Mineral target area delineation method based on resistivity and polarizability image fusion

Abstract

The invention discloses a mineral target area delineation method based on electrical resistivity and polarizability image fusion, which comprises the following steps of: firstly, preprocessing actually measured electromagnetic data of an exploration area, including data review, gridding registration and image normalization, so as to respectively obtain a standardized electrical resistivity image and a standardized polarizability image; decomposing the two source images into low-frequency sub-band images and high-frequency sub-band images by using non-subsampled contourlet transform; fusing the low-frequency sub-band image and the high-frequency sub-band image by using a fusion rule based on weighted average and a new measurement parameter; and finally, a fusion image is obtained through NSCT inverse transformation, and the fusion image is compared with prior geological data to delineate a prospecting target area. Through a multi-scale image fusion technology, complementary information of resistivity and polarizability is effectively integrated, the accuracy and reliability of mineralization abnormal area identification are remarkably improved, and a new technical means is provided for mineral exploration.

Description

Technical Field

[0001] This invention belongs to the field of geophysical exploration and image processing technology, specifically relating to a method for delineating mineral target areas based on resistivity and polarizability image fusion. Background Technology

[0002] Geophysical exploration plays a vital role in resource exploration and engineering geology due to its non-destructive nature, wide coverage, and ability to acquire multi-parameter information. Electromagnetic methods, as one of its main techniques, can effectively infer the spatial distribution of subsurface targets by measuring differences in the conductivity and induced polarization of the Earth's crustal media.

[0003] Currently, the interpretation of geophysical parameters such as resistivity and polarizability largely relies on the subjective interpretation of single images by technicians. This method of interpretation is easily influenced by personal experience, and the interpretation results for the same dataset may differ. Furthermore, it is difficult to quantitatively integrate the comprehensive information reflected by different physical property parameters, thus limiting the objectivity and accuracy of the interpretation.

[0004] Image fusion technology provides a new approach for the comprehensive analysis and interpretation of multi-source geophysical data. Unsampled contour wave transform, as an advanced multi-scale geometric analysis tool, possesses translation invariance and excellent orientation and detail capture capabilities, making it highly suitable for fusing geophysical images with complex structural features.

[0005] Chinese patent application CN120686372A discloses a method and system for deep underground multiphysics field detection. The method includes the following steps: S1, acquiring seismic wavefield data; S2, processing based on the original multiphysics field dataset; S3, processing based on a set of fundamental characteristic parameters; and S4, calling a comprehensive feature fusion matrix. In the process of deep underground multiphysics field detection, this invention uses monitoring instruments and sensors to collect waveform, wave velocity, and amplitude parameters from seismic wavefield data, as well as electromagnetic field strength, resistivity, and polarizability parameters from electromagnetic field data, generating an original multiphysics field dataset. This dataset provides comprehensive foundational data for subsequent processing. Based on this dataset, the wave velocity and amplitude characteristics of each waveform segment of the seismic wavefield data, and the resistivity and polarizability characteristics of the electromagnetic field data, are determined, and a set of fundamental characteristic parameters is integrated to accurately grasp the key characteristics of different physical fields. The core of this patent application lies in constructing a comprehensive feature fusion matrix to weightedly fuse the characteristic parameters of different physical fields, such as seismic wavefield data (e.g., wave velocity, amplitude) and electromagnetic field data (e.g., resistivity, polarizability), to achieve the identification of deep geological targets. However, compared with this invention, it has the following obvious shortcomings in terms of technical implementation and final effect: It employs a feature-level or decision-level fusion approach. It directly performs a weighted summation of macroscopic feature parameters (such as mean wave velocity and mean resistivity) extracted from different physical fields. This method loses a large amount of spatial detail and structural information in the original image, and cannot accurately describe the morphology of geological body boundaries, the internal texture of mineralized alteration zones, and other details that are crucial for target area delineation.

[0006] Because its fusion process loses detailed geometric structural information and the fusion rules are relatively simple, the resulting "comprehensive feature fusion matrix" is likely to be a highly generalized and smooth data volume. This leads to unclear boundary identification of small ore bodies and complex mineralized zones, and fails to provide target area information with sufficient spatial accuracy. Therefore, it is difficult to use directly for precise mineral target area delineation and is more suitable for large-scale, macroscopic geological structural analysis.

[0007] Chinese patent application CN 118068428 A discloses a method and system for joint air-ground-well investigation of contaminated sites based on electromagnetic methods. It integrates the advantages of semi-airborne transient electromagnetic methods, surface and well-based high-density resistivity methods, induced polarization methods, and drilling technology. It proposes a zonal gradient method that integrates resistivity extrema and gradients, fully utilizing the high efficiency and wide range of electromagnetic methods to effectively guide well placement during the initial and detailed investigation stages. By fusing multi-source data from air, ground, and well electromagnetic methods through constraint equations between electromagnetic methods, it comprehensively considers the physical property relationship between electromagnetic parameters and contamination concentration, as well as the coefficient of determination after parameter fitting, fully integrating multi-source heterogeneous data from electromagnetic parameters and drilling sampling analysis. The core of this patent application lies in constructing a joint "air, ground, and well" exploration process, achieving comprehensive utilization of multi-source data through process optimization and data correction. However, compared with this invention, it has the following shortcomings in core technology and final effect: it focuses on the accuracy and representativeness of data values, and the final result is a contamination concentration distribution model. Although this is also a spatial distribution, the generation process relies on interpolation and weighted averaging, and such algorithms naturally smooth out and blur sharp boundaries. Therefore, this method is less effective at accurately characterizing the boundaries of contaminated sites and identifying small or complex-shaped contamination clumps.

[0008] Chinese patent application CN 117805915 A discloses a method and system for identifying geological anomalies based on induced polarization. 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 through resistivity and polarizability attribute modeling and induced polarization anomaly coefficient calculation. It achieves accurate identification of geological anomalies through physical property and anomaly coefficient cluster analysis and anomaly cluster extraction. Finally, it achieves accurate identification of geological anomalies through geological anomaly target area weight distribution and multi-attribute fusion analysis and comparison. Its core lies in identifying and enhancing physical property anomalies through the construction of mathematical models and statistical clustering. Although it introduces machine learning for cluster analysis, its technical path is fundamentally different from this invention and has the following shortcomings: Anomaly enhancement relies on preset mathematical formulas (MF / GJ coefficients). Although these formulas are designed based on physical property relationships, they are fixed and global. For a complex exploration area, this enhancement method may over-enhance noise in some areas while failing to effectively highlight true anomalies in others, lacking adaptive optimization capabilities for local image content.

[0009] Compared with Chinese patent application CN 118068428 A, the NSCT fusion method used in this invention pays close attention to the spatial geometric features (edges, textures, contours) of the image. Its NMP fusion rules for high-frequency subbands are specifically designed to adaptively preserve the clearest and most significant boundary details, which gives the fusion results a natural advantage in delineating ore body boundaries and identifying complex morphologies. Summary of the Invention

[0010] The purpose of this invention is to overcome the shortcomings of existing geophysical data interpretation methods, such as strong subjectivity and insufficient comprehensive utilization of multi-source information, and to provide a method that can objectively and accurately integrate resistivity and polarizability information and effectively delineate mineral target areas.

[0011] To achieve the above objectives, the present invention adopts the following technical solution: A method for delineating mineral target areas based on resistivity and polarizability image fusion, comprising the following steps: (1). Data preprocessing and parameter acquisition: The measured electromagnetic data collected in the field were cleaned, gridded and registered and normalized to obtain standardized resistivity and polarizability images; (2). Image fusion processing: Non-subsampled contourlet transform is applied to decompose the two source images into multiple scales and directions; for the low-frequency sub-band representing the background field, a weighted average method is used for fusion; for the high-frequency sub-band representing the detailed features, a fusion rule based on the metric parameter NMP is used for fusion. (3). Image reconstruction and target area delineation: The fused image is obtained by inverse non-subsampled contour wave transform (NSCT), and the anomalies in the fused image are interpreted in combination with geological data to delineate the mineral exploration target area.

[0012] NSCT, short for Nonsubsampled Contourlet Transform, is a translation-invariant, multi-scale, multi-directional image decomposition algorithm. It consists of a cascaded Nonsubsampled Pyramid Filter Bank (NSPFB) and a Nonsubsampled Directional Filter Bank (NSDFB). The former decomposes the image into low-frequency and high-frequency subbands at different scales, while the latter further separates the high-frequency subbands into multiple directional components, thus more effectively capturing geometric features such as edges and textures in the image. Because this transform eliminates the downsampling operation in traditional algorithms, it effectively avoids artifacts during image reconstruction, significantly improving the quality of image fusion. It is particularly suitable for multi-source image fusion applications requiring high structural fidelity, such as geophysical data.

[0013] Furthermore, in step (1), data preprocessing specifically includes: First, the raw measurement data is thoroughly reviewed to identify and eliminate outliers and distorted data caused by instrument malfunction, environmental interference, or human error. Next, through interpolation, sorting, and coordinate transformation methods, data points of different types or with different geometric distributions are transformed into grid values ​​with the same resolution, thereby ensuring that all data types in the same region can be precisely aligned in a unified coordinate system and registered strictly according to the grid position; Subsequently, the standardized grid values ​​are configured with different color band mapping relationships according to the preset physical property combination characteristics and converted into color images to enhance specific physical property anomaly combinations before fusion; Next, the color image is converted to grayscale to prepare for subsequent fusion operations; Finally, the generated grayscale image will be normalized to digitize the image information, and the normalized source image will be used as the input for image fusion.

[0014] Furthermore, the image decomposition part in step (2) includes: The NSCT transform consists of two stages: a nonsubsampled pyramid filter bank (NSPFB) and a nonsubsampled directional filter bank (NSDFB), which can achieve sparse representation of images at multiple scales and in multiple directions.

[0015] First, the non-downsampled pyramid filter bank performs multi-scale decomposition on the source image, decomposing the image into a low-frequency sub-band and a series of high-frequency sub-bands through iterative filtering; this process does not perform downsampling, thus possessing translation invariance. The decomposition and synthesis filters of a non-subsampled pyramid filter bank must satisfy the following complete reconstruction condition: H0(Z)H1(Z)+G0(Z)G1(Z)=1, Where H0(Z) and H1(Z) are decomposition filters, and G0(Z) and G1(Z) are synthesis filters; After decomposition by the k-th level non-subsampled pyramid filter bank, the nth level equivalent filter H n The mathematical expression for (Z) is: , Subsequently, the non-downsampled directional filter bank performs directional decomposition on each high-frequency subband obtained by the non-downsampled pyramid filter bank. The non-downsampled directional filter bank also eliminates the downsampling operation in the traditional directional filter bank. It consists of a set of fan-shaped filters and can decompose the high-frequency subband into multiple subband images in different directions at a specific scale. The decomposition and synthesis filters of non-downsampling direction filter banks must satisfy the following complete reconstruction condition: U0(Z)V0(Z)+U1(Z)V1(Z)=1, Wherein, U0(Z) and U1(Z) are directional decomposition filters, and V0(Z) and V1(Z) are directional synthesis filters; Performing l-level non-downsampling directional filter bank directional decomposition on any high-frequency subband yields 2 l The source image is divided into a directional sub-band with the same size as the source image. By cascading the non-downsampled pyramid filter bank and the non-downsampled directional filter bank, NSCT finally decomposes the source image into a low-frequency sub-band and multiple high-frequency sub-bands distributed at different scales and in different directions, thereby accurately capturing the contour and texture information in the image.

[0016] Furthermore, in step (2), for the resistivity low-frequency subband image L ρ _k and low-frequency subband image of polarizability L η The fusion rule considers the average characteristics of the low-frequency sub-band image and the contribution of the target ore body's physical properties. Therefore, a weighted average fusion rule is adopted, which is defined as follows: , FL represents the fused low-frequency sub-band image, which is the final result after weighted averaging; w1 and w2 represent weight coefficients, which are pre-set positive numbers and satisfy w1 + w2 = 1; Lρ kThe low-frequency subband image representing the resistivity image is the low-frequency component containing its overall structure and background information obtained at the k-th layer after NSCT decomposition of the resistivity source image; Lη k The low-frequency subband image representing the polarizability model is the low-frequency component containing the overall structure and background information obtained at the k-th layer after the polarizability source image is decomposed by NSCT; k represents the number of layers or scales of NSCT decomposition, indicating that fusion is performed separately at each decomposition scale.

[0017] The weighting coefficients w1 and w2 can be differentiated based on the physical properties of the target to highlight key information. For example, for low-resistivity, high-polarization ore bodies, a higher weight is assigned to polarizability (i.e., w2 > w1); this yields the low-frequency resistivity sub-band image L. ρ _k and low-frequency subband image of polarizability L η The fusion result FL of _k.

[0018] Furthermore, in step (2), the resistivity high-frequency subband image and the polarizability high-frequency subband image are fused using the NMP fusion rule; High-frequency subband resistivity image H ρ _k and polarizability high-frequency subband image H η _k describes the detail information corresponding to the source image, using a metric parameter NMP; NMP is defined as follows: , Where α1, β1, and γ1 are the parameters used to adjust PC, LSCM, and LE in NMP, α1=1, β1=2, and γ1=2; after obtaining NMP, the fused high-frequency subband image is obtained through the following formula: , Where FH represents the high-frequency fused image, and NMP represents the NMP image. ρ (x,y) and NMPη(x,y) are the NMP values ​​of the resistivity and polarizability images at (x,y), respectively; Hρ k The coefficients of the high-frequency subband image representing the resistivity image at the k-th level decomposition are the detail components obtained after NSCT decomposition of the resistivity source image; Hη k The coefficients of the high-frequency subband image representing the polarimetric image at the k-th level decomposition are the detail components obtained after NSCT decomposition of the polarimetric source image; "otherwise" is a conditional statement meaning "otherwise," and in this decision rule, it indicates that if the condition [NMP] is met, then... ρ The operation performed when [(x,y)≥NMPη(x,y)] does not hold, i.e., when the NMP value of resistivity is less than the NMP value of polarizability.

[0019] Furthermore, in step (3), the resistivity image I is obtained by reconstructing the fused low-frequency subband image and high-frequency subband image using the inverse non-subsampled contour wave transform. ρ and polarization image I η The IF (Image Fusion Process) formula is as follows: IF=INNSCT(FL,FH) INNSCT stands for Inverse Non-Subsampled Profilometry Transform. The non-subsampled pyramid filter bank and non-subsampled directional filter bank used in this process are consistent with those used in the decomposition process. The reconstructed fused image, by comparing it with known geological information, can significantly improve the reliability and positioning accuracy of mineralization anomaly identification.

[0020] Compared with Chinese patent application CN120686372A, the core of the technical solution of this invention is non-subsampled contourlet transform (NSCT). This is an advanced multi-scale geometric analysis tool that can decompose images into different scales and directions, thereby accurately capturing key geometric features such as edges, contours, and textures contained in resistivity and polarizability images. This pixel-level fusion can remove redundant and complementary information from the source, achieving true image information integration.

[0021] Due to NSCT's excellent edge preservation capabilities and NMP's preferential fusion of significant details, the final fused image of this patent can clearly highlight the boundaries and internal structure of mineralization anomalies, significantly reducing the ambiguity of single-method interpretation and providing a very intuitive and accurate basis for drilling positioning.

[0022] Compared with Chinese patent application CN 118068428 A, the NSCT fusion method used in this invention pays close attention to the spatial geometric features (edges, textures, contours) of the image. Its NMP fusion rules for high-frequency subbands are specifically designed to adaptively preserve the clearest and most significant boundary details, which gives the fusion results a natural advantage in delineating ore body boundaries and identifying complex morphologies.

[0023] Compared to Chinese patent application CN 117805915 A, the high-frequency fusion rule (based on NMP) of this invention can intelligently determine and retain information with clearer structures and more prominent edges from resistivity or polarizability images at each pixel. Because NSCT has translation invariance and the fusion rule operates at the pixel level, the final fused image can preserve and even enhance the boundaries of geological bodies with high fidelity, generating a detailed and structurally clear "new image," which is very suitable for accurately delineating target areas.

[0024] The beneficial effects of this invention are: 1. By systematically preprocessing data and fusing NSCT images, effective complementarity and integration of resistivity and polarizability data were achieved, reducing the ambiguity of interpretation by a single method.

[0025] 2. The fusion rules adopted have clear physical meanings, the weights of low-frequency fusion are adjustable, and the high-frequency fusion can adaptively select significant features, making the fusion results more in line with the needs of geological interpretation.

[0026] 3. This method has a clear process and is highly operable, providing a new, more objective and reliable technical means for target area delineation in mineral exploration. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the overall structure of the present invention; Figure 2 This is a plane diagram of the contour lines of the apparent polarizability in the normalized induced polarization. Figure 3 This is a plane diagram of the contour lines of the apparent resistivity in the normalized induced polarization. Figure 4 This is a contour map of the fused data obtained based on NSCT. Detailed Implementation

[0028] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0029] The structures, proportions, and sizes illustrated in the accompanying drawings are merely for illustrative purposes and to aid those skilled in the art in understanding and reading the invention. They are not intended to limit the scope of the invention and therefore have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size, provided they do not affect the effectiveness or purpose of the invention, should still fall within the scope of the technical content disclosed herein. Furthermore, the terms "upper," "lower," "left," "right," "middle," and "one" used in this specification are merely for clarity and not intended to limit the scope of the invention. Changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention's implementation.

[0030] The implementation examples of this invention are based on the Mingyang copper-nickel polymetallic mine exploration area in Guazhou County, Gansu Province.

[0031] The implementation process of this embodiment strictly follows... Figure 1 The diagram shows the overall structure.

[0032] Step 1. Data Acquisition and Preprocessing In this embodiment, measured data from the Mingyang copper-nickel polymetallic deposit exploration area in Guazhou County, Gansu Province, were used. The Bouguer anomaly in this area is a near-east-west trending stepped zone, a favorable spatial region for hydrothermal deposit formation. Physical property measurements show that the polarizability values ​​in the area are generally high, and the gabbro associated with copper-nickel mineralization exhibits low resistivity and high polarizability.

[0033] A 1:10000 induced polarization gradient scan was conducted in this area, with a grid spacing of 100 meters (line spacing) × 40 meters (point spacing), and 37 survey lines were laid out. The raw data obtained were apparent resistivity and apparent polarizability data.

[0034] Data preprocessing is fundamental to ensuring effective data fusion. The specific steps are as follows: (1) Data review: First, a comprehensive review of the original measured data is conducted to identify and remove outliers and distorted data points caused by poor electrode contact, environmental electromagnetic interference or human error, so as to ensure the reliability of the data source.

[0035] (2) Gridding and registration: Using professional software, the irregularly distributed measurement point data is transformed into regular grid data through Kriging interpolation, and coordinate transformation and unification are performed to ensure that resistivity and polarizability data are strictly spatially registered in the same coordinate system.

[0036] (3) Imaging and Normalization: Standardized grid values ​​were plotted as color contour maps. To facilitate enhanced display of different property combinations in subsequent fusion analysis, a unified color band scale was used for visualization of resistivity and polarizability images. Specifically, to highlight the typical mineralization response pattern of "low resistance, high polarization," the color band mapping was inverted when generating the polarizability contour map. This artificial setting of the color band mapping ensured that, before subsequent fusion, the anomalous areas (low resistance and high polarization areas) related to the target mineralization in the two source images appeared visually consistent, both being highlighted features. This method, through flexible configuration of the color band mapping relationship, can also be applied to pre-fusion enhancement of other property combinations such as "high-high" and "low-low," demonstrating its flexibility and specificity in the collaborative identification of multiple property anomalies. The images were then converted to grayscale. Finally, the grayscale image is normalized to unify the pixel value range to the [0,1] interval, resulting in a standard resistivity source image I with a size of 284×300 pixels. ρ and polarization source image I η This serves as the input for NSCT image fusion. The normalized image is as follows: Figure 2 and Figure 3 As shown.

[0037] Step 2. NSCT image fusion Step2.1 NSCT decomposition Preprocessed resistivity image I ρ and polarization image I η Perform NSCT decomposition. Decomposition parameter settings: Based on the image size and detail richness, the decomposition level is set to (4,4,4,4), resulting in a 4-level scale decomposition. The pyramid filter (NSPFB) is set to "maxflat", and the directional filter (NSDFB) is set to "dmaxflat7". This combination achieves excellent multi-scale directional features while maintaining computational efficiency.

[0038] Decomposition process: First, NSPFB performs multi-scale decomposition on the source image to obtain a low-frequency sub-band image (L). ρ， L η The image contains images of high-frequency subbands at four different scales. NSDFB then performs directional decomposition on the high-frequency subbands at each scale. For example, with a directional series vector [2,3,3,4], when the directional series is 2, the high-frequency subband can be decomposed into 2... 2 =4 directional sub-bands; when the directional series is 4, it can be decomposed into 2 4 =16 directional sub-bands. Ultimately, each source image is decomposed into one low-frequency sub-band and a series of high-frequency sub-bands distributed at different scales and in different directions.

[0039] Step 2.2 Subband Image Fusion Low-frequency subband fusion: The low-frequency subband carries the main structural and background field information of the image. A weighted average fusion rule is used: FL=w1L ρk +w2L ηk (w1+w2=1) In this embodiment, the target rock mass (basic and ultrabasic rocks) exhibits characteristics of "low resistivity and high polarization," with high polarization being a more direct and crucial indicator of mineralization. Therefore, the polarization image is given a higher weight in the fusion process, with w1=0.25 (resistivity weight) and w2=0.75 (polarization weight) to better highlight mineralization information and clearly identify the boundary between the ore body and the surrounding rock.

[0040] High-frequency subband fusion: The high-frequency subband contains detailed information such as image edges and textures. A fusion rule based on the new metric parameter NMP is adopted.

[0041] NMP calculation: NMP consists of three components: phase coherence (PC), local sharpness variation (LSCM), and local energy (LE). Its definition is as follows: In this embodiment, the parameters are set to α1=1, β1=2, γ1=2 to enhance the response to local contrast and detail sharpness.

[0042] Fusion Decision: For each pixel (x, y), calculate the NMP values ​​corresponding to the resistivity high-frequency subband and the polarizability high-frequency subband, denoted as NMP_ρ(x, y) and NMP_η(x, y), respectively. The fusion decision rule is: select the high-frequency coefficients of the source image with the larger NMP value as the fused high-frequency coefficients.

[0043] This rule can adaptively preserve sharper and more prominent edge and texture details from either source image.

[0044] Step 3. Image reconstruction and target delineation The inverse NSCT transform INNSCT is used to reconstruct the fused low-frequency subband (FL) and high-frequency subband (FH) to obtain the final fused image (IF). The decomposition filter and directional filter used in the reconstruction process are consistent with those used in the decomposition stage to ensure lossless reconstruction of information.

[0045] IF=INNSCT(FL,FH) The reconstructed fused image IF (see Figure 4 It also includes electrical structure information of resistivity and mineralization response information of polarizability. The IF is compared and analyzed with prior data such as geological maps and rock mass outcrop extents.

[0046] In this embodiment, a judgment coefficient threshold (e.g., <0.45) is set to quantify and identify "low-resistivity, high-polarization" anomaly areas. Results show that anomaly areas below this threshold in the fused image highly overlap with the locations of previously discovered basic and ultrabasic rock mineralization alteration zones on the surface, and the high-polarization features in the source images are clearly preserved in the fused results. Based on this, we successfully delineated several favorable target areas for mineral exploration, providing accurate and reliable evidence for subsequent drilling verification work.

[0047] The above embodiments fully demonstrate that the method of the present invention can effectively integrate resistivity and polarizability data, significantly improving the ability to identify mineralized alteration zones and the accuracy of delineating prospecting target areas.

[0048] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for delineating mineral target areas based on resistivity and polarizability image fusion, characterized by the following steps: as follows: (1). Data preprocessing and parameter acquisition: The measured electromagnetic data collected in the field were cleaned, gridded and registered and normalized to obtain standardized resistivity and polarizability images; (2). Image fusion processing: Non-subsampled contourlet transform is applied to decompose the two source images into multiple scales and directions; for the low-frequency sub-band representing the background field, a weighted average method is used for fusion; for the high-frequency sub-band representing the detailed features, a fusion rule based on the metric parameter NMP is used for fusion. (3). Image reconstruction and target area delineation: The fused image is obtained by inverse transformation of non-subsampled contour wave transform. The anomalies in the fused image are interpreted in combination with geological data to delineate the mineral exploration target area.

2. The mineral target area delineation method based on resistivity and polarizability image fusion as described in claim 1, characterized in that, In step (1), data preprocessing specifically includes: First, the raw measurement data is thoroughly reviewed to identify and eliminate outliers and distorted data caused by instrument malfunction, environmental interference, or human error. Next, through interpolation, sorting, and coordinate transformation methods, data points of different types or with different geometric distributions are transformed into grid values ​​with the same resolution, thereby ensuring that all data types in the same region can be precisely aligned in a unified coordinate system and registered strictly according to the grid position; Subsequently, the standardized grid values ​​are configured with different color band mapping relationships according to the preset physical property combination characteristics and converted into color images to enhance specific physical property anomaly combinations before fusion; Next, the color image is converted to grayscale to prepare for subsequent fusion operations; Finally, the generated grayscale image will be normalized to digitize the image information, and the normalized source image will be used as the input for image fusion.

3. The mineral target area delineation method based on resistivity and polarizability image fusion as described in claim 1, characterized in that, In step (2), the image decomposition part includes: The non-subsampled contourlet transform consists of two stages: a non-subsampled pyramid filter bank and a non-subsampled directional filter bank. It can achieve sparse representation of images at multiple scales and in multiple directions. First, the non-downsampled pyramid filter bank performs multi-scale decomposition on the source image, decomposing the image into a low-frequency sub-band and a series of high-frequency sub-bands through iterative filtering; this process does not perform downsampling, thus possessing translation invariance. The decomposition and synthesis filters of a non-subsampled pyramid filter bank must satisfy the following complete reconstruction condition: H0(Z)H1(Z)+G0(Z)G1(Z)=1, Where H0(Z) and H1(Z) are decomposition filters, and G0(Z) and G1(Z) are synthesis filters; After decomposition by the k-th level non-subsampled pyramid filter bank, the nth level equivalent filter H n The mathematical expression for (Z) is: ， Subsequently, the non-downsampled directional filter bank performs directional decomposition on each high-frequency subband obtained by the non-downsampled pyramid filter bank. The non-downsampled directional filter bank also eliminates the downsampling operation in the traditional directional filter bank. It consists of a set of fan-shaped filters and can decompose the high-frequency subband into multiple subband images in different directions at a specific scale. The decomposition and synthesis filters of non-downsampling direction filter banks must satisfy the following complete reconstruction condition: U0(Z)V0(Z)+U1(Z)V1(Z)=1, Wherein, U0(Z) and U1(Z) are directional decomposition filters, and V0(Z) and V1(Z) are directional synthesis filters; Performing l-level non-downsampling directional filter bank directional decomposition on any high-frequency subband yields 2 l The source image is divided into a directional sub-band with the same size as the source image. By cascading the non-downsampled pyramid filter bank and the non-downsampled directional filter bank, NSCT finally decomposes the source image into a low-frequency sub-band and multiple high-frequency sub-bands distributed at different scales and in different directions, thereby accurately capturing the contour and texture information in the image.

4. The mineral target area delineation method based on resistivity and polarizability image fusion as described in claim 1, characterized in that, In step (2), for the resistivity low-frequency subband image L ρ _k and low-frequency subband image of polarizability L η The fusion rule considers the average characteristics of the low-frequency sub-band image and the contribution of the target ore body's physical properties. Therefore, a weighted average fusion rule is adopted, which is defined as follows: , Where FL represents the fused low-frequency sub-band image, which is the final result after weighted averaging; w1 and w2 represent weight coefficients, which are pre-set positive numbers and satisfy w1 + w2 = 1; Lρ k The low-frequency subband image representing the resistivity image is the low-frequency component containing its overall structure and background information obtained at the k-th layer after NSCT decomposition of the resistivity source image; Lη k The low-frequency subband image representing the polarizability model is the low-frequency component containing the overall structure and background information obtained at the k-th layer after the polarizability source image is decomposed by NSCT; k represents the number of layers or scales of NSCT decomposition, indicating that fusion is performed separately at each decomposition scale.

5. The mineral target area delineation method based on resistivity and polarizability image fusion as described in claim 1, characterized in that, In step (2), the NMP fusion rule is used to fuse the resistivity high-frequency subband image and the polarizability high-frequency subband image; resistivity high-frequency subband image H ρ _k and polarizability high-frequency subband image H η _k describes the detail information corresponding to the source image, using a metric parameter NMP; NMP is defined as follows: , Where α1, β1, and γ1 are the parameters used to adjust PC, LSCM, and LE in NMP, α1=1, β1=2, and γ1=2; after obtaining NMP, the fused high-frequency subband image is obtained through the following formula: , Where FH represents the high-frequency fused image, and NMP represents the NMP image. ρ (x,y) and NMPη(x,y) are the NMP values ​​of the resistivity and polarizability images at (x,y), respectively; Hρ k The coefficients of the high-frequency subband image representing the resistivity image at the k-th level decomposition are the detail components obtained after NSCT decomposition of the resistivity source image; Hη k The coefficients of the high-frequency subband image representing the polarimetric image at the k-th level decomposition are the detail components obtained after NSCT decomposition of the polarimetric source image; "otherwise" is a conditional statement meaning "otherwise," and in this decision rule, it indicates that if the condition [NMP] is met, then... ρ The operation performed when (x,y)≥NMPη(x,y)] does not hold, that is, when the NMP value of resistivity is less than the NMP value of polarizability.

6. The mineral target area delineation method based on resistivity and polarizability image fusion as described in claim 1, characterized in that, In step (3), the resistivity image I is obtained by reconstructing the fused low-frequency subband image and high-frequency subband image using the inverse non-subsampled contour wave transform. ρ and polarization image I η The IF (Image Fusion Process) formula is as follows: IF=INNSCT(FL,FH) INNSCT stands for Inverse Non-Subsampled Profile Wave Transform. The non-subsampled pyramid filter bank and non-subsampled directional filter bank used in this process are consistent with those used in the decomposition process. The reconstructed fused image simultaneously contains the spatial distribution characteristics of resistivity and polarizability.

Images