Application of the Magnetotelluric Method in Geothermal Exploration in Jianshi County, Hubei Province,China

Application of the Magnetotelluric Method in Geothermal Exploration in Jianshi County, Hubei Province,China | 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 Application of the Magnetotelluric Method in Geothermal Exploration in Jianshi County, Hubei Province,China simeng Peng, xingbing Xie, lianqun Zhang, song Rao, lei Zhou, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5902191/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Geothermal resource is a kind of renewable green and clean energy stored in the earth's interior. Under the impetus of the global energy revolution and the goal of “double-carbon”, the development and utilization of geothermal energy has become an important strategy for the energy development of all countries in the world. Magnetotelluric (MT) sounding method, which is not shielded by high-resistance layers and provides high resolution for low-resistance layers, has unique advantages in revealing geothermal system structures, delineating hydrothermal geoelectric structures, and assessing the development potential of geothermal regions. Based on an MT profile in Maotian Township, Jianshi County, this paper processed data from 51 broadband MT measurement points using Fourier transform, Robust estimation, and impedance tensor decomposition techniques. After a detailed analysis of the dimensional characteristics and electrical axes, a 2D electrical structure model from the surface to a depth of 9 km was obtained via topography-based 2D inversion. The fault distribution was confirmed, and the potential geothermal resource areas were inferred based on geological information. The MT sounding survey confirmed the favorable geothermal resource storage range in the study area and demonstrated the advantages of MT in geothermal exploration, with important practical value for investigating the underground electrical structure and geothermal resource distribution. magnetotelluric sounding qualitative analysis 2D inversion geothermal exploration. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 0. Introduction Geothermal resources refer to the renewable heat energy stored within the earth's interior [ 1 ] . This kind of thermal energy is generally concentrated along the edges of tectonic plates and originates from the earth's molten magma and the decay of radioactive substances. Due to its large reserves, high energy utilization efficiency, low operational costs, and benefits in energy conservation and emission reduction, geothermal energy has become a focal point in the field of renewable energy. The study area is located in Jianshi County, Enshi City, Hubei Province [ 2 ] . The region is characterized by rich fault structures and abundant geothermal resources, making it highly valuable for exploration [ 3 ] . Previous research in this area has extensively investigated the crustal structure and deep tectonic features using various geophysical methods such as deep seismic reflection profiles, background noise imaging, and non-seismic methods. The results indicate that the electrical layering in the region is primarily composed of six sets, with a general electrical characteristic of "high-low-low-high-low-high." The main structures in the area include the Lichuan syncline, central anticline belt, Enshi Basin, and Huaguoping syncline, with significant fault development [ 4 – 6 ] . Meanwhile, geothermal resource studies in the region have been relatively simplistic, primarily relying on water chemistry, isotope features, and hydrogen-oxygen isotope testing to delineate geothermal resource boundaries [ 7 ] .As a natural source electromagnetic method, Magnetotellurics (MT) has the advantages of depth detection capability, high resolution, non-invasiveness, etc. It has unique advantages in revealing the structure of geothermal systems in sedimentary basins, circling the structure of hydrothermal geothermal basins, and evaluating the potential of geothermal area development in the rift valley system, and it has been widely used in the fields of hydrocarbon and mineral exploration, water resources investigation, and geothermal resource’s exploration [ 8 – 14 ] . In recent years, MT technology for geothermal resource exploration has become relatively mature. In 1999, Volpi G et al. conducted an MT survey at the southern edge of the Mount Amiata geothermal area (Tuscany, Italy), where the interpretation of the data by inverting them and calculating two-dimensional models of the resistivity and impedance phases revealed a good correlation between the characteristics of the geothermal temperature field and the resistivity distribution at depth, with the objective of determining the relationship between the shallow and deep electrical structures associated with local geothermal reservoirs and systematic heat recharge [ 15 ] . Guo Baodong et al. used the MT method and its inverse resistivity characteristics to reflect the distribution and production of water-conducting and heat-conducting formations in the geothermal field in the northern Ordos Basin [ 16 ] , and utilized the results of the previous geothermal well logging to delineate the distribution pattern of thermal reservoirs within the depth of 4 km in the horizontal and vertical directions, to encircle the range of geothermal anomalies and the characteristics of the spatial distribution, and to identify water-rich parts of the subsurface hot water and the favorable exploration wells locations. In this paper, 51 MT measuring points collected in Maotian Township, Jianshi County, were used to obtain the underground electrical structure of the study area after systematic data processing, qualitative analysis, and two-dimensional inversion, and combined with the regional geologic data to carry out a comprehensive geologic interpretation and to circle the range of geothermal resource storage. 1. Geological background and distribution of measurement Points Maotian Township, located in the southeastern part of Enshi Tujia and Miao Autonomous Prefecture, is situated within the tectonically active region of the East Chongqing-Western Hubei area, at the junction between the ancient Three Gorges seismic belt and the Qinling uplift. This area belongs to the late-stage tectonic movement zone of the Mindong region. Exposed strata in the area mainly consist of the Upper Cambrian-Ordovician Lower Series Loushanguan Formation, Middle Triassic Badong Formation, Upper Cretaceous Paomagang Formation, and Quaternary strata. The area spans the Hanjiang River Bend, Shuanghekou Fault, and Beimenpo Fault, with a complex geologic system, a large undulating topography, and a geomorphic distribution with obvious tectonic features. In geological history, Maotian Township in Jianshi County has experienced a number of geological movements, most notably the seismic tectonic landforms triggered by the Beimenpo Fracture Group. The stratigraphic details of the Baiyun Mountain District adjacent to the Hanjiang River have been preserved, and dense faults hundreds of kilometers long have revealed tectonic patterns at the scale of mountain ranges. There are various types of rocks in the district, mainly consisting of metamorphic rocks, volcanic rocks and sedimentary rocks. Among them, metamorphic rocks mainly include mica schist, quartzite, marble, etc. Volcanic rocks are dominated by andesite, rhyolite, tuff, etc. Sedimentary rocks are dominated by sandstone, mudstone, limestone, etc. According to the analysis of the spatial relationship of geological structure, the oldest strata exposed in and around the study area are gray-light gray thick-bedded massive dolomite and muddy dolomite in the Loushanguan Formation of the Upper Cambrian-Ordovician Lower Ordovician. The Ordovician-Cambrian carbonate rocks with good hydraulic conductivity and the red sandstone of Cretaceous Paomagang Formation with poor permeability and thermal conductivity, the Silurian clastic rocks (with the Paomagang Formation not consolidated in contact) and the sandstone of Cambrian Lower Tianheban Formation and Shipai Formation constitute a closed and complete thermal storage unit, with certain conditions of heat storage and water storage. The Enshi fault in the area is a regional north-east oriented active fracture, which is the main factor controlling the aggregation and transportation of geothermal fluids [3- 7] . One MT survey line (Fig. 1), with a profile length of 5.2 km and a total of 51 survey points, was deployed in the study area. Due to the influence of the actual terrain, human interference and other factors, under the premise of meeting the requirements of the survey purpose, some of the survey points were shifted appropriately within a reasonable range relative to the designed points. The study area is located within the central anticlinal belt shown in Fig. 2. The region features a complex system of fault structures, with major faults including the Enshi Fault, Jianshi Fault, and Daqingshan Fault. These faults exhibit multiple linear or belt-like distributions with different orientations in the plan view. 2. Data acquisition and processing 2.1 Data acquisition The MT profile is located at the southwestern corner of Maotian Township, where the terrain is undulating and the national highway G209 passes through. The profile extends 5.2 km with 51 measurement points. The field data acquisition was conducted using the MTU-5 series MT system from Phoenix Geophysics, Canada, which collects four horizontal components of the electromagnetic field (Ex, Ey, Hx, Hy) using a tensor-based observation approach. To ensure the quality of data observation, the far-reference method was employed [17] . Most measurement points had observation times exceeding 40 hours, yielding effective data with periods up to 5000s. The time series were fast Fourier transformed using the SSMT2000 and MT-Editor software that comes with the instrument, and after Robust processing with far reference and careful power spectrum selection [18] , valid data of geodetic electromagnetic impedance tensor response were obtained for all the 51 measurement points with frequencies ranging from 320 Hz to 2000 s. The resistivity phase curves of some of the measurement points are shown in Fig. 3, which indicate that the overall data quality is very good. Figure 2 shows the resistivity phase curves of some measurement points, which show that the overall quality of the data is very good, but some of the measurement points are aberrated due to the influence of the topography and shallow anomalies, which is manifested as the phenomenon of super-phase or phase tends to 0, which requires impedance tensor decomposition technique to correct the aberration. 2.2 Data preprocessing 2.2.1 Denoising based on orthogonal polynomial fitting Orthogonal polynomial fitting for noise removal is an efficient and stable data processing method that effectively reduces noise while preserving useful signals. By adaptively determining the polynomial order, this method scientifically processes noise in different time-domain signals and minimizes the aliasing effects between useful signals and noise. Additionally, its consistent stability across various data conditions makes it a reliable noise removal technique. In this study, orthogonal polynomial fitting is employed, using Legendre polynomials to approximate the data for noise removal. This method eliminates noise by fitting and regressing the observed data, minimizing the overall error. When applied to real data, two-dimensional polynomial approximation is used to constrain the overall trend of data changes and mitigate the effects of topography and local anomalies. Figure 4(a)(b) shows the TM-polarized apparent resistivity profiles before and after the denoising of the survey line. Comparison of the figures shows that the original measured apparent resistivity profile is more seriously disturbed, and the denoising method based on orthogonal polynomial fitting better eliminates the influence of noise, and also has a certain effect on eliminating the influence of topography and local anomalies at some measurement points [19] . 2.2.2 Static shift correction method The problem of static shift of MT curves is caused by surface inhomogeneities, and the interpretation of apparent resistivity curves that undergo static shifts can lead to erroneous conclusions, which can seriously disfigure the subsurface electrical structure. Currently, the conventional static correction methods are: low-pass filtering, curve translation and impedance tensor decomposition, and the more effective ones are impedance tensor decomposition and low-pass filtering [20] . In this paper, a combination of impedance tensor decomposition and low-pass filtering is used to correct for static offset. The combination of mathematical low-pass filtering and physical impedance tensor decomposition can maximize the elimination of the “static effect”. Figure 4(b)(c) shows the cross-sections of the line before and after the static correction of the TM-polarized apparent resistivity. From the figure, it can be seen that the original apparent resistivity profile contours have serious “hanging noodles” phenomenon, and the static offset is serious. From Fig. 4(c), it can be seen that the apparent resistivity profile after applying the static offset correction technique has been well improved, and the stratification of the profile is good. 3. Qualitative analysis of data After the time series processing was completed, the power spectrum file (EDI file) of each measurement point was obtained. On the basis of the power spectrum data, all subsequent qualitative analyses of electrical principal axes, tectonic dimensionality and other qualitative analyses as well as two-dimensional inversion calculations were carried out by using MT-Pioneer, a visualization and integration software for geomagnetic data processing and inversion interpretation developed by Chen Xiaobin [21] . 3.1 Impedance tensorimaging analysis Multi-site-multi-frequency tensor decomposition statistical imaging analysis technology is a newly developed geomagnetic data processing and analysis technology, which is integrated in MT-Pioneer software [21] . In this technique, the frequency distribution cloud map can be used to analyze the change of the electrical main axis from shallow to deep in the survey area, which can be used to know the depth conversion of the geometrical features of the geological structure, and then can be used to speculate the depth contact relationship of the tectonic units and the integration of the different tectonic units; according to the measurement point distribution cloud map can be used to analyze the change of the electrical main axis of the survey area in the transverse direction, which can be used to know the change of the geological tectonic geometrical features in the transverse direction, and then can be used to speculate the geometrical features of the geological tectonic units. Based on the point distribution maps, we can analyze the lateral changes of the electrical main axes in the surveyed area, and thus obtain the lateral changes of the geometric features of geological structures, and then speculate the lateral contact and transformation relationship of the tectonic units; and based on the 2D effective factor distribution maps, we can speculate and identify the distribution of the linear tectonic structures (often fault zones). We used this technique to analyze the statistical imaging of MT lines with multi-frequency-multi-site tensor decomposition. The rose diagram of the full-band multi-frequency-multi-site statistical imaging can show the dominant orientation of the electrical major axis. From Fig. 5, it can be seen that the overall dominant major axis orientation of the survey line is NEE/NNW orientation, and there is a sub-major axis orientation of NNE/NWW orientation, which indicates that the electrical major axis orientation in the survey area is not single, and the structure is more complex. On the frequency distribution map (Fig. 6(a)), the direction of the electrical major axis of the survey line is relatively scattered, and the electrical major axis in the middle and high frequency bands (320Hz-0.1s) of the survey line is more concentrated in the NEE/NNW and NNE/NWW directions, and there is no clear major axis orientation in the low frequency band. On the cloud map of the measurement point distribution (Fig. 6(b)), the electrical spindle orientation generally changes regularly around 50° (-40°), indicating that along the direction of the measurement line, the electrical spindle rotates within a certain range. 3.2 Tectonic dimensionality analysis The results of 2D deviation and 2D effective factor of the survey line are shown in Fig. 7(a)(b). 2D deviation was proposed by Swift in 1967, which is defined as when the value of 2D deviation is less than the threshold (usually 0.3), the subsurface medium can be approximated as 2D or 1D, and greater than the threshold value, it is 3D [22] . The two-dimensional validity factor describes the “pure two-dimensionality” of the structure, and its high value indicates that the structure corresponding to the data is good in two-dimensionality, but bad in one-dimensionality and three-dimensionality: its small value indicates that the pure “two-dimensionality” is weak, and good in one-dimensionality or three-dimensionality [21] . Combined with Fig. 7(a)(b), it can be seen that the two-dimensionality is stronger in the middle and high frequencies (320 Hz-0.01 Hz) in the study area, and the three-dimensionality is significantly enhanced in the low frequency band. 3.3 Impedance tensor distortion correction The impedance tensor decomposition technique can also be used for aberration correction, this paper is based on the Groom-Bailey decomposition method, by decomposing the impedance tensor, separating the local aberration and the regional response to eliminate the influence caused by shallow aberration, so as to recover the real information of the deep geologic structure [23] . Figure 8 shows the apparent resistivity and phase curve of a measurement point before and after the impedance tensor distortion correction, from which it can be seen that the over-phase phenomenon is obviously corrected after the distortion correction. 4. 2D Inversion Calculation 4.1 Inversion method The 2D inversion is performed by a 2D nonlinear conjugate gradient inversion algorithm based on the MT-Pioneer platform [24] . Some recently developed new techniques are more often used in the inversion, such as the gradual inversion mode from blocky coarse grid to fine grid, the center grid technique of the measurement points with topography, the technique of the impression method, the technique of the L-curve analysis, the technique of the forward validation of the inversion results, etc., and the application of these techniques improves the reliability of the final inversion results [25- 26] . The terrain-adaptive measurement-point-centered meshing technique has significant advantages in magnetotelluric inversion. It accurately positions measurement points based on their elevation, ensuring alignment with forward modeling responses and enhancing inversion precision. The technique generates smooth, high-quality lateral meshes that better reflect subsurface geological changes. By partitioning meshes according to terrain features, it accurately captures terrain’s impact on electromagnetic fields, reducing distortion and improving result reliability. Mesh density can be adjusted according to electrical structure distribution, reducing computational load and boosting inversion efficiency. Additionally, its high automation minimizes human intervention, avoiding operational errors and enhancing inversion stability and consistency. Given these benefits, this study employs the terrain-adaptive measurement-point-centered meshing technique with a 2D nonlinear conjugate gradient inversion algorithm based on the MT-Pioneer platform for 2D inversion. 4.2 Measurement point-centered grids with topography The automatic generation of the center grid of the measurement points can be used to obtain a smooth and high-quality transverse grid; the incremental scale factor is used to construct the longitudinal grid; and the automatic search method is used to form the grid of the terrain part based on the elevation data of the measurement points, in which the longitudinal grid size of the terrain part is set to be the same in order to ensure the accuracy of the orthogonal computation of all the measurement points. The grid dissection process and the inversion process are carried out iteratively to make the sparsity of the grid as consistent as possible with the high and low resistance distribution of the electrical structure. The final subsurface grid obtained (Fig. 9(a)). 4.3 TM mode 2D inversion The identification problem of TE and TM modes is based on the geological data (such as stratigraphic strike, fault direction, etc.) in the study area to initially determine the main direction of the regional geological structure, and then compared and verified by the principal axis analysis of the impedance tensor, which finally determines the TM and TE directions. According to the existing studies, the TE mode requires much more two-dimensionality of the model than the TM mode [27] . The construction of the studied profiles is characterized by a certain degree of three-dimensionality. In this paper, the two-dimensional inversion was started with the TM model, TE+TM model, and TE model, respectively. It is found that the TE+TM mode and TM mode have more consistent inversion results, and the results of TM mode based on qualitative analysis are more reliable than those of TE+TM mode, so TM mode is used in this paper to carry out 2D inversion. The two-dimensional inversion uses the apparent resistivity and phase data of 51 measurement points, and the impedance is rotated to the optimal principal axis orientation of the region according to the qualitative analysis results, with the inversion threshold error of 5%, and the frequency range of 320 Hz-1000 s. The inversion consists of a total of 94 × 128 grids, with the first grid thickness of 20 m in the vertical direction, and the downward grid thickness segments grow in different ratios. The growth factor is 1.02 within 2 km, 1.1 within 2-5.2 km, 1.2 within 5-10 km, and 1.3 within 10-50 km, and finally a total of 69 vertical grids are divided. At the end of 64 iterations of inversion, the final root mean square error RMS was 2.94 (Fig. 9(b)). 5. Reliability analysis of 2D inversion results 5.1 Data fitting A good data fit is essential to evaluate the reliability of an inversion result. Figure 10 shows the comparison between the theoretical response of the 2D electrical model and the proposed cross-section of the observed data. It can be seen that there is a good agreement between the observed data and the theoretical response of the inversion in terms of both apparent resistivity and impedance phase, the data fit is very good, indicating that the inversion results fully reflect the constraints of the observed data. Of course, due to the non-uniqueness of the inversion, the inversion result is one of several models that can produce the response of the observed data. 6. Results analysis 6.1 Electrical structure analysis Because of the good data fit, subsequent inversion can be carried out. The electrical structure of the profile obtained by two-dimensional inversion is shown in Figure 11. The electrical structure is relatively complex, and can be roughly summarized as “vertical layering, horizontal blocking”. Vertically, it can be divided into three layers of “high-middle-low-low” electrical structure, and there is a high-resistance zone of about 1200 m thick under the measurement points 9~42. Continuing downward, the resistance gradually decreases, sea level 0~-9000 m resistivity decreases, the extension direction from top to bottom, there is a “gourd-shaped” low-resistance area, the resistance is significantly lower than the east and west sides, and connected with the low-resistance anomalies below the measuring point 45~49, presumed to be an aquifer. In the transverse direction, it can be roughly divided into five obvious electrical zones, the west side of the No.3 measuring point is a high-resistance anomaly area with a thickness of less than 200 m; the east side of the No.5 measuring point to the west side of the No.7 measuring point is a small high-resistance anomaly area with a thickness of about 300 m, and the anomaly area below the anomaly area steeply changes into medium-resistance and low-resistance anomalies. There is a discontinuity in the resistivity near the profile points 7 to 10, and the resistivity is abnormally lowered here, and the extension direction is from up to down, so it is assumed that a fault (F 1 ) is developed in this area, which tends to be SE-directed, with a dipping angle of about 60°, and the width of the fault band is about 50~100 m. Combined with the geologic background of the area, this fault is assumed to be a high-steep reversed fault, and it may form a good channel for the conductivity of water in the area;From the east of point 10 to the west of point 42, the lower part of the section shows a large block of medium-resistance and high-resistance anomalies with a thickness of nearly 1,500 m. From the east side of point 41 to the west side of point 43, the high-resistance anomalies have a thickness of only about 100 m, and the lower part of the section steeply changes into low-resistance anomalies; in the section, there is a discontinuity in the left and right of the resistivity near points 41~43, and there is anomalous lowering of the resistivity in this area, with the direction of extension being from the upper part of the section downwards. It is inferred that there is a small branch fault (F 2 ) at this location, which tends to be SE-directed with a small dip angle of about 40°. The medium-high resistance anomalous zone below measuring points 43-51 is small in this area, with a thickness of about 500m, and then there is a large low-resistance anomalous zone in the shape of “crocodile mouth”, which may be a concealed water-conducting channel (F 3 ) in this area, with a width of about 500 m, and constitutes an anti-“Y” structure with F 1 . 6.2 Geothermal Formation Mechanism Analysis The study area is located in the Enshi Basin, and the drilling data reveals that the main stratigraphic layers in the basin are Cretaceous, Silurian, Ordovician and Cambrian, among which the Ordovician and Cambrian are the main thermal reservoirs explored in this well. The Ordovician-Cambrian carbonate rocks with good hydraulic conductivity and the red sandstone of Cretaceous Paomagang Formation with poor permeability and thermal conductivity, the Zhiliu clastic rocks (not integrated contact with Paomagang Formation) and the sandstones of Lower Cambrian Tianheban Formation and Shipai Formation form a closed and complete thermal storage unit, which has a certain condition of heat storage and water storage. The Enshi Great Fault in the area is a regional north-east active great fault, which is the main factor controlling the aggregation and transportation of geothermal fluids [3- 7] . (1) Thermal reservoir characteristics. According to the existing regional comprehensive geological survey and physical exploration, shallow drilling data, the thermal reservoir in the study area belongs to carbonate karst fissure thermal reservoir, both layered and banded thermal reservoir characteristics, thermal reservoir aquifer belongs to the Ordovician-Cambrian carbonate rocks, lithology is mainly bioclastic tuff, thick laminated graywacke, dolomitic graywacke, dolomite, gray dolomite, karst is strongly developed, strong water-rich. Carbonate rock fissure karst system is the main storage and transportation place of underground hot water in the area [8] . (2) Thermal storage cover. Thermal storage cap layer in the geothermal system mainly plays the role of heat insulation and thermal insulation, and the cap layer of the geothermal water in the study area is mainly the Silurian (with) muddy rocks. Groundwater in the process of circulation, along the fracture and dissolution channels for deep circulation, the Silurian (with) mudstone and Ordovician carbonate rocks are easy to form a semi-closed groundwater pooling area at the boundary. (3) Channel. The fracture structure of Maotian Township is very complex, with multiple lines or bands with different orientations distributed in the plane. They mainly include the Huayuanao Fault Zone, the Kuanping Fault Zone, and the Qinglongping-Shetoushan Fault Zone. The voids in the fracture zones are enlarged ,and the permeability is increased, which provides water and heat conduction channels for the geothermal system. In addition, the development of dissolution phenomenon in the region, the formation of a large number of solution tanks, holes, caves, etc., the existence of fissure karst system for the underground hot water to provide a good storage space and transportation channel, F 3 in the figure is the main hydraulic conduit in the region. (4) Heat source. There is no heat source from intrusive rocks and magmatic rocks in the study area, so the heat source mainly comes from the natural warming of groundwater along the deep fractures for deep circulation [28] . It is assumed that the heat source in this area is mainly geothermal heat flow. According to the above analysis, the study area has a complete heat storage and thermal conductivity system, and fractures F 1 , F 2 , and F 3 have a certain strike and undercutting depth, which can easily form a channel for convection and circulation of geothermal resources. Therefore, the study area is of great value for geothermal resources exploration and application. 7. Conclusions In this paper, the two-dimensional electrical structure model at a certain depth in the area is obtained by data preprocessing, impedance tensor decomposition and dimensional analysis of 51 geomagnetic measurement points distributed in Maotian Township, Jianshi County, and the two-dimensional inversion of the topography is utilized, and the following conclusions are drawn on the basis of the previous research and the corresponding geological data: (1) The electrical structure of the study area is relatively complex, and the overall characteristics can be summarized as follows: the resistivity of the surface layer along the direction of the survey line is a discontinuous distribution of high and low resistance, there is a relatively stable high-resistance body in the target area (Points 9~42) within the range of 0 m above sea level, and there is a low-resistance anomalous zone from the surface to the depth on the east and west of the target area respectively, inferred to be the location of fracture confluence, and the fracture extends to the depth of the electrical low-resistance zone. (2) There is a high resistance zone of about 1200 m below the measurement points 9~42. Continuing downward, the resistance gradually decreases, the resistivity of 0~9000 m at the sea level decreases continuously, and the extension direction is from top to bottom, and there is a “gourd-shaped” low-resistance zone, with resistance significantly lower than that of the east and west sides, and it is connected with the low-resistance anomalous zone below the measuring points 45~51, which is presumed to be an aquifer. (3) According to the regional geological background and related research data, it is assumed that the thermal storage aquifer belongs to Ordovician-Cambrian carbonate rocks, and its lithology mainly consists of thick laminated tuff, bioclastic tuff, dolomitic tuff and dolomite, and gray dolomite. (4) According to the results of the compilation of geothermal heat flow data in the China Earth region [29] . The geothermal temperature gradient in the study area is 20℃/km [30] , and taking the depth of the thermostatic zone as 30 m and the temperature as 12℃ [31] , it is presumed that the stratigraphic temperature corresponding to the depth of the target aquifer from 1500 to 9000 m is 41.4℃~67.4℃ [32] . Declarations Funding: This work was supported by National Natural Science Foundation of China(Grant numbers 2474117 and 42274103) Competing interests: The authors have no conflicts of interest to declare that are relevant to the content of this article.All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Data Availability declaration: The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Ethics and Consent to Participate declarations:This study complies with the Code of Academic Ethics. References Su, K., Ai, H., Alvandi, A., Lyu, C., Wei, X., Qin, Z., Tu, Y., Yan, Y. & Nie, T. (2024). Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies. Open Geosciences, 16(1), 20220641. Hu C, Bai D B. Analysis of the Current Situation and Prospect of Underground Hot Water Resources Development and Utilization in Hubei Province[J]. China Steel Focus, 2020, (17):82–83. Yuan F, Yang B Y, Fu Q, Li M L, Li Z W, Wan K. Study on Hydrochemical Characteristics and Genesis of Xiaolongtan Geothermal Field in Northern Enshi Basin[J]. Resources Environment & Engineering, 2022, 36(03):337–343. Chen M, Liu Z D, Ruan X M, Yan J Y, Zhang H. Crustal structure of the Enshi-Lichuan area based on deep seismic reflection profiles[A] Proceedings of the Second National Mineral Exploration Conference[C]. Chinese Geophysical Society, Metallic Ore Exploration Committee, Chinese Geophysical Society, China, 2023:4. Zhong R F. Study on velocity structure of Enshi-Lichuan area based on ambient noise tomography[D].East China University of Technology, 2022. Yang W. The Application of Geological and GeophysicalComprehensive Interpretation Technology in Enshi-Lichuan Oiland Gas Prospect Area[D].Chengdu University Of Technology,2018. Li Z W, Yuan F, Li M L, Zhao J, Wan K, Li G S. Indicative significance of hydrochemical characterizatcs in geothermal resource investigations in the Enshi Basin[J].Bulletin of Geological Science and Technology, 2023, 42(04):83–94 + 106. Zhu H L, Xu B W, Liu Z L, Shi F, Xin Y Q, Cao X G, Cheng G Q. The application of magnetotelluric sounding to geothermal resources assessment in Yinchuan Basin[J]. Geophysical and Geochemical Exploration, 2019, 43(04):718–725. Niu P, Han J T, Zeng Z F, Hou H S, Liu L J, Ma G Q, Guan Y W. Deep controlling factors of the geothermal field in the northern Songliao basin derived from magnetotelluric survey[J].Chinese Journal of Geophysics, 2021, 64(11):4060–4074. Shao B S, Shi F, Liu Z L, et al. Application of the magnetotelluric sounding method in the exploration of geothermal resources in the west of the Taikang uplift[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2023, 45(03):379–388. Gao J, Zhang H, Zhang S, et al. Three-dimensional magnetotelluric imaging of the geothermal system beneath the Gonghe Basin, Northeast Tibetan Plateau[J]. Geothermics, 2018, 76: 15–25. Delhaye R, Rath V, Jones A G, et al. Quantitative geothermal interpretation of electrical resistivity models of the Rathlin Basin, Northern Ireland[J]. Geothermics, 2019, 77: 175–187. Volpi G, Manzella A, Fiordelisi A. Investigation of geothermal structures by magnetotellurics (MT): an example from the Mt. Amiata area, Italy[J]. Geothermics, 2003, 32(2): 131–145. Zaher M A, Saibi H, Nishijima J, et al. Exploration and assessment of the geothermal resources in the Hammam Faraun hot spring, Sinai Peninsula, Egypt[J]. Journal of Asian Earth Sciences, 2012, 45: 256–267. Volpi G, Manzella A, Fiordelisi A. Investigation of geothermal structures by magnetotellurics (MT): an example from the Mt. Amiata area, Italy[J]. Geothermics, 2003, 32(2): 131–145. Guo B D, Sun Z R, Shi Y X, et al. Application of magnetotelluric sounding method in geothermal resources exploration in northern Ordos Basin[J]. Geology and Resources, 2023, 32(01):64–69. Gamble T D, Goubau W M, Clarke J. Magnetotellurics with a remote magnetic reference[J]. Geophysics, 1979, 44(1): 53–68. Egbert G D. Robust multiple-station magnetotelluric data processing[J]. Geophysical Journal International, 1997, 130(2): 475–496. Yang Y F, Wang H Z, Cao J Z. Using orthogonal polynomials for eliminating white noise [J]. Oil Geophysical Prospecting, 2001, (06):80 – 79 + 78–0. Yang J, Xiao H Y, Jiang Y D, et al.The study and application of static displacement correction for magnetotelluric sounding data processing [J].Computing Techniques for Geophysical and Geochemical Exploration, 2015, 37(02):187–192. Chen X B, Cai J T, Wang L F, et al. Refined techniques for magnetotelluric data processing and two-dimensional inversion(IV):Statistical image method based on multi-site, multi-frequency tensor decomposition[J]. Chinese Journal of Geophysics, 2014, 57(06):1946–1957. Swift C M. A magnetotelluric investigation of an electrical conductivity anomaly in the southwestern United States[D]. Massachusetts Institute of Technology, 1967. Groom R W, Bailey R C. Decomposition of magnetotelluric impedance tensors in the presence of local three-dimensional galvanic distortion[J]. Journal of Geophysical Research: Solid Earth, 1989, 94(B2): 1913–1925. Rodi W, Mackie R L. Nonlinear conjugate gradients algorithm for 2-D magnetotelluric inversion[J]. Geophysics, 2001, 66(1): 174–187. Chen X B, Zhao G Z. Automatic construction of a Site Centered Grid (SCG) for 2D magnetotelluric inversion[J]. Chinese Journal of Geophysics, 2009, 52(06):1564–1572. Ye T, Chen X B, Yan L J. Refined techniques for data processing and two-dimensional inversion in magnetotelluric (III):using the Impressing Method to construct starting model of 2D magnetotelluric inversion[J]. Chinese Journal of Geophysics, 2013, 56(10):3596–3606. Qiu G G, Zhang X B, Pei F G, et al.Effectiveness of magnetotelluric sounding inversion technique[J]. Geophysical and Geochemical Exploration, 2015, 39(01):118–124. Wang X, Ye J, Fan W, et al. Study on Genetic Model of Geothermal Resources of Sodium Chloride Type Karst Heat Reservoir in Western Hubei[J]. Resources Environment & Engineering, 2022, 36(05):588–593. Jiang G Z, Gao p, Rao S, et al.Compilation of heat flow data in the continental area of China(4th edition)[J]. Chinese Journal of Geophysics, 2016, 59(08):2892–2910. Gaber, G.M.; Saleh, S.; Kotb, A. Integrating radiometric and aeromagnetic data for assessment of geothermal potential: A case study in Central Eastern Desert, Egypt. Acta Geophys. 2024. Gaber, G.M.; Saleh, S.; Kotb, A. 3D Gravity and magnetic inversion modelling for geothermal assessment and temperature modelling in the central eastern desert and Red Sea, Egypt. Sci. Rep. 2024, 14, 15266. Gaber, G.M.; Saleh, S.; Kotb, A. Investigating geothermal resources in the Central Eastern Desert of Red Sea, Egypt, using aeromagnetic data. J. Earth Syst. Sci. 2024, 133, 137. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 May, 2025 Reviews received at journal 23 May, 2025 Reviews received at journal 28 Apr, 2025 Reviews received at journal 20 Apr, 2025 Reviewers agreed at journal 20 Apr, 2025 Reviewers agreed at journal 18 Apr, 2025 Reviewers invited by journal 18 Apr, 2025 Submission checks completed at journal 11 Apr, 2025 Editor assigned by journal 03 Apr, 2025 First submitted to journal 02 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-5902191","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445319571,"identity":"2bcfc1c8-d48e-47a2-8e92-db06e21f3127","order_by":0,"name":"simeng Peng","email":"","orcid":"","institution":"Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education (Yangtze University)","correspondingAuthor":false,"prefix":"","firstName":"simeng","middleName":"","lastName":"Peng","suffix":""},{"id":445319572,"identity":"4f77549a-51e1-4304-8622-03f7a676f42e","order_by":1,"name":"xingbing Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBAC+2bGhsM/KmyQxRLwazFgbz74mOFMGojN2ECcFp5jycaMbYdJ0GIukWMmXcB2PrF/dvv1hz8qDjPws+cYMPzcgVuL5Qyglhk8txNn3DlT2CBx5jCDZM8bA8beM3isuZFjJsEjcTux4UZOYoMh0IUGN3IMmBnbCGkxOJc4H6Ql8d9hBntCWgzOAL3Pk3AgccON9IMNBxuAtkgQ0CLZ3nzw4YwDycYbb+Qwzmw4ls4jceZZwcFePFr4mRkbDnz8Zyc770b6g48/aqzl+NuTNz74ic8vUODYwMBjAGLwgIgDhDUAEw4DA/sDYhSOglEwCkbBCAQAE85dWMvSAPQAAAAASUVORK5CYII=","orcid":"","institution":"Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education (Yangtze University)","correspondingAuthor":true,"prefix":"","firstName":"xingbing","middleName":"","lastName":"Xie","suffix":""},{"id":445319573,"identity":"2d5efcbf-9ef4-4905-ba23-135b328051c2","order_by":2,"name":"lianqun Zhang","email":"","orcid":"","institution":"Research Institute of Petroleum Exploration \u0026 Development","correspondingAuthor":false,"prefix":"","firstName":"lianqun","middleName":"","lastName":"Zhang","suffix":""},{"id":445319574,"identity":"3fb895e1-3288-4564-a917-1b7874a9f472","order_by":3,"name":"song Rao","email":"","orcid":"","institution":"Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education (Yangtze University)","correspondingAuthor":false,"prefix":"","firstName":"song","middleName":"","lastName":"Rao","suffix":""},{"id":445319576,"identity":"d258eacc-150d-43b6-8bde-3b579bb1f794","order_by":4,"name":"lei Zhou","email":"","orcid":"","institution":"Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education (Yangtze University)","correspondingAuthor":false,"prefix":"","firstName":"lei","middleName":"","lastName":"Zhou","suffix":""},{"id":445319577,"identity":"16b236b4-df38-43e6-b7df-a7bac912e846","order_by":5,"name":"chuyang Lu","email":"","orcid":"","institution":"Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education (Yangtze University)","correspondingAuthor":false,"prefix":"","firstName":"chuyang","middleName":"","lastName":"Lu","suffix":""},{"id":445319578,"identity":"2ecc53d6-708a-4221-9d4e-94f078e1fed6","order_by":6,"name":"yuxi Li","email":"","orcid":"","institution":"Enshi New Territories Cultural Tourism Development Co","correspondingAuthor":false,"prefix":"","firstName":"yuxi","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-01-25 14:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5902191/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5902191/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81064241,"identity":"44d7dc35-4a08-4fc0-bf2b-af0b3231a3b7","added_by":"auto","created_at":"2025-04-21 20:30:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":238044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of measuring points in the study area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5902191/v1/d14f0ddf7613df3302e09bbf.jpg"},{"id":81064249,"identity":"09a8d7b5-d18e-42e6-b21f-8f311d78b4e4","added_by":"auto","created_at":"2025-04-21 20:30:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":282629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTectonic Division Map of Western Hubei - Eastern Chongqing Area(The study area in Jianshi is indicated by a green box.)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5902191/v1/1e3de69508e7c00e5431140c.jpg"},{"id":81064245,"identity":"81ca43fc-d01b-4cd1-90d6-c173f56f21c8","added_by":"auto","created_at":"2025-04-21 20:30:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":693450,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlot of apparent resistivity and phase profiles at selected MT measurement points\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5902191/v1/4383bbc5ea5028bd5767efee.jpg"},{"id":81064242,"identity":"559ca0ea-18a4-43f2-b1ea-7fe55474789a","added_by":"auto","created_at":"2025-04-21 20:30:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69803,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)TM-polarized apparent resistivity profiles before line denoising(b)TM-polarized apparent resistivity profile after line denoising(c)Line TM-polarized apparent resistivity static-corrected cross sections\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5902191/v1/5b5dfdb9f2c33da1f2b1ed93.jpg"},{"id":81064244,"identity":"32f1b8b6-2610-428b-9f22-60b91142eeaf","added_by":"auto","created_at":"2025-04-21 20:30:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":30468,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMulti-frequency-multi-site statistical imaging statistical rose diagrams\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5902191/v1/13f442289dd5212e45f37d46.jpg"},{"id":81064246,"identity":"e443a838-9886-4060-ad56-1583e8b22816","added_by":"auto","created_at":"2025-04-21 20:30:13","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":95622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)Multi-frequency-multi-site statistical imaging frequency distribution maps(b)Multi-frequency-multi-site statistical imaging of site distribution clouds\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5902191/v1/fdf0d10c98af4b72e7c190b4.jpg"},{"id":81065087,"identity":"433ce9c6-9e8e-4f84-8895-f74736afd15a","added_by":"auto","created_at":"2025-04-21 20:54:13","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":101241,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)Multi-frequency-multi-site statistical imaging 2D deviation map\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b)Multi-frequency-multi-site statistical imaging of effective 2D factors\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5902191/v1/4088e382007a6797ed180984.jpg"},{"id":81064250,"identity":"87a07831-4091-4b79-a3bc-3ca4643dd46f","added_by":"auto","created_at":"2025-04-21 20:30:13","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":67698,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpedance tensor distortion correction foresight resistivity and phase curves (left: original, right: corrected)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5902191/v1/00aa5a0e3139e1c6dc23afb4.jpg"},{"id":81064394,"identity":"5d15f500-feaa-4a63-b22e-2dd8b08f17c3","added_by":"auto","created_at":"2025-04-21 20:38:13","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":120653,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)inversion lattice(b)2D inversion RMS plot\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5902191/v1/5a31059fe6f85a5f49bd2610.jpg"},{"id":81064814,"identity":"6e12657d-11a7-4a9b-84f1-d18ce9902cb3","added_by":"auto","created_at":"2025-04-21 20:46:13","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":122246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)TM-model raw apparent resistivity (b) Inverse model-calculated apparent resistivity profiles (c)TM model raw phase (d)Inversion model calculated phase profile\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5902191/v1/dbbc079ecfc8a7b248578f30.jpg"},{"id":81064392,"identity":"102ea16b-e614-4276-aa66-1e1e412fc40b","added_by":"auto","created_at":"2025-04-21 20:38:13","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":80384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2D inverse resistivity and geologic interpretation profiles (F\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e、 F\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e、 F\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e are inferred faults)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5902191/v1/9fb85fc8210f10867ffcbb33.jpg"},{"id":81065345,"identity":"ccb80bbf-dac7-4485-b0b9-1ce38da7d4cd","added_by":"auto","created_at":"2025-04-21 21:02:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3019378,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5902191/v1/f5cb4675-c151-47ac-9cbe-60a96a5c9963.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of the Magnetotelluric Method in Geothermal Exploration in Jianshi County, Hubei Province,China","fulltext":[{"header":"0. Introduction","content":"\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eGeothermal resources refer to the renewable heat energy stored within the earth's interior\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. This kind of thermal energy is generally concentrated along the edges of tectonic plates and originates from the earth's molten magma and the decay of radioactive substances. Due to its large reserves, high energy utilization efficiency, low operational costs, and benefits in energy conservation and emission reduction, geothermal energy has become a focal point in the field of renewable energy.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe study area is located in Jianshi County, Enshi City, Hubei Province\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The region is characterized by rich fault structures and abundant geothermal resources, making it highly valuable for exploration\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Previous research in this area has extensively investigated the crustal structure and deep tectonic features using various geophysical methods such as deep seismic reflection profiles, background noise imaging, and non-seismic methods. The results indicate that the electrical layering in the region is primarily composed of six sets, with a general electrical characteristic of \"high-low-low-high-low-high.\" The main structures in the area include the Lichuan syncline, central anticline belt, Enshi Basin, and Huaguoping syncline, with significant fault development \u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, geothermal resource studies in the region have been relatively simplistic, primarily relying on water chemistry, isotope features, and hydrogen-oxygen isotope testing to delineate geothermal resource boundaries \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.As a natural source electromagnetic method, Magnetotellurics (MT) has the advantages of depth detection capability, high resolution, non-invasiveness, etc. It has unique advantages in revealing the structure of geothermal systems in sedimentary basins, circling the structure of hydrothermal geothermal basins, and evaluating the potential of geothermal area development in the rift valley system, and it has been widely used in the fields of hydrocarbon and mineral exploration, water resources investigation, and geothermal resource\u0026rsquo;s exploration \u003csup\u003e[\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12 CR13\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn recent years, MT technology for geothermal resource exploration has become relatively mature. In 1999, Volpi G et al. conducted an MT survey at the southern edge of the Mount Amiata geothermal area (Tuscany, Italy), where the interpretation of the data by inverting them and calculating two-dimensional models of the resistivity and impedance phases revealed a good correlation between the characteristics of the geothermal temperature field and the resistivity distribution at depth, with the objective of determining the relationship between the shallow and deep electrical structures associated with local geothermal reservoirs and systematic heat recharge\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Guo Baodong et al. used the MT method and its inverse resistivity characteristics to reflect the distribution and production of water-conducting and heat-conducting formations in the geothermal field in the northern Ordos Basin\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, and utilized the results of the previous geothermal well logging to delineate the distribution pattern of thermal reservoirs within the depth of 4 km in the horizontal and vertical directions, to encircle the range of geothermal anomalies and the characteristics of the spatial distribution, and to identify water-rich parts of the subsurface hot water and the favorable exploration wells locations. In this paper, 51 MT measuring points collected in Maotian Township, Jianshi County, were used to obtain the underground electrical structure of the study area after systematic data processing, qualitative analysis, and two-dimensional inversion, and combined with the regional geologic data to carry out a comprehensive geologic interpretation and to circle the range of geothermal resource storage.\u003c/p\u003e"},{"header":"1. Geological background and distribution of measurement Points","content":"\u003cp\u003eMaotian Township, located in the southeastern part of Enshi Tujia and Miao Autonomous Prefecture, is situated within the tectonically active region of the East Chongqing-Western Hubei area, at the junction between the ancient Three Gorges seismic belt and the Qinling uplift. This area belongs to the late-stage tectonic movement zone of the Mindong region. Exposed strata in the area mainly consist of the Upper Cambrian-Ordovician Lower Series Loushanguan Formation, Middle Triassic Badong Formation, Upper Cretaceous Paomagang Formation, and Quaternary strata. The area spans the Hanjiang River Bend, Shuanghekou Fault, and Beimenpo Fault, with a complex geologic system, a large undulating topography, and a geomorphic distribution with obvious tectonic features. In geological history, Maotian Township in Jianshi County has experienced a number of geological movements, most notably the seismic tectonic landforms triggered by the Beimenpo Fracture Group. The stratigraphic details of the Baiyun Mountain District adjacent to the Hanjiang River have been preserved, and dense faults hundreds of kilometers long have revealed tectonic patterns at the scale of mountain ranges. There are various types of rocks in the district, mainly consisting of metamorphic rocks, volcanic rocks and sedimentary rocks. Among them, metamorphic rocks mainly include mica schist, quartzite, marble, etc. Volcanic rocks are dominated by andesite, rhyolite, tuff, etc. Sedimentary rocks are dominated by sandstone, mudstone, limestone, etc.\u003c/p\u003e\n\u003cp\u003eAccording to the analysis of the spatial relationship of geological structure, the oldest strata exposed in and around the study area are gray-light gray thick-bedded massive dolomite and muddy dolomite in the Loushanguan Formation of the Upper Cambrian-Ordovician Lower Ordovician. The Ordovician-Cambrian carbonate rocks with good hydraulic conductivity and the red sandstone of Cretaceous Paomagang Formation with poor permeability and thermal conductivity, the Silurian clastic rocks (with the Paomagang Formation not consolidated in contact) and the sandstone of Cambrian Lower Tianheban Formation and Shipai Formation constitute a closed and complete thermal storage unit, with certain conditions of heat storage and water storage. The Enshi fault in the area is a regional north-east oriented active fracture, which is the main factor controlling the aggregation and transportation of geothermal fluids\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e[3-\u003c/sup\u003e\u003csup\u003e7]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOne MT survey line (Fig. 1), with a profile length of 5.2 km and a total of 51 survey points, was deployed in the study area. Due to the influence of the actual terrain, human interference and other factors, under the premise of meeting the requirements of the survey purpose, some of the survey points were shifted appropriately within a reasonable range relative to the designed points. The study area is located within the central anticlinal belt shown in Fig. 2. The region features a complex system of fault structures, with major faults including the Enshi Fault, Jianshi Fault, and Daqingshan Fault. These faults exhibit multiple linear or belt-like distributions with different orientations in the plan view.\u003c/p\u003e"},{"header":"2. Data acquisition and processing","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data acquisition\u003c/strong\u003e\u003cbr\u003e The MT profile is located at the southwestern corner of Maotian Township, where the terrain is undulating and the national highway G209 passes through. The profile extends 5.2 km with 51 measurement points. The field data acquisition was conducted using the MTU-5 series MT system from Phoenix Geophysics, Canada, which collects four horizontal components of the electromagnetic field (Ex, Ey, Hx, Hy) using a tensor-based observation approach. To ensure the quality of data observation, the far-reference method was employed\u003csup\u003e[17]\u003c/sup\u003e. Most measurement points had observation times exceeding 40 hours, yielding effective data with periods up to 5000s. The time series were fast Fourier transformed using the SSMT2000 and MT-Editor software that comes with the instrument, and after Robust processing with far reference and careful power spectrum selection\u003csup\u003e[18]\u003c/sup\u003e, valid data of geodetic electromagnetic impedance tensor response were obtained for all the 51 measurement points with frequencies ranging from 320 Hz to 2000 s. The resistivity phase curves of some of the measurement points are shown in Fig. 3, which indicate that the overall data quality is very good. Figure 2 shows the resistivity phase curves of some measurement points, which show that the overall quality of the data is very good, but some of the measurement points are aberrated due to the influence of the topography and shallow anomalies, which is manifested as the phenomenon of super-phase or phase tends to 0, which requires impedance tensor decomposition technique to correct the aberration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Data preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1 Denoising based on orthogonal polynomial fitting\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Orthogonal polynomial fitting for noise removal is an efficient and stable data processing method that effectively reduces noise while preserving useful signals. By adaptively determining the polynomial order, this method scientifically processes noise in different time-domain signals and minimizes the aliasing effects between useful signals and noise. Additionally, its consistent stability across various data conditions makes it a reliable noise removal technique. In this study, orthogonal polynomial fitting is employed, using Legendre polynomials to approximate the data for noise removal. This method eliminates noise by fitting and regressing the observed data, minimizing the overall error. When applied to real data, two-dimensional polynomial approximation is used to constrain the overall trend of data changes and mitigate the effects of topography and local anomalies.\u003c/p\u003e\n\u003cp\u003eFigure 4(a)(b) shows the TM-polarized apparent resistivity profiles before and after the denoising of the survey line. Comparison of the figures shows that the original measured apparent resistivity profile is more seriously disturbed, and the denoising method based on orthogonal polynomial fitting better eliminates the influence of noise, and also has a certain effect on eliminating the influence of topography and local anomalies at some measurement points \u003csup\u003e[19]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 Static shift correction method\u003c/strong\u003e\u003cbr\u003e The problem of static shift of MT curves is caused by surface inhomogeneities, and the interpretation of apparent resistivity curves that undergo static shifts can lead to erroneous conclusions, which can seriously disfigure the subsurface electrical structure. Currently, the conventional static correction methods are: low-pass filtering, curve translation and impedance tensor decomposition, and the more effective ones are impedance tensor decomposition and low-pass filtering \u003csup\u003e[20]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn this paper, a combination of impedance tensor decomposition and low-pass filtering is used to correct for static offset. The combination of mathematical low-pass filtering and physical impedance tensor decomposition can maximize the elimination of the \u0026ldquo;static effect\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eFigure 4(b)(c) shows the cross-sections of the line before and after the static correction of the TM-polarized apparent resistivity. From the figure, it can be seen that the original apparent resistivity profile contours have serious \u0026ldquo;hanging noodles\u0026rdquo; phenomenon, and the static offset is serious. From Fig. 4(c), it can be seen that the apparent resistivity profile after applying the static offset correction technique has been well improved, and the stratification of the profile is good.\u003c/p\u003e"},{"header":"3. Qualitative analysis of data","content":"\u003cp\u003eAfter the time series processing was completed, the power spectrum file (EDI file) of each measurement point was obtained. On the basis of the power spectrum data, all subsequent qualitative analyses of electrical principal axes, tectonic dimensionality and other qualitative analyses as well as two-dimensional inversion calculations were carried out by using MT-Pioneer, a visualization and integration software for geomagnetic data processing and inversion interpretation developed by Chen Xiaobin\u003csup\u003e[21]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Impedance tensorimaging analysis\u003c/strong\u003e\u003cbr\u003e Multi-site-multi-frequency tensor decomposition statistical imaging analysis technology is a newly developed geomagnetic data processing and analysis technology, which is integrated in MT-Pioneer software \u003csup\u003e[21]\u003c/sup\u003e. In this technique, the frequency distribution cloud map can be used to analyze the change of the electrical main axis from shallow to deep in the survey area, which can be used to know the depth conversion of the geometrical features of the geological structure, and then can be used to speculate the depth contact relationship of the tectonic units and the integration of the different tectonic units; according to the measurement point distribution cloud map can be used to analyze the change of the electrical main axis of the survey area in the transverse direction, which can be used to know the change of the geological tectonic geometrical features in the transverse direction, and then can be used to speculate the geometrical features of the geological tectonic units. Based on the point distribution maps, we can analyze the lateral changes of the electrical main axes in the surveyed area, and thus obtain the lateral changes of the geometric features of geological structures, and then speculate the lateral contact and transformation relationship of the tectonic units; and based on the 2D effective factor distribution maps, we can speculate and identify the distribution of the linear tectonic structures (often fault zones). We used this technique to analyze the statistical imaging of MT lines with multi-frequency-multi-site tensor decomposition.\u003c/p\u003e\n\u003cp\u003eThe rose diagram of the full-band multi-frequency-multi-site statistical imaging can show the dominant orientation of the electrical major axis. From Fig. 5, it can be seen that the overall dominant major axis orientation of the survey line is NEE/NNW orientation, and there is a sub-major axis orientation of NNE/NWW orientation, which indicates that the electrical major axis orientation in the survey area is not single, and the structure is more complex.\u003c/p\u003e\n\u003cp\u003eOn the frequency distribution map (Fig. 6(a)), the direction of the electrical major axis of the survey line is relatively scattered, and the electrical major axis in the middle and high frequency bands (320Hz-0.1s) of the survey line is more concentrated in the NEE/NNW and NNE/NWW directions, and there is no clear major axis orientation in the low frequency band. On the cloud map of the measurement point distribution (Fig. 6(b)), the electrical spindle orientation generally changes regularly around 50\u0026deg; (-40\u0026deg;), indicating that along the direction of the measurement line, the electrical spindle rotates within a certain range.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Tectonic dimensionality analysis\u003c/strong\u003e\u003cbr\u003e The results of 2D deviation and 2D effective factor of the survey line are shown in Fig. 7(a)(b). 2D deviation was proposed by Swift in 1967, which is defined as when the value of 2D deviation is less than the threshold (usually 0.3), the subsurface medium can be approximated as 2D or 1D, and greater than the threshold value, it is 3D\u003csup\u003e[22]\u003c/sup\u003e. The two-dimensional validity factor describes the \u0026ldquo;pure two-dimensionality\u0026rdquo; of the structure, and its high value indicates that the structure corresponding to the data is good in two-dimensionality, but bad in one-dimensionality and three-dimensionality: its small value indicates that the pure \u0026ldquo;two-dimensionality\u0026rdquo; is weak, and good in one-dimensionality or three-dimensionality\u003csup\u003e[21]\u003c/sup\u003e. Combined with Fig. 7(a)(b), it can be seen that the two-dimensionality is stronger in the middle and high frequencies (320 Hz-0.01 Hz) in the study area, and the three-dimensionality is significantly enhanced in the low frequency band.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Impedance tensor distortion correction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe impedance tensor decomposition technique can also be used for aberration correction, this paper is based on the Groom-Bailey decomposition method, by decomposing the impedance tensor, separating the local aberration and the regional response to eliminate the influence caused by shallow aberration, so as to recover the real information of the deep geologic structure\u003csup\u003e[23]\u003c/sup\u003e. Figure 8 shows the apparent resistivity and phase curve of a measurement point before and after the impedance tensor distortion correction, from which it can be seen that the over-phase phenomenon is obviously corrected after the distortion correction.\u003c/p\u003e"},{"header":"4. 2D Inversion Calculation","content":"\u003cp\u003e\u003cstrong\u003e4.1 Inversion method\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The 2D inversion is performed by a 2D nonlinear conjugate gradient inversion algorithm based on the MT-Pioneer platform\u003csup\u003e[24]\u003c/sup\u003e. Some recently developed new techniques are more often used in the inversion, such as the gradual inversion mode from blocky coarse grid to fine grid, the center grid technique of the measurement points with topography, the technique of the impression method, the technique of the L-curve analysis, the technique of the forward validation of the inversion results, etc., and the application of these techniques improves the reliability of the final inversion results\u003csup\u003e[25-\u003c/sup\u003e\u003csup\u003e26]\u003c/sup\u003e. The terrain-adaptive measurement-point-centered meshing technique has significant advantages in magnetotelluric inversion. It accurately positions measurement points based on their elevation, ensuring alignment with forward modeling responses and enhancing inversion precision. The technique generates smooth, high-quality lateral meshes that better reflect subsurface geological changes. By partitioning meshes according to terrain features, it accurately captures terrain’s impact on electromagnetic fields, reducing distortion and improving result reliability. Mesh density can be adjusted according to electrical structure distribution, reducing computational load and boosting inversion efficiency. Additionally, its high automation minimizes human intervention, avoiding operational errors and enhancing inversion stability and consistency. Given these benefits, this study employs the terrain-adaptive measurement-point-centered meshing technique with a 2D nonlinear conjugate gradient inversion algorithm based on the MT-Pioneer platform for 2D inversion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Measurement point-centered grids with topography\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The automatic generation of the center grid of the measurement points can be used to obtain a smooth and high-quality transverse grid; the incremental scale factor is used to construct the longitudinal grid; and the automatic search method is used to form the grid of the terrain part based on the elevation data of the measurement points, in which the longitudinal grid size of the terrain part is set to be the same in order to ensure the accuracy of the orthogonal computation of all the measurement points. The grid dissection process and the inversion process are carried out iteratively to make the sparsity of the grid as consistent as possible with the high and low resistance distribution of the electrical structure. The final subsurface grid obtained (Fig. 9(a)).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 TM mode 2D inversion\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The identification problem of TE and TM modes is based on the geological data (such as stratigraphic strike, fault direction, etc.) in the study area to initially determine the main direction of the regional geological structure, and then compared and verified by the principal axis analysis of the impedance tensor, which finally determines the TM and TE directions. According to the existing studies, the TE mode requires much more two-dimensionality of the model than the TM mode\u003csup\u003e[27]\u003c/sup\u003e. The construction of the studied profiles is characterized by a certain degree of three-dimensionality. In this paper, the two-dimensional inversion was started with the TM model, TE+TM model, and TE model, respectively. It is found that the TE+TM mode and TM mode have more consistent inversion results, and the results of TM mode based on qualitative analysis are more reliable than those of TE+TM mode, so TM mode is used in this paper to carry out 2D inversion.\u003c/p\u003e\n\u003cp\u003eThe two-dimensional inversion uses the apparent resistivity and phase data of 51 measurement points, and the impedance is rotated to the optimal principal axis orientation of the region according to the qualitative analysis results, with the inversion threshold error of 5%, and the frequency range of 320 Hz-1000 s. The inversion consists of a total of 94 × 128 grids, with the first grid thickness of 20 m in the vertical direction, and the downward grid thickness segments grow in different ratios. The growth factor is 1.02 within 2 km, 1.1 within 2-5.2 km, 1.2 within 5-10 km, and 1.3 within 10-50 km, and finally a total of 69 vertical grids are divided. At the end of 64 iterations of inversion, the final root mean square error RMS was 2.94 (Fig. 9(b)).\u003c/p\u003e"},{"header":"5. Reliability analysis of 2D inversion results","content":"\u003cp\u003e\u003cstrong\u003e5.1 Data fitting\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;A good data fit is essential to evaluate the reliability of an inversion result. Figure 10 shows the comparison between the theoretical response of the 2D electrical model and the proposed cross-section of the observed data. It can be seen that there is a good agreement between the observed data and the theoretical response of the inversion in terms of both apparent resistivity and impedance phase, the data fit is very good, indicating that the inversion results fully reflect the constraints of the observed data. Of course, due to the non-uniqueness of the inversion, the inversion result is one of several models that can produce the response of the observed data.\u003c/p\u003e"},{"header":"6. Results analysis","content":"\u003cp\u003e\u003cstrong\u003e6.1 Electrical structure analysis\u003c/strong\u003e\u003cbr\u003eBecause of the good data fit, subsequent inversion can be carried out. The electrical structure of the profile obtained by two-dimensional inversion is shown in Figure 11. The electrical structure is relatively complex, and can be roughly summarized as “vertical layering, horizontal blocking”. Vertically, it can be divided into three layers of “high-middle-low-low” electrical structure, and there is a high-resistance zone of about 1200 m thick under the measurement points 9~42. Continuing downward, the resistance gradually decreases, sea level 0~-9000 m resistivity decreases, the extension direction from top to bottom, there is a “gourd-shaped” low-resistance area, the resistance is significantly lower than the east and west sides, and connected with the low-resistance anomalies below the measuring point 45~49, presumed to be an aquifer. In the transverse direction, it can be roughly divided into five obvious electrical zones, the west side of the No.3 measuring point is a high-resistance anomaly area with a thickness of less than 200 m; the east side of the No.5 measuring point to the west side of the No.7 measuring point is a small high-resistance anomaly area with a thickness of about 300 m, and the anomaly area below the anomaly area steeply changes into medium-resistance and low-resistance anomalies. There is a discontinuity in the resistivity near the profile points 7 to 10, and the resistivity is abnormally lowered here, and the extension direction is from up to down, so it is assumed that a fault (F\u003csub\u003e1\u003c/sub\u003e) is developed in this area, which tends to be SE-directed, with a dipping angle of about 60°, and the width of the fault band is about 50~100 m. Combined with the geologic background of the area, this fault is assumed to be a high-steep reversed fault, and it may form a good channel for the conductivity of water in the area;From the east of point 10 to the west of point 42, the lower part of the section shows a large block of medium-resistance and high-resistance anomalies with a thickness of nearly 1,500 m. From the east side of point 41 to the west side of point 43, the high-resistance anomalies have a thickness of only about 100 m, and the lower part of the section steeply changes into low-resistance anomalies; in the section, there is a discontinuity in the left and right of the resistivity near points 41~43, and there is anomalous lowering of the resistivity in this area, with the direction of extension being from the upper part of the section downwards. It is inferred that there is a small branch fault (F\u003csub\u003e2\u003c/sub\u003e) at this location, which tends to be SE-directed with a small dip angle of about 40°. The medium-high resistance anomalous zone below measuring points 43-51 is small in this area, with a thickness of about 500m, and then there is a large low-resistance anomalous zone in the shape of “crocodile mouth”, which may be a concealed water-conducting channel (F\u003csub\u003e3\u003c/sub\u003e) in this area, with a width of about 500 m, and constitutes an anti-“Y” structure with F\u003csub\u003e1\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2 Geothermal Formation Mechanism Analysis\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The study area is located in the Enshi Basin, and the drilling data reveals that the main stratigraphic layers in the basin are Cretaceous, Silurian, Ordovician and Cambrian, among which the Ordovician and Cambrian are the main thermal reservoirs explored in this well. The Ordovician-Cambrian carbonate rocks with good hydraulic conductivity and the red sandstone of Cretaceous Paomagang Formation with poor permeability and thermal conductivity, the Zhiliu clastic rocks (not integrated contact with Paomagang Formation) and the sandstones of Lower Cambrian Tianheban Formation and Shipai Formation form a closed and complete thermal storage unit, which has a certain condition of heat storage and water storage. The Enshi Great Fault in the area is a regional north-east active great fault, which is the main factor controlling the aggregation and transportation of geothermal fluids\u003csup\u003e[3-\u003c/sup\u003e\u003csup\u003e7]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(1) Thermal reservoir characteristics. According to the existing regional comprehensive geological survey and physical exploration, shallow drilling data, the thermal reservoir in the study area belongs to carbonate karst fissure thermal reservoir, both layered and banded thermal reservoir characteristics, thermal reservoir aquifer belongs to the Ordovician-Cambrian carbonate rocks, lithology is mainly bioclastic tuff, thick laminated graywacke, dolomitic graywacke, dolomite, gray dolomite, karst is strongly developed, strong water-rich. Carbonate rock fissure karst system is the main storage and transportation place of underground hot water in the area\u003csup\u003e[8]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(2) Thermal storage cover. Thermal storage cap layer in the geothermal system mainly plays the role of heat insulation and thermal insulation, and the cap layer of the geothermal water in the study area is mainly the Silurian (with) muddy rocks. Groundwater in the process of circulation, along the fracture and dissolution channels for deep circulation, the Silurian (with) mudstone and Ordovician carbonate rocks are easy to form a semi-closed groundwater pooling area at the boundary.\u003c/p\u003e\n\u003cp\u003e(3) Channel. The fracture structure of Maotian Township is very complex, with multiple lines or bands with different orientations distributed in the plane. They mainly include the Huayuanao Fault Zone, the Kuanping Fault Zone, and the Qinglongping-Shetoushan Fault Zone. The voids in the fracture zones are enlarged ,and the permeability is increased, which provides water and heat conduction channels for the geothermal system. In addition, the development of dissolution phenomenon in the region, the formation of a large number of solution tanks, holes, caves, etc., the existence of fissure karst system for the underground hot water to provide a good storage space and transportation channel, F\u003csub\u003e3\u003c/sub\u003e in the figure is the main hydraulic conduit in the region.\u003c/p\u003e\n\u003cp\u003e(4) Heat source. There is no heat source from intrusive rocks and magmatic rocks in the study area, so the heat source mainly comes from the natural warming of groundwater along the deep fractures for deep circulation\u003csup\u003e[28]\u003c/sup\u003e. It is assumed that the heat source in this area is mainly geothermal heat flow.\u003c/p\u003e\n\u003cp\u003eAccording to the above analysis, the study area has a complete heat storage and thermal conductivity system, and fractures F\u003csub\u003e1\u003c/sub\u003e, F\u003csub\u003e2\u003c/sub\u003e, and F\u003csub\u003e3\u003c/sub\u003e have a certain strike and undercutting depth, which can easily form a channel for convection and circulation of geothermal resources. Therefore, the study area is of great value for geothermal resources exploration and application.\u003c/p\u003e"},{"header":"7. Conclusions","content":"\u003cp\u003eIn this paper, the two-dimensional electrical structure model at a certain depth in the area is obtained by data preprocessing, impedance tensor decomposition and dimensional analysis of 51 geomagnetic measurement points distributed in Maotian Township, Jianshi County, and the two-dimensional inversion of the topography is utilized, and the following conclusions are drawn on the basis of the previous research and the corresponding geological data:\u003c/p\u003e\n\u003cp\u003e(1) The electrical structure of the study area is relatively complex, and the overall characteristics can be summarized as follows: the resistivity of the surface layer along the direction of the survey line is a discontinuous distribution of high and low resistance, there is a relatively stable high-resistance body in the target area (Points 9~42) within the range of 0 m above sea level, and there is a low-resistance anomalous zone from the surface to the depth on the east and west of the target area respectively, inferred to be the location of fracture confluence, and the fracture extends to the depth of the electrical low-resistance zone.\u003c/p\u003e\n\u003cp\u003e(2) There is a high resistance zone of about 1200 m below the measurement points 9~42. Continuing downward, the resistance gradually decreases, the resistivity of 0~9000 m at the sea level decreases continuously, and the extension direction is from top to bottom, and there is a “gourd-shaped” low-resistance zone, with resistance significantly lower than that of the east and west sides, and it is connected with the low-resistance anomalous zone below the measuring points 45~51, which is presumed to be an aquifer.\u003c/p\u003e\n\u003cp\u003e(3) According to the regional geological background and related research data, it is assumed that the thermal storage aquifer belongs to Ordovician-Cambrian carbonate rocks, and its lithology mainly consists of thick laminated tuff, bioclastic tuff, dolomitic tuff and dolomite, and gray dolomite.\u003c/p\u003e\n\u003cp\u003e(4) According to the results of the compilation of geothermal heat flow data in the China Earth region\u003csup\u003e[29]\u003c/sup\u003e. The geothermal temperature gradient in the study area is 20℃/km\u003csup\u003e[30]\u003c/sup\u003e, and taking the depth of the thermostatic zone as 30 m and the temperature as 12℃\u003csup\u003e[31]\u003c/sup\u003e, it is presumed that the stratigraphic temperature corresponding to the depth of the target aquifer from 1500 to 9000 m is 41.4℃~67.4℃\u003csup\u003e[32]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding:\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China(Grant numbers 2474117 and 42274103)\u003c/p\u003e\n\u003cp\u003eCompeting interests:\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003eData Availability declaration:\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eEthics and Consent to Participate declarations:This study complies with the Code of Academic Ethics.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSu, K., Ai, H., Alvandi, A., Lyu, C., Wei, X., Qin, Z., Tu, Y., Yan, Y. \u0026amp; Nie, T. (2024). Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies. Open Geosciences, 16(1), 20220641.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu C, Bai D B. Analysis of the Current Situation and Prospect of Underground Hot Water Resources Development and Utilization in Hubei Province[J]. China Steel Focus, 2020, (17):82\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan F, Yang B Y, Fu Q, Li M L, Li Z W, Wan K. Study on Hydrochemical Characteristics and Genesis of Xiaolongtan Geothermal Field in Northern Enshi Basin[J]. Resources Environment \u0026amp; Engineering, 2022, 36(03):337\u0026ndash;343.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen M, Liu Z D, Ruan X M, Yan J Y, Zhang H. Crustal structure of the Enshi-Lichuan area based on deep seismic reflection profiles[A] Proceedings of the Second National Mineral Exploration Conference[C]. Chinese Geophysical Society, Metallic Ore Exploration Committee, Chinese Geophysical Society, China, 2023:4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong R F. Study on velocity structure of Enshi-Lichuan area based on ambient noise tomography[D].East China University of Technology, 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang W. The Application of Geological and GeophysicalComprehensive Interpretation Technology in Enshi-Lichuan Oiland Gas Prospect Area[D].Chengdu University Of Technology,2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z W, Yuan F, Li M L, Zhao J, Wan K, Li G S. Indicative significance of hydrochemical characterizatcs in geothermal resource investigations in the Enshi Basin[J].Bulletin of Geological Science and Technology, 2023, 42(04):83\u0026ndash;94\u0026thinsp;+\u0026thinsp;106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu H L, Xu B W, Liu Z L, Shi F, Xin Y Q, Cao X G, Cheng G Q. The application of magnetotelluric sounding to geothermal resources assessment in Yinchuan Basin[J]. Geophysical and Geochemical Exploration, 2019, 43(04):718\u0026ndash;725.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu P, Han J T, Zeng Z F, Hou H S, Liu L J, Ma G Q, Guan Y W. Deep controlling factors of the geothermal field in the northern Songliao basin derived from magnetotelluric survey[J].Chinese Journal of Geophysics, 2021, 64(11):4060\u0026ndash;4074.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao B S, Shi F, Liu Z L, et al. Application of the magnetotelluric sounding method in the exploration of geothermal resources in the west of the Taikang uplift[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2023, 45(03):379\u0026ndash;388.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao J, Zhang H, Zhang S, et al. Three-dimensional magnetotelluric imaging of the geothermal system beneath the Gonghe Basin, Northeast Tibetan Plateau[J]. Geothermics, 2018, 76: 15\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelhaye R, Rath V, Jones A G, et al. Quantitative geothermal interpretation of electrical resistivity models of the Rathlin Basin, Northern Ireland[J]. Geothermics, 2019, 77: 175\u0026ndash;187.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolpi G, Manzella A, Fiordelisi A. Investigation of geothermal structures by magnetotellurics (MT): an example from the Mt. Amiata area, Italy[J]. Geothermics, 2003, 32(2): 131\u0026ndash;145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaher M A, Saibi H, Nishijima J, et al. Exploration and assessment of the geothermal resources in the Hammam Faraun hot spring, Sinai Peninsula, Egypt[J]. Journal of Asian Earth Sciences, 2012, 45: 256\u0026ndash;267.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolpi G, Manzella A, Fiordelisi A. Investigation of geothermal structures by magnetotellurics (MT): an example from the Mt. Amiata area, Italy[J]. Geothermics, 2003, 32(2): 131\u0026ndash;145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo B D, Sun Z R, Shi Y X, et al. Application of magnetotelluric sounding method in geothermal resources exploration in northern Ordos Basin[J]. Geology and Resources, 2023, 32(01):64\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGamble T D, Goubau W M, Clarke J. Magnetotellurics with a remote magnetic reference[J]. Geophysics, 1979, 44(1): 53\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgbert G D. Robust multiple-station magnetotelluric data processing[J]. Geophysical Journal International, 1997, 130(2): 475\u0026ndash;496.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y F, Wang H Z, Cao J Z. Using orthogonal polynomials for eliminating white noise [J]. Oil Geophysical Prospecting, 2001, (06):80\u0026thinsp;\u0026ndash;\u0026thinsp;79\u0026thinsp;+\u0026thinsp;78\u0026ndash;0.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Xiao H Y, Jiang Y D, et al.The study and application of static displacement correction for magnetotelluric sounding data processing [J].Computing Techniques for Geophysical and Geochemical Exploration, 2015, 37(02):187\u0026ndash;192.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X B, Cai J T, Wang L F, et al. Refined techniques for magnetotelluric data processing and two-dimensional inversion(IV):Statistical image method based on multi-site, multi-frequency tensor decomposition[J]. Chinese Journal of Geophysics, 2014, 57(06):1946\u0026ndash;1957.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwift C M. A magnetotelluric investigation of an electrical conductivity anomaly in the southwestern United States[D]. Massachusetts Institute of Technology, 1967.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGroom R W, Bailey R C. Decomposition of magnetotelluric impedance tensors in the presence of local three-dimensional galvanic distortion[J]. Journal of Geophysical Research: Solid Earth, 1989, 94(B2): 1913\u0026ndash;1925.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodi W, Mackie R L. Nonlinear conjugate gradients algorithm for 2-D magnetotelluric inversion[J]. Geophysics, 2001, 66(1): 174\u0026ndash;187.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X B, Zhao G Z. Automatic construction of a Site Centered Grid (SCG) for 2D magnetotelluric inversion[J]. Chinese Journal of Geophysics, 2009, 52(06):1564\u0026ndash;1572.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe T, Chen X B, Yan L J. Refined techniques for data processing and two-dimensional inversion in magnetotelluric (III):using the Impressing Method to construct starting model of 2D magnetotelluric inversion[J]. Chinese Journal of Geophysics, 2013, 56(10):3596\u0026ndash;3606.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiu G G, Zhang X B, Pei F G, et al.Effectiveness of magnetotelluric sounding inversion technique[J]. Geophysical and Geochemical Exploration, 2015, 39(01):118\u0026ndash;124.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Ye J, Fan W, et al. Study on Genetic Model of Geothermal Resources of Sodium Chloride Type Karst Heat Reservoir in Western Hubei[J]. Resources Environment \u0026amp; Engineering, 2022, 36(05):588\u0026ndash;593.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang G Z, Gao p, Rao S, et al.Compilation of heat flow data in the continental area of China(4th edition)[J]. Chinese Journal of Geophysics, 2016, 59(08):2892\u0026ndash;2910.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaber, G.M.; Saleh, S.; Kotb, A. Integrating radiometric and aeromagnetic data for assessment of geothermal potential: A case study in Central Eastern Desert, Egypt. Acta Geophys. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaber, G.M.; Saleh, S.; Kotb, A. 3D Gravity and magnetic inversion modelling for geothermal assessment and temperature modelling in the central eastern desert and Red Sea, Egypt. Sci. Rep. 2024, 14, 15266.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaber, G.M.; Saleh, S.; Kotb, A. Investigating geothermal resources in the Central Eastern Desert of Red Sea, Egypt, using aeromagnetic data. J. Earth Syst. Sci. 2024, 133, 137.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"magnetotelluric sounding, qualitative analysis, 2D inversion, geothermal exploration.","lastPublishedDoi":"10.21203/rs.3.rs-5902191/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5902191/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGeothermal resource is a kind of renewable green and clean energy stored in the earth's interior. Under the impetus of the global energy revolution and the goal of \u0026ldquo;double-carbon\u0026rdquo;, the development and utilization of geothermal energy has become an important strategy for the energy development of all countries in the world. Magnetotelluric (MT) sounding method, which is not shielded by high-resistance layers and provides high resolution for low-resistance layers, has unique advantages in revealing geothermal system structures, delineating hydrothermal geoelectric structures, and assessing the development potential of geothermal regions. Based on an MT profile in Maotian Township, Jianshi County, this paper processed data from 51 broadband MT measurement points using Fourier transform, Robust estimation, and impedance tensor decomposition techniques. After a detailed analysis of the dimensional characteristics and electrical axes, a 2D electrical structure model from the surface to a depth of 9 km was obtained via topography-based 2D inversion. The fault distribution was confirmed, and the potential geothermal resource areas were inferred based on geological information. The MT sounding survey confirmed the favorable geothermal resource storage range in the study area and demonstrated the advantages of MT in geothermal exploration, with important practical value for investigating the underground electrical structure and geothermal resource distribution.\u003c/p\u003e","manuscriptTitle":"Application of the Magnetotelluric Method in Geothermal Exploration in Jianshi County, Hubei Province,China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 20:30:08","doi":"10.21203/rs.3.rs-5902191/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-23T17:04:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T16:14:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T08:48:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-20T10:30:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189458520394902469613366592163740303647","date":"2025-04-20T10:19:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100531016242426944266427481030669167819","date":"2025-04-18T19:12:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-18T07:56:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-11T06:20:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-03T05:51:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Geoscience","date":"2025-04-02T05:53:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3049ab7-c765-47c3-a33e-d2ac349e92e2","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T20:08:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-21 20:30:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5902191","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5902191","identity":"rs-5902191","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}