A method and system for identifying a geological abnormal area based on an induced polarization method

Abstract

The application discloses a kind of geological abnormal area identification method and system based on excitation polarization method, the present application is based on geological data fusion, target area positioning is realized by geological modeling and target weight analysis, the purpose of enhancing the amplitude of physical property anomaly is reached by resistivity, polarization rate attribute modeling, excitation polarization method anomaly coefficient calculation, accurate identification of geological anomaly is reached by physical property and anomaly coefficient clustering analysis and anomaly cluster extraction, the purpose of accurate identification of geological anomaly is reached by geological anomaly target area weight distribution and multi-attribute fusion analysis and comparison.The present application uses multiple data information by data fusion, weight analysis, physical property parameter modeling, anomaly coefficient calculation, clustering analysis and multi-attribute fusion analysis etc. Key technologies, improve exploration accuracy and efficiency, can more accurately identify geological abnormal area.The present application enhances weak physical property anomaly by excitation polarization method anomaly feature calculation, improves the accuracy of anomaly identification.

Description

Technical Field

[0001] This invention belongs to the field of geophysical exploration technology, specifically relating to a method and system for identifying geological anomaly zones based on induced polarization. Background Technology

[0002] The induced polarization method, also known as the induced polarization method, is an electrical exploration method that uses the differences in induced polarization effects among different rocks, minerals, and surrounding media in the Earth's crust as its material basis. It involves observing and studying the distribution patterns of artificially established DC (time domain) or AC (frequency domain) induced electric fields to explore for minerals and solve geological problems. Its principle is to artificially establish an underground DC or AC electric field, and simultaneously measure the resistivity parameters using two or more sets of electrodes. At the same time, it observes the secondary electric field response signal of the slowly changing secondary electric field caused by electrochemical effects underground. Based on the resistivity of rocks and minerals and the induced polarization variation patterns of geological bodies, it infers the resistivity and polarization characteristics of the underground medium, thereby inferring the underground geological structure, stratigraphic properties, and hydrogeological conditions.

[0003] Induced polarization (IP) is a geophysical exploration method based on dielectric properties. It boasts advantages such as large detection depth and high resolution, and has been widely applied in mineral resource exploration, environmental geological surveys, and engineering investigations. Depending on the field source, IPVs are classified into time-domain (DC) or frequency-domain (AC) IPVs. The property profiles (two-dimensional) or property volumes (three-dimensional) used for interpretation include resistivity (ρ), polarizability (η), charge rate (M), half-life (St), deviation (r), and apparent phase (φs).

[0004] Anomaly identification based on geophysical data is an important research area in geophysical exploration. However, current research on induced polarization (IPD) anomaly identification methods is relatively limited. Currently, common methods for identifying geological anomalies based on IPD mainly include the following two:

[0005] 1. Visual Interpretation Method: Anomalies are identified by visually observing geophysical data images. This method is generally used for anomaly identification in two-dimensional profiles and slices.

[0006] 2. Feature extraction method: This method identifies anomalous bodies by extracting attribute features (such as iso-envelope surfaces) from the probe data. It is generally used for anomaly identification of three-dimensional data volumes.

[0007] The methods described above rely directly on physical properties such as resistivity and polarizability. However, in actual production, topography, geological conditions, and uneven overburden can affect exploration resolution. The resistivity and polarizability responses caused by some geological structures (such as karst, ore bodies, and mining voids) are extremely weak, not clearly reflected in two-dimensional profiles and three-dimensional data volumes, and easily overlooked during interpretation, leading to omissions and oversights. Furthermore, these methods are subjective, have low identification efficiency and accuracy, and do not fully utilize geological and geophysical data. Therefore, existing technologies cannot meet practical application needs. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of existing technologies. This invention provides a method and system for identifying geological anomalies based on the induced polarization method. On the basis of geophysical attribute modeling, this invention uses mathematical statistics and mathematical methods such as principal component analysis and gradient calculation to enhance the amplitude of weak geological anomalies. It uses machine learning to classify and extract anomaly clusters from multi-attribute geophysical data. By fusion analysis and cross-comparison of anomaly clusters formed by exploration target weights and anomaly coefficients, and anomaly clusters formed by physical property clusters, it identifies geological anomalies.

[0009] To achieve the desired effect, the present invention adopts the following technical solution:

[0010] This invention discloses a method for identifying geological anomaly zones based on induced polarization, comprising:

[0011] S1. Construct a surface model and a geological block model of the area to be explored based on the topographic and geological data of the area to be explored;

[0012] S2. Perform weight analysis on the surface model and geological block model of the area to be explored to obtain the weight distribution of the area to be explored;

[0013] S3. Based on the induced polarization inversion data, establish resistivity and polarizability attribute models and extract the abnormal distributions of resistivity and polarizability from them;

[0014] S4. Different anomaly coefficients are used to quantify the physical property anomaly characteristics of geological blocks for different anomaly distributions; when the exploration target is low resistivity and high polarizability, the MF anomaly coefficient is selected; when the exploration target is high resistivity and high polarizability, the GI anomaly coefficient is selected.

[0015] S5. Perform cluster analysis on the resistivity and polarizability attribute models and the quantified physical property anomaly characteristics to obtain each cluster of anomalies;

[0016] S6. Perform fusion analysis and cross-comparison on the weight distribution of the area to be explored in S2, the abnormal distribution of resistivity and polarizability in S3, and the various clusters of abnormal clusters in S5 to identify geological anomaly areas.

[0017] Furthermore, the topographic data includes at least one of topographic maps and remote sensing images, and the geological data includes at least one of geological maps, well logging data, geophysical data, and geophysical data.

[0018] Furthermore, S3 specifically includes: based on the excited polarization inversion data, using an interpolation algorithm to establish a resistivity and polarizability attribute model and extracting the abnormal distribution of resistivity and polarizability from it.

[0019] Furthermore, the interpolation algorithm includes at least one of Kriging interpolation, inverse distance weighted interpolation, and radial basis function interpolation.

[0020] Furthermore, the MF anomaly coefficient is:

[0021] Furthermore, the GI anomaly coefficient is:

[0022] Furthermore, the planning parameters are at least one of polarization, charge rate, half-life, deviation, and phase.

[0023] Furthermore, the electrical parameter is at least one of resistivity, conductivity, and natural electric field.

[0024] Furthermore, the algorithms used for cluster analysis include at least one of K-means, random forest, and DBSCAN.

[0025] This invention discloses a geological anomaly zone identification system based on induced polarization method, comprising:

[0026] The data acquisition module is used to collect various data for the identification of geological anomaly zones;

[0027] The identification module is used to identify geological anomaly zones according to any of the methods described above.

[0028] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides a method and system for identifying geological anomalies based on induced polarization. Based on geological data fusion, this invention achieves target area localization through geological modeling and target weight analysis; enhances the magnitude of physical property anomalies through resistivity and polarizability attribute modeling and induced polarization anomaly coefficient calculation; accurately identifies geological anomalies through physical property and anomaly coefficient cluster analysis and anomaly cluster extraction; and achieves accurate identification of geological anomalies through geological anomaly target area weight distribution and multi-attribute fusion analysis and comparison. This invention comprehensively utilizes various data information through key technologies such as data fusion, weight analysis, physical property parameter modeling, anomaly coefficient calculation, cluster analysis, and multi-attribute fusion analysis, improving exploration accuracy and efficiency, and enabling more accurate identification of geological anomaly areas. This invention enhances weak physical property anomalies through induced polarization anomaly feature calculation, improving the accuracy of anomaly identification. This invention integrates various data such as geological data, topographic data, geological survey data, and geophysical data, making full use of geological and geophysical data, thus improving exploration accuracy and efficiency. This invention utilizes machine learning to classify and extract anomaly clusters from multi-attribute geophysical data. By fusing and cross-comparing anomaly clusters formed by exploration target weights and anomaly coefficients, as well as anomaly clusters formed by physical properties, it can identify geological anomalies more efficiently and accurately. Compared to existing technologies, this invention significantly improves the accuracy and precision of anomaly area identification through attribute modeling, cluster analysis, multi-attribute fusion analysis, and cross-comparison. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart of a geological anomaly identification method based on induced polarization method provided in an embodiment of the present invention.

[0031] Figure 2 This is a schematic diagram of cluster analysis provided in an embodiment of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] See Figure 1 This invention discloses a method for identifying geological anomaly zones based on induced polarization, comprising:

[0034] S1. Construct a surface model and a geological block model of the area to be explored based on the topographic and geological data of the area to be explored;

[0035] Preferably, the topographic data includes at least one of topographic maps and remote sensing images, and the geological data includes at least one of geological maps, well logging data, geophysical data, and geophysical data.

[0036] Based on the topographic data of the exploration area and integrated with the geological map of the exploration area, a surface model of the exploration area is constructed. The surface model is a reconstruction model obtained by dividing the surface of the exploration area based on the topographic data and geological map. Preferably, the surface model includes data such as lithology, stratigraphic boundaries, and structural distribution.

[0037] Based on the geological data of the exploration area and the geological survey data of the exploration area, a geological block model of the exploration area is constructed on the basis of the surface model. The geological survey data of the exploration area includes, but is not limited to, stratigraphic boundaries, structural distribution, lithology, etc. The geological block model is a three-dimensional shape restoration model of the geological conditions based on geological exploration data combined with three-dimensional graphic construction technology.

[0038] This invention analyzes various data, including topographic and geological data, geological maps of the exploration area, and geological survey data. By making full use of the topographic, geological, and geophysical data of the exploration area, it can make the identification of geological anomalies more accurate.

[0039] S2. Perform weight analysis on the surface model and geological block model of the area to be explored to obtain the weight distribution of the area to be explored;

[0040] For example, a surface model of the exploration area is constructed based on topographic data, and the stratigraphic distribution characteristics are obtained by integrating the geological map of the area to be explored and relevant survey data. Combining regional geological data and geological survey data, a weight analysis is performed according to the distribution range of the stratigraphic distribution characteristics of the exploration target to obtain the weight of the surface model based on the area to be explored. Weight analysis is then performed on the geological blocks within the exploration area to obtain the weights of different geological blocks. Based on the weights of the surface model of the area to be explored and the weights of different geological blocks, the weight distribution of the area to be explored is determined. According to the purpose of the exploration work, combined with the geological data and geological survey data of the exploration area, a weight analysis is performed on the geological block models within the exploration area according to the distribution range of the stratigraphic distribution characteristics to obtain the weights of different geological block models. The weight results can be represented by corresponding data, and a reasonable threshold is set. Geological block models with weight results greater than the threshold can be identified as target areas of the exploration area.

[0041] S3. Based on the induced polarization inversion data, establish resistivity and polarizability attribute models and extract the abnormal distributions of resistivity and polarizability from them;

[0042] In one embodiment, S3 specifically includes: establishing resistivity and polarizability attribute models based on induced polarization inversion data using an interpolation algorithm, and extracting the abnormal distributions of resistivity and polarizability from them.

[0043] In another embodiment, the interpolation algorithm includes at least one of Kriging interpolation, inverse distance weighted interpolation, and radial basis function interpolation.

[0044] For example, the abnormal distribution generally includes high-resistivity abnormal regions, low-resistivity abnormal regions, high-polarity abnormal regions, low-amplitude abnormal regions, etc.

[0045] Specifically, an initial resistivity model is determined based on the topographic and geological data of the area to be explored. Then, forward modeling is performed based on the corresponding current field equation to obtain the corresponding theoretical apparent resistivity. The difference between the observed and theoretical values ​​is compared. Based on the measured apparent resistivity profile, calculations and analyses are performed to obtain the resistivity distribution in the strata, thereby enabling the division of strata and the determination of the distribution of anomalous areas.

[0046] S4. Different anomaly coefficients are used to quantify the physical property anomaly characteristics of geological blocks for different anomaly distributions; when the exploration target is low resistivity and high polarizability, the MF anomaly coefficient is selected; when the exploration target is high resistivity and high polarizability, the GI anomaly coefficient is selected.

[0047] MF anomaly coefficient (good conduction charging anomaly coefficient): Low resistivity and high polarizability anomaly coefficient, mainly used to highlight anomalous areas of low resistivity (or high conductivity) and high polarizability, suitable for identifying good conduction charging areas (e.g., scattering sulfides, semi-scattering or massive sulfides related to fracture zones and tectonic zones, or water-rich infill karst development areas).

[0048] GI anomaly coefficient (poor conductor charging anomaly coefficient): High resistivity and high polarizability anomaly coefficient, mainly used to highlight abnormal regions of high resistivity (or low conductivity) and high polarizability, suitable for identifying poor conductor charging regions (e.g., scattered sulfidation associated with silicide or carbonate alteration zones).

[0049] On the one hand, the anomaly coefficient of the MF is:

[0050] Specifically, the calculation of MF anomaly coefficient is applicable to the calculation of anomaly coefficients of induced polarization data. It can be used, but is not limited to the calculation of anomaly coefficients of resistivity and conductivity. It can also be used to calculate anomaly coefficients by combining resistivity, conductivity with polarizability, charge rate, half-life, deviation, and phase in pairs.

[0051] On the other hand, the GI anomaly coefficient is:

[0052] Specifically, the calculation of GI anomaly coefficient is applicable to the calculation of anomaly coefficients of induced polarization data. It can be used, but is not limited to the calculation of anomaly coefficients of resistivity and conductivity. It can also be used to calculate anomaly coefficients by combining resistivity, conductivity with polarizability, charge rate, half-life, deviation, and phase in pairs.

[0053] Preferably, the planned parameters in the calculation formulas for the MF anomaly coefficient and the GI anomaly coefficient are at least one of the following: polarizability (unit: dimensionless %), charge rate (unit: milliseconds ms, millivolts-seconds / volts mV·S / V, millivolts / volts mV / V), half-life (unit: milliseconds ms, seconds s), deviation (unit: dimensionless %), and phase (unit: milliradians mrad).

[0054] Furthermore, the electrical parameter is at least one of resistivity, conductivity, and natural electric field. Specifically, the unit of resistivity is ohm-meter (Ω·m).

[0055] It is worth noting that when the physical properties of the exploration target are clear, one or more anomaly coefficients can be selected for calculation; when the physical properties of the exploration target are unclear, all anomaly coefficients can be selected for calculation. Under normal conditions, when the exploration target exhibits polarization characteristics, both MF and GI anomaly coefficients can be selected for calculation. Specifically, when the exploration target has low resistivity and high polarizability, the MF anomaly coefficient should be selected; when the exploration target has high resistivity and high polarizability, the GI anomaly coefficient should be selected. Quantifying the physical property anomaly characteristics of geological bodies through MF and GI anomaly coefficient calculations aims to enhance the amplitude of weak geological anomalies and improve the accuracy of anomaly identification.

[0056] S5. Perform cluster analysis on the resistivity and polarizability attribute models and the quantified physical property anomaly characteristics to obtain each cluster of anomalies;

[0057] Preferably, the algorithms used for cluster analysis include at least one of K-means, random forest, and DBSCAN.

[0058] Specifically, this invention utilizes unsupervised learning clustering algorithms to perform cluster analysis on resistivity and polarizability attribute models and quantified physical property anomaly features, respectively. Preferably, the cluster analysis data can be single attribute data or a combination of multiple attribute data. The cluster analysis algorithm is reasonably selected based on the data combination and data distribution characteristics, and can include, but is not limited to, various clustering algorithms such as K-means, random forest, and DBSCAN. Furthermore, a suitable cluster analysis algorithm can be selected based on the physical properties (resistivity, polarizability), anomaly distribution characteristics, and the needs of the exploration target.

[0059] Preferably, step S5 specifically includes: performing cluster analysis on the resistivity and polarizability attribute models and the quantified physical property anomaly features to obtain attribute clustering models and anomaly feature clustering models, and extracting anomaly clusters from the attribute clustering models and anomaly feature clustering models to obtain each clustered anomaly cluster.

[0060] S6. Perform fusion analysis and cross-comparison on the weight distribution of the area to be explored in S2, the abnormal distribution of resistivity and polarizability in S3, and the various clusters of abnormal clusters in S5 to identify geological anomaly areas.

[0061] For example, such as Figure 2 As shown, K-means is used for cluster analysis of anomaly coefficients and attributes. Different clustering algorithms can also be used, with various combinations of clustering analyses based on data types. Anomaly clusters are identified through comparative analysis, and finally, a spatial analysis model of the geological anomaly area is obtained through weighted and anomaly cluster fusion comparative analysis. In addition to conventional methods, the fusion comparative analysis can also be implemented using neural network models for machine learning to improve accuracy.

[0062] Preferably, the analysis results of identifying geological anomaly zones can be displayed in a variety of visualization methods, including but not limited to two-dimensional profiles, three-dimensional data volumes, contour maps, and anomaly cluster feature maps.

[0063] Preferably, the geological anomaly areas identified by the present invention can be displayed in the form of spatial distribution maps of geological anomaly areas, development range maps of anomaly differences, and characteristic descriptions of anomaly clusters.

[0064] This invention, based on geological data fusion, achieves target area localization through geological modeling and target weight analysis. It enhances the magnitude of physical property anomalies by modeling resistivity and polarizability attributes and calculating anomaly coefficients using the induced polarization method. It accurately identifies geological anomalies through physical property and anomaly coefficient cluster analysis and anomaly cluster extraction. Finally, it achieves precise identification of geological anomalies through the weight distribution of geological anomaly target areas and multi-attribute fusion analysis and comparison. This invention comprehensively utilizes various data information by employing key technologies such as data fusion, weight analysis, physical property parameter modeling, anomaly coefficient calculation, cluster analysis, and multi-attribute fusion analysis, improving exploration accuracy and efficiency, and enabling more accurate identification of geological anomaly areas. This invention enhances weak physical property anomalies through induced polarization anomaly feature calculation, improving the accuracy of anomaly identification. This invention integrates various data, including geological data, topographic data, geological survey data, and geophysical data, making full use of geological and geophysical data to improve exploration accuracy and efficiency. This invention utilizes machine learning to classify and extract anomaly clusters from multi-attribute geophysical data. By fusing and cross-comparing anomaly clusters formed by exploration target weights and anomaly coefficients, as well as physical property clusters, it can more efficiently and accurately identify geological anomalies. Compared to existing technologies, this invention significantly improves the accuracy and precision of anomaly identification through attribute modeling, cluster analysis, multi-attribute fusion analysis, and cross-comparison. This method is applicable to the detection of geological bodies with significant differences in underground resistivity and polarizability (such as mineral deposits, karst, underground rivers, mined-out areas, and groundwater), and has been widely applied in mineral resource exploration, environmental geological surveys, water resource exploration, and engineering investigation.

[0065] Based on the same inventive concept, this invention also discloses a geological anomaly zone identification system based on induced polarization method, comprising:

[0066] The data acquisition module is used to collect various data for the identification of geological anomaly zones;

[0067] The identification module is used to identify geological anomaly zones according to any of the methods described above.

[0068] The system embodiments described herein can be implemented one-to-one with the aforementioned method embodiments, and will not be repeated here.

[0069] Based on the same inventive concept, this invention also discloses an electronic device, which may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other through the communication bus. The processor can call logical instructions in the memory to execute a geological anomaly zone identification method based on induced polarization, including:

[0070] S1. Construct a surface model and a geological block model of the area to be explored based on the topographic and geological data of the area to be explored;

[0071] S2. Perform weight analysis on the surface model and geological block model of the area to be explored to obtain the weight distribution of the area to be explored;

[0072] S3. Based on the induced polarization inversion data, establish resistivity and polarizability attribute models and extract the abnormal distributions of resistivity and polarizability from them;

[0073] S4. Different anomaly coefficients are used to quantify the physical property anomaly characteristics of geological blocks for different anomaly distributions; when the exploration target is low resistivity and high polarizability, the MF anomaly coefficient is selected; when the exploration target is high resistivity and high polarizability, the GI anomaly coefficient is selected.

[0074] S5. Perform cluster analysis on the resistivity and polarizability attribute models and the quantified physical property anomaly characteristics to obtain each cluster of anomalies;

[0075] S6. Perform fusion analysis and cross-comparison on the weight distribution of the area to be explored in S2, the abnormal distribution of resistivity and polarizability in S3, and the various clusters of abnormal clusters in S5 to identify geological anomaly areas.

[0076] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0077] On the other hand, embodiments of the present invention also provide a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, and when the program instructions are executed by a computer, the computer is able to execute a geological anomaly zone identification method based on induced polarization method provided in the above-described method embodiments, including:

[0078] S1. Construct a surface model and a geological block model of the area to be explored based on the topographic and geological data of the area to be explored;

[0079] S2. Perform weight analysis on the surface model and geological block model of the area to be explored to obtain the weight distribution of the area to be explored;

[0080] S3. Based on the induced polarization inversion data, establish resistivity and polarizability attribute models and extract the abnormal distributions of resistivity and polarizability from them;

[0081] S4. Different anomaly coefficients are used to quantify the physical property anomaly characteristics of geological blocks for different anomaly distributions; when the exploration target is low resistivity and high polarizability, the MF anomaly coefficient is selected; when the exploration target is high resistivity and high polarizability, the GI anomaly coefficient is selected.

[0082] S5. Perform cluster analysis on the resistivity and polarizability attribute models and the quantified physical property anomaly characteristics to obtain each cluster of anomalies;

[0083] S6. Perform fusion analysis and cross-comparison on the weight distribution of the area to be explored in S2, the abnormal distribution of resistivity and polarizability in S3, and the various clusters of abnormal clusters in S5 to identify geological anomaly areas.

[0084] In another aspect, embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for identifying geological anomalies based on induced polarization provided in the above embodiments, comprising:

[0085] S1. Construct a surface model and a geological block model of the area to be explored based on the topographic and geological data of the area to be explored;

[0086] S2. Perform weight analysis on the surface model and geological block model of the area to be explored to obtain the weight distribution of the area to be explored;

[0087] S3. Based on the induced polarization inversion data, establish resistivity and polarizability attribute models and extract the abnormal distributions of resistivity and polarizability from them;

[0088] S4. Different anomaly coefficients are used to quantify the physical property anomaly characteristics of geological blocks for different anomaly distributions; when the exploration target is low resistivity and high polarizability, the MF anomaly coefficient is selected; when the exploration target is high resistivity and high polarizability, the GI anomaly coefficient is selected.

[0089] S5. Perform cluster analysis on the resistivity and polarizability attribute models and the quantified physical property anomaly characteristics to obtain each cluster of anomalies;

[0090] S6. Perform fusion analysis and cross-comparison on the weight distribution of the area to be explored in S2, the abnormal distribution of resistivity and polarizability in S3, and the various clusters of abnormal clusters in S5 to identify geological anomaly areas.

[0091] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0092] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying geological anomaly zones based on induced polarization, characterized in that, include: S1. Construct a surface model and a geological block model of the area to be explored based on the topographic and geological data of the area to be explored; S2. Perform weight analysis on the surface model and geological block model of the area to be explored to obtain the weight distribution of the area to be explored; S3. Based on the induced polarization inversion data, establish resistivity and polarizability attribute models and extract the abnormal distributions of resistivity and polarizability from them; S4. Different anomaly coefficients are used to quantify the physical property anomaly characteristics of geological blocks for different anomaly distributions; when the exploration target is low resistivity and high polarizability, the MF anomaly coefficient is selected; when the exploration target is high resistivity and high polarizability, the GI anomaly coefficient is selected. S5. Perform cluster analysis on the resistivity and polarizability attribute models and the quantified physical property anomaly characteristics to obtain each cluster of anomalies; S6. Perform fusion analysis and cross-comparison on the weight distribution of the area to be explored in S2, the abnormal distribution of resistivity and polarizability in S3, and the various clusters of abnormal clusters in S5 to identify geological anomaly areas. The MF anomaly coefficient is: ; The GI anomaly coefficient is: .

2. The method for identifying geological anomaly zones based on induced polarization as described in claim 1, characterized in that, The topographic data includes at least one of topographic maps and remote sensing images, and the geological data includes at least one of geological maps, well logging data, geophysical data, and geophysical data.

3. The geological anomaly zone identification method based on induced polarization as described in claim 1, characterized in that, Specifically, S3 includes: establishing resistivity and polarizability attribute models based on induced polarization inversion data and extracting abnormal distributions of resistivity and polarizability from them using interpolation algorithms.

4. The geological anomaly zone identification method based on induced polarization as described in claim 3, characterized in that, The interpolation algorithm includes at least one of Kriging interpolation, inverse distance weighted interpolation, and radial basis function interpolation.

5. The method for identifying geological anomaly zones based on induced polarization as described in claim 1, characterized in that, The polarization parameter is at least one of polarizability, charge rate, half-life, deviation, and phase.

6. The method for identifying geological anomaly zones based on induced polarization as described in claim 1, characterized in that, The electrical parameter is at least one of resistivity, conductivity, and natural electric field.

7. The method for identifying geological anomaly zones based on induced polarization as described in claim 6, characterized in that, Algorithms used for cluster analysis include at least one of K-means, random forest, and DBSCAN.

8. A geological anomaly zone identification system based on induced polarization method, characterized in that, include: The data acquisition module is used to collect various data for the identification of geological anomaly zones; An identification module is used to identify geological anomaly zones according to any one of the methods described in claims 1-7.

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