A method for geology map space database assisted resistivity inversion

Abstract

The application discloses a method for resistivity inversion assisted by a geological map space database, and comprises the following steps: S1, selecting a geological map of a specified scale and constructing a geological map database, and extracting specified parameters of resistivity inversion; S2, constructing a gridded geological surface body, and obtaining the lithology distribution and physical property parameters in each grid unit; S3, constructing an underground physical property distribution model by using the obtained parameter combination; S4, introducing geological constraints and obtaining regional regularization parameters of spatial variation according to the underground physical property distribution model based on a preset inversion optimization algorithm, and the obtained regional regularization parameters are used for performing resistivity inversion calculation; S5, performing information data superposition and comparison in GIS software based on the resistivity obtained by the inversion calculation; and S6, defining a consistency scoring function, and quantifying the matching degree of the inversion result and the geological boundary and the structural belt. The application can improve the accuracy and efficiency of inversion modeling and enhance the geological consistency of the inversion result by using existing geological information.

Description

Technical Field

[0001] This invention relates to the field of geological information processing technology, specifically to a method, device, and storage medium for resistivity inversion in geophysics based on geological map spatial database modeling. Background Technology

[0002] With the in-depth development of large-scale regional geological surveys at scales of 1:50,000, geological map spatial database technology has gradually realized the digital storage and standardized management of geological information. This technology, by integrating multi-source geological data and constructing high-precision geological map spatial models, has significantly improved the ability to express and analyze geological information. At the same time, geophysical exploration technologies, such as resistivity inversion, have seen a significant increase in data acquisition density and processing resolution driven by hardware equipment and algorithm optimization, providing massive data support for underground structure detection.

[0003] However, the collaborative application of geological map spatial databases and geophysical inversion technology is still in its early stages. Traditional inversion methods rely heavily on human experience to construct prior models or sparse borehole data, failing to fully utilize the spatial constraints of high-resolution geological maps, resulting in the incomplete release of technological potential.

[0004] Furthermore, in the interdisciplinary field of geophysical forward and inverse modeling theory and digital geology integration, the research and development of existing technologies mainly focuses on inverse algorithm optimization and geostatistical model construction. Some research directions attempt to combine geostatistics with three-dimensional geological modeling technology, and improve inverse stability through methods such as Kriging interpolation and stochastic simulation. In addition, digital geology technology provides spatial constraints for geophysical inversion through gridded surface data, such as surface lithology distribution and tectonic unit boundaries, which can theoretically reduce the risk of multiple solutions.

[0005] However, in practical applications, there are still technical bottlenecks in the deep integration of the multi-attribute data of geological map spatial databases and geophysical data, especially the insufficient modeling accuracy of shallow geological bodies, which leads to a low spatial matching degree between the prior model and the real geological structure.

[0006] The aforementioned technical deficiencies directly limit the reliability of inversion under complex geological conditions:

[0007] First, existing methods struggle to convert volumetric data from high-resolution geological map spatial databases into refined three-dimensional resistivity initial models. Profile layering relies on artificially simplified geological profiles, leading to significant deviations between the inversion results and the actual geological structure.

[0008] Secondly, when the accuracy of the geological map is insufficient or the shallow geological phenomena are complex, the artificially drawn prior model is prone to introducing systematic errors, which exacerbates the instability and multiple solutions of the inversion process.

[0009] Third, the spatial scale difference between sparse borehole data and dense geophysical data further weakens the resolution capability of joint geological-geophysical inversion.

[0010] These problems together make it difficult for existing technologies to meet the dual requirements of detailed characterization of shallow geological bodies and high-precision inversion of deep structures in large-scale geological surveys.

[0011] Therefore, this application proposes a method for resistivity inversion based on a geological map spatial database to solve the above-mentioned technical problems. Summary of the Invention

[0012] The main objective of this invention is to provide a method for resistivity inversion based on a geological map spatial database, which utilizes existing geological information to improve the accuracy and efficiency of inversion modeling and enhance the geological consistency of the inversion results, thereby solving the technical problems mentioned in the background art.

[0013] The present invention solves the above-mentioned technical problems by adopting the following technical solutions:

[0014] A method for resistivity inversion based on geological map spatial database includes the following steps:

[0015] S1. Based on the resistivity measurement point density and wiring length, select a geological map of a specified scale, construct a geological map database using the specified geological map, and extract the specified parameters for resistivity inversion;

[0016] S2. Construct a gridded geological surface and obtain the lithological distribution and physical property parameters, including resistivity, within each grid cell of the geological surface;

[0017] S3. Construct a subsurface property distribution model using the data processed in step S1 and the lithological property parameter data of the gridded geological surface processed in step S2.

[0018] S4. Perform geological constraint inversion based on the preset inversion optimization algorithm, construct regional regularization parameters through the underground physical property distribution model, and obtain underground resistivity data to construct an underground resistivity model;

[0019] S5. Based on the resistivity obtained from the inversion calculation, information data is overlaid and compared in GIS software;

[0020] S6. Define a consistency scoring function to quantify the degree of matching between the inversion results and geological boundaries and tectonic zones after data overlay and comparison.

[0021] Preferably, the dataset in step S1 comprises 24 datasets in total, consisting of five element datasets: a basic element dataset, a comprehensive element dataset, an external table dataset, and an in-map integral dataset. These include: geological bodies (GEOPOLYGON), geological boundaries (GEOLINE), attitudes (ATTITUDE), samples (SAMPLE), photographs (PHOTOGRAPH), isotopic ages (ISOTOPE), springs (SPRING), river and lake coastlines (LINE_GEOGRAPHY), tectonic deformation zones (TECOZONE), volcanic rock facies (VOLCA_FACIES), map frames (MAP_FRAME), map sheets (MAP_SHEET), legends (LEGEND), comprehensive columnar sections (COLUMNAR_SECTION), cutting profiles (CUTTING_PROFILE), duty tables (DUTY_TABLE), and various annotation data.

[0022] Preferably, the parameters specified in step S1 include:

[0023] Regional boundary and stratigraphic distribution data: Extract data such as "geological body entities", "geological boundaries", and "tectonic deformation zones" to determine the boundaries of geological units within the survey area.

[0024] Geological attribute parameters: Reference resistivity, lithology and structural information of each unit are collected from datasets such as "occurrence", "lithology", "sample" and "isotope age".

[0025] Auxiliary information: Use legends, columnar sections, and profile data to confirm parameters such as formation thickness, strike, and dip angle.

[0026] Preferably, step S1 further includes the following specified parameters:

[0027] Data storage format and attribute table structure, standardized layer data, GPS coordinate fields representing the precise latitude and longitude information of various geological units, tectonic zones, and sample points, tectonic zone classification data, geological boundary classification, and physical property parameters of various geological units.

[0028] Preferably, the process of constructing the gridded geological surface in step S2 includes:

[0029] S21. Generate regular grid cells within the specified area, wherein the grid resolution is determined based on the data density and accuracy requirements of the survey area;

[0030] S22. Let each grid cell be G. i (i = 1, 2, ..., M; M is the total number of grid cells), each G i There is a unique coordinate index (x) i ,y i ,z i ).

[0031] Preferably, the process for obtaining lithological distribution and physical property parameters in step S2 includes:

[0032] (1) Overlay the existing geological surface onto the grid, for each grid cell G i Extract geological surface information within its coverage area;

[0033] (2) The GPS coordinates of the drilling location, the deep lateral resistivity curve with depth as the independent variable, and the formation and lithology records were measured and obtained as resistivity data. The average resistivity of the formation was calculated based on the resistivity range and lithology of each group of formations. Where j represents different strata;

[0034] (3) Based on empirical data, initial lithological resistivity values ​​are set for each lithology. This serves as a reference value for the resistivity of stratum j.

[0035] Preferably, the construction process of the underground physical property distribution model in step S3 includes:

[0036] S31. Construct an initial surface model as the distribution range. Based on the geological map, obtain the distribution pattern and boundary range, and construct a two-dimensional surface resistivity model, which includes:

[0037]

[0038] in, Let i be the two-dimensional fundamental resistivity of the grid cell i. This represents the average resistivity of cell i in the drilling data. is the lithological empirical resistivity value corresponding to unit i in the drilling data, and w is the confidence weight of the drilling data.

[0039] S32. To unify the effects of stratigraphic dip angle, fault displacement, and resistivity attenuation, a vertical correction function is introduced to describe the resistivity variation of each geological unit along the depth direction. This extends the two-dimensional lithological assignment to the three-dimensional model. The calculation formula for the vertical correction function is as follows:

[0040] f depth (z)=exp[-α·cos(θ)·(z-z0-β·δz)]

[0041] Where z is the current depth, α is the attenuation coefficient of resistivity with depth, cos(θ) is the dip angle adjustment factor, z0 is the initial depth of the formation, β is the fault influence factor, and δz is the fault depth offset.

[0042] S33. Each two-dimensional grid cell G i (x i ,yi resistivity By using the vertical correction function f depth (z) Mapped to the depth direction, the final resistivity values ​​of the three-dimensional mesh cells are:

[0043]

[0044] in, Let be the final resistivity of the i-th grid at depth z, used to form the initial resistivity model of the geological map prior to the data.

[0045] Preferably, the process for obtaining the regularization parameter in step S4 includes:

[0046] S41. Applying smoothing constraints of different intensities in the horizontal and vertical directions yields:

[0047] Horizontal smoothing constraint: using the horizontal smoothing operator L h To suppress drastic changes in the model in the x,y plane, there are Where m represents the resistivity distribution in the inversion iterative model. It is the gradient vector of the model on the horizontal x,y plane, representing the rate of change of the model values ​​in the x and y directions;

[0048] Vertical smoothing constraint: using the vertical smoothing operator L v The second derivative enhances the sensitivity to changes in formation thickness and fault displacement along the depth direction.

[0049] S42. Use generalized cross-validation or L-curve method to determine the global regularization factor λ0, so that data fitting and model smoothing are in balance;

[0050] S43. Based on the parameters obtained in steps S1 and S2, determine the region constraint weights w(x,y,z);

[0051] S44. Based on the regional constraint weight w(x,y,z), calculate and obtain the regionalization regularization parameter λ(x,y,z), where λ(x,y,z)=λ0w(x,y,z);

[0052] S45. Iteratively update the regularization parameters, with the iterative update formula as follows:

[0053]

[0054] Where, d obsThe data represents measured resistivity. f(m) is the forward modeling function for calculating the potential response based on the underground conductivity distribution. L is a regularization operator including the gradient or Laplace operator. m0 is the initial resistivity model from the geological map. m is the resistivity distribution in the inversion iterative model, i.e., m(x,y,z). λ old (x,y,z) are the regionalization regularization parameters before iteration, λ new (x,y,z) are the regionalization regularization parameters after iteration.

[0055] Preferably, the specific operation process of step S43 includes:

[0056] S431. Divide the study area into a specified number of geologically consistent regions. Each region (x, y, z) is assigned a fixed spatial variation weight, and the weight function is defined as w(x, y, z). Regions with continuous, homogeneous strata are defined as having high weight values, while regions with boundaries or complex strata are defined as having low weight values.

[0057] (1) Divide the horizontal weights into: a geologically continuous and stable homogeneous stratigraphic zone w h (x,y)=1.0, lithological transition zone w at the boundary of lithological gradient or weak unconformity h (x,y)∈(0.6,0.8), the area near the fault zone with complex structure and strong discontinuity w h (x,y)∈(0.2,0.4), lacking geological data to explain the ambiguous high uncertainty region w h (x,y)=0.1;

[0058] (2) Using vertical weight w v (x,y,z) represents the reality that the uncertainty of the model gradually increases with the depth of the data. It is used to indicate that the greater the depth of a specified point, the smaller the weight, and the lower the model's reliability.

[0059] w v (w,y,z)=exp(-γz)

[0060] Where γ>0 is the depth attenuation factor, which controls the rate of descent. In sedimentary basins, γ∈[0.02,0.05] indicates good data, in bedrock areas γ∈(0.05,0.15) indicates poor data, and in highly complex areas including fold belts γ∈(0.10,0.25); z is the depth.

[0061] S432. In the resistivity inversion model, combining the horizontal and vertical weights into a total spatial constraint yields:

[0062] w(x,y,z)=w h (x,y)·w v (x,y,z)

[0063] This is used to enable the model to simultaneously reflect the continuity of geological structures and the uncertainty and complexity of the depth direction in space.

[0064] Preferably, the method for calculating resistivity in step S4 includes:

[0065] The subsurface resistivity model *m* is solved iteratively. In each iteration, *m* is updated and the model is adjusted according to the local regularization parameter until the preset convergence condition is met. We have:

[0066]

[0067] Where Φ(m) is the inversion objective function, m is the resistivity distribution in the inversion iterative model, i.e., m(x,y,z), m0 is the prior resistivity initial model of the geological map, and d obs The resistivity data are measured values, f(m) is the forward modeling function for the potential response derived from the underground conductivity distribution, and L... h For horizontal gradient operators Used to control lateral smoothing, L v The vertical second derivative Used to control depth continuity, λ is a regularization parameter, which is determined by the above iterative update regularization parameter strategy;

[0068] In the resistivity inversion calculation process, the region regularization parameters constructed in step S4 are adjusted to achieve adaptive optimization.

[0069] Preferably, the specific operation process of step S5 includes:

[0070] S51. Store the geological map data of each layer in the geological map database in Shapefile or GeoDatabase format, convert the resistivity inversion calculation results into a format and import them into the GIS software as resistivity model data;

[0071] S52. Use GIS software to perform overlay analysis, overlay each dataset according to its spatial location, distinguish the geological element layers from the inversion results through transparency and color symbols, adjust the scale, position and symbols of each layer, so that the profile reflects the changes in underground electrical properties while displaying geological zoning and structural information.

[0072] S53. Display overlaid data.

[0073] Preferably, the specific calculation formula for the consistency scoring function in step S6 is as follows:

[0074]

[0075] Where S is the consistency score, ρ invThe inverse resistivity of the region (x,y,z) is obtained by solving for the resistivity in step S5, ρ. geo (x,y,z) represents the resistivity of the region (x,y,z) predicted by geological information, Ω represents the overall spatial extent, A is the normalization factor, and χ represents the volume or area of ​​the effective integration region. boundary (x,y,z) is a boundary function used to extract spatial points near geological boundaries, and In the integral calculation of the consistency scoring function, only grid cells close to the structural or stratigraphic boundaries are considered, while flat and continuous areas are ignored.

[0076] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0077] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0078] As can be seen from the above technical solution, the present invention provides a method for resistivity inversion based on a geological map spatial database. Compared with the prior art, the present invention has the following advantages:

[0079] 1. This invention transforms complex geological information into computable prior constraints, which can significantly improve the analytical accuracy of inversion models for structural features such as faults and folds, thereby reducing the subjective errors of manual modeling.

[0080] 2. This invention, by setting a vertical attenuation function that integrates stratum dip angle and fault displacement in the construction of a three-dimensional model, can dynamically reflect the non-uniform change of resistivity with depth, thus breaking through the limitations of traditional two-dimensional planar models, accurately depicting the effect of electrical jumps in steeply dipping strata and fault zones, making the inversion results more consistent with the real geological structure, and improving the ability to detect deep structures including thrust bodies and rift basins.

[0081] 3. By setting a weight function with fixed spatial variation weights in the inversion objective function, this invention can dynamically adjust the smoothing intensity of the model in different regions to achieve strong constraints in homogeneous strata, weak constraints in fault zones, and reduced weights in deep regions. This effectively balances the multiple solutions in the inversion with geological rationality, generating a resistivity model that combines high resolution and geological consistency.

[0082] 4. This invention expands the theoretical framework of prior information constraints in geophysical inversion, facilitating a more accurate revelation of deep geological structures and providing a new approach to solving the problem of inversion non-uniqueness. By deeply integrating standardized hierarchical information from geological map spatial databases, three-dimensional physical property modeling technology, adaptive regularization constraints driven by geology, and a closed-loop evaluation system, it can significantly enhance the utilization efficiency of geological prior information in geophysical inversion. This solves the problems of model distortion and multiple solutions caused by the coarseness of manual modeling and the failure to consider tectonic scale and vertical variations in traditional methods, providing a high-precision and high-reliability resistivity structure model for resource exploration and engineering geological safety assessment in complex geological areas.

[0083] 5. This invention improves the accuracy and efficiency of inversion modeling by utilizing existing geological information, enhances the geological consistency of inversion results, and establishes a full-process standard from geological map selection and Shapefile storage to attribute table design. It automatically segments and vectorizes multi-source geological map data, significantly improves the accuracy of geological element extraction, and achieves efficient integration and management of multi-source data such as geological bodies, geological boundaries, and hierarchical tectonic zones.

[0084] It should be understood that the descriptions in this section are not intended to identify key or essential features of embodiments of the invention, nor are they intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Of course, implementing any product of the invention does not necessarily require achieving all of the advantages described above simultaneously. Attached Figure Description

[0085] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0086] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0087] Figure 2 This is a schematic diagram of the geological profile of the present invention;

[0088] Figure 3 This is a schematic diagram of the resistivity inversion profile of the present invention;

[0089] Figure 4 This is a schematic diagram of the constrained resistivity inversion profile of the present invention. Detailed Implementation

[0090] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0091] For details in the embodiments, please refer to Figures 1 to 4 .

[0092] This invention proposes a method for resistivity inversion based on a geological map spatial database, which expands the theoretical framework of prior information constraints in geophysical inversion. This method can more accurately reveal deep geological structures and provides a new approach to solving the problem of inversion non-uniqueness. It fully utilizes existing geological information to improve the accuracy and efficiency of inversion modeling and enhances the geological consistency of the inversion results. A complete workflow standard has been established, from geological map selection and Shapefile storage to attribute table design. This standard automatically segments and vectorizes multi-source geological map data, significantly improving the accuracy of geological element extraction. It also enables efficient integration and management of multi-source data such as geological terraces, geological boundaries, and hierarchical tectonic zones, providing a solid data foundation for surface and subsurface development.

[0093] like Figure 1 As shown, the specific steps include:

[0094] S1. Based on the resistivity measurement point density and wiring length, select geological maps of appropriate scales and utilize existing geological map databases built on 1:50,000 or 1:250,000 geological maps. The datasets consist of 5 element datasets, including basic element datasets, comprehensive element datasets, external table datasets, and map integral datasets, totaling 24 datasets. Then, perform data preprocessing operations and extract parameters closely related to resistivity inversion.

[0095] Among them, reference Figure 2The dataset includes: geological bodies (GEOPOLYGON), geological boundaries (GEOLINE), attitudes (ATTITUDE), samples (SAMPLE), photographs (PHOTOGRAPH), isotopic ages (ISOTOPE), springs (SPRING), river and lake coastlines (LINE_GEOGRAPHY), tectonic deformation zones (TECOZONE), volcanic rock facies (VOLCA_FACIES), map frames (MAP_FRAME), map sheets (MAP_SHEET), legends (LEGEND), columnar sections (COLUMNAR_SECTION), cutting profiles (CUTTING_PROFILE), duty tables (DUTY_TABLE), and various annotation data.

[0096] The parameters closely related to resistivity inversion mainly include:

[0097] Regional boundary and stratigraphic distribution data: Extract data such as "geological body entities", "geological boundaries", and "tectonic deformation zones" to determine the boundaries of geological units within the survey area;

[0098] Geological attribute parameters: Reference resistivity, lithology and structural information of each unit are collected from datasets such as "occurrence", "lithology", "sample" and "isotope age".

[0099] Auxiliary information: Use legends, columnar sections, and profile data to confirm parameters such as formation thickness, strike, and dip angle.

[0100] Furthermore, the following preprocessing operations are also included:

[0101] Data storage format and attribute table structure: Each dataset (such as geological bodies, geological boundaries, tectonic deformation zones, etc.) is stored as a separate Shapefile, including geometric information (points, lines, polygons) and attribute tables. Multiple data layers are integrated into the same database for easy management and querying, such as a File Geodatabase (.gdb) or a personal geodatabase (.mdb). Attribute tables include necessary fields such as ID, name, classification code (for hierarchical information), GPS coordinate fields (longitude, latitude, preferably using a unified standard coordinate system, such as WGS84 or EPSG:4326), and possible numerical attributes (such as reference resistivity, dip angle, strike, displacement value, etc.). Each layer (such as tectonic zones, geological boundaries, attitude, etc.) should retain complete metadata records, including information such as data source, measurement unit, acquisition time, and spatial resolution.

[0102] Establish standardized layers: Classify the dataset according to function and purpose. Create a basic geometric layer to store information on geological bodies and geological boundaries; create a structural layer to store information on tectonic deformation zones, faults, and attitudes; and create auxiliary layers to store auxiliary data such as samples, photographs, isotopic ages, and annotation data. Each layer should include information describing its classification as much as possible. For example, for tectonic zones and geological boundaries, a "classification" field can be added to clearly identify level 1, level 2, or level 3 (or other classification standards).

[0103] GPS coordinate integration: To ensure that each element in the database accurately corresponds to the resistivity profile measurement points, a GPS coordinate field (using a standard coordinate system, such as WGS84) is required to be added to the attribute table of each dataset to record the precise latitude and longitude information of each geological unit, structural zone, sample point, etc. This GPS information is used to accurately locate the actual spatial position of each geological unit and auxiliary element, ensuring that the corresponding area can be accurately found when matching with resistivity measurement points later.

[0104] Tectonic zone classification: Based on existing geotectonic unit division schemes, tectonic zones within the work area are classified into three levels. Level 1 tectonic zones typically represent regional main tectonic units, reflecting large-scale tectonic backgrounds; Level 2 tectonic zones reflect secondary regional tectonic features, such as local fault depressions and fold belts; Level 3 tectonic zones represent local tectonic features, such as faults and shear zones. From the "Tectonic Deformation Zone_TECOZONE" dataset, tectonic zones at each level are extracted and labeled according to pre-defined classification criteria (based on tectonic zone width, strike continuity, deformation intensity, etc.). A unique attribute table identifier (ID) is used to associate these zones with the main geometric layer, ensuring that different scales of tectonic influences can be distinguished in subsequent inversion of the prior model.

[0105] Geological boundary classification: Geological boundaries are classified according to stratigraphic boundaries, angular unconformities, and pulsating intrusive boundaries. Stratigraphic boundaries: Reflect the boundaries between different strata and can serve as a basis for regional stratigraphic zoning; angular unconformities: Used to identify areas with significant tectonic deformation and abrupt changes in sedimentary environment; pulsating intrusive boundaries: Reflect areas with significant intrusive activity and are often associated with tectonic activity. From the “Geological Boundary_GEOLINE” dataset, each boundary is assigned a corresponding classification attribute based on the above classification criteria, and a unique identifier (ID) from the attribute table is used to associate it with the main geometric layer. This classification information can be used to differentiate and process prior models during inversion, determining the physical parameters and constraint strength of each boundary region.

[0106] Other parameter processing: Data such as "occurrence" and "lithology" are processed to determine the physical properties of each geological unit, obtain references for the resistivity model of geological bodies, and uniformly associate them with the main geometric layer using the unique identifier ID of the attribute table. Legends, columnar sections, and profile data are used to obtain information such as stratigraphic thickness, dip angle, and strike, providing more constraints for constructing the prior model.

[0107] S2. Construct a gridded geological surface, referencing Figure 2 A regular grid (e.g., square or rectangular cells) is generated within the study area. The grid resolution can be determined based on the data density and accuracy requirements of the survey area. Let each grid cell be G. i (i = 1, 2, ..., M; M is the total number of grid cells), each G i There is a unique coordinate index (x) i ,y i ,z i This involves overlaying existing geological bodies (e.g., planar data represented in vector format, such as stratigraphic boundaries and tectonic zones) onto a grid. For each grid cell G... i It extracts geological surface information within its coverage area and obtains relevant attributes such as lithological distribution and physical properties including resistivity within each grid cell of the geological surface.

[0108] The resistivity is obtained using the following process:

[0109] (1) Collection of regional drilling resistivity: Drilling resistivity measurement data includes: GPS coordinates of the drilling location, deep lateral resistivity curves with depth as the independent variable, formation and lithology records, and calculation of the average formation resistivity based on the resistivity range and lithology of each group of formations. Where j represents different strata;

[0110] (3) Based on literature, laboratory test data, or empirical data from other similar regions, initial lithological resistivity values ​​are set for each lithology. (For example, in a certain group of main lithologies, fine-grained sandstone interbedded with thin mudstone has an initial lithological resistivity of 60-80 □Ω·m), which is used as a reference value for the resistivity of stratum j, so as to further calculate the regional lithological resistivity.

[0111] S3. Construct a subsurface property distribution model using the data processed in step S1 and the lithological property parameter data of the gridded geological face after step S2.

[0112] The construction process of the underground physical property distribution model includes:

[0113] S31. Construct an initial surface model as the distribution range to provide a reasonable starting point for the inversion process, overcome the multiple solutions of the inversion problem, obtain the distribution pattern and boundary range based on the geological map range, and construct a two-dimensional surface resistivity model. If there are formation resistivity values ​​measured by drilling, then:

[0114]

[0115] Among them, ρinit(G i (Obtained from lithological reference values, literature, or experimental data from adjacent areas) Let i be the two-dimensional fundamental resistivity of the grid cell i. This represents the average resistivity of cell i in the drilling data. is the lithological empirical resistivity value corresponding to unit i in the drilling data, and w∈[0.7,0.9] is the confidence weight of the drilling data;

[0116] S32. To unify the effects of stratigraphic dip angle, fault displacement, and resistivity attenuation, a vertical correction function is introduced to describe the resistivity variation of each geological unit along the depth direction, focusing on stratigraphic dip angle and fault displacement parameters. This extends the two-dimensional lithological assignment to the three-dimensional model, enabling the generated three-dimensional resistivity a priori model to more realistically reflect changes in subsurface structure. The calculation formula with the vertical correction function is as follows:

[0117] f depth (z)=exp[-α·cos(θ)·(z-z0-β·δz)]

[0118] Where z is the current depth, α is the attenuation coefficient of resistivity with depth, cos(θ) is the dip angle adjustment factor, z0 is the initial depth of the formation, β is the fault influence factor, and δz is the fault depth offset.

[0119] Furthermore, in low-dipping strata (dip angle 0°–15°), the deviation of the rock strata from the horizontal plane is small, and the resistivity changes more gradually with depth, allowing for the use of a lower attenuation coefficient α. i The value is approximately 0.02–0.03; the resistivity changes more significantly in rock strata with moderate dip angles (15°–30°), requiring a moderately accelerated change in resistivity, hence a moderate attenuation coefficient α is used. i The value is approximately 0.03–0.04; in steep strata (dip angle > 30°), the rock strata change vertically rapidly, and the resistivity decreases quickly with depth, making a higher attenuation coefficient α suitable. i Values ​​are approximately 0.04–0.05; for small fault displacements of 1–2 m, β is recommended. i Values ​​are approximately 0.10–0.15; β is recommended for 2–3m fault displacement. i Values ​​are approximately 0.15–0.30; for fault displacements greater than 3m, β is recommended. iThe value is approximately 0.20 to 0.30;

[0120] S33. Each two-dimensional grid cell G i (x i ,y i resistivity By using the vertical correction function f depth (z) Mapped to the depth direction, the final resistivity values ​​of the three-dimensional mesh cells are:

[0121]

[0122] in, Let be the final resistivity of the i-th grid at depth z, used to form the initial resistivity model of the geological map prior to the data.

[0123] S4. Based on smoothing constraints of different intensities in the horizontal and vertical directions, geological constraint inversion is performed in combination with a preset inversion optimization algorithm. Regional regularization parameters are constructed through the underground physical property distribution model, and underground resistivity data is obtained to construct an underground resistivity model.

[0124] At this point, by applying smoothing constraints of different intensities in the horizontal and vertical directions, the horizontal continuity of the strata is maintained while highlighting the characteristics of depth interfaces and fault jumps, so as to construct regional regularization parameters.

[0125] The specific operating procedures include:

[0126] S41. Applying smoothing constraints of different intensities in the horizontal and vertical directions yields:

[0127] Horizontal smoothing constraint: using the horizontal smoothing operator L h To suppress drastic changes in the model in the x,y plane and maintain formation connectivity, we have:

[0128]

[0129] Where m represents the resistivity distribution in the inversion iterative model. It is the gradient vector of the model on the horizontal x,y plane, representing the rate of change of the model values ​​in the x and y directions;

[0130] Vertical smoothing constraint: using the vertical smoothing operator L v The second derivative enhances the sensitivity to changes in formation thickness and fault displacement along the depth direction, as follows:

[0131] S42. Use generalized cross-validation or L-curve method to determine the global regularization factor λ0, so that data fitting and model smoothing are in balance;

[0132] S43. Based on the geological parameters obtained in steps S1 and S2, determine the regional constraint weights w(x,y,z) according to the stratigraphic continuity, fault distribution, and structural complexity of each region. The operation process includes:

[0133] S431. Divide the study area into a specified number of geologically consistent regions. Assign a fixed spatial variation weight to each region (x, y, z). Define the weight function as w(x, y, z). In regions with relatively clear geological information (e.g., continuous, homogeneous strata), a high weight value is defined, while in boundary or complex regions, a low weight value is defined.

[0134] (1) Divide the horizontal weights into: a geologically continuous and stable homogeneous stratigraphic zone w h (x,y)=1.0, lithological transition zone w at the boundary of lithological gradient or weak unconformity h (x,y)∈(0.6,0.8), the area near the fault zone with complex structure and strong discontinuity w h (x,y)∈(0.2,0.4), lacking geological data to explain the ambiguous high uncertainty region w h (x,y)=0.1;

[0135] (2) Using vertical weight w v (x,y,z) represents the reality that the uncertainty of the model gradually increases with the depth of the data. It is used to indicate that the greater the depth of a specified point, the smaller the weight, and the lower the model's reliability.

[0136] w v (x,y,z)=exp(-γz)

[0137] Where γ>0 is the depth attenuation factor, which controls the rate of descent. In sedimentary basins, γ∈[0.02,0.05] indicates good data, in bedrock areas γ∈(0.05,0.15) indicates poor data, and in highly complex areas including fold belts γ∈(0.10,0.25); z is the depth.

[0138] S432. In the resistivity inversion model, combining the horizontal and vertical weights into a total spatial constraint yields:

[0139] w(x,y,z)=w h (x,y)·w v (x,y,z)

[0140] This is used to enable the model to simultaneously reflect the continuity of geological structures and the uncertainty and complexity of the depth direction in space;

[0141] S44. Based on the regional constraint weight w(x,y,z), calculate and obtain the regional regularization parameter λ(x,y,z), so that the resistivity inversion can be dynamically adjusted with geological basis, and the inversion result with reliable, interpretable and geologically consistent λ(x,y,z)=λ0w(x,y,z);

[0142] S45. Iteratively update the regularization parameter λ, and after each iteration, adaptively update λ based on the ratio of the residual to the regularization term. The iterative update formula is as follows:

[0143]

[0144] Where, d obs The data represents measured resistivity. f(m) is the forward modeling function for calculating the potential response based on the underground conductivity distribution. L is a regularization operator including the gradient or Laplace operator. m0 is the initial resistivity model from the geological map. m is the resistivity distribution in the inversion iterative model, i.e., m(x,y,z). λ old (x,y,z) are the regionalization regularization parameters before iteration, λ new (x,y,z) are the regionalization regularization parameters after iteration.

[0145] In summary, by setting a vertical attenuation function that integrates stratigraphic dip angle and fault displacement in the construction of the 3D model, the non-uniform change of resistivity with depth can be dynamically reflected. This overcomes the limitations of traditional 2D planar models, accurately depicts the effect of electrical jumps in steeply dipping strata and fault zones, makes the inversion results more consistent with the real geological structure, and improves the ability to detect deep structures, including thrust bodies and rift basins.

[0146] S46. Solve for resistivity by inversion based on the preset inversion optimization algorithm.

[0147] At this point, a schematic reference is provided for the inversion profile based on geological resistivity. Figure 3 And based on the geological resistivity constraint, the inversion profile is a schematic reference. Figure 4 Methods for obtaining resistivity include:

[0148] The subsurface resistivity model *m* is solved iteratively. In each iteration, *m* is updated and the model is adjusted according to the local regularization parameter until the preset convergence condition is met. We have:

[0149]

[0150] Where Φ(m) is the inversion objective function, m is the resistivity distribution in the inversion iterative model, i.e., m(x,y,z), m0 is the prior resistivity initial model of the geological map, and d obsThe resistivity data are measured, f(m) is the forward modeling function for the potential response derived from the underground conductivity distribution, λ is the regularization parameter, determined by the iterative update strategy described above, and L h For horizontal gradient operators Used to control lateral smoothing, L v The vertical second derivative Used to control depth continuity;

[0151] In the resistivity inversion calculation process, the latest geological information feedback is combined in real time to dynamically adjust the constructed regional regularization parameters through backpropagation, so as to achieve adaptive optimization and further improve the reliability of the inversion results.

[0152] In summary, by setting a weight function w(x,y,z) with fixed spatial variation weights in the inversion objective function (integrating horizontal geological continuity classification and vertical depth attenuation factor), the smoothing intensity of the model in different regions can be dynamically adjusted to achieve strong constraints in homogeneous stratigraphic areas (maintaining continuity), weak constraints in fault zones (preserving abrupt change characteristics), and reduced weights in deep areas (accommodating data uncertainty). This effectively balances the multiple solutions in the inversion with geological rationality, generating a resistivity model that combines high resolution and geological consistency.

[0153] S5. Based on the resistivity obtained from the inversion calculation, information data is overlaid and compared in GIS software.

[0154] The specific operational procedures at this time include:

[0155] S51. Convert the resistivity inversion results from step S4 into a format supported by GIS software (such as GeoTIFF). Then, store the geological map layer data in the geological map database from step S1 in the form of Shapefile or GeoDatabase. Import the resistivity model data and geological map layers (including structural zone classification, geological boundary classification and auxiliary elements) into GIS software (such as ArcGIS, QGIS, or ArcGIS Pro).

[0156] S52. Utilize GIS software to perform overlay analysis, superimposing various datasets according to their spatial location. Distinguish between geological element layers and inversion results using transparency and color symbols. Adjust the scale, position, and symbols of each layer to ensure a more accurate correspondence between geological data and resistivity variation curves. This guarantees that the profile map not only reflects underground electrical variations but also visually displays geological zoning and structural information. For example, the resistivity raster can be set to a semi-transparent color band, while geological boundaries and structural zones can be clearly marked with boundary lines and symbols.

[0157] S53. Overlaying data displays can intuitively compare the relationship between resistivity changes and geological boundaries and tectonic zone classifications, providing a basis for the verification of subsequent inversion results and geological interpretation.

[0158] In summary, by setting up a tectonic zone classification and geological boundary classification system in the geological map spatial database, the scale of geological structures and boundary types can be accurately quantified. Then, complex geological information can be transformed into computable prior constraints, which can significantly improve the analytical accuracy of inversion models for structural features such as faults and folds, and reduce the subjective errors of manual modeling.

[0159] S6. Define a consistency scoring function to quantify the degree of matching between the inversion results and geological boundaries and tectonic zones after data overlay and comparison. Then, evaluate the inversion results based on the scoring results and provide identification basis for potential anomaly areas. Finally, feed back the consistency evaluation results to the inversion module. For local errors found in the evaluation, adjust the regularization parameters or prior models, and perform secondary inversion if necessary to further optimize the model.

[0160] The specific formula for calculating the consistency scoring function is as follows:

[0161]

[0162] Where S is the consistency score, ρ inv The inverse resistivity of the region (x,y,z) is obtained by solving for the resistivity in step S4, ρ. geo (x,y,z) represents the resistivity of the region (x,y,z) predicted by geological information, Ω represents the overall spatial extent, A is the normalization factor, and χ represents the volume or area of ​​the effective integration region. boundary (x,y,z) is a boundary function used to extract spatial points near geological boundaries, and In the integral calculation of the consistency scoring function, only grid cells close to the structural or stratigraphic boundaries are considered, while flat and continuous areas are ignored.

[0163] At this point, by setting a boundary-focused consistency scoring function S in the post-processing stage (calculating the difference between inverted and geologically predicted resistivity only near the tectonic / stratigraphic boundary), the geological credibility of the inversion results can be objectively quantified, thereby quickly locating areas of conflict between the model and geological information (such as misjudgment of concealed faults). This can then drive the dynamic iterative optimization of regularization parameters, forming a "closed loop" of "inversion-evaluation-feedback" and continuously improving the reliability of interpretation.

[0164] In summary, this method, by setting standardized Shapefile / Geodatabase storage structures and attribute table fields (such as GPS coordinates, hierarchical codes, and lithological resistivity) during the data processing stage, facilitates unified management of multi-source geological elements (geological bodies, boundaries, tectonic zones, etc.), enabling seamless GIS spatial overlay analysis of geological map database → resistivity model → inversion results. This significantly reduces the costs of multi-format conversion and manual integration, providing an efficient platform for geological-geophysical collaborative interpretation. Furthermore, by deeply integrating standardized hierarchical information from the geological map spatial database, 3D physical property modeling technology, geologically driven adaptive regularization constraints, and closed-loop evaluation systems, this method significantly enhances the efficiency of utilizing prior geological information in geophysical inversion. This solves the problems of model distortion and multiple solutions caused by the coarseness of manual modeling and the failure to consider tectonic scale and vertical variations in traditional methods, providing high-precision and high-reliability resistivity structure models for resource exploration and engineering geological safety assessment in complex geological areas.

[0165] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0166] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0167] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the methods for resistivity inversion based on a geological map spatial database as described above.

[0168] It is understood that the system provided in the embodiments of the present invention corresponds to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0169] This application also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus.

[0170] Memory, used to store computer programs;

[0171] When the processor executes the program stored in memory, it implements the above-mentioned method for resistivity inversion based on a geological map spatial database.

[0172] The communication bus mentioned in the above-mentioned electronic devices can be a standard bus for interconnecting peripheral components or an extended industrial standard structure bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0173] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0174] The memory may include random access memory or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0175] The processors mentioned above can be general-purpose processors, including central processing units, network processors, etc.; they can also be digital signal processors, application-specific integrated circuits, field-programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0176] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium, or a semiconductor medium, etc.

[0177] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0178] Furthermore, it should be noted that if any directional indication (such as up, down, left, right, front, back, etc.) is involved in the embodiments of the present invention, the directional indication is only used to explain the relative positional relationship and movement of each component in a specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0179] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, in the embodiments of this invention, "multiple" refers to two or more. Moreover, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

Claims

1. A method for resistivity inversion based on a geological map spatial database, characterized in that, Includes the following steps: S1. Based on the resistivity measurement point density and wiring length, select a geological map of a specified scale, construct a geological map database using the specified geological map, and extract the specified parameters for resistivity inversion; S2. Construct a gridded geological surface and obtain the lithological distribution and physical property parameters, including resistivity, within each grid cell of the geological surface; S3. Construct a subsurface property distribution model using the data processed in step S1 and the lithological property parameter data of the gridded geological surface processed in step S2. S4. Based on the preset inversion optimization algorithm, geological constraints are introduced and the regional regularization parameters of spatial variation are calculated according to the underground physical property distribution model. These parameters are used to perform resistivity inversion calculations to construct an underground resistivity model. S5. Based on the resistivity obtained from the inversion calculation, information data is overlaid and compared in GIS software; S6. Define a consistency scoring function to quantify the degree of matching between the inversion results and geological boundaries and tectonic zones after data overlay and comparison; The construction process of the underground physical property distribution model in step S3 includes: S31. Construct an initial surface model as the distribution range. Based on the geological map, obtain the distribution pattern and boundary range, and construct a two-dimensional surface resistivity model, which includes: in, For this grid cell The two-dimensional fundamental resistivity, For this unit in drilling data The corresponding average resistivity, For this unit in drilling data The corresponding empirical resistivity value of the lithology, Weights for the confidence level of drilling data; S32. To unify the effects of stratigraphic dip angle, fault displacement, and resistivity attenuation, a vertical correction function is introduced to describe the resistivity variation of each geological unit along the depth direction, extending the two-dimensional lithological assignment to the three-dimensional model. The calculation formula for the vertical correction function is as follows: in, At the current depth, This is the attenuation coefficient of resistivity as a function of depth. This is the tilt adjustment factor. This is the initial depth of the stratum. Fault influence factor This refers to fault depth offset; S33. Each two-dimensional grid cell resistivity Through the vertical correction function Mapping to the depth direction, the final resistivity values ​​of the three-dimensional mesh cells are: in, For the first Each grid at depth The final resistivity is used to form the initial resistivity model of the geological map.

2. The method for resistivity inversion based on geological map spatial database as described in claim 1, characterized in that, The parameters specified in step S1 include: Regional boundary and stratigraphic distribution data are used to determine the boundaries of geological units within the survey area; Reference resistivity, lithology, and structural information; Formation thickness, strike, and dip angle parameters; Data storage format and attribute table structure; Standardized layer data; The GPS coordinate field represents the precise latitude and longitude information of each geological unit, tectonic zone, and sample point; Construct hierarchical data; Geological boundary classification; Physical properties of each geological unit.

3. The method for resistivity inversion based on geological map spatial database as described in claim 1, characterized in that, The process for constructing the gridded geological surface in step S2 includes: S21. Generate regular grid cells within the specified area, wherein the grid resolution is determined based on the data density and accuracy requirements of the survey area; S22. Let each grid cell be... , , The total number of grid cells, each Has a unique coordinate index .

4. The method for resistivity inversion based on geological map spatial database as described in claim 3, characterized in that, The process for obtaining lithological distribution and physical property parameters in step S2 includes: (1) Overlay the existing geological surface with the grid, for each grid cell Extract geological surface information within its coverage area; (2) The GPS coordinates of the drilling location, the deep lateral resistivity curve with depth as the independent variable, and the contents of the formation and lithology record table are used as resistivity data. The average resistivity of the formation is calculated based on the resistivity range and lithology of each group of formations. ,in Indicates different strata; (3) Based on empirical data, set initial lithological resistivity values ​​for each lithology. As this stratum The resistivity reference value.

5. The method for resistivity inversion based on geological map spatial database as described in claim 1, characterized in that, The process of obtaining the regularization parameter in step S4 includes: S41. Applying smoothing constraints of different intensities in the horizontal and vertical directions yields: Horizontal smoothing constraint: using the horizontal smoothing operator Inhibition model in Dramatic changes in the plane, have ;in, To retrieve the resistivity distribution in the inversion iterative model, Is the model at the level The gradient vector on the plane represents the rate of change of the model values ​​in the x and y directions; Vertical smoothing constraint: using vertical smoothing operator The second derivative enhances the sensitivity to changes in formation thickness and fault displacement along the depth direction. ; S42. Use generalized cross-validation or L... Curve method for determining global regularization factor This achieves a balance between data fitting and model smoothing; S43. Based on the parameters obtained in steps S1 and S2, determine the regional constraint weights. ; S44. Weighting Based on Regional Constraints Calculate and obtain the region regularization parameters ,have ; S45. Iteratively update the regularization parameters. The iterative update formula is: in, These are measured resistivity data. To obtain the forward modeling function for the potential response based on the underground conductivity distribution, Regularization operators, including gradient or Laplacian operators, This is the initial model of the resistivity prior to the geological map. For the resistivity distribution in the inversion iterative model, i.e. , These are the regionalization regularization parameters before iteration. These are the regionalization regularization parameters after iteration.

6. The method for resistivity inversion based on geological map spatial database as described in claim 5, characterized in that, The specific operation process of step S43 includes: S431. Divide the study area into a specified number of geologically consistent zones, each zone... Assign a fixed spatial variation weight, and define the weight function as follows: In continuous, homogeneous regions, values ​​are defined as high-weighted, while in boundary or complex regions, values ​​are defined as low-weighted. (1) Divide the horizontal weight into: a region of uniform stratigraphy with a stable geological continuous structure. Lithological transition zones at lithological gradients or weak unconformities Areas near fault zones with complex and discontinuous structures High uncertainty areas lacking geological data for interpretation ; (2) Use vertical weights This reflects the increasing uncertainty of the model with respect to deep data. It indicates that as the depth of a given point increases, the weight decreases, and the model's reliability declines. Examples include: in, As a depth attenuation factor, it controls the descent rate, in sedimentary basins This indicates that the data is relatively good, especially in the bedrock area. This indicates poor data, particularly in highly complex regions including folded zones. ; For depth; S432. In the resistivity inversion model, combining the horizontal and vertical weights into a total spatial constraint yields: This is used to enable the model to simultaneously reflect the continuity of geological structures and the uncertainty and complexity of the depth direction in space.

7. The method for resistivity inversion based on geological map spatial database as described in claim 5, characterized in that, The method for calculating resistivity in step S4 includes: The subsurface resistivity model *m* is solved iteratively. In each iteration, *m* is updated and the model is adjusted according to the regional regularization parameter until the preset convergence condition is met. Thus: in, For the inversion objective function, For the resistivity distribution in the inversion iterative model, i.e. , This is the initial model of the resistivity prior to the geological map. These are measured resistivity data. To obtain the forward modeling function for the potential response based on the underground conductivity distribution, For level regularization parameters, For vertical regularization parameters, For horizontal gradient operators Used to control lateral smoothing The vertical second derivative , used to control depth continuity; In the resistivity inversion calculation process, the region regularization parameters constructed in step S4 are adjusted to achieve adaptive optimization.

8. The method for resistivity inversion based on geological map spatial database as described in claim 1, characterized in that, The specific operation process of step S5 includes: S51. Store the geological map data of each layer in the geological map database in the form of Shapefile or GeoDatabase, convert the resistivity inversion calculation results into a format and import them into the GIS software as resistivity model data. S52. Use GIS software to perform overlay analysis, overlay each dataset according to its spatial location, distinguish the geological element layers from the inversion results through transparency and color symbols, adjust the scale, position and symbols of each layer, so that the profile reflects the changes in underground electrical properties while displaying geological zoning and structural information. S53. Display overlaid data.

9. The method for resistivity inversion based on geological map spatial database as described in claim 1, characterized in that, The specific calculation formula for the consistency scoring function in step S6 is as follows: in, For consistency scoring, For the region The inverse resistivity is obtained by solving for the resistivity in step S5. For areas predicted by geological information resistivity, Represents the overall spatial extent, where A is the normalization factor and represents the volume or area of ​​the effective integration region. This is a boundary function used to extract spatial points near geological boundaries, and In the integral calculation of the consistency scoring function, only grid cells close to the structural or stratigraphic boundaries are considered, while flat and continuous areas are ignored.

Images