A method, system, medium and device for apparent resistivity correction based on resistivity method

Abstract

The application provides a resistivity-based apparent resistivity correction method, system, medium and equipment, and relates to the technical field of electric field detection. The technical scheme provided by the application generates an in-group control scheme by matching a preset optimization target corresponding to an attribute label, adjusts a target control scheme according to inter-group influencing factors, and can adaptively adjust a measurement strategy according to geological characteristics. After collecting a vector receiving voltage, the initial apparent resistivity is vector synthesized according to a horizontal angle to obtain a target apparent resistivity, effectively solving the problem that a single component signal is weak and susceptible to noise interference in the traditional resistivity method. When a certain direction component signal is particularly weak or abnormal, the vector synthesis mechanism can avoid the appearance of negative apparent resistivity, improving data stability. The improvement of the final input data quality and the full use of vector information significantly improve the convergence of the inversion algorithm and the stability of the solution, can perform vector synthesis on the component signal, and improve the inversion effect of the resistivity method.

Description

Technical Field

[0001] This application relates to the field of electric field detection technology, specifically to a method, system, medium, and device for correcting apparent resistance based on resistivity method. Background Technology

[0002] Resistivity detection is an important geophysical exploration method. It involves injecting an artificial current into the subsurface and measuring the potential difference at different locations on the surface or underground to calculate the apparent resistivity distribution of the subsurface medium, thereby inferring the subsurface geological structure and physical properties. This method is based on the physical principle that different geological bodies have different resistivities. For example, the resistivity of media such as rocks, minerals, groundwater, and oil and gas varies significantly. By analyzing the spatial distribution and variation patterns of apparent resistivity, the location, morphology, and electrical characteristics of subsurface anomalies can be effectively identified. Resistivity detection has advantages such as relatively simple equipment, low cost, and wide applicability, and is widely used in mineral exploration, engineering geological surveys, environmental monitoring, and groundwater resource investigations.

[0003] In related technologies, a certain directional component is often used for calculation and inversion. However, because the signal of this component is particularly weak, it is easily affected by environmental noise, instrument drift, electromagnetic interference and other factors. In some areas with complex underground structures, the voltage value measured near the voltage equipotential surface may have the opposite sign to the device coefficient, resulting in the appearance of negative apparent resistivity and poor inversion effect. Summary of the Invention

[0004] This application provides a method, system, medium, and device for apparent resistance correction based on resistivity method, which can perform vector synthesis of component signals and improve the inversion effect of resistivity method.

[0005] In a first aspect, this application provides a method for correcting apparent resistance based on the resistivity method, the method comprising:

[0006] Obtain a feature map of the exploration area, determine the attribute labels corresponding to different feature regions in the feature map, and the attribute labels characterize the degree of influence of different feature regions on resistivity exploration.

[0007] The placement positions of multiple transmitting electrodes and multiple receiving electrodes are mapped to the feature region, and multiple electrode groups are divided according to the attribute tags. Each electrode group contains multiple vector receiving units, and each vector receiving unit consists of three receiving electrodes forming a horizontal angle.

[0008] Match the preset optimization targets corresponding to the attribute tags of the electrode group to generate the group-specific control scheme for the electrode group.

[0009] Based on the inter-group influence factors between adjacent electrode groups, the intra-group control scheme is adjusted to generate a target control scheme for each electrode group, and the received voltage of the vector receiving unit is collected after the target control scheme is executed.

[0010] Calculate the initial apparent resistance corresponding to the vector receiving unit based on the vector received voltage, and perform vector synthesis on the initial apparent resistance based on the horizontal angle to obtain the target apparent resistance;

[0011] The apparent resistivity of the target area is used as input data for inversion calculation to obtain the apparent resistivity distribution results of the exploration area.

[0012] By adopting the above technical solution, a pre-defined optimization target corresponding to the attribute label is matched to generate an intra-group control scheme, and adjustments are made based on inter-group influencing factors to form a target control scheme. This allows for adaptive adjustment of the measurement strategy according to geological characteristics. After acquiring the vector received voltage, the initial apparent resistance is vector-synthesized based on the horizontal angle to obtain the target apparent resistance, effectively solving the problem of weak single-component signals being easily interfered with by noise in the traditional resistivity method. When the component signal in a certain direction is particularly weak or abnormal, the vector synthesis mechanism can avoid the occurrence of negative apparent resistivity, improving data stability. Finally, the improvement in the quality of the input data and the full utilization of vector information significantly improve the convergence and solution stability of the inversion algorithm, enabling vector synthesis of component signals and improving the inversion effect of the resistivity method.

[0013] Optionally, the step of using the target apparent resistivity as input data for inversion calculation to obtain the apparent resistivity distribution result of the exploration area includes:

[0014] Obtain the first device coefficient and the second device coefficient corresponding to the two received components in the vector receiving unit, respectively;

[0015] The first device coefficient and the second device coefficient are vector-synthesized based on the horizontal angle to obtain the target device coefficient;

[0016] The target apparent resistivity is obtained by multiplying the target apparent resistivity by the target device coefficient.

[0017] The apparent resistivity distribution of the exploration area is obtained by inversion calculation using the target apparent resistivity.

[0018] Optionally, the step of using the target apparent resistivity to perform inversion calculations to obtain the apparent resistivity distribution results of the exploration area includes:

[0019] Two apparent resistance components are defined from the vector receiving unit, and the composite azimuth angle of the vector receiving unit is determined based on the apparent resistance component in one of the directions.

[0020] Based on the target apparent resistivity and the synthetic azimuth, calculate the Jacobian matrix of the subsurface resistivity model;

[0021] The apparent resistivity distribution of the exploration area was obtained by inversion calculation using the Jacobian matrix.

[0022] The Jacobian matrix is:

[0023] ;

[0024] in, This represents the partial derivative of the target apparent resistance with respect to the subsurface resistivity model. This represents the apparent resistance component in the first direction. This represents the apparent resistance component in the second direction. This indicates the target apparent resistance. This represents the horizontal angle between the two apparent resistance components. This represents the partial derivative of the apparent resistance component in the first direction with respect to the target apparent resistivity. This represents the partial derivative of the apparent resistivity component in the second direction with respect to the target apparent resistivity. This represents the partial derivative of the synthesized azimuth angle with respect to the subsurface resistivity model. This represents the partial derivative of the apparent resistance component in the first direction with respect to the subsurface resistivity model. This represents the partial derivative of the synthesized apparent resistance with respect to the subsurface resistivity model.

[0025] Optionally, the step of vector synthesis of the initial apparent resistance based on the horizontal angle to obtain the target apparent resistance includes:

[0026] The receiving electrode at the position forming the horizontal angle in the vector receiving unit is determined as the starting electrode, and the remaining two receiving electrodes in the vector receiving unit are determined as target electrodes, thus obtaining two initial apparent resistances along the direction from the starting electrode to the target electrode;

[0027] The target apparent resistance is obtained by vector synthesis of the initial apparent resistance based on the horizontal angle.

[0028] Optionally, the step of acquiring a feature map of the exploration area and determining the attribute labels corresponding to different feature regions in the feature map includes:

[0029] Acquire topographic and geomorphological data and surface cover data of the exploration area, and construct a feature map of the exploration area based on the topographic and geomorphological data and the surface cover data;

[0030] Based on the influence of the topographic data and the surface cover data on resistivity exploration, the feature map is divided into regions to obtain multiple feature regions.

[0031] Each of the aforementioned feature regions is assigned a corresponding attribute label, which includes topographic relief, geological complexity, and surface conductivity.

[0032] Optionally, the step of matching the attribute tags of the electrode group with preset optimization targets to generate an intra-group control scheme for the electrode group includes:

[0033] Match the attribute tags of the electrode group with preset optimization targets from a preset test database. The preset optimization targets include a signal-to-noise ratio threshold, a measurement progress threshold, and a signal strength threshold.

[0034] According to the preset optimization target, adjust the power supply parameters of the transmitting electrode and the sampling parameters of the receiving electrode in the electrode group;

[0035] Calculate the relative positional relationship between each vector receiving unit in the electrode group, and determine the sampling timing of each vector receiving unit based on the relative positional relationship;

[0036] Based on the power supply parameters, the sampling parameters, and the sampling timing, an intra-group control scheme is generated for the electrode group.

[0037] Optionally, the inter-group influencing factors include the voltage gradient change rate and the distance between electrodes. The step of adjusting the intra-group control scheme based on the inter-group influencing factors between adjacent electrode groups to generate a target control scheme for each electrode group includes:

[0038] Calculate the rate of change of voltage gradient at the boundary of adjacent electrode groups, and measure the actual distance between electrodes at the boundary of adjacent electrode groups;

[0039] When the voltage gradient change rate is greater than a preset change rate threshold and the actual distance is less than a preset distance, the boundary region corresponding to the actual distance is set as a transition region.

[0040] Adjust the working time interval of the electrode group in the transition region, and set the working timing of the electrode group in the transition region to an alternating start mode;

[0041] The vector receiving unit in the transition region is set to a dual data acquisition mode to superimpose the acquired signals of the electrode group in the transition region.

[0042] Based on the alternating start mode and the dual data acquisition mode, update the corresponding parameter settings in the group control scheme to generate the target control scheme corresponding to the transition region.

[0043] Secondly, this application provides an apparent resistance correction system based on the resistivity method, the system comprising:

[0044] The map feature analysis module is used to acquire a feature map of the exploration area and determine the attribute labels corresponding to different feature regions in the feature map. The attribute labels represent the degree of influence of different feature regions on resistivity exploration.

[0045] An electrode group division module is used to map the placement positions of multiple transmitting electrodes and multiple receiving electrodes to the feature region, and to divide multiple electrode groups according to the attribute tags. Each electrode group contains multiple vector receiving units, and each vector receiving unit consists of three receiving electrodes forming a horizontal angle.

[0046] The first processing module is used to match the preset optimization target corresponding to the attribute label of the electrode group and generate the group control scheme corresponding to the electrode group.

[0047] The second processing module is used to adjust the intra-group control scheme according to the inter-group influence factors between adjacent electrode groups, generate a target control scheme corresponding to each electrode group, and collect the received voltage of the vector receiving unit after executing the target control scheme.

[0048] The calculation module is used to calculate the initial apparent resistance corresponding to the vector receiving unit based on the vector receiving voltage, and to perform vector synthesis on the initial apparent resistance based on the horizontal angle to obtain the target apparent resistance;

[0049] The inversion module is used to perform inversion calculations using the target apparent resistivity as input data to obtain the apparent resistivity distribution results of the exploration area.

[0050] Thirdly, this application provides a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing any of the methods described above.

[0051] Fourthly, this application provides an electronic device including a processor, a memory, and a transceiver, wherein the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform any of the methods described above.

[0052] In summary, the beneficial effects of the technical solution of this application include:

[0053] By adopting the above technical solution, a pre-defined optimization target corresponding to the attribute label is matched to generate an intra-group control scheme, and adjustments are made based on inter-group influencing factors to form a target control scheme. This allows for adaptive adjustment of the measurement strategy according to geological characteristics. After acquiring the vector received voltage, the initial apparent resistance is vector-synthesized based on the horizontal angle to obtain the target apparent resistance, effectively solving the problem of weak single-component signals being easily interfered with by noise in the traditional resistivity method. When the component signal in a certain direction is particularly weak or abnormal, the vector synthesis mechanism can avoid the occurrence of negative apparent resistivity, improving data stability. Finally, the improvement in the quality of the input data and the full utilization of vector information significantly improve the convergence and solution stability of the inversion algorithm, enabling vector synthesis of component signals and improving the inversion effect of the resistivity method. Attached Figure Description

[0054] Figure 1 This is a schematic flowchart of an apparent resistance correction method based on resistivity method according to an embodiment of this application;

[0055] Figure 2 This is a color diagram illustrating the model space and planar position provided in an embodiment of this application;

[0056] Figure 3 This is a color schematic diagram of an inversion result provided in an embodiment of this application;

[0057] Figure 4 This is a schematic diagram of the structure of an apparent resistance correction system based on resistivity method according to an embodiment of this application;

[0058] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0059] Explanation of reference numerals in the attached drawings: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0060] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0061] In the description of the embodiments of this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0062] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0063] Please see Figure 1 This is a flowchart illustrating an apparent resistance correction method based on resistivity, provided in an embodiment of this application. This method can be implemented using a computer program, a microcontroller, or run on an apparent resistance correction system based on resistivity using the von Neumann architecture. The computer program can be integrated into an application or run as a standalone utility application. The specific steps of the apparent resistance correction method based on resistivity are described in detail below.

[0064] S101: Obtain the feature map of the exploration area, determine the attribute labels corresponding to different feature regions in the feature map, and the attribute labels represent the degree of influence of different feature regions on resistivity exploration.

[0065] In this context, an exploration area refers to the target region requiring geophysical exploration, typically a geographical area with specific geological survey needs. A feature map is a spatial information layer constructed based on topography, surface cover, geological structures, and other elements, used to represent the surface and shallow geological features at different locations within the exploration area. Feature regions represent spatial units with similar geophysical characteristics, divided according to factors such as topographic relief, geological structure, and surface medium; these are typically local areas ranging from tens to hundreds of meters in size. Attribute labels are digital identifiers assigned to each feature region, indicating the degree and type of influence that region has on resistivity exploration, including quantitative indicators such as topographic relief, geological complexity, and surface conductivity. For example, within an exploration area, multiple feature regions may be defined; one region might be marked as a high-conductivity region due to the presence of shallow clay cover, while another region might be marked as a low-conductivity region due to bedrock exposure.

[0066] Specifically, firstly, a comprehensive feature map containing topographic features, surface cover distribution, and geological structure information is constructed using multi-source data such as remote sensing imagery, digital elevation data, and geological data. Then, geographic information system (GIS) technology is used to conduct a refined spatial analysis of the work area, identifying key elements such as topographic relief, lithological distribution differences, and surface medium types using tens of meters as basic units. Based on the spatial distribution characteristics and combination relationships of these elements, the entire exploration area is divided into several small-scale feature regions with relatively uniform characteristics. The size of each region is controlled within an appropriate range to ensure relatively uniform surface conditions within the region. Finally, based on factors such as the degree of topographic relief, geological structure complexity, and surface medium conductivity of each feature region, corresponding attribute labels are assigned to each region, establishing a correspondence between feature regions and resistivity exploration adaptability.

[0067] In some embodiments, feature maps can be constructed and attribute labels determined in various ways: Optionally, a gridded fine analysis method is adopted. First, the exploration area is divided into regular grids, and basic data such as topographic elevation, slope, and surface cover type are obtained within each grid cell. Physical parameters such as soil type, water content, and resistivity of key grids are measured through ground surveys to establish a parameter database. Then, spatial interpolation methods are used to calculate the distribution of physical parameters across all grids. Adjacent grids are merged based on the similarity of parameter values ​​to form feature regions. Each feature region contains several grid cells. Finally, the average value of parameters within the region is used to determine the feature map. The coefficient of variation determines the attribute labels; optionally, a method combining feature identification and field verification is adopted. High-resolution aerial photography or UAV imagery is used to identify surface features in the work area, including vegetation cover, soil type, water accumulation areas, rock outcrops, etc. The boundaries of different features are automatically extracted through image segmentation technology to form a preliminary feature area division. Then, technical personnel are organized to conduct field verification in key areas, measuring parameters such as soil resistivity, terrain slope, and groundwater level to correct and improve the boundaries and attributes of feature areas, ensuring that the area of ​​each feature area is moderate and the internal conditions are relatively uniform. Accurate attribute labels are assigned to each area based on the measured data.

[0068] S102: Map the placement positions of multiple transmitting electrodes and multiple receiving electrodes to the feature region, and divide multiple electrode groups according to attribute labels. Each electrode group contains multiple vector receiving units, and each vector receiving unit consists of three receiving electrodes forming a horizontal angle.

[0069] In this system, the transmitting electrode refers to the conductive device used to inject excitation current into the ground, typically a rod-shaped or plate-shaped electrode made of stainless steel or copper; the receiving electrode refers to the voltage acquisition device used to measure the distribution of the underground electric field, generally an electrode with high input impedance; the deployment location refers to the specific coordinate position of each electrode on the ground, including planar coordinates and elevation information; mapping refers to the process of establishing a correspondence between the geographical coordinates of the electrodes and their spatial positions in a feature map; the electrode group refers to a set of electrodes combined according to the characteristics of the feature area and measurement requirements, used for coordinated control and data acquisition; the vector receiving unit refers to a measurement unit consisting of three receiving electrodes, capable of simultaneously measuring voltage signals in two different directions; the horizontal angle refers to the angle between the two receiving directions in the vector receiving unit, theoretically a right angle, but usually controlled within a certain range in actual engineering. For example, in a feature area, several receiving and transmitting electrodes may be deployed to form multiple vector receiving units, each covering a certain area.

[0070] Specifically, the electrode spacing and density are first determined based on the exploration accuracy requirements and geological target characteristics. Electrodes are typically placed at appropriate intervals within each characteristic area. Then, the preset coordinate positions of all transmitting and receiving electrodes are projected onto the constructed characteristic map. Spatial analysis is used to determine the specific characteristic area where each electrode is located. Next, the electrodes are grouped according to the attribute labels of the characteristic areas to ensure that electrodes within the same electrode group have similar surface environmental conditions. At the same time, the spatial range of the electrode group does not exceed the boundary of a single characteristic area. Within each electrode group, receiving electrodes are configured according to the requirements of vector reception. With one receiving electrode as the center, two other receiving electrodes are placed within an appropriate range around it to form a three-electrode vector receiving unit with a specific horizontal angle. Finally, the geometric configuration of each vector receiving unit is checked to ensure that it meets the measurement accuracy requirements. If necessary, the electrode positions are fine-tuned near the boundary of the characteristic area to optimize the receiving geometry.

[0071] In some embodiments, the mapping of electrode placement locations and the division of electrode groups can be achieved in various ways: Optionally, an automated placement method based on feature region boundaries can be adopted. First, the boundary coordinates and attribute information of each region in the feature map are read. Electrode placement points are automatically generated in each region according to a triangular or rectangular grid. The grid spacing is adjusted according to the area and complexity of the region. Spatial overlay analysis is used to ensure that each electrode is strictly located within the corresponding feature region to avoid attribute confusion caused by cross-region placement. Then, electrode groups are formed by feature region. Within each electrode group, a relatively centrally located receiving electrode is selected as the center point of the vector receiving unit. Paired electrodes are placed in different directions to form a vector receiving configuration with a horizontal angle close to a right angle. Finally, the effectiveness of each vector unit is verified by geometric calculation.

[0072] S103: Match the preset optimization target corresponding to the attribute label of the electrode group and generate the intra-group control scheme corresponding to the electrode group;

[0073] Among them, the preset optimization target refers to the measurement quality indicators pre-set for different surface environmental conditions, including quantitative parameters such as signal-to-noise ratio threshold, measurement progress threshold, and signal strength threshold. The signal-to-noise ratio threshold represents the minimum requirement for the ratio of the measurement signal to the background noise, used to ensure data quality. The measurement progress threshold refers to the minimum efficiency requirement for completing the measurement per unit time. The signal strength threshold represents the minimum detectable value of the received voltage. The group control scheme refers to the measurement parameter configuration and operation process formulated for a specific electrode group, including power supply parameters, sampling parameters, timing control, etc. The power supply parameters represent the excitation signal characteristics such as current intensity, frequency, and waveform of the transmitting electrode. The sampling parameters refer to the data acquisition parameters such as the sampling frequency, filtering settings, and gain configuration of the receiving electrode. For example, for a characteristic area with a surface of moist clay, its electrode group may need to be set with a lower power supply current and a higher sampling accuracy to adapt to the measurement requirements of a highly conductive environment.

[0074] Specifically, firstly, a knowledge base containing optimal measurement parameters under different local surface environmental conditions is established. This knowledge base is built based on a large amount of small-scale practical engineering experience and numerical simulation results, covering best practices for various characteristic areas. Then, the attribute label information of each electrode group is read, including characteristic parameters such as the topographic relief, geological complexity, and surface conductivity of the local area. Based on these parameters, matching optimization targets are retrieved from the knowledge base to determine the signal-to-noise ratio requirements, measurement efficiency indicators, and signal quality standards that the electrode group should achieve. Considering the relatively uniform conditions within a small area, more precise parameter settings can be adopted. Next, the required technical parameters such as power supply current intensity, voltage sampling accuracy, and data acquisition duration are derived from the optimization targets. At the same time, the spatial distribution characteristics and mutual distances of each vector receiving unit within the electrode group are considered to calculate the relative positional relationships and geometric coupling effects between units. Based on this, the measurement timing and synchronization control strategy of each unit are determined, ultimately forming an intra-group control scheme containing complete parameter configuration and operation procedures.

[0075] In some embodiments, the optimization of target matching and control scheme generation can be achieved in a variety of ways: Optionally, a precise matching method based on local environmental characteristics can be adopted. First, a detailed parameter library covering different small-scale regional environmental conditions is established, including the optimal measurement parameters under different combinations of soil types, water content, and terrain slope. The specific attribute parameters of the characteristic area where the electrode group is located are compared and matched with the standard conditions in the parameter library. The parameter configuration with the highest similarity is selected as the basic template. Then, fine-tuning is performed according to the specific conditions of the current area. For example, the power supply current intensity is adjusted according to the soil resistivity, the electrode grounding resistance compensation is adjusted according to the terrain slope, and the measurement point density and sampling time are adjusted according to the area. Finally, the rationality of the parameter configuration is verified through pre-tests to ensure that high-quality measurement data can be obtained in this local area.

[0076] S104: Based on the inter-group influence factors between adjacent electrode groups, adjust the intra-group control scheme, generate the target control scheme corresponding to each electrode group, and collect the received voltage of the vector receiving unit after executing the target control scheme.

[0077] Among these, inter-group influencing factors refer to physical effects such as electromagnetic coupling, signal interference, and geoelectric field interaction between adjacent electrode groups; voltage gradient change rate refers to the spatial rate of change of voltage gradient at the boundary of adjacent electrode groups, reflecting the degree of lateral change of underground electrical structure; inter-electrode distance refers to the actual physical distance between electrodes at the boundary of adjacent electrode groups; adjustment refers to the process of modifying the original control scheme according to the degree of inter-group influence; target control scheme refers to the final measurement parameter configuration and operation procedure after inter-group influence correction; transition zone refers to the buffer zone set at the boundary of adjacent electrode groups to reduce inter-group measurement interference; alternating start mode refers to the working mode in which adjacent electrode groups alternately perform measurements according to a time sequence; dual data acquisition mode refers to the method of simultaneously using two different parameter configurations for data acquisition in the transition zone; superposition processing refers to the signal processing technique of weighted averaging of multiple measurement results to improve data quality. For example, when the voltage gradient change rate at the boundary of two adjacent feature regions exceeds a preset threshold, a transition zone of appropriate width needs to be set near the boundary.

[0078] Specifically, firstly, the electric field distribution generated by each electrode group under the current control scheme is calculated through numerical simulation. The electric field variation within a certain range near the boundary of adjacent feature regions is analyzed in detail. The electric field overlap area and interference intensity between adjacent electrode groups are identified, and the voltage gradient and its spatial rate of change at the boundary are calculated. At the same time, the actual distance between electrodes at the boundary of adjacent electrode groups is measured in the field. Considering that the boundary effect is more obvious in small feature regions, when the voltage gradient rate of change exceeds the preset threshold and the distance between electrodes is less than the critical distance, it is determined that there is a significant inter-group influence. A transition region needs to be set between adjacent feature regions for special processing. The width of the transition region is determined according to the electrode spacing and the influence intensity. The measurement strategy is redesigned in the transition region. The adjacent electrode groups that originally worked independently according to the feature region are changed to an alternating start mode. Electromagnetic interference caused by synchronous measurement is avoided by time separation. At the same time, the vector receiving unit in the transition region adopts a dual data acquisition method, and measurements are performed according to the parameter configuration of the two adjacent electrode groups respectively. Finally, the multiple sets of data collected are weighted and superimposed. The weighting coefficient is determined according to the theoretical accuracy and actual signal-to-noise ratio of each measurement scheme.

[0079] S105: Calculate the initial apparent resistance corresponding to the vector receiving unit based on the vector receiving voltage, and perform vector synthesis on the initial apparent resistance based on the horizontal angle to obtain the target apparent resistance;

[0080] In this context, vector received voltage represents the voltage signal measured by each receiving electrode in the vector receiving unit; initial apparent resistance refers to the apparent resistance value calculated based on the unidirectional received voltage and the corresponding device coefficient; horizontal angle represents the angular parameter between the two receiving directions in the vector receiving unit; vector synthesis refers to the mathematical process of merging the apparent resistance components of two different directions into a single composite value according to vector operation rules; target apparent resistance represents the final apparent resistance value obtained after vector synthesis, characterized by being always positive and having strong noise immunity; starting electrode refers to the center electrode located at the vertex of the angle between the two receiving directions in the vector receiving unit; target electrode refers to the other two receiving electrodes arranged along two different directions starting from the starting electrode. For example, in a vector receiving unit within a characteristic region, two initial apparent resistance values ​​can be calculated by measuring the voltage from the center electrode to the electrodes in the two directions, and then the target apparent resistance can be obtained through the vector synthesis formula.

[0081] Specifically, firstly, the voltage data collected by each vector receiving unit after the target control scheme is executed is read, including the voltage measurement values ​​from the starting electrode to the two target electrodes. Considering that the surface conditions in the small area are relatively uniform and the consistency of the measurement data is good, then the initial apparent resistance in each direction is calculated using Ohm's law based on the basic principle of the resistivity method. The calculation formula is that the apparent resistance equals the measured voltage divided by the excitation current. Since the conditions in the characteristic area are relatively stable, higher calculation accuracy can be used. Next, the geometric configuration parameters of the vector receiving unit are obtained, especially the horizontal angle between the two receiving directions. In the small area, due to the relatively flat terrain, the angle deviation is usually small. The vector synthesis formula is applied for calculation. The synthesis formula is that the target apparent resistance equals the sum of the squares of the two initial apparent resistance components plus the square root of the product of twice the component product and the cosine of the angle. This synthesis method ensures that even if the apparent resistance value in a certain direction is very small or even negative, the synthesized target apparent resistance is still positive. At the same time, since there are fewer interference factors in the small area, the synthesis effect is more stable and reliable. Finally, high-quality target apparent resistance data that can be used for subsequent inversion calculations are obtained.

[0082] In some embodiments, the initial apparent resistance calculation and vector synthesis can be implemented in several ways: Optionally, a precise calculation method considering local surface conditions can be adopted. First, the precise coordinate positions of the three electrodes in the vector receiving unit are determined by high-precision GPS measurement. The influence of micro-topographical changes in the characteristic area on the electrode spacing is considered, and the geometric deviation caused by local topographical undulations is corrected. Then, the effective device coefficients in each direction are calculated based on the surface resistivity distribution characteristics of the characteristic area. The device coefficient calculation considers the influence of surface inhomogeneity, and an integral method is used to obtain more accurate geometric factors. The initial apparent resistance is calculated based on the corrected device coefficients and the measured voltage. The calculation is then performed using a three-dimensional vector synthesis formula that considers the influence of topography. The formula introduces a topographic correction coefficient, and an adaptive coefficient is obtained through spatial vector operations. The target apparent resistance value under local conditions is then used. Finally, the synthesized result undergoes a regional consistency check to remove outliers that are significantly inconsistent with the surrounding data. Optionally, an adaptive weighted synthesis method based on regional characteristics is adopted. Dynamic weight coefficients are assigned to the initial apparent resistance in each direction within the characteristic region according to the statistical characteristics and data quality. The weight allocation considers the surface conductivity characteristics and signal propagation conditions of the region. Directions with high signal-to-noise ratios and conforming to regional characteristics are given larger weights, while abnormal or low-quality directions are given smaller weights. The target apparent resistance is calculated using an adaptive weighted vector synthesis formula. The formula parameters are adjusted in real time according to the data distribution characteristics within the region. At the same time, a local optimization algorithm is used to continuously improve the weight allocation strategy so that the synthesized target apparent resistance maintains both mathematical stability and conforms to the geophysical characteristics of the characteristic region.

[0083] S106: Use the target apparent resistivity as input data for inversion calculation to obtain the apparent resistivity distribution results of the exploration area.

[0084] In this context, inversion calculation refers to the process of solving the mathematical inverse problem of inferring the subsurface resistivity distribution based on observational data. Input data refers to the observational dataset used for inversion calculation, including the target apparent resistivity and its corresponding geometric parameters. The apparent resistivity distribution result represents the spatial distribution of subsurface three-dimensional resistivity obtained through inversion calculation. Device coefficients are geometric factors determined by the electrode geometry, used to convert apparent resistivity into apparent resistivity. Target device coefficients represent the device coefficients corresponding to the target apparent resistivity after vector synthesis. Target apparent resistivity is the product of the target apparent resistivity and the target device coefficients, representing the synthesized resistivity observation value. The Jacobian matrix represents the partial derivative matrix of the observational data with respect to the parameters and is a key mathematical tool in inversion calculation. The synthesized azimuth angle is the equivalent direction angle of the apparent resistivity after vector synthesis, used to maintain the continuity of directional information during the inversion process. For example, the target apparent resistivity of a vector receiving unit within a certain characteristic region is obtained by multiplying the target apparent resistivity by the corresponding target device coefficients; this value will be used as the representative resistivity observation value of that local region in the inversion calculation.

[0085] Specifically, firstly, the device coefficients corresponding to the two receiving components in the vector receiving unit within each feature region are obtained. The device coefficient calculation takes into account the influence of local surface conditions and employs a more accurate calculation method. Then, the two device coefficients are vector-synthesized based on the horizontal angle between the vector receiving units to obtain the target device coefficients that match the target apparent resistivity. Since the conditions are relatively uniform within a small area, the synthesized result has better stability. Next, the product of the target apparent resistivity and the target device coefficient is calculated to obtain the target apparent resistivity used for inversion. This value represents the comprehensive electrical characteristics of the corresponding feature region. Simultaneously, based on the ratio of the two apparent resistivity components... The composite azimuth is calculated. The azimuth changes relatively gently within a small area, which helps to maintain the continuity of directional information during the inversion process. Then, a Jacobian matrix adapted to the small-scale features is constructed for the inversion calculation. This matrix takes into account the boundary effects of the feature area and the influence of local anomalies. The calculation accuracy is improved by refining the grid. Finally, the constrained least squares method or regularized inversion algorithm is used. The target apparent resistivity of each feature area is used as the observation data, and the high-precision Jacobian matrix is ​​used as the sensitivity information. The underground resistivity distribution is solved through multi-scale iterative optimization to obtain high-resolution three-dimensional resistivity distribution results that reflect the differences in local feature areas.

[0086] Based on the above embodiments, as an optional implementation method, the method of obtaining the feature map of the exploration area and determining the attribute labels corresponding to different feature areas in the feature map in step S101 can be specifically implemented through the following steps S201-S203.

[0087] S201: Obtain topographic and geomorphological data and surface cover data of the exploration area, and construct a feature map of the exploration area based on the topographic and geomorphological data and surface cover data;

[0088] Topographic data refers to a set of spatial data describing terrain features such as ground undulation and slope distribution. This data directly affects the feasibility of electrode deployment and signal propagation characteristics. Surface cover data represents the spatial distribution information of different types of surface cover, including elements such as soil type, vegetation cover, building distribution, and water body distribution. Surface cover data encompasses information such as vegetation type and density, soil texture and thickness, location of artificial structures, and the extent of surface water bodies. These elements have a significant impact on the grounding conditions and noise environment of resistivity measurements.

[0089] Specifically, the basic data required for constructing the feature map is obtained through multi-source data fusion technology. First, topographic data of the exploration area is acquired using digital elevation data (DEM), including elevation, slope, aspect, and surface curvature parameters. This data is obtained through methods such as lidar scanning, photogrammetry, or digitization of existing topographic maps, and the data accuracy must meet the geometric requirements for subsequent vector receiving electrode deployment. Simultaneously, land cover data is acquired using remote sensing image interpretation technology. Multispectral satellite imagery or high-resolution aerial imagery is used to identify vegetation types, exposed soil areas, water bodies, buildings, and other land cover types using supervised classification or object-oriented classification methods, establishing a spatial distribution database of land cover types. Then, a geographic information system (GIS) platform is used for data preprocessing, including coordinate system unification, spatial resolution resampling, and geometric correction, ensuring accurate spatial registration of the topographic data and land cover data. Next, spatial overlay analysis is used to fuse the two types of data, generating a data layer containing comprehensive information on topography and land cover using raster computation or vector overlay, forming a feature map that reflects the spatial differentiation characteristics of the surface conditions in the exploration area.

[0090] S202: Based on the influence of topographic and geomorphological data and surface cover data on resistivity exploration, the feature map is divided into regions to obtain multiple feature regions;

[0091] Among them, the degree of influence refers to the strength of the impact of topography and surface cover on the accuracy of resistivity exploration and measurement, signal quality, and operational difficulty; regional division refers to the process of dividing a continuous geographical space into several relatively homogeneous spatial units according to specific criteria.

[0092] Specifically, based on the feature map constructed using S201, the automatic division of feature areas is achieved by quantitatively analyzing the influence of topography and surface cover on vector receiver resistivity exploration. First, an evaluation index system for the degree of influence is established. Regarding topography, the impact of slope on the geometric accuracy of electrode placement, the impact of topographic relief on the uniformity of current distribution, and the impact of topographic type on maintaining the horizontal angle of vector receiver units are considered. A quantitative evaluation function for the degree of topographic influence is established by calculating parameters such as slope value, local topographic relief, and surface roughness for each grid unit. Regarding surface cover, the impact of vegetation cover on electrode grounding resistance, the impact of soil type on signal transmission, and the impact of surface conductivity on vector receiver voltage measurement are analyzed. A comprehensive evaluation algorithm for the degree of cover influence is constructed by extracting parameters such as vegetation cover density, soil resistivity, and surface moisture content. Then, a weighted linear combination method is used to synthesize the influence of topography and surface cover, calculating the comprehensive influence index for each spatial unit. This index reflects the suitability and technical difficulty of conducting vector receiver resistivity measurements at that location. Based on the spatial distribution pattern of the influence degree index, a threshold-based region growth algorithm or k-means clustering algorithm is used to divide the region, and adjacent spatial units with similar influence degrees are grouped into the same feature region.

[0093] S203: Assign corresponding attribute labels to each feature region. The attribute labels include topographic relief, geological complexity, and surface conductivity.

[0094] Specifically, the calculation of terrain relief employs statistical analysis methods. Statistical analysis is performed on the digital elevation data within each characteristic region, calculating statistical parameters such as the standard deviation, coefficient of variation, and range of elevations within the region. A weighted average is then used to obtain a comprehensive relief value, which is subsequently divided into several levels according to its magnitude. Each level is assigned a corresponding numerical label, and the relief label is directly linked to the parameter settings for controlling the horizontal angle of the vector receiving unit and assessing inter-group influence. Determining geological complexity requires a comprehensive analysis of geological elements within the region, including the degree of geological structural development, the complexity of lithological distribution, and the density of fault structures. A spatial database of geological elements is established by collecting geological data. The analytic hierarchy process (AHP) is used to determine the weight coefficients of each geological element, and a comprehensive geological complexity index for each characteristic region is calculated. Levels are then classified and labels are assigned according to the index values. The complexity labels are used to guide the selection of preset optimization targets and the setting of power supply parameters. Surface conductivity is determined through field resistivity measurements and soil electrical parameter surveys. Representative measuring points are selected within each characteristic area for shallow resistivity measurements. Combined with information on soil type, water content, and mineral composition, the resistivity-conductivity conversion formula is used to calculate surface conductivity values. Conductivity is then classified into levels and assigned corresponding labels. These conductivity labels are directly used for parameter selection in vector synthesis calculations and for determining target device coefficients. Ultimately, a comprehensive attribute labeling system is formed, encompassing three dimensions: topographic relief, geological complexity, and surface conductivity. A quantitative correspondence is established between these attribute labels and the vector receiver resistivity exploration technical parameters.

[0095] Based on the above embodiments, as an optional implementation method, step S103, which matches the preset optimization target corresponding to the attribute label of the electrode group and generates the intra-group control scheme corresponding to the electrode group, specifically includes S301-S304.

[0096] S301: Match the preset optimization targets corresponding to the attribute tags of the electrode group from the preset test database. The preset optimization targets include signal-to-noise ratio threshold, measurement progress threshold and signal strength threshold.

[0097] The preset test database refers to a pre-established data set containing measurement parameter configurations under different geological exploration conditions; matching refers to the process of finding projects that match the current conditions through comparison; the signal-to-noise ratio threshold refers to the minimum standard of the ratio of useful signal power to noise signal power; the measurement progress threshold refers to the minimum efficiency requirement for completing the measurement task per unit time; the signal strength threshold refers to the minimum numerical standard that the voltage signal amplitude detected by the receiving electrode should reach.

[0098] This step matches electrode group attribute labels with preset optimization targets through database retrieval. The three attribute labels of the electrode group—topographic relief, geological complexity, and surface conductivity—are used as query conditions to retrieve corresponding optimization target parameters from a preset test database. The database is categorized and stored according to label combinations, with each label combination corresponding to a set of optimization target parameter configurations. The retrieval process calculates the similarity between the current label and each label combination in the database, selects the label combination with the highest similarity, and obtains its corresponding signal-to-noise ratio threshold, measurement progress threshold, and signal strength threshold.

[0099] S302: Adjust the power supply parameters of the transmitting electrode and the sampling parameters of the receiving electrode in the electrode group according to the preset optimization target;

[0100] Power supply parameters refer to the technical parameters such as current intensity, frequency, and waveform when the transmitting electrode injects current into the ground; sampling parameters refer to the technical parameters such as sampling frequency, sampling accuracy, and filtering bandwidth when the receiving electrode collects voltage signals; adjustment refers to the process of modifying the equipment operating parameters according to the target requirements.

[0101] This step adjusts the operating parameters of the electrode assembly according to preset optimization targets. Adjusting the power supply parameters of the transmitting electrodes includes: calculating the required transmitting current intensity based on the signal strength threshold, establishing the relationship function between the current intensity and the received signal amplitude, and determining the transmitting current value considering the influence of ground conductivity; selecting the optimal operating frequency based on the signal-to-noise ratio threshold, and analyzing the target frequency range and noise spectrum distribution; designing the transmitting waveform based on the measurement progress threshold, and optimizing the pulse width and duty cycle to improve measurement efficiency. Adjusting the sampling parameters of the receiving electrodes includes: determining the sampling frequency based on the transmitting signal frequency, setting the sampling accuracy and quantization bits based on the signal-to-noise ratio threshold, and setting the filter bandwidth based on the signal spectrum characteristics and noise suppression requirements.

[0102] S303: Calculate the relative positional relationship between each vector receiving unit in the electrode group, and determine the sampling timing of each vector receiving unit based on the relative positional relationship;

[0103] The relative positional relationship refers to the geometrical distribution of each vector receiving unit in space and the distance and angular relationship between them; the sampling timing sequence refers to the time arrangement scheme for each vector receiving unit to sample voltage in a specific time sequence.

[0104] This step determines the positional relationships between vector receiving units and establishes the sampling timing sequence through spatial geometric calculations. A local coordinate system is established with the center of the electrode group as the origin, and the coordinate position of each vector receiving unit is obtained. The straight-line distance, azimuth angle, and elevation difference between each unit are calculated. The spatial distribution pattern of each unit is analyzed, and the electromagnetic coupling coefficient and signal crosstalk degree between units that are close to each other are calculated. The sampling timing sequence determination process includes: calculating the signal propagation time from the transmitting electrode to each vector receiving unit, analyzing the electromagnetic interference characteristics between each unit, arranging the sampling timing sequence using time-division multiplexing for units that are spatially close, and arranging parallel sampling for units with less mutual interference, forming a timing scheme that balances measurement accuracy and work efficiency.

[0105] S304: Generate the intra-group control scheme corresponding to the electrode group based on the power supply parameters, sampling parameters and sampling timing.

[0106] Intra-group control scheme refers to the comprehensive control strategy and parameter configuration scheme for the coordinated operation of all electrodes within a single electrode group.

[0107] This step integrates power supply parameters, sampling parameters, and sampling timing to generate a complete electrode group control scheme. The control scheme adopts a layered architecture, including three layers: overall control logic, parameter management, and device driver interfaces. The transmitting electrode control section generates current control commands based on the power supply parameters, including current intensity setpoints, frequency parameters, and waveform parameters. The receiving electrode control section configures the data acquisition system based on the sampling parameters, setting the sampling frequency, quantization accuracy, amplifier gain, and filter parameters. The timing control section generates a time reference signal according to the sampling timing scheme and controls the sampling time of each vector receiving unit through a programmable delay. The data synchronization section ensures that all sampled data has a unified time label, providing a time reference for subsequent vector synthesis calculations.

[0108] Based on the above embodiments, as an optional implementation method, the inter-group influencing factors include the voltage gradient change rate and the distance between electrodes. In step S104, the intra-group control scheme is adjusted according to the inter-group influencing factors between adjacent electrode groups to generate the target control scheme corresponding to each electrode group. This can be specifically achieved through the following steps S401-S405.

[0109] S401: Calculate the rate of change of voltage gradient at the boundary of adjacent electrode groups and measure the actual distance between electrodes at the boundary of adjacent electrode groups;

[0110] Among them, the voltage gradient change rate refers to the rate at which the voltage gradient value at the boundary of adjacent electrode groups changes with spatial position, reflecting the degree of non-uniformity of electric field distribution; the actual distance represents the real spatial distance between electrodes at the boundary of adjacent electrode groups, which is a physical quantity value directly obtained through measuring equipment.

[0111] The voltage gradient rate of change is calculated using a numerical differential method. First, several measurement points are established at the boundary of adjacent electrode groups. High-precision voltmeters are used to measure the potential value at each point. The voltage gradient value at each point is obtained by dividing the potential difference between adjacent measurement points by the distance. The central difference algorithm is used to calculate the voltage gradient point-by-point within the boundary region, obtaining the rate of change of the voltage gradient in space. Through multi-point measurements along the boundary direction, the spatial distribution characteristics of the voltage gradient rate of change in the boundary region are obtained. Actual distance measurements are performed using a laser rangefinder or GPS differential positioning technology to accurately measure the key electrode positions at the boundary of adjacent electrode groups, with a measurement accuracy required to be at the centimeter level. The influence of terrain undulations must be considered during the measurement process, and a three-dimensional spatial distance calculation method is used to ensure the accuracy of the distance measurement.

[0112] S402: When the voltage gradient change rate is greater than the preset change rate threshold and the actual distance is less than the preset distance, the boundary region corresponding to the actual distance is set as the transition region.

[0113] Among them, the preset rate of change threshold refers to the pre-set critical value of the voltage gradient rate of change, which is used as a quantitative standard to judge the degree of electric field interference between adjacent electrode groups; the preset distance refers to the pre-determined critical value of the distance between electrodes, and when the actual distance is less than this value, it is considered that there is mutual influence; the transition region represents the spatial range where there is significant mutual influence between adjacent electrode groups that requires special treatment.

[0114] In practice, by analyzing the relationship between the rate of change of voltage gradient and measurement error at different distances, a threshold value for the rate of change that significantly affects measurement accuracy is set. The preset distance is set based on the geometric dimensions of the electrode group and the current diffusion range. The effective radius of the electrode group is calculated through numerical simulation of the current field. When the boundary distance between adjacent electrode groups is less than a specific multiple of the effective radius, significant mutual influence is considered to exist. The condition judgment process uses logical AND operation. The transition region is triggered only when the rate of change of voltage gradient is greater than the preset rate of change threshold and the actual distance is less than the preset distance. The spatial range of the transition region is determined using a buffer analysis method. The transition region is formed by extending a certain distance to both sides from the boundary point that satisfies the condition. The extension distance is determined according to the size of the electrode group and the influence range to ensure that the transition region can completely cover the spatial range of mutual influence.

[0115] S403: Adjust the working time interval of the electrode group in the transition region and set the working timing of the electrode group in the transition region to an alternating start mode;

[0116] The working time interval refers to the length of time required for the electrode group to perform a complete measurement operation, including power supply time and signal stabilization time; the alternating start mode means that adjacent electrode groups start working in turn according to a predetermined time sequence to avoid mutual interference caused by simultaneous operation.

[0117] In practical implementation, the adjustment of the working time interval is based on signal propagation characteristics and electric field stability requirements. First, the mutual influence intensity of electrode groups within the transition area is analyzed. The time constant for influence dissipation is determined through coupling coefficient calculation, and the original working time interval is appropriately extended to ensure that the electric field influence of the previous electrode group is completely dissipated before starting the next electrode group. The alternating start mode design adopts the time-division multiplexing principle, numbering and sorting the electrode groups within the transition area according to their spatial location, and establishing a schedule for their rotation. The start time difference between adjacent electrode groups is set as the adjusted working time interval. The specific implementation of the start timing sequence uses digital timer control. Each electrode group is configured with an independent start control signal, and precise timing control is achieved through a programmable logic controller. To improve measurement efficiency, a pipeline operation method is adopted. When the first electrode group completes measurement and enters the signal stabilization stage, the second electrode group starts measurement, and so on, ensuring continuous measurement operations throughout the transition area. Simultaneously, a conflict detection mechanism is established to monitor the working status of each electrode group in real time. When abnormal start-up or timing conflicts are detected, the start time of subsequent electrode groups is automatically adjusted to ensure the correct execution of the alternating start mode.

[0118] S404: Set the vector receiving unit in the transition area to dual data acquisition mode to superimpose the acquired signals of the electrode group in the transition area.

[0119] Dual data acquisition mode refers to the working mode in which the vector receiving unit simultaneously or in time-division acquires signals from two different electrode groups, thereby improving signal quality through multiple acquisitions; superposition processing refers to a data processing method that combines multiple acquired signal data through mathematical operations to enhance useful signals and suppress random noise.

[0120] This step improves measurement accuracy and signal quality in the transition region by enhancing data acquisition capabilities. Implementing a dual data acquisition mode requires adjustments to the hardware and software configuration of the vector receiving unit. Hardware-wise, this involves increasing data storage capacity and processing power, while software-wise, it involves expanding the functional modules of the data acquisition program. Specific implementation methods include time-division acquisition and synchronous acquisition. The time-division acquisition scheme utilizes the time interval of the alternating start mode to acquire signals from each electrode group during its operation, with each vector receiving unit performing two independent acquisitions at the same measurement location. The synchronous acquisition scheme increases the number of acquisition channels to simultaneously receive signals from two adjacent electrode groups. The superposition processing employs a coherent accumulation algorithm. First, the acquired signals are time-aligned and amplitude-corrected to eliminate systematic errors caused by differences in acquisition time and equipment. Then, a weighted average of the two acquired signals is calculated. The weighting coefficients are determined based on signal quality and signal-to-noise ratio, with higher-quality acquired data receiving a larger weight.

[0121] S405: Based on the alternating start mode and dual data acquisition mode, update the corresponding parameter settings in the group control scheme and generate the target control scheme corresponding to the transition area.

[0122] The target control scheme refers to a comprehensive control strategy formulated for the special conditions of the transition zone, which includes parameter configuration and execution logic for alternating start-up mode and dual data acquisition mode.

[0123] This step integrates the parameter settings of the previously determined alternating start mode and dual data acquisition mode into the group control scheme, generating a target control scheme suitable for the transition area. The parameter update process adopts a modular replacement method, retaining the parameter settings that are not affected in the group control scheme, and only modifying the parameters related to timing control and data acquisition. The parameter integration of the alternating start mode includes: writing the adjusted working time interval into the timing control module, updating the delay setting of the electrode group start trigger signal, and configuring the control logic for conflict detection and automatic adjustment functions. The parameter integration of the dual data acquisition mode includes: expanding the cache capacity configuration of the data acquisition module, adding parameter settings for the overlay processing algorithm, and configuring the judgment threshold and processing flow for abnormal data detection. The generation of the target control scheme adopts a configuration file management method, saving all parameter settings as a structured configuration file, which includes three main parts: equipment control parameters, timing control parameters, and data processing parameters. Equipment control parameters include the power supply parameters, sampling parameters, and working status settings of each electrode group; timing control parameters include time-related settings such as start delay, working interval, and synchronization signal; data processing parameters include overlay algorithm parameters, filtering parameters, and quality control thresholds.

[0124] Based on the above embodiments, as an optional implementation method, the method of using the target apparent resistivity as input data for inversion calculation in step S106 to obtain the apparent resistivity distribution result of the exploration area can be specifically implemented through the following steps S501-S504.

[0125] S501: Obtain the first device coefficient and the second device coefficient corresponding to the two received components in the vector receiving unit, respectively;

[0126] The first device coefficient refers to the device coefficient corresponding to the first received component in the vector receiving unit, which is determined by the geometric positional relationship between the power supply electrode and the electrode of that received component. The second device coefficient refers to the device coefficient corresponding to the second received component in the vector receiving unit, which is also determined by the geometric positional relationship between the power supply electrode and the electrode of that received component. The device coefficient reflects the geometric characteristics of the electrode configuration, and its value is composed of the reciprocal difference of the distances between each electrode, directly affecting the accuracy of the apparent resistivity calculation.

[0127] This step calculates the device coefficients corresponding to the two orthogonal receiving components of the vector receiving unit. For each of the two receiving components in the vector receiving unit, the device coefficients of the quadrupole device formed by the component and the power supply electrode are calculated. The calculation of the first device coefficient is based on the spatial position of the power supply electrode and the electrode of the first receiving component. It is obtained by measuring the distance between each electrode and using the device coefficient calculation formula. The calculation process requires precise measurement of the distance from the power supply electrode to each receiving electrode, and these distance values ​​are substituted into the device coefficient expression for calculation. The calculation method for the second device coefficient is the same, based on the spatial positional relationship between the power supply electrode and the electrode of the second receiving component.

[0128] S502: Perform vector synthesis of the first device coefficient and the second device coefficient based on the horizontal angle to obtain the target device coefficient;

[0129] Among them, the horizontal angle refers to the angle between the two receiving components in the horizontal plane in the vector receiving unit, with a theoretical value of ninety degrees; vector synthesis refers to the mathematical operation process of merging two directional physical quantities according to the vector operation rules; the target device coefficient refers to the new device coefficient obtained through vector synthesis, representing the equivalent device characteristics after synthesis.

[0130] This step uses a vector synthesis method to combine the coefficients of two devices into the target device coefficients. Let the angle between the two received components be the horizontal angle, and understand the two device coefficients as vector components along their respective receiving directions. The vector synthesis process uses the principle of vector addition, considering the magnitudes of the two device coefficients and the angular relationship between them. The calculation of the synthesized device coefficients uses the law of cosines. First, calculate the sum of the squares of the two device coefficients, then add twice the product of the device coefficients and the cosine of the angle, and finally take the square root of the result. Theoretically, the horizontal angle should be 90 degrees, but in actual fieldwork, it is generally between 80 and 100 degrees.

[0131] S503: Calculate the product of the target apparent resistivity and the target device coefficient to obtain the target apparent resistivity;

[0132] Target apparent resistivity refers to the apparent resistivity value obtained through vector synthesis, representing the equivalent resistance characteristics after synthesis; target apparent resistivity refers to the product of target apparent resistivity and target device coefficient, representing the equivalent resistivity characteristics of the underground medium. Apparent resistivity is an important physical parameter characterizing the electrical conductivity of underground media, and its value reflects the electrical differences between different underground rock layers.

[0133] This step calculates the target apparent resistivity using multiplication, providing input data for subsequent inversion calculations. The calculation process directly multiplies the target apparent resistance by the target device coefficient to obtain the target apparent resistivity value. The synthesized apparent resistivity is always positive, effectively solving the problem of negative apparent resistivity that can occur in traditional methods. During the calculation, it is necessary to ensure the consistency of units between the target apparent resistance and the target device coefficient. The unit of the target apparent resistance is ohms, the unit of the target device coefficient is meters, and the unit of the product is ohm-meters, conforming to the dimensional requirements of resistivity. In the numerical calculation process, it is necessary to consider the control of numerical precision and significant digits to avoid calculation errors caused by precision loss. For large-scale data processing, batch calculation is adopted to improve processing efficiency, while data quality control is performed to identify and handle outliers.

[0134] S504: Use the target apparent resistivity to perform inversion calculations to obtain the apparent resistivity distribution results of the exploration area.

[0135] The apparent resistivity distribution result refers to the resistivity distribution characteristics at different locations and depths underground in the exploration area, obtained through inversion calculation. The inversion process is a reverse solution process that reconstructs the true underground electrical structure from surface observation data.

[0136] This step uses the target apparent resistivity as input data for inversion calculation to obtain the distribution of the subsurface electrical structure of the exploration area. The inversion calculation employs numerical optimization algorithms such as the least squares method, minimizing the error between the theoretical response and the observed data through iterative solutions. Using synthesized apparent resistivity for inversion effectively reduces false anomalies around anomalies, improves inversion accuracy, and more closely approximates the electrical distribution of the actual model. The inversion process first establishes a three-dimensional subsurface mesh model, dividing the exploration area into several mesh cells, each assigned an initial resistivity value. Then, the theoretical response is calculated, i.e., the theoretical apparent resistivity value of each observation point is calculated based on the current resistivity model. Next, the objective function is calculated, i.e., the weighted sum of squared residuals between the theoretical response and the observed data. By calculating the Jacobian matrix, i.e., the partial derivative matrix of the objective function with respect to the model parameters, the update direction of the model parameters is determined. Damped least squares method or other optimization algorithms are used to update the model parameters, reducing the objective function value. The above process is repeated until the convergence condition is met or the maximum number of iterations is reached.

[0137] Based on the above embodiments, as an optional implementation, step S504 further includes: defining two apparent resistance components from the vector receiving unit, and determining the composite azimuth angle of the vector receiving unit based on the apparent resistance component of one of the directions.

[0138] Based on the target apparent resistivity and the composite azimuth, the Jacobian matrix of the subsurface resistivity model is calculated;

[0139] The apparent resistivity distribution of the exploration area was obtained by inversion calculation using the Jacobian matrix.

[0140] The Jacobian matrix is:

[0141] ;

[0142] in, This represents the partial derivative of the target apparent resistance with respect to the subsurface resistivity model. This represents the apparent resistance component in the first direction. This represents the apparent resistance component in the second direction. Indicates the target apparent resistance. This represents the horizontal angle between the two apparent resistance components. This represents the partial derivative of the apparent resistivity component in the first direction with respect to the apparent resistivity of the target. This represents the partial derivative of the apparent resistivity component in the second direction with respect to the apparent resistivity of the target. This represents the partial derivative of the composite azimuth angle with respect to the subsurface resistivity model. This represents the partial derivative of the apparent resistivity component in the first direction with respect to the subsurface resistivity model. This represents the partial derivative of the synthesized apparent resistance with respect to the subsurface resistivity model.

[0143] Among them, the apparent resistance component refers to the apparent resistance value measured in each independent direction in the vector receiving unit, reflecting the resistivity characteristics of the underground medium in that direction; the composite azimuth angle refers to the equivalent azimuth angle calculated through vector synthesis, representing the main directional characteristics of the composite apparent resistance; the underground resistivity model refers to a mathematical model that discretizes the underground space of the exploration area into a three-dimensional grid, with each grid cell assigned a corresponding resistivity value; the Jacobian matrix refers to the partial derivative matrix of the objective function with respect to the model parameters, used to describe the sensitivity of the observed data to changes in the underground resistivity model parameters; the partial derivative represents the rate of change of the objective function with respect to a single model parameter, reflecting the degree of influence of the parameter change on the objective function value; the apparent resistivity distribution result is the final output of the inversion calculation, representing the resistivity distribution characteristics of different locations and depths underground in the exploration area in the form of a three-dimensional grid.

[0144] The formula set contains two main partial derivative calculation expressions used to describe the sensitivity relationship between the target apparent resistance and the composite azimuth angle to the parameters of the subsurface resistivity model.

[0145] The left side of the first formula represents the partial derivative of the target apparent resistance with respect to the m-th parameter of the subsurface resistivity model. The right side of the formula is divided into three main parts: the first part is the product of the apparent resistance component in the first direction and the apparent resistance component in the second direction with the cosine of the included angle, divided by the target apparent resistance value, and then multiplied by the partial derivative of the apparent resistance component in the first direction with respect to the m-th parameter; the second part is the product of the apparent resistance component in the second direction and the apparent resistance component in the first direction with the cosine of the included angle, divided by the target apparent resistance value, and then multiplied by the partial derivative of the apparent resistance component in the second direction with respect to the m-th parameter; the third part is the partial derivative of the apparent resistance component in the second direction with respect to the m-th parameter.

[0146] The left side of the second formula represents the partial derivative of the synthesized azimuth angle with respect to the m-th parameter of the subsurface resistivity model. The right side of the formula is the reciprocal of the target apparent resistivity value, multiplied by the expression within square brackets. The square brackets contain two terms: the first term is the partial derivative of the apparent resistivity component in the first direction with respect to the m-th parameter minus the ratio of the apparent resistivity component in the first direction to the target apparent resistivity value, multiplied by the partial derivative of the target apparent resistivity with respect to the m-th parameter; the second term is the partial derivative of the target apparent resistivity with respect to the m-th parameter.

[0147] These two formulas are based on the chain rule and the principle of chain rule for differentiation. The first formula describes the sensitivity of the target apparent resistance to model parameters during vector synthesis. It synthesizes apparent resistance components in two orthogonal directions according to vector operation rules, with the contribution of each component represented by a weighting coefficient. The weighting coefficient considers the magnitude of the component itself and its coupling relationship with the other component; the coupling strength is determined by the cosine of the included angle. When the two components are orthogonal, the coupling term is zero, simplifying to a linear combination of independent components. The second formula describes the sensitivity of the synthesized azimuth angle. Based on the derivative rule of the arctangent function, it decomposes the azimuth angle change into the contribution of each component change.

[0148] In the vector resistivity method data processing of this application, the main purpose of using this set of formulas is to establish a precise mathematical relationship between the observed data and the parameters of the subsurface model, providing a reliable sensitivity matrix for inversion calculations. Traditional scalar resistivity methods can only process resistivity information in a single direction, while vector resistivity methods need to process data from multiple directions simultaneously, requiring a more complex mathematical framework. This set of formulas allows for the simultaneous incorporation of both directional and amplitude information from vector observation data into the inversion calculations, improving the accuracy and reliability of the inversion results. The weighting mechanism in the formulas automatically adjusts the contribution of components from different directions; when the signal in a certain direction is weak or interfered with, the weight of that direction is automatically reduced, avoiding the adverse effects of noise on the inversion results. Simultaneously, the calculation of azimuth sensitivity provides directional constraints for three-dimensional resistivity imaging, enabling more accurate determination of the spatial location and geometry of anomalies, significantly improving the resolution and application effectiveness of resistivity exploration.

[0149] This sequence of steps first defines two apparent resistance components in the vector receiving unit, and then determines the composite azimuth of the vector receiving unit based on one of the apparent resistance components. After obtaining the apparent resistance components in the two orthogonal directions of the vector receiving unit, the apparent resistance component used for calculating the composite azimuth is determined according to a preset selection strategy. The selection strategy includes signal strength comparison, signal-to-noise ratio evaluation, or a preset priority order. The calculation of the composite azimuth is based on the selected apparent resistance component and its corresponding spatial direction angle. The final composite azimuth value is determined through trigonometric functions, typically within the range of 0 to 360 degrees. Next, based on the target apparent resistivity and the composite azimuth, the Jacobian matrix of the subsurface resistivity model is calculated. The calculation process uses the chain rule, decomposing the derivative of the composite function into the product of the derivatives of multiple simple functions. According to the Jacobian matrix calculation formula, the partial derivative of the target apparent resistivity with respect to the subsurface resistivity model is equal to the partial derivative of the weighted sum of the two apparent resistance components. The weighting coefficients are determined by the values ​​of each component and the cosine of the included angle. In the specific calculation, firstly, the partial derivatives of the apparent resistivity components in the first and second directions with respect to each grid cell of the subsurface resistivity model are calculated. Then, weighting coefficients are calculated based on the mathematical relationship of vector synthesis. The partial derivatives of each component are multiplied by the corresponding weighting coefficients and then divided by the target apparent resistivity value to obtain the partial derivative of the target apparent resistivity with respect to the subsurface resistivity model. The partial derivative of the synthesized azimuth angle is calculated using the derivative rule of the arctangent function, combined with the component partial derivatives and the synthesis relationship. Finally, the calculated Jacobian matrix is ​​used for inversion calculation to obtain the apparent resistivity distribution results of the exploration area. The inversion calculation uses the nonlinear least squares method, and the error between the theoretical response and the observed data is minimized through iterative solution. In each iteration, the theoretical observed data is calculated using the current subsurface resistivity model, and the residual vector between the observed data and the theoretical data is calculated. The objective function is the weighted sum of squares of the residual vector. The update direction and step size of the model parameters are calculated using the Jacobian matrix, and the normal equations are solved using the Gauss-Newton method or the Levenberg-Marquardt method to obtain the correction amount of the model parameters.

[0150] Optionally, the receiving electrode at the position forming a horizontal angle in the vector receiving unit is determined as the starting electrode, and the remaining two receiving electrodes in the vector receiving unit are determined as target electrodes, thus obtaining two initial apparent resistances along the direction from the starting electrode to the target electrode.

[0151] The target apparent resistance is obtained by vector synthesis of the initial apparent resistance based on the horizontal angle.

[0152] The specific synthesis formula is as follows:

[0153] ;

[0154] The left side of the formula represents the synthesized target apparent resistance value. The right side of the formula is a square root expression containing three main terms: the first term is the square of the apparent resistance component in the first direction; the second term is the square of the apparent resistance component in the second direction; and the third term is twice the product of the apparent resistance component in the first direction and the apparent resistance component in the second direction, multiplied by the cosine of the angle between the two components.

[0155] In the vector resistivity data processing of this application, the main purpose of using this synthesis formula is to obtain stable and reliable apparent resistivity values, avoiding the limitations and instability of single-directional components. When the value of a component in a certain direction is particularly small, contaminated by noise, or has poor signal quality, the weight of that component in the synthesis process is automatically reduced. The synthesized apparent resistivity value is mainly determined by the component with better signal quality, thereby greatly reducing the weight of small-value components and avoiding their adverse effects on subsequent inversion calculations. This processing method has an adaptive filtering effect, which can automatically identify and suppress abnormal data and improve the overall data quality. At the same time, the synthesized apparent resistivity value integrates the subsurface electrical information from two directions, which is more comprehensive and reliable than the measurement results from a single direction, providing higher-quality input data for subsequent three-dimensional resistivity imaging.

[0156] In one embodiment of this application, a specific experiment is provided to verify the effectiveness of the technical solution provided in this application. Please refer to [link / reference]. Figure 2 , Figure 2 A color diagram illustrating the model space and planar position provided for an embodiment of this application.

[0157] like Figure 2 As shown, the model includes five anomalies, each a cube. The background resistivity of the model is 100 Ω·m. Anomalies 1 and 2 are low-resistivity bodies, corresponding to the blue areas in the diagram, with a resistivity of 50 Ω·m. Anomalies 3, 4, and 5 are high-resistivity bodies, corresponding to the red areas in the diagram, with a resistivity of 500 Ω·m. The range of anomaly 1 is X: 4~9m, Y: 6~9m, Z: -1~-0.2m; the range of anomaly 2 is X: 13-19m, Y: 5-11m, Z: -4~-1.2m; the range of anomaly 3 is X: 1~5m, Y: 2~6m, Z: -2.2~-0.2m; the range of anomaly 4 is X: 7~11m, Y: 1~4m, Z: -1.5~-0.1m; and the range of anomaly 5 is X: 17~21m, Y: 2~6m, Z: -1.2~-0.1m.

[0158] Forward modeling was performed using the aforementioned model and apparatus, and a 5% Gaussian error was added to the forward modeling results to synthesize the simulation data. The simulation data was then inverted using the least squares method. Method one involved removing small values ​​from vector elements whose apparent resistivity differed by more than a factor in two directions, followed by a standard inversion. Method two involved calculating the synthesized apparent resistivity and synthesized azimuth angle for all vector elements using the method described in the previous technical solution. The inversion parameters and iteration counts were kept consistent for both methods. The results are as follows: Figure 3 As shown, Figure 3 A color schematic diagram of an inversion result provided in an embodiment of this application.

[0159] Figure 3 In the middle (a), the original inversion result is shown. Figure 3 In (b), the horizontal slice images at different depths obtained by the two inversion methods are compared to correct the inversion results (solid boxes indicate that the anomalous body exists at the slice depth, and dashed boxes indicate that the anomalous body does not exist at the slice depth). By comparing the inversion results, it can be seen that using the synthesized apparent resistance and azimuth angle for inversion can effectively reduce false anomalies around the anomalous body, improve the inversion accuracy, and more closely approximate the electrical distribution of the real model.

[0160] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of the application.

[0161] Please see Figure 4 This illustration shows a schematic diagram of an apparent resistance correction system based on resistivity method provided in an exemplary embodiment of this application. The system can be implemented as all or part of a system through software, hardware, or a combination of both. An apparent resistance correction system based on resistivity method includes:

[0162] The map feature analysis module is used to obtain the feature map of the exploration area, determine the attribute labels corresponding to different feature areas in the feature map, and the attribute labels represent the degree of influence of different feature areas on resistivity exploration.

[0163] The electrode group division module is used to map the placement positions of multiple transmitting electrodes and multiple receiving electrodes to the feature area, and divide multiple electrode groups according to attribute tags. Each electrode group contains multiple vector receiving units, and each vector receiving unit consists of three receiving electrodes forming a horizontal angle.

[0164] The first processing module is used to match the preset optimization targets corresponding to the attribute labels of the electrode group and generate the group control scheme corresponding to the electrode group.

[0165] The second processing module is used to adjust the intra-group control scheme according to the inter-group influence factors between adjacent electrode groups, generate the target control scheme corresponding to each electrode group, and collect the received voltage of the vector receiving unit after executing the target control scheme.

[0166] The calculation module is used to calculate the initial apparent resistance corresponding to the vector receiving unit based on the vector receiving voltage, and to perform vector synthesis of the initial apparent resistance based on the horizontal angle to obtain the target apparent resistance;

[0167] The inversion module is used to perform inversion calculations using the target apparent resistivity as input data to obtain the apparent resistivity distribution results of the exploration area.

[0168] This application also provides a computer storage medium that can store multiple instructions. The instructions are adapted to be loaded and executed by a processor as described above for the apparent resistance correction method based on resistivity. For the specific execution process, please refer to the detailed description of the embodiments, which will not be repeated here.

[0169] Please see Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 300 may include: at least one processor 301, at least one network interface 304, user interface 303, memory 305, and at least one communication bus 302.

[0170] The communication bus 302 is used to enable communication between these components.

[0171] The user interface 303 may include a display screen and a camera.

[0172] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0173] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of digital signal processing, field-programmable gate array, or programmable logic array. The processor 301 may integrate one or more of the following: a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0174] The memory 305 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 305 may include a non-transitory computer-readable medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. Figure 5 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for an apparent resistance correction method based on resistivity.

[0175] exist Figure 5 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 301 can be used to call an application program stored in the memory 305 that is a resistivity-based apparent resistance correction method. When executed by one or more processors, the electronic device executes one or more methods as described in the above embodiments.

[0176] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and practical application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure.

Claims

1. A method for correcting apparent resistance based on resistivity, characterized in that, The method includes: Obtain a feature map of the exploration area, determine the attribute labels corresponding to different feature regions in the feature map, and the attribute labels characterize the degree of influence of different feature regions on resistivity exploration. The placement positions of multiple transmitting electrodes and multiple receiving electrodes are mapped to the feature region, and multiple electrode groups are divided according to the attribute tags. Each electrode group contains multiple vector receiving units, and each vector receiving unit consists of three receiving electrodes forming a horizontal angle. Match the preset optimization targets corresponding to the attribute tags of the electrode group to generate the group-specific control scheme for the electrode group. Based on the inter-group influence factors between adjacent electrode groups, the intra-group control scheme is adjusted to generate a target control scheme for each electrode group, and the received voltage of the vector receiving unit is collected after the target control scheme is executed. Calculate the initial apparent resistance corresponding to the vector receiving unit based on the vector received voltage, and perform vector synthesis on the initial apparent resistance based on the horizontal angle to obtain the target apparent resistance; The apparent resistivity of the target area is used as input data for inversion calculation to obtain the apparent resistivity distribution results of the exploration area.

2. The method according to claim 1, characterized in that, The step of using the target apparent resistivity as input data for inversion calculation to obtain the apparent resistivity distribution result of the exploration area includes: Obtain the first device coefficient and the second device coefficient corresponding to the two received components in the vector receiving unit, respectively; The first device coefficient and the second device coefficient are vector-synthesized based on the horizontal angle to obtain the target device coefficient; The target apparent resistivity is obtained by multiplying the target apparent resistivity by the target device coefficient. The apparent resistivity distribution of the exploration area is obtained by inversion calculation using the target apparent resistivity.

3. The method according to claim 2, characterized in that, The inversion calculation using the target apparent resistivity to obtain the apparent resistivity distribution results of the exploration area includes: Two apparent resistance components are defined from the vector receiving unit, and the composite azimuth angle of the vector receiving unit is determined based on the apparent resistance component in one of the directions. Based on the target apparent resistivity and the synthetic azimuth, calculate the Jacobian matrix of the subsurface resistivity model; The apparent resistivity distribution of the exploration area was obtained by inversion calculation using the Jacobian matrix. The Jacobian matrix is: ； in, This represents the partial derivative of the target apparent resistance with respect to the subsurface resistivity model. This represents the apparent resistance component in the first direction. This represents the apparent resistance component in the second direction. This indicates the target apparent resistance. This represents the horizontal angle between the two apparent resistance components. This represents the partial derivative of the apparent resistance component in the first direction with respect to the target apparent resistivity. This represents the partial derivative of the apparent resistivity component in the second direction with respect to the target apparent resistivity. This represents the partial derivative of the synthesized azimuth angle with respect to the subsurface resistivity model. This represents the partial derivative of the apparent resistance component in the first direction with respect to the subsurface resistivity model. This represents the partial derivative of the synthesized apparent resistance with respect to the subsurface resistivity model.

4. The method according to claim 1, characterized in that, The step of vector synthesis of the initial apparent resistance based on the horizontal angle to obtain the target apparent resistance includes: The receiving electrode at the position forming the horizontal angle in the vector receiving unit is determined as the starting electrode, and the remaining two receiving electrodes in the vector receiving unit are determined as target electrodes, thus obtaining two initial apparent resistances along the direction from the starting electrode to the target electrode; The target apparent resistance is obtained by vector synthesis of the initial apparent resistance based on the horizontal angle.

5. The method according to claim 1, characterized in that, The process of acquiring a feature map of the exploration area and determining the attribute labels corresponding to different feature regions in the feature map includes: Acquire topographic and geomorphological data and surface cover data of the exploration area, and construct a feature map of the exploration area based on the topographic and geomorphological data and the surface cover data; Based on the influence of the topographic data and the surface cover data on resistivity exploration, the feature map is divided into regions to obtain multiple feature regions. Each of the aforementioned feature regions is assigned a corresponding attribute label, which includes topographic relief, geological complexity, and surface conductivity.

6. The method according to claim 1, characterized in that, The process of matching the attribute tags of the electrode group with preset optimization targets to generate an intra-group control scheme for the electrode group includes: Match the attribute tags of the electrode group with preset optimization targets from a preset test database. The preset optimization targets include a signal-to-noise ratio threshold, a measurement progress threshold, and a signal strength threshold. According to the preset optimization target, adjust the power supply parameters of the transmitting electrode and the sampling parameters of the receiving electrode in the electrode group; Calculate the relative positional relationship between each vector receiving unit in the electrode group, and determine the sampling timing of each vector receiving unit based on the relative positional relationship; Based on the power supply parameters, the sampling parameters, and the sampling timing, an intra-group control scheme is generated for the electrode group.

7. The method according to claim 1, characterized in that, The inter-group influencing factors include the voltage gradient change rate and the distance between electrodes. The step of adjusting the intra-group control scheme based on the inter-group influencing factors between adjacent electrode groups to generate a target control scheme for each electrode group includes: Calculate the rate of change of voltage gradient at the boundary of adjacent electrode groups, and measure the actual distance between electrodes at the boundary of adjacent electrode groups; When the voltage gradient change rate is greater than a preset change rate threshold and the actual distance is less than a preset distance, the boundary region corresponding to the actual distance is set as a transition region. Adjust the working time interval of the electrode group in the transition region, and set the working timing of the electrode group in the transition region to an alternating start mode; The vector receiving unit in the transition region is set to a dual data acquisition mode to superimpose the acquired signals of the electrode group in the transition region. Based on the alternating start mode and the dual data acquisition mode, update the corresponding parameter settings in the group control scheme to generate the target control scheme corresponding to the transition region.

8. A system for correcting apparent resistance based on resistivity method, characterized in that, The system includes: The map feature analysis module is used to acquire a feature map of the exploration area and determine the attribute labels corresponding to different feature regions in the feature map. The attribute labels represent the degree of influence of different feature regions on resistivity exploration. An electrode group division module is used to map the placement positions of multiple transmitting electrodes and multiple receiving electrodes to the feature region, and to divide multiple electrode groups according to the attribute tags. Each electrode group contains multiple vector receiving units, and each vector receiving unit consists of three receiving electrodes forming a horizontal angle. The first processing module is used to match the preset optimization target corresponding to the attribute label of the electrode group and generate the group control scheme corresponding to the electrode group. The second processing module is used to adjust the intra-group control scheme according to the inter-group influence factors between adjacent electrode groups, generate a target control scheme corresponding to each electrode group, and collect the received voltage of the vector receiving unit after executing the target control scheme. The calculation module is used to calculate the initial apparent resistance corresponding to the vector receiving unit based on the vector receiving voltage, and to perform vector synthesis on the initial apparent resistance based on the horizontal angle to obtain the target apparent resistance; The inversion module is used to perform inversion calculations using the target apparent resistivity as input data to obtain the apparent resistivity distribution results of the exploration area.

9. A computer storage medium, characterized in that, The computer storage medium stores a plurality of instructions, which are adapted to be loaded by a processor and executed as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The device includes a processor, a memory, and a transceiver, wherein the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 7.

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