Geological abnormal region identification method and system based on earth resistivity method

Abstract

The invention belongs to the technical field of geological exploration, and provides a geological abnormal area identification method and system based on a ground resistivity method, which are characterized in that the correlation degree between a plurality of longitudinal electrode distances and horizontal exploration is analyzed and evaluated on the basis of the longitudinal electrode distances in a longitudinal electrode distance interval, and if a transverse tight signal is generated, the longitudinal electrode distances can be identified. The method comprises the following steps: constructing a transverse correlation model, obtaining a transverse electrode formula, obtaining a preset detection horizontal range, substituting the preset detection horizontal range into the transverse electrode formula, obtaining a transverse electrode distance interval, carrying out comprehensive analysis on the transverse electrode distance interval and a longitudinal electrode distance interval, and determining an optimal electrode distance, thereby comprehensively considering factors of two dimensions of horizontal detection and depth detection. The influence of different dimensions is eliminated through standardization, so that when the optimal electrode distance is determined, the horizontal and depth detection effects are balanced, and the finally obtained optimal electrode distance can better meet the comprehensive requirements of geological abnormal region identification on horizontal and depth detection.

Description

Technical Field

[0001] The present invention belongs to the technical field of geological exploration, and specifically relates to a method and system for identifying geological anomaly areas based on the geoelectric resistivity method. Background Technique

[0002] As a commonly used geological exploration method, the geoelectric resistivity method utilizes the different resistivity characteristics of different geological bodies and controls the detection depth by changing the electrode spacing. Common measurement devices include the symmetric four-electrode device, dipole device, and triode device. However, in practical applications, changing the electrode spacing not only affects the detection depth but also affects the lateral resolution. Therefore, how to determine the electrode spacing suitable for identifying geological anomaly areas has become a key issue in improving the accuracy and effectiveness of geological anomaly area identification.

[0003] A Chinese patent application with the application number CN117805915A discloses a method and system for identifying geological anomaly areas based on the induced polarization method. By calculating the anomaly characteristics of the induced polarization method, the weak physical property anomalies are enhanced, and the accuracy of anomaly identification is improved. However, when controlling the detection depth by changing the electrode spacing through the geoelectric resistivity method, it not only affects the detection depth but also affects the lateral resolution. Therefore, by separately analyzing the correlation degrees between the electrode spacing and the detection depth and the detection horizontal degree, the longitudinal electrode sequence and the lateral electrode sequence are obtained, and the two are combined and analyzed to obtain the optimal electrode spacing, which solves the problem that it is difficult to determine the electrode spacing when identifying geological anomaly areas. At the same time, by weighing the effects of horizontal and depth detection, the finally obtained optimal electrode spacing can better meet the comprehensive requirements of geological anomaly area identification in horizontal and depth detection.

[0004] Therefore, the present invention provides a method and system for identifying geological anomaly areas based on the geoelectric resistivity method. Summary of the Invention

[0005] In order to make up for the deficiencies of the prior art and solve at least one of the technical problems proposed in the background technique.

[0006] The technical solution adopted by the present invention to solve its technical problems is as follows: In a first aspect, a method for identifying geological anomaly areas based on the geoelectric resistivity method includes the following steps: According to multiple historical detection reports, based on the electrode spacing and detection depth of each historical detection, a historical electrode sequence and a historical depth sequence are respectively constructed, and the longitudinal correlation degree between the electrode spacing and depth detection is evaluated; If the longitudinal correlation degree is close, obtain the longitudinal electrode formula, and obtain the longitudinal electrode spacing interval according to the required depth detection range; Based on the longitudinal electrode distance within the longitudinal electrode distance range and the corresponding detection level each time, analyze and evaluate the horizontal correlation degree between the longitudinal electrode distance and the detection level; If the horizontal correlation is tight, obtain the horizontal electrode formula, and according to the required detection level range, obtain the horizontal electrode distance range, and perform an intersection analysis with the longitudinal electrode distance range to determine the optimal electrode distance.

[0007] Preferably, the process of evaluating the longitudinal correlation degree between the electrode distance and the depth detection is as follows: Extract the electrode distance and the detection depth from the historical detection reports respectively, and sort them according to the time series to construct the historical electrode sequence and the historical depth sequence respectively; In the historical electrode sequence and the historical depth sequence, combine the adjacent sorted electrode distances and detection depths respectively to obtain the adjacent electrode groups and the adjacent depth groups; Output each adjacent electrode group and the corresponding adjacent depth group through the Spearman rank correlation coefficient model to obtain the longitudinal correlation value; If the longitudinal correlation value is within or within , , generate a longitudinal tight signal.

[0008] Preferably, the method for obtaining the longitudinal electrode formula by the least squares method is as follows: Construct the longitudinal curve of the electrode, and arbitrarily connect the coordinates of two points on the longitudinal curve to obtain the longitudinal suspected fitting line and the longitudinal suspected fitting equation; Substitute the X coordinates in the coordinates of all points on the longitudinal curve into the longitudinal suspected fitting equation to obtain multiple suspected fitting coordinates respectively, and input them into the suspected fitting analysis model together with the Y coordinates in the coordinates of all points on the longitudinal curve to output the longitudinal suspected analysis value; Extract the longitudinal suspected fitting line corresponding to the minimum longitudinal suspected analysis value as the reference fitting line, and use the fitting equation corresponding to the reference fitting line as the longitudinal electrode formula.

[0009] Preferably, the process of obtaining the longitudinal electrode distance range is as follows: Input the two endpoint values of the required depth detection range into the longitudinal electrode formula respectively to obtain the longitudinal electrode distance range.

[0010] Preferably, the process of evaluating the horizontal correlation degree between the longitudinal electrode distance and the detection level is as follows: Arbitrarily select a longitudinal electrode distance from the longitudinal electrode distance range, and according to the historical detection reports, obtain the detection level corresponding to the longitudinal electrode distance, and sort them according to the size of the corresponding longitudinal electrode distance to obtain the detection level sequence; Process the vertical electrode spacing and the detection levelness respectively through the Spearman rank correlation coefficient model, and output the obtained horizontal correlation value; If the horizontal correlation value is within or within , , a horizontal tight signal is generated.

[0011] Preferably, the acquisition method of the horizontal electrode formula is as follows: Construct a horizontal correlation model, connect the coordinates of any two points on the horizontal correlation model, and obtain multiple horizontal suspected fitting lines; Arbitrarily select a horizontal suspected fitting line to obtain a horizontal suspected fitting equation, and according to the X values of all the point coordinates on the horizontal correlation model, obtain the Y coordinates within all the point coordinates on the horizontal suspected fitting line, and combine the Y coordinates within all the point coordinates on the horizontal correlation model, and output the obtained horizontal suspected analysis value through the suspected fitting analysis model; Extract the horizontal suspected fitting line corresponding to the minimum horizontal suspected analysis value as the horizontal reference line, that is, the corresponding horizontal suspected fitting equation as the horizontal electrode formula.

[0012] Preferably, the acquisition method of the horizontal electrode spacing interval is as follows: If the slope in the horizontal electrode formula is negative, input the two endpoint values in the preset detection level range into the horizontal electrode formula to obtain the horizontal electrode spacing interval.

[0013] Preferably, the intersection analysis of the horizontal electrode spacing interval and the vertical electrode spacing interval is as follows: Perform minimum-maximum normalization processing on the intersection electrode interval, the intersection level interval, and the intersection depth interval to obtain the detection level normalization value and the detection depth normalization value respectively, and sum them to obtain the best electrode judgment value.

[0014] Preferably, the acquisition method of the best electrode spacing is as follows: Extract the electrode spacing corresponding to the maximum best electrode judgment value as the best electrode spacing.

[0015] In a second aspect, a geological anomaly area identification system based on the georesistivity method includes: Vertical correlation analysis module: According to multiple historical detection reports, respectively construct a historical electrode sequence and a historical depth sequence based on the electrode spacing and detection depth of each historical detection, and evaluate the vertical correlation degree between the electrode spacing and the depth detection; Vertical interval processing module: If the vertical correlation degree is tight, obtain the vertical electrode formula, and according to the required depth detection range, obtain the vertical electrode spacing interval; Horizontal correlation analysis module: Based on the vertical electrode spacing within the vertical electrode spacing range and the corresponding horizontal detection level each time, analyze and evaluate the horizontal correlation degree between the vertical electrode spacing and the detection level; Optimal electrode determination module: If the horizontal correlation is tight, obtain the horizontal electrode formula, and based on the required horizontal detection range, obtain the horizontal electrode spacing range, and perform intersection analysis with the vertical electrode spacing range to determine the optimal electrode spacing.

[0016] The beneficial effects of the present invention are as follows: 1. According to multiple historical detection reports, the present invention obtains the electrode spacing and detection depth of each historical detection, and evaluates the longitudinal correlation degree between the electrode spacing and depth detection. If the correlation degree is tight, the historical electrode sequence and historical depth sequence are fitted by the least squares method to obtain the longitudinal electrode formula, and based on the required depth detection range, the vertical electrode spacing range is obtained, so that the longitudinal curve of the electrode can be more robustly fitted under various data distribution conditions, accurately find the curve that can represent the data trend, and thus better identify the geological anomaly area. Moreover, the reasonable value range of the electrode spacing corresponding to a specific depth range helps to determine the electrode spacing range to be used when detecting a specific depth, improving the effectiveness of identifying the geological anomaly area; 2. Based on the vertical electrode spacing within the vertical electrode spacing range, the present invention analyzes and evaluates the correlation degree between multiple vertical electrode spacings and horizontal detection. If a horizontal tight signal is generated, a horizontal correlation model is constructed, the horizontal electrode formula is obtained, the preset detection horizontal range is substituted into the horizontal electrode formula to obtain the horizontal electrode spacing range, and comprehensive analysis is performed with the vertical electrode spacing range to determine the optimal electrode spacing. Therefore, the factors of horizontal detection and depth detection in two dimensions are comprehensively considered, and the influence of different dimensions is eliminated through standardization, so that when determining the optimal electrode spacing, the effects of horizontal and depth detection are weighed, and the finally obtained optimal electrode spacing can better meet the comprehensive requirements of horizontal and depth detection for identifying the geological anomaly area. Description of the Drawings

[0017] The present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 is the step flow chart of a method for identifying geological anomaly areas based on the ground resistivity method of the present invention; Figure 2 is the structure diagram of a system for identifying geological anomaly areas based on the ground resistivity method of the present invention. Specific Embodiments

[0019] In order to make the technical means, creative features, achieved purposes and effects of the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0020] Example 1 As Figure 1 shown, a method for identifying geological anomaly areas based on the geoelectric resistivity method according to an embodiment of the present invention, wherein the geoelectric resistivity method is based on different resistivity characteristics of different geological bodies, and the detection depth is controlled by changing the electrode spacing. Usually, the measuring devices include a symmetric quadrupole device, a dipole device, a triode device, etc. However, changing the electrode spacing will not only affect the detection depth but also affect the lateral resolution. Therefore, in the process of identifying geological anomaly areas through the measuring device, it is urgent to determine the electrode spacing suitable for identifying geological anomaly areas. The specific implementation process is as follows, including: Step 1: According to multiple historical detection reports, obtain the electrode spacing and detection depth of each historical detection, and evaluate the longitudinal correlation degree between the electrode spacing and depth detection; It should be noted that the historical detection report includes the electrode spacing, detection depth, and detection level; Exemplarily, taking the historical detection report as an Excel table as an example, the electrode spacing, detection depth, and detection level are respectively screened through the data screening function; Extract the electrode spacing and detection depth respectively, and sort them according to the time series to construct a historical electrode sequence and a historical depth sequence respectively; It should be noted that the sorting of the electrode spacing in the historical electrode sequence corresponds one-to-one with the sorting of the detection depth in the historical depth sequence in the time dimension, and the number of elements in the historical electrode sequence and the historical depth sequence is equal; For example, if the sorting of the electrode spacing in the historical electrode sequence is the first, the detection depth corresponding to the electrode spacing in the historical electrode sequence is the first in the sorting of the historical depth sequence; In the historical electrode sequence, combine the adjacent electrode spacings to obtain adjacent electrode groups, and extract the detection depths corresponding to the adjacent electrode spacings from the historical depth sequence respectively to form adjacent depth groups; Output each adjacent electrode group and the corresponding adjacent depth group through the Spearman rank correlation coefficient model to obtain a longitudinal correlation value. The specific implementation process is as follows: Perform a difference process on the adjacent detection depths in the adjacent depth group, and output to obtain the depth rank difference ; Input all the depth rank differences into the Spearman rank correlation coefficient model (Equation 1) to obtain the longitudinal correlation value ; Specifically, Equation 1: ; Wherein, represents the total number of elements in the historical depth sequence, represents the sum of the squares of the depth rank differences; In detail, the role of the Spearman rank correlation coefficient model is: Function 1: From the essence of the Spearman rank correlation coefficient model, the Spearman rank correlation coefficient is a non-parametric statistical method, which does not require the data to obey a specific distribution. In geological detection by the georesistivity method, the data distribution of electrode distance and detection depth is often complex and diverse. Therefore, it can adapt to complex data processing situations without preprocessing or converting the data, thereby improving the efficiency of data processing; Function 2: It can be used to detect the monotonic relationship between variables (electrode distance and depth detection). Therefore, in the process of geological detection by georesistivity method, whether it is a linear relationship or a nonlinear relationship, it can reflect the true degree of correlation between electrode distance and depth detection. It can be understood that the meaning of the longitudinal correlation value is: the quantitative index calculated by the Spearman rank correlation coefficient model of the electrode distance and the detection depth data in the historical detection report is used to measure the degree of correlation between the electrode distance and the depth detection. Specifically, when the longitudinal correlation value is in a specific interval (although part of the interval content in the question is suspected to be missing, it can be inferred that it is two specific intervals), it indicates that the longitudinal correlation between the electrode distance and the depth detection is close, and there is a strong monotonic correlation (positive or negative correlation) between the two, and a longitudinal close signal will be generated at this time; when the longitudinal correlation value is 0, it means that the longitudinal correlation between the electrode distance and the depth detection is distant, and there is no obvious monotonic correlation between the two, and a longitudinal weak signal will be generated; If the vertical correlation value is or in , If the value is within , it means that the longitudinal correlation between the electrode distance and the depth detection is close, and a longitudinal close signal is generated; If the longitudinal correlation value is 0, it means that the longitudinal correlation between the electrode and the depth detection is remote, the longitudinal correlation is weak, and a longitudinal weak signal is generated; Step 2: If a longitudinal tight signal is generated, the longitudinal electrode formula is obtained by fitting the historical electrode sequence and the historical depth sequence through the least square method, and the longitudinal electrode distance interval is obtained according to the required depth detection range; A two-dimensional coordinate system is constructed with the X-axis as the electrode distance and the Y-axis as the detection depth; Substitute the historical electrode sequence and historical depth sequence into the two-dimensional coordinate system to obtain the electrode longitudinal curve; The electrode longitudinal curve is fitted by the least square method, and the process is as follows: Exemplarily, the coordinates of all points on the electrode longitudinal curve are extracted, and the coordinates of any two points on the electrode longitudinal curve are connected to obtain a longitudinal suspected fitting line, that is, the longitudinal suspected fitting equation is: ; in, is expressed as the slope of the longitudinal suspected fitting line, is expressed as a constant; Substitute the X coordinates within the coordinates of all points on the longitudinal curve of the electrode into the longitudinal suspected fitting equation to respectively obtain multiple suspected fitting coordinates (X, ), where is expressed as the total number of point coordinates on the longitudinal suspected fitting line and is equal to the total number of point coordinates on the longitudinal curve of the electrode, is expressed as the th Y coordinate of a point on the longitudinal suspected fitting line; Obtain the Y coordinates within the coordinates of all points on the longitudinal curve of the electrode and the coordinates within multiple suspected fitting coordinates, and respectively input them into the suspected fitting analysis model to output the longitudinal suspected analysis value; The specific execution process is as follows: B1. Based on the X coordinates of the point coordinates on the longitudinal curve of the electrode, combine the Y coordinates of the curve with the same X coordinate and the suspected fitting coordinates to obtain multiple Y coordinate analysis groups; B2. Input multiple Y coordinate analysis groups into the suspected fitting analysis model (Equation 2) respectively to output the longitudinal suspected analysis value; More specifically, Equation 2: ; where is expressed as the distance between the Y coordinate on the curve with the same X coordinate and the suspected fitting coordinates, is expressed as the total number of point coordinates on the longitudinal curve of the electrode; It should be noted that , where is expressed as the Y coordinate within the th point coordinate on the longitudinal curve of the electrode, is expressed as the Y coordinate within the th point coordinate on the longitudinal suspected fitting line, is expressed as the total number of point coordinates on the longitudinal curve of the electrode or the total number of point coordinates on the longitudinal suspected fitting line; Specifically, the suspected fitting analysis model is constructed by combining the Manhattan distance formula principle and the least squares method principle, and its function is: Function 1: The Manhattan distance has strong adaptability to data distribution. Unlike some methods based on the Euclidean distance that have strict requirements on data normality, etc., after combining with the least squares method, it can more robustly fit the longitudinal curve of the electrode in various data distribution situations, accurately find the curve that can represent the data trend, thereby better identifying geological anomaly areas and avoiding fitting deviations when dealing with non-normally distributed data, which may lead to misjudgment or missed judgment of anomaly areas; Function 2: When measuring the difference between data points, the Manhattan distance highlights the absolute difference in coordinate components. In the identification of geological anomaly areas based on the geoelectric resistivity method, it helps to highlight the variation differences between the detection depths corresponding to electrode spacings at different positions; It can be further understood that the meaning represented by the longitudinal suspected analysis value is: used to measure the fitting degree of the longitudinal suspected fitting line to the longitudinal curve of the electrode, reflecting the overall deviation degree between the actual data points on the longitudinal curve of the electrode and the corresponding points on the longitudinal suspected fitting line. Specifically, if the longitudinal suspected analysis value is smaller, it indicates that the longitudinal suspected fitting line is closer to the longitudinal curve of the electrode, which helps to improve the accuracy of identifying geological anomaly areas based on the geoelectric resistivity method; Compare the magnitudes of the longitudinal suspected analysis values corresponding to all longitudinal suspected fitting lines, extract the longitudinal suspected fitting line corresponding to the minimum longitudinal suspected analysis value as the reference fitting line, and use the fitting equation corresponding to the reference fitting line as the longitudinal electrode formula; Input the two endpoint values of the required depth detection range into the longitudinal electrode formula respectively to obtain the longitudinal electrode spacing interval; The meaning represented by the longitudinal electrode spacing interval is: it defines the reasonable value range of the electrode spacing corresponding to a specific depth range during geological exploration, helps to determine the electrode spacing range to be used during detection at a specific depth, improves the accuracy and effectiveness of identifying geological anomaly areas, and the two endpoint values within the longitudinal electrode spacing interval are closed intervals; The solution of this embodiment is summarized as follows: According to multiple historical detection reports, obtain the electrode spacing and detection depth of each historical detection, and evaluate the longitudinal correlation between the electrode spacing and depth detection. If the correlation is close, fit the historical electrode sequence and historical depth sequence by the least squares method to obtain the longitudinal electrode formula, and obtain the longitudinal electrode spacing interval according to the required depth detection range, so as to more robustly fit the longitudinal curve of the electrode in various data distribution situations, accurately find the curve that can represent the data trend, thereby better identifying geological anomaly areas, and the reasonable value range of the electrode spacing corresponding to a specific depth range helps to determine the electrode spacing range to be used during detection at a specific depth, improving the effectiveness of identifying geological anomaly areas.

[0021] Embodiment 2 As Figure 1As shown in the figure, based on Embodiment 1, a method for identifying geological anomaly areas based on the ground resistivity method according to an embodiment of the present invention further includes the following steps: Step 3: Based on the longitudinal electrode spacing within the longitudinal electrode spacing range, analyze and evaluate the correlation degree between multiple longitudinal electrode spacings and horizontal detection; In some embodiments, randomly select a longitudinal electrode spacing from within the longitudinal electrode spacing range, and according to the historical detection report, obtain the detection level corresponding to the longitudinal electrode spacing; Sort the detection levels corresponding to all longitudinal electrode spacings within the longitudinal electrode spacing range according to the size of the corresponding longitudinal electrode spacing to obtain a detection level sequence; It should be noted that the total number of elements in the detection level sequence is equal to the total number of longitudinal electrode spacings within the longitudinal electrode spacing range; Input all longitudinal electrode spacings within the longitudinal electrode spacing range and all detection levels within the detection level sequence into the Spearman rank correlation coefficient model (Equation 1) to obtain a horizontal correlation value; The specific execution process is as follows: C1. Within the longitudinal electrode spacing range, combine adjacent sorted longitudinal electrode spacings to obtain adjacent analysis groups, and extract the detection levels corresponding to the adjacent longitudinal electrode spacings from within the longitudinal electrode spacing region to form adjacent level groups; C2. Perform a difference process on adjacent detection levels within the adjacent level group, and output to obtain a horizontal rank difference ; C3. Input all horizontal rank differences into the Spearman rank correlation coefficient model (Equation 1), and output to obtain a horizontal correlation value ; Specifically, Equation 1: ; Among them, e represents the total number of longitudinal electrode spacings within the longitudinal electrode spacing range, represents the sum of the squares of the horizontal rank differences; It can be understood that the meaning represented by the horizontal correlation value is: used to measure the correlation degree between the longitudinal electrode spacing and horizontal detection, which helps to further clarify the relationship between the electrode spacing and horizontal detection during the identification of geological anomaly areas based on the ground resistivity method, and provides a basis for more accurately selecting a suitable electrode spacing to improve the accuracy and effectiveness of geological anomaly area identification; Compare the horizontal correlation value with the horizontal correlation threshold, and the process is as follows: If the horizontal correlation value is within or within , , it indicates that the correlation degree between the longitudinal electrode spacing and the detection level is tight, and a horizontal tight signal is generated; If the horizontal correlation value is 0, it indicates that the correlation between the vertical electrode distance and the detection level is weak, and a horizontal weak signal is generated. Step 4: If a horizontal tight signal is generated, construct a horizontal correlation model, obtain a horizontal electrode formula, substitute the preset detection level range into the horizontal electrode formula to obtain a horizontal electrode distance interval, and perform comprehensive analysis with the vertical electrode distance interval to determine the optimal electrode distance. In a preferred embodiment, with the X-axis as the vertical electrode distance and the Y-axis as the detection level, construct a horizontal correlation model. Fit the horizontal correlation model by the least squares method, and the process is as follows: Exemplarily, extract all the point coordinates on the horizontal correlation model, connect any two point coordinates on the horizontal correlation model to obtain multiple horizontal suspected fitting lines. Arbitrarily select a horizontal suspected fitting line to obtain a horizontal suspected fitting equation: ; Wherein, represents the slope of the horizontal correlation model, represents a constant; Substitute the X values of all the point coordinates on the horizontal correlation model into the horizontal suspected fitting equation to respectively obtain multiple horizontal suspected fitting Y coordinates , wherein, represents the total number of point coordinates on the horizontal suspected fitting line, and is equal to the total number of point coordinates on the horizontal correlation model, represents the th Y coordinate of a point on the horizontal suspected fitting line; Obtain the Y coordinates of all the point coordinates on the horizontal correlation model and the Y coordinates of all the point coordinates on the horizontal suspected fitting line, respectively input them into the suspected fitting analysis model, and output to obtain a horizontal suspected analysis value. Compare the magnitudes of the horizontal suspected analysis values corresponding to all the horizontal suspected fitting lines, extract the horizontal suspected fitting line corresponding to the minimum horizontal suspected analysis value as the horizontal reference line, that is, the horizontal suspected fitting equation corresponding to the horizontal reference line as the horizontal electrode formula. If the slope in the horizontal electrode formula is positive, it indicates that there is a positive correlation between the vertical electrode distance and the horizontal detection; If the slope in the horizontal electrode formula is negative, it indicates that there is a negative correlation between the vertical electrode distance and the horizontal detection, then input the two endpoint values on the preset detection level range into the horizontal electrode formula to obtain a horizontal electrode distance interval. Perform an intersection process on the horizontal electrode distance interval and the vertical electrode distance interval to form an intersection electrode distance interval; Obtain the detection level and detection depth corresponding to the electrode distance within the intersection electrode distance interval, and construct an intersection horizontal interval and an intersection depth interval; Perform min-max normalization on the intersection electrode interval, intersection horizontal interval, and intersection depth interval. The specific implementation process is as follows: S1. Perform pre-normalization processing on the intersection horizontal interval and intersection depth interval respectively; S2. Extract the maximum detection level and minimum detection level within the intersection horizontal interval respectively. Similarly, extract the maximum detection depth and minimum detection depth within the intersection depth interval respectively; S3. Input the maximum detection level and minimum detection level into the min-max normalization formula to obtain the detection level normalization value; S4. Input the maximum detection depth and minimum detection depth into the min-max normalization formula and output the detection depth normalization value; S5. Sum the detection level normalization value and detection depth normalization value corresponding to each electrode distance within the intersection electrode interval to obtain the optimal electrode judgment value; S6. Compare the sizes of the optimal electrode judgment values corresponding to all electrode distances within the intersection electrode interval, and extract the electrode distance corresponding to the maximum optimal electrode judgment value as the optimal electrode distance; The solution of this embodiment is summarized as follows: Based on the longitudinal electrode distances within the longitudinal electrode distance interval, analyze and evaluate the correlation degree between multiple longitudinal electrode distances and horizontal detection. If a transverse tight signal is generated, construct a transverse correlation model, obtain the transverse electrode formula, substitute the preset detection level range into the transverse electrode formula to obtain the transverse electrode distance interval, and conduct comprehensive analysis with the longitudinal electrode distance interval to determine the optimal electrode distance. Thus, factors in two dimensions of horizontal detection and depth detection are comprehensively considered, and the influence of different dimensions is eliminated through standardization, enabling the trade-off between the effects of horizontal and depth detection when determining the optimal electrode distance. The finally obtained optimal electrode distance can better meet the comprehensive requirements of geological anomaly area identification in horizontal and depth detection.

[0022] Embodiment 3 As Figure 2 shown, a geological anomaly area identification system based on the geoelectric resistivity method according to an embodiment of the present invention includes the following modules: Longitudinal correlation analysis module: According to multiple historical detection reports, construct a historical electrode sequence and a historical depth sequence respectively based on the electrode distance and detection depth of each historical detection, and evaluate the longitudinal correlation degree between the electrode distance and depth detection; Longitudinal interval processing module: If the longitudinal correlation degree is tight, obtain the longitudinal electrode formula, and obtain the longitudinal electrode distance interval according to the required depth detection range; Transverse correlation analysis module: Based on the longitudinal electrode distances within the longitudinal electrode distance interval and the corresponding detection level of each detection, analyze and evaluate the transverse correlation degree between the longitudinal electrode distance and the detection level; Optimal electrode determination module: If the horizontal correlation is close, obtain the horizontal electrode formula, and based on the required horizontal detection range, obtain the horizontal electrode distance interval, and perform intersection analysis with the vertical electrode distance interval to determine the optimal electrode distance.

[0023] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying geological anomaly areas based on georesistivity method, characterized by: include: According to multiple historical detection reports, based on the electrode distance and detection depth of each historical detection, the historical electrode sequence and historical depth sequence are constructed respectively, and the longitudinal correlation between the electrode distance and depth detection is evaluated; If the longitudinal correlation is close, obtain the longitudinal electrode formula, and according to the required depth detection range, obtain the longitudinal electrode distance interval; Based on the longitudinal electrode distance within the longitudinal electrode distance interval and the corresponding detection level each time, the lateral correlation between the longitudinal electrode distance and the detection level is analyzed and evaluated; If the lateral correlation is close, the lateral electrode formula is obtained, and the lateral electrode distance interval is obtained according to the required horizontal detection range, and the intersection analysis is performed with the longitudinal electrode distance interval to determine the optimal electrode distance.

2. The method for identifying geological anomaly areas based on the georesistivity method according to claim 1, characterized in that: To evaluate the longitudinal correlation between electrode spacing and depth detection, the process is as follows: Extract electrode distance and detection depth from historical detection reports, sort them according to time series, and construct historical electrode series and historical depth series respectively; In the historical electrode sequence and the historical depth sequence, the adjacently ordered electrode distances and detection depths are respectively combined to obtain adjacent electrode groups and adjacent depth groups; Each adjacent electrode group and the corresponding adjacent depth group were output through the Spearman rank correlation coefficient model to obtain the longitudinal correlation value; If the vertical correlation value is or in , If the vertical tight signal is generated, 3. The method for identifying geological anomaly areas based on the georesistivity method according to claim 1, characterized in that: The longitudinal electrode formula is obtained by fitting through the least squares method: Constructing the electrode longitudinal curve, and arbitrarily connecting the coordinates of two points on the longitudinal curve to obtain a longitudinal suspected fitting line and a longitudinal suspected fitting equation; Substitute the X coordinates of all points on the longitudinal curve into the longitudinal suspected fitting equation to obtain multiple suspected fitting coordinates, and input them into the suspected fitting analysis model together with the Y coordinates of all points on the longitudinal curve to obtain the longitudinal suspected analysis value; The longitudinal suspected fitting line corresponding to the minimum longitudinal suspected analysis value is extracted as the reference fitting line, and the fitting equation corresponding to the reference fitting line is used as the longitudinal electrode formula.

4. The method for identifying geological anomaly areas based on the georesistivity method according to claim 1, characterized in that: The process of obtaining the longitudinal electrode distance interval is as follows: The two end point values ​​of the required depth detection range are respectively input into the longitudinal electrode formula to obtain the longitudinal electrode distance interval.

5. The method for identifying geological anomaly areas based on the georesistivity method according to claim 4, characterized in that: To evaluate the lateral correlation between the longitudinal electrode spacing and the detection level, the process is as follows: Randomly select a longitudinal electrode distance from the longitudinal electrode distance interval, and obtain the detection level corresponding to the longitudinal electrode distance according to the historical detection report, and sort them according to the size of the corresponding longitudinal electrode distance to obtain the detection level sequence; The longitudinal electrode distance and detection level are processed separately through the Spearman rank correlation coefficient model, and the lateral correlation value is output; If the horizontal correlation value is or in , If the horizontal tight signal is generated.

6. The method for identifying geological anomaly areas based on the georesistivity method according to claim 1, characterized in that: The lateral electrode formula is obtained as follows: Construct a horizontal correlation model, and connect the coordinates of any two points on the horizontal correlation model to obtain multiple horizontal suspected fitting lines; A transverse suspected fitting line is randomly selected to obtain a transverse suspected fitting equation, and the Y coordinates of all points on the transverse suspected fitting line are obtained according to the X values ​​of the coordinates of all points on the transverse correlation model, and the transverse suspected analysis value is obtained by combining the Y coordinates of all points on the transverse correlation model with the output of the suspected fitting analysis model; The lateral suspected fitting line corresponding to the minimum lateral suspected analysis value is extracted as the lateral reference line, that is, the corresponding lateral suspected fitting equation is used as the lateral electrode formula.

7. The method for identifying geological anomaly areas based on the georesistivity method according to claim 1, characterized in that: The method for obtaining the horizontal electrode distance interval is: If the slope in the transverse electrode formula is negative, the two end point values ​​on the preset detection level range are input into the transverse electrode formula to obtain the transverse electrode distance interval.

8. The method for identifying geological anomaly areas based on the georesistivity method according to claim 1, characterized in that: The intersection analysis of the horizontal electrode spacing interval and the vertical electrode spacing interval is performed as follows: The horizontal electrode distance interval and the longitudinal electrode distance interval are intersected to form an intersection electrode distance interval. The detection level and detection depth corresponding to the electrode distance in the intersection electrode distance interval are obtained respectively. The intersection level interval and the intersection depth interval are constructed, and the minimum-maximum normalization processing is performed to obtain the optimal electrode judgment value.

9. The method for identifying geological anomaly areas based on the georesistivity method according to claim 8, characterized in that: The best electrode distance is obtained as follows: The electrode distance corresponding to the maximum optimal electrode judgment value is extracted as the optimal electrode distance.

10. A geological anomaly area identification system based on georesistivity method, characterized by: Longitudinal correlation analysis module: Based on multiple historical detection reports, the historical electrode sequence and historical depth sequence are constructed based on the electrode distance and detection depth of each historical detection, and the longitudinal correlation between the electrode distance and depth detection is evaluated; Longitudinal interval processing module: if the longitudinal correlation is close, obtain the longitudinal electrode formula, and obtain the longitudinal electrode distance interval according to the required depth detection range; Horizontal correlation analysis module: based on the longitudinal electrode distance within the longitudinal electrode distance interval and the corresponding detection level each time, analyze and evaluate the horizontal correlation between the longitudinal electrode distance and the detection level; Optimal electrode determination module: If the lateral correlation is close, obtain the lateral electrode formula, and according to the required horizontal detection range, obtain the lateral electrode distance interval, and perform intersection analysis with the longitudinal electrode distance interval to determine the optimal electrode distance.

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