map, in the igneous area, several points have low values; this is probably due to the condition of the igneous rocks that are weathered and easily destroyed. Based on the RTP map of the southwest area, there is a high magnetic value that enters the Wuni formation; based on field observations, the high magnetic anomaly is caused by a large number of andesite rocks in that location.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;5\u003c/strong\u003e (a). Limestone and black clay, (b). Weathered igneous rock\u003c/p\u003e\n\u003cp\u003eCorrelation between field geological observations with reduce to pole (RTP) maps can be identified as the causes of high and low magnetic anomalies and their constituent lithology. The value of the magnetic anomaly on the RTP map needs clarification, so a 2D incision model is created to present the shape and source of the magnetic anomaly measured laterally and vertically. 2D modeling uses ZondGM2D software inversion by entering IGRF parameter values 45035.9 nT, inclination \u0026minus;\u0026thinsp;32.5099\u0026deg;, and declination 0.7550\u0026deg;. The RTP map \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan\u003e6\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e comprises 4 models of incisions; these incisions are made to obtain X, Y, and Z values and magnetic values, which are then inverted into a 2D model. The 4 incisions consist of A-A\u0026apos; with a length of 1800 m, B-B\u0026apos; with a length of 2800 m, C-C\u0026apos; with a length of 3200 m, and D-D\u0026apos; with a length of 2400 m. The depth of the incision obtained varies from 750 m to 1200 m. In 2D model inversion, depth weighting is used to produce a smooth model with better vertical resolution.\u003c/p\u003e\n\u003cp\u003eThe selection of incisions is made based on consideration of regional geological data, the distribution of anomalies on the RTP map, the presence of faults, and the location of geological disasters. Based on the regional geological map \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan\u003e2\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e, it is known that in the northeastern area, there is a fault stretching from southeast-northwest in the Wonosari formation (Twml), which consists of intercalated limestone, sand marl and mudstone. Then, based on the distribution of anomalies on the RTP map \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan\u003e6\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e, high anomalies are dominated in the northeast direction, so the incision was selected from southwest to northeast. This incision was chosen because it is perpendicular to the fault, where low to high anomalies meet, and there are many points where geological disasters occur in the research area. The results of the 2D model in \u003cstrong\u003eFig.\u0026nbsp;7\u003c/strong\u003e can show magnetic anomalies that are spread vertically and laterally. The A-A\u0026apos; incision has values ranging from \u0026minus;\u0026thinsp;14 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e SI to 18 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e SI, B-B\u0026rsquo; incision \u0026minus;\u0026thinsp;50 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003eSI to 90 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e SI, C-C\u0026rsquo; incision \u0026minus;\u0026thinsp;40 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e SI to 90 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e SI, and D-D\u0026rsquo; incision \u0026minus;\u0026thinsp;30 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e SI to 30 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e SI. High magnetic anomaly values are between 20 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e SI to 90 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e SI, marked in yellow-red, while low magnetic anomaly values have values of 0 to -40 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e SI, marked in green-dark blue. In the 2D incision model, paramagnetic and diamagnetic rocks are characterized by low anomalous values. Based on theory and field observations, the low anomalous areas are breccias weakened by weathering. Meanwhile, rocks with ferromagnetic properties are estimated to be limestone (Reynolds, \u003cspan\u003e2011\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;7\u003c/strong\u003e The result of the incision on the Reduce to Pole (RTP) map\u003c/p\u003e\n\u003cp\u003eBased on the results of observations and mapping in the field, the disaster points that occurred mostly occurred in the zones marked in red. Disaster observations consist of landslides, subsidence, and damage caused by earthquakes. Figure\u0026nbsp;\u003cspan\u003e8\u003c/span\u003e shows several examples of damage from disasters, such as damaged roads, cracked houses, and even collapsed bridges. Geological disasters that occur in the red zone can disrupt the activities of residents and can cause economic, physical, and fatalities (Imani et al, \u003cspan\u003e2021\u003c/span\u003e; Jahangiri et al, \u003cspan\u003e2022\u003c/span\u003e; Kamiński et al, \u003cspan\u003e2021\u003c/span\u003e; Priyono et al, \u003cspan\u003e2020\u003c/span\u003e; Shanmugam \u0026amp; Wang, \u003cspan\u003e2015\u003c/span\u003e; Uhlemann et al, \u003cspan\u003e2017\u003c/span\u003e). The red area in the northeastern part has topography dominated by fields and valleys and there are rivers in each valley \u003cstrong\u003e(Fig.\u0026nbsp;3b).\u003c/strong\u003e The varied topography causes the slopes to become steep and steep. Steep slopes with an average slope of 40⁰-60⁰ can increase the risk of landslides and the addition of rivers at the base of the slope, which can cause soil erosion (\u0026Ccedil;ellek, \u003cspan\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe areas with the highest levels of disaster occurrence are shown in Fig. \u003cspan\u003e8\u003c/span\u003e, then studied geologically based on the observations shown in \u003cstrong\u003eFig.\u0026nbsp;9\u003c/strong\u003e. Figure \u003cspan\u003e8\u003c/span\u003e shows disaster risk zones or areas that experience the most geological natural disasters, such as landslides, earthquake damage, and ground movements. Based on the magnetic anomaly values on the RTP map in \u003cstrong\u003eFig.\u0026nbsp;9 (a)\u003c/strong\u003e, areas with high disaster risk have anomaly values that tend to be high; the constituent lithology may consist of rock types that have ferromagnetic properties. Igneous rock is one of the rocks with ferromagnetic properties, so it can produce high magnetic anomalies. Still, in the areas marked in red, there are no igneous rocks but limestone with calcite mineral inserts (Reynolds, \u003cspan\u003e2011\u003c/span\u003e); it can be concluded that the measured high magnetic value is due to the presence of this mineral. Theoretically, rocks with a high density level can conduct seismic waves well; if an earthquake occurs, the area will easily experience vibration or receive greater shocks so that the resulting impact and damage will also be greater.\u003c/p\u003e\n\u003cp\u003eBased on the regional geological map information shown in \u003cstrong\u003eFig.\u0026nbsp;9 (b)\u003c/strong\u003e, disaster risk areas are found in 2 formations, the Wonosari and Nampol formations, which are dominated by limestone, sand, and clay (Sujanto et al, \u003cspan\u003e1992\u003c/span\u003e). This is following the RTP map reading that this area has a high anomaly value and is possibly caused by limestone. The existence of faults and contact of solid rock layers with sandy clay layers causes rocks stable to become unstable. In addition, the area has a thick layer of clay on the surface, followed by limestone in the next layer; this can become a slip plane that triggers landslides and ground movements (Marino et al, \u003cspan\u003e2020\u003c/span\u003e). Based on the geological map (Fig. \u003cspan\u003e2\u003c/span\u003e), it is known that in the northeastern part, there are faults that enter the research area so this can also confirm the cause of the many geological disasters that occur. The presence of faults in an area can be an indication that the area is in a disaster-prone area. When an earthquake occurs in an area where there is a local fault due to the movement of the subduction zone, the earthquake vibration waves can cause the local fault to resonate. When a fault resonates due to an earthquake, it can cause other geological disasters such as landslides (Liu et al, \u003cspan\u003e2020\u003c/span\u003e), ground movement, and liquefaction (Kusumawardani et al, \u003cspan\u003e2021\u003c/span\u003e). The large amount of damage caused by earthquakes proves that this area has a high level of soil vulnerability. This research is a preliminary study, so it is necessary to carry out further research with other geophysical methods to determine the subsurface layer in detail.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;9\u003c/strong\u003e (a). Reduce to Pole (RTP) map of disaster risk, (b). Geological map of disaster risk\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe reduced to pole (RTP) map provides information on the distribution of magnetic anomalies with values of -682.6 nT to 1022 nT. Correlation between RTP maps, regional geology, and field observations provide mutually correlated results. Theoretically, igneous rock's susceptibility value is more significant than limestone's. However, based on the RTP map, the igneous rock area several points have low values due to the condition of the igneous rocks that are weathered and easily destroyed. Information from the RTP map is obtained from the Disaster Risk Zone, where disaster points are marked in the red area. The types of disasters in this zone vary, such as landslides, ground movements, and earthquake damage. The Disaster Risk Zone on the RTP map has a high magnetic anomaly; based on the geological map, the magnetic value is interpreted as limestone and igneous rock in the bedrock layer. The areas with the highest levels of disaster occurrence are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e8\u003c/span\u003e, then studied geologically based on the observations shown in \u003cb\u003eFig.\u0026nbsp;9.\u003c/b\u003e Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows disaster risk zones or areas that experience the most geological natural disasters, such as landslides, earthquake damage, and ground movements. Based on the magnetic anomaly values on the RTP map in \u003cb\u003eFig.\u0026nbsp;9 (a)\u003c/b\u003e, areas with high disaster risk have anomaly values that tend to be high; the constituent lithology may consist of rock types that have ferromagnetic properties. While, the meeting between compact rock layers with sandy clay layers and faults causes rocks that were originally stable to become unstable, so the potential for disaster is large.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe writers gratefully acknowledge the support of the\u0026nbsp;Indonesian Collaborative Research Grant (RKI) 2023 by Universitas Negeri Malang, Brawijaya University, and Andalas University. The author also thanks the Srimulyo Village, Dampit District, Malang Regency residents for their support during the data acquisition process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials availability\u0026nbsp;\u003c/strong\u003eResearch data can be obtained by request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eSiti Zulaikah: Conceptualisation, Supervision, writing-original draft preparation, investigation, and methodology.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAdi Susilo: Validation, Conceptualisation, Critical Review Article, and Resources.\u003c/li\u003e\n \u003cli\u003eAhmad Fauzi Pohan: Validation and Resources.\u003c/li\u003e\n \u003cli\u003eMuhammad Fathur Rouf Hasan: Writing-original draft preparation, investigation, and methodology.\u003c/li\u003e\n \u003cli\u003eMohammad Habiby Idmi: Writing-original draft preparation, investigation, and methodology.\u003c/li\u003e\n \u003cli\u003eMochamad Aryono Adhi: Validation and Critical Review Article.\u003c/li\u003e\n \u003cli\u003eDaeng Achmad Suaidi: Analysis, Validation and Critical Review Article.\u003c/li\u003e\n \u003cli\u003eNordiana Mohd. Muztaza: Analysis, Validation and Critical Review Article.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis research received funding from the 2023 Indonesian Collaborative Research Grant (RKI)\u0026nbsp;with Number: 10.5.59/UN32.20.1/LT/2023 from Universitas Negeri Malang, Number: 801.14/UN10.C10/TU/2023 from Brawijaya University, and Number: 5/UN16.19/PT.01.03/IS-RKI+Skema A(Mitra)/2023 from Andalas University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eAll ethical standards have been followed during this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdebo BA, Layade GO, Ilugbo SO, Hamzat AA, Otobrise, HK (2019) Mapping of subsurface geological structures using ground magnetic and electrical resistivity methods within lead City University, Southwestern Nigeria. 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Geodesy and Geodynamics, 9(5): 367\u0026ndash;371. https://doi.org/10.1016/j.geog.2018.05.002\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":true,"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":"Disaster Susceptibility Areas, Geological Disaster, Geomagnetic, Malang Regency, Rock Typology, RTP Map","lastPublishedDoi":"10.21203/rs.3.rs-3608588/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3608588/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBased on data from the BPBD Malang Regency, more than 100 geological disasters, such as earthquakes, landslides, and subsidence, yearly with losses of up to 3\u0026nbsp;Billion IDR. Based on field observations, one of the villages that frequently experience landslides and subsidence almost every year is Srimulyo Village, Dampit District. This condition requires research on the subsurface to analyze the trigger factors for geological disasters to increase disaster mitigation and awareness. This study aims to analyze the typological characteristics of the rocks in the study area using the geomagnetic method as a geological disaster mitigation strategy. The method is geomagnetic; measurement designs regularly cover the entire study area with a distance of 300 meters between measurement points, while research is presented in 2D models and the analysis is based on the measured magnetic anomaly values on the reduce to pole (RTP) map. The results showed that the correlation between the RTP maps, regional geology, and field observations gave mutually correlated results. Based on the interpretation result of the RTP map, we create a Disaster Risk Zone map marked with the highest magnetic anomaly values in the northwest and northeast areas. The types of disasters in this zone vary, such as landslides, ground movements, and earthquake damage. The meeting between compact rock layers with sandy clay layers and faults causes rocks that were originally stable to become unstable, so the potential for disaster is large. The results of this study contribute to the local government in carrying out disaster mitigation and development planning.\u003c/p\u003e","manuscriptTitle":"Characterization of typological rocks using the geomagnetic method for mapping geological disaster susceptibility areas in Malang Regency, Indonesia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-02 07:00:07","doi":"10.21203/rs.3.rs-3608588/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":"d1909195-1d47-418f-8bec-2c14dff0c680","owner":[],"postedDate":"January 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-14T10:02:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-02 07:00:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3608588","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3608588","identity":"rs-3608588","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}