A remote sensing identification method for ore-controlling folds in vegetation-covered areas of mid-altitude mountains

Abstract

The present invention provides a remote sensing identification method for ore-controlling folds in mid-alpine vegetation-covered areas. First, based on the DEM data of the study area, a relatively complete continuous slope is extracted for post-processing to obtain a complete and smooth fold structure; at the same time, WorldView‑3 images and ASTER images of the study area are obtained, vegetation and water bodies in the images are masked, and alteration anomalies are extracted respectively and points with inconsistent positions are removed to obtain mineralized alteration anomaly points; finally, the obtained fold structure and mineralized alteration anomaly points are superimposed and analyzed, and the ore-controlling effect of the fold is determined according to the relationship between the distribution characteristics of the mineralized alteration anomaly points and the surface morphology of the fold structure. The present invention macroscopically, quickly and directly identifies the spatial distribution characteristics of the fold structure and the mineralized alteration anomaly points in the mid-alpine vegetation-covered areas, and through superimposed analysis, the relationship between the fold structure and the mineralization is determined, providing spatial information support for in-depth research on the fold ore-controlling mechanism.

Description

Technical Field

[0001] The invention relates to a remote sensing identification method, in particular to a remote sensing identification method for ore-controlling folds in a mid-high mountain vegetation coverage area. Background Art

[0002] Fold structures are of great significance for predicting mineral exploration. Folds before and during mineralization have direct and indirect control effects on mineralization, from which mineralization laws can be analyzed. Folds after mineralization can help us understand the transformation, enrichment or destruction of ore deposits, as well as analyze and determine the superposition and enrichment laws of multi-stage mineralization. Therefore, studying the spatiotemporal relationship between folds and mineralization is very important for regional mineral resource prediction and exploration. Traditional field geological work is difficult in areas covered by vegetation in mid- and high-mountain areas, and the geological information that can be directly observed and obtained, such as fold structures and regional mineralization, is limited, and there are certain limitations in the understanding of the regional fold control mechanism. Summary of the invention

[0003] In view of the technical problems existing in the above-mentioned background technology, the present invention proposes a remote sensing identification method for ore-controlling folds in mid-to-high mountain vegetation coverage areas. The method has a reasonable conception and can identify the spatial distribution characteristics of fold structures and mineralization and alteration anomaly information in a macroscopic, rapid and direct manner through high-resolution satellite remote sensing images and DEM elevation data. By superimposing and analyzing the spatial distribution characteristics of the two, the relationship between fold structure and mineralization can be determined, providing spatial information support for in-depth research on the fold-controlling ore-controlling mechanism.

[0004] In order to solve the above technical problems, the present invention provides a remote sensing identification method for ore-controlling folds in mid-to-high mountain vegetation coverage areas. First, based on the DEM data of the study area, relatively complete continuous slopes are extracted and post-processed to obtain complete and smooth fold structures; at the same time, Worl dView-3 images and ASTER images of the study area are obtained, the vegetation and water bodies in the images are masked, and the alteration anomalies are extracted respectively and the points with inconsistent positions are removed to obtain the mineralized alteration anomaly points; finally, the obtained fold structure and the mineralized alteration anomaly points are superimposed and analyzed, and the ore-controlling effect of the folds is determined according to the relationship between the distribution characteristics of the mineralized alteration anomaly points and the surface morphology of the fold structure.

[0005] The remote sensing identification method for ore-controlling folds in mid-altitude vegetation-covered areas, wherein the acquisition process of the fold structure is:

[0006] (1.1) Obtain the spatial resolution ALOSDEM data of the study area;

[0007] (1.2) For each pixel a in the DEM data, take the eight neighborhoods of 3×3 pixels one by one, that is, Calculate the rate of change dx of pixel a in the x direction and the rate of change dy in the y direction. The formula is:

[0008] dx=[(a3+2a5+a8)-(a1+2a4+a6)] / (8×r);

[0009] dy=[(a6+2a7+a8)-(a1+2a2+a3)] / (8×r);

[0010] In the formula, r represents the resolution of DEM data, r = 12.5;

[0011] Then calculate the slope S of the study area, the formula is:

[0012]

[0013] Traverse each pixel by moving the neighborhood and assign the calculated slope value to each pixel;

[0014] (1.3) Reclassify the slope S, set the slope threshold, eliminate the flat areas, and obtain the slope S' of the middle and high mountain areas;

[0015] (1.4) Using the object-oriented contrast splitting segmentation algorithm, setting the contrast splitting segmentation algorithm threshold, performing segmentation operations on S', and obtaining the segmented middle and high mountain slope map S";

[0016] (1.5) Using the formula Density = N / R, where N represents the number of pixels of a certain spot and R represents the approximate radius of the spot, calculate the shape feature Density of each spot in the medium and high mountain slope map S”, set the Density threshold, and obtain the initial slope result P;

[0017] (1.6) Converting the initial slope result P of the raster type into vector data, and processing the vector data to obtain the smoothed slope result P' of the vector type;

[0018] (1.7) Convert the smoothed slope result P' into raster data, and use ArcScan tool to extract the center line of each patch of the raster data;

[0019] (1.8) The center line of the image spot is superimposed on the optical image of the study area, and non-structural lines are manually removed to obtain the correct fold structure.

[0020] The remote sensing identification method for ore-controlling folds in the vegetation-covered area of ​​mid-altitude mountains, wherein the process of obtaining the mineralized alteration abnormal points is as follows:

[0021] (2.01) Obtain the image data of Wor l dView-3 and ASTER images in the study area;

[0022] (2.02) The Wor l dView-3 image and ASTER image were uniformly resampled respectively;

[0023] (2.03) Then pre-process the Wor l dView-3 image and ASTER image respectively;

[0024] (2.04) Based on the spectral index, the vegetation and water bodies in the Worl dView-3 image and ASTER image of the study area are masked to obtain the target image;

[0025] (2.05) Perform principal component analysis on the target images after vegetation and water masking in Wor l dView-3 images and ASTER images, and select characteristic principal component components based on statistical characteristics;

[0026] (2.06) Statistical band information of the principal components obtained based on the Worl dView-3 image and the ASTER image, and extract the alteration anomalies of iron staining, hydroxyl and carbonate based on the statistical band information;

[0027] (2.07) The various abnormal results obtained from the Worl dView-3 image and the ASTER image are subjected to median filtering to enhance the regularity of the distribution of abnormal points and obtain the point set Q ij , i is the image type, j is the abnormality type;

[0028] (2.08) For each outlier set Q ij , use DBSCAN algorithm for spatial clustering, and cluster each outlier set Q ij Divide into several subsets according to spatial distance, that is, Q = {q1, q2, ..., q n}, n represents the number of subsets;

[0029] (2.09) For each subset q k , where k = 1, 2, ..., n, and the point increment algorithm is used for each subset q k Generate a minimum outer circle C k ;

[0030] (2.10) The outer circles of various abnormal results of the Worl dView-3 image and the ASTER image are superimposed correspondingly, the non-intersecting outer circles are eliminated, and the abnormal points at the positions of the remaining outer circles are selected as the final abnormal results.

[0031] The remote sensing identification method for ore-controlling folds in mid-high mountain vegetation coverage areas, wherein: after the step (2.05) is completed, the main component components in the target image obtained after the vegetation and water body are respectively low-pass filtered to obtain a smoothed image.

[0032] The remote sensing identification method for ore-controlling folds in mid-altitude vegetation-covered areas, wherein the specific process of the overlay analysis is:

[0033] (3.1) Superimpose the acquired fold structure and mineralized alteration anomaly points;

[0034] (3.2) Delineate the area where the fold structure and the mineralized alteration anomaly points overlap, and analyze the spatial and temporal relationship between the fold structure and the mineralized alteration anomaly points;

[0035] (3.3) The distribution of mineralized alteration anomalies along the folded rock layers. If there is a certain continuity and the information of mineralized alteration anomalies in the parts with strong rock deformation is significantly different from that in other parts, it indicates that the folds were formed after mineralization;

[0036] (3.4) The mineralized alteration anomalies are distributed along the rock layers of the folds. If they have poor continuity and are concentrated in the fold fracture zone, it indicates that the folds were formed before mineralization.

[0037] The remote sensing identification method for mineral-controlling folds in mid-to-high mountain vegetation coverage areas, wherein: in the step (3.4), the mineralized alteration anomaly points are distributed along the rock strata of the folds. If they have poor continuity and are concentrated in the fold fracture zone, they can also indicate that they were formed before the mineralization and at the same time as the mineralization.

[0038] By adopting the above technical solution, the present invention has the following beneficial effects:

[0039] The remote sensing identification method for ore-controlling folds in mid-altitude vegetation-covered areas of the present invention is well-conceived and solves the problem that it is difficult to quickly and macroscopically identify regional fold structures and mineralization information in field surveys in mid-altitude vegetation-covered areas due to the low exposure of geological bodies and complex topography; starting from the spatial relationship between the surface morphological characteristics of the fold structure and the distribution of mineralization alteration anomalies, an analysis of the control of the fold structure on regional mineralization is carried out, which can provide spatial information support for regional remote sensing prospecting prediction and in-depth research on the fold control mechanism; comprehensive use of high-resolution satellite remote sensing data and DEM data to carry out remote sensing identification of ore-controlling folds has achieved good results, providing a method innovation for remote sensing geological interpretation and remote sensing investigation of mineral resources BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 The present invention is a specific flow chart of the remote sensing identification method for ore-controlling folds in mid-altitude and high mountain vegetation coverage areas. DETAILED DESCRIPTION

[0042] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] The present invention is further explained below in conjunction with specific implementation modes.

[0044] like Figure 1 As shown, the present embodiment provides a remote sensing identification method for ore-controlling folds in mid-to-high mountain vegetation coverage areas. First, based on the DEM data of the study area, relatively complete continuous slopes are extracted and post-processed to obtain relatively complete and smooth fold structures. At the same time, Worl dView-3 images and ASTER images of the study area are obtained, the vegetation and water bodies in the images are masked, and the alteration anomalies are extracted respectively, and the points with inconsistent positions are removed to obtain the mineralized alteration anomaly points. Finally, the obtained fold structure and the mineralized alteration anomaly points are superimposed and analyzed, and the ore-controlling effect of the folds is determined according to the relationship between the distribution characteristics of the mineralized alteration anomaly points and the surface morphology of the fold structure.

[0045] like Figure 1 As shown, the remote sensing identification method of the present invention for ore-controlling folds in a mid-altitude vegetation-covered area specifically comprises the following steps:

[0046] S100, fold structure acquisition

[0047] S101. Obtain 12.5m spatial resolution ALOSDEM data of the study area; 12.5m spatial resolution means that the ground distance corresponding to the side length of each pixel in the DEM data is 12.5m. Commonly used DEM data have three resolutions: 12.5m, 30m, and 90m. Compared with 90m, 12.5m and 30m can extract slopes more accurately;

[0048] S102, for each pixel a in the DEM data, take the eight neighborhoods of 3×3 pixels one by one, that is, Calculate the rate of change dx of pixel a in the x direction and the rate of change dy in the y direction. The formula is:

[0049] dx=[(a3+2a5+a8)-(a1+2a4+a6)] / (8×r)

[0050] dy=[(a6+2a7+a8)-(a1+2a2+a3)] / (8×r)

[0051] In the formula, r represents the resolution of DEM data, r = 12.5;

[0052] Then calculate the slope S of the study area, the formula is:

[0053]

[0054] Traverse each pixel by moving the neighborhood and assign the calculated slope value to each pixel;

[0055] S103, reclassifying the slope S, setting a slope threshold, eliminating flat areas, and obtaining the slope S' of the middle and high mountain areas;

[0056] S104, using an object-oriented contrast splitting segmentation algorithm, setting a contrast splitting segmentation algorithm threshold, performing a segmentation operation on S', and obtaining a segmented middle and high mountain slope map S".

[0057] S105, using the formula Density=N / R (N represents the number of pixels of a certain spot, and R represents the approximate radius of the spot) to calculate the shape feature Density of each spot in S", set the Density threshold, and obtain the initial slope result P;

[0058] S106, converting the initial slope result P of the raster type into vector data, performing surface element clustering, boundary smoothing and other processing on the vector data, and obtaining a smoothed slope result P' of the vector type;

[0059] S107, convert P' into raster data, and use ArcScan tool to extract the center line of each spot of the raster data;

[0060] S108. Superimpose the center line of the image spot on the optical image of the study area, manually remove non-structural lines, and obtain the correct fold structure.

[0061] S200, acquisition of mineralization alteration abnormal points

[0062] S201, obtain the image data of Worl dView-3 and ASTER images of the study area;

[0063] S202. Resample the Worl dView-3 images to a uniform spatial resolution of 7.50 m, and resample the ASTER images to a uniform spatial resolution of 30 m. In order to unify the spatial resolutions of different bands, theoretically, it can be any value greater than 0. In actual operation, it is generally based on a certain band, and the resolutions of other bands are modified to the basic band resolution.

[0064] S203, perform radiometric calibration, atmospheric correction, cropping and other preprocessing on the Worl dView-3 image and ASTER image respectively;

[0065] S204, masking vegetation and water bodies in the Worl dView-3 image and ASTER image of the study area based on spectral indices to obtain a target image;

[0066] S205, respectively performing principal component analysis on the target images after vegetation and water body masking in the Worl dView-3 image and the ASTER image, and selecting characteristic principal component components based on statistical characteristics; wherein, by respectively performing low-pass filtering on the principal component components, a smoothed image can be obtained;

[0067] S206, respectively statistically analyzing the band information of the principal components obtained based on the Worl dView-3 image and the ASTER image, and extracting the alteration anomaly points of iron staining, hydroxyl and carbonate based on the band statistical information;

[0068] S207, median filtering is performed on various abnormal results obtained from the Worl dView-3 image and the ASTER image to enhance the regularity of the distribution of abnormal points and obtain the point set Q ij (i is the image type, j is the abnormality type);

[0069] S208. For each abnormal point set Q ij , use DBSCAN algorithm for spatial clustering, and cluster each outlier set Q ij Divide into several subsets according to spatial distance, that is, Q = {q1, q2, ..., q n}, n represents the number of subsets;

[0070] S209, for each subset q k , where k = 1, 2, ..., n; use the point increment algorithm for each subset q k Generate a minimum outer circle C k ;

[0071] S210, superimposing the outer circles of various abnormal results of the Worl dView-3 image and the ASTER image accordingly, eliminating the outer circles without intersection, and selecting the abnormal points at the positions of the remaining outer circles as the final abnormal results.

[0072] S300, Overlay Analysis

[0073] S301, superimposing the acquired fold structure and the mineralized alteration abnormal points;

[0074] S302, delineate the area where the fold structure and the mineralized alteration abnormal points overlap, and analyze the temporal and spatial relationship between the fold structure and the mineralized alteration abnormal points;

[0075] S303. The mineralized alteration anomalies are distributed along the folded rock layers. If they have a certain continuity and the mineralized alteration anomalies in the parts where the rock layers are strongly deformed are significantly different from those in other parts, it indicates that the folds were formed after the mineralization, and the ore deposits were transformed, enriched or destroyed;

[0076] S304. Mineralized alteration anomalies are distributed along the folded rock strata. If they have poor continuity and are mostly concentrated in the fold fracture zones or fault sites, it indicates that the folds were formed before or at the same time as the mineralization, directly or indirectly providing mineralization space and controlling the distribution, spatial morphology and occurrence of the ore bodies.

[0077] The present invention has a reasonable concept. Through high-resolution satellite remote sensing images and DEM elevation data, the spatial distribution characteristics of fold structures and abnormal mineralization and alteration information can be directly and macroscopically identified. By superimposing and analyzing the spatial distribution characteristics of the two, the relationship between fold structures and mineralization can be determined, providing spatial information support for in-depth research on the fold-controlling ore mechanism.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote sensing identification method for ore-controlling folds in vegetation-covered areas of mid- and high mountains, characterized in that : First, based on the DEM data of the study area, a relatively complete continuous slope was extracted and post-processed to obtain a complete and smooth fold structure; at the same time, the WorldView-3 image and ASTER image of the study area were obtained, the vegetation and water bodies in the image were masked, and the alteration anomalies were extracted respectively and the points with inconsistent positions were removed to obtain the mineralized alteration anomaly points; finally, the obtained fold structure and the mineralized alteration anomaly points were superimposed and analyzed, and the ore-controlling effect of the fold was determined based on the relationship between the distribution characteristics of the mineralized alteration anomaly points and the surface morphology of the fold structure; The process of obtaining the fold structure is as follows: (1.1) Obtain the spatial resolution ALOSDEM data of the study area; (1.2) For each pixel a in the DEM data, take the eight neighborhoods of 3×3 pixels one by one, that is, Calculate the rate of change dx of pixel a in the x direction and the rate of change dy in the y direction. The formula is: dx=[(a3+2a5+a8)-(a1+2a4+a6)] / (8×r); dy=[(a6+2a7+a8)-(a1+2a2+a3)] / (8×r); In the formula, r represents the resolution of DEM data, r = 12.5; Then calculate the slope S of the study area, the formula is: Traverse each pixel by moving the neighborhood and assign the calculated slope value to each pixel; (1.3) Reclassify the slope S, set the slope threshold, eliminate the flat areas, and obtain the slope S' of the middle and high mountain areas; (1.4) Using the object-oriented contrast splitting segmentation algorithm, setting the contrast splitting segmentation algorithm threshold, performing segmentation operations on S', and obtaining the segmented middle and high mountain slope map S"; (1.5) Using the formula Density = N / R, where N represents the number of pixels of a certain spot and R represents the approximate radius of the spot, calculate the shape feature Density of each spot in the medium and high mountain slope map S'', set the Density threshold, and obtain the initial slope result P; (1.6) Converting the initial slope result P of the raster type into vector data, and processing the vector data to obtain the smoothed slope result P' of the vector type; (1.7) Convert the smoothed slope result P' into raster data, and use ArcScan tool to extract the center line of each patch of the raster data; (1.8) Superimpose the center line of the image spot on the optical image of the study area, manually remove the non-structural lines, and obtain the correct fold structure; The process of obtaining the mineralized alteration abnormal points is as follows: (2.01) Obtain image data of WorldView-3 and ASTER images of the study area; (2.02) Uniformly resample the WorldView-3 images and ASTER images; (2.03) Then pre-process the WorldView-3 image and ASTER image respectively; (2.04) Mask the vegetation and water bodies in the WorldView-3 image and ASTER image of the study area based on the spectral index to obtain the target image; (2.05) Perform principal component analysis on the target images after vegetation and water masking in WorldView-3 and ASTER images, and select characteristic principal component components based on statistical features; (2.06) Statistical band information of the principal components obtained based on WorldView-3 images and ASTER images, and extract the alteration anomalies of iron staining, hydroxyl and carbonate based on the statistical band information; (2.07) Median filtering is performed on various abnormal results obtained from WorldView-3 images and ASTER images to enhance the regularity of abnormal point distribution and obtain the point set Q ij , i is the image type, j is the abnormality type; (2.08) For each outlier set Q ij , use DBSCAN algorithm for spatial clustering, and cluster each outlier set Q ij Divide into several subsets according to spatial distance, that is, Q = {q1, q2, ..., q n }, n represents the number of subsets; (2.09) For each subset q k , where k = 1, 2, ..., n, and the point increment algorithm is used for each subset q k Generate a minimum outer circle C k ; (2.10) The outer circles of various abnormal results of WorldView-3 images and ASTER images are superimposed, the non-intersecting outer circles are eliminated, and the abnormal points at the positions of the remaining outer circles are selected as the final abnormal results.

2. The remote sensing identification method for ore-controlling folds in mid-altitude vegetation-covered areas according to claim 1, characterized in that: After the step (2.05) is completed, a smoothed image can be obtained by performing low-pass filtering on the principal components in the target image obtained after masking the vegetation and water bodies.

3. The remote sensing identification method for ore-controlling folds in mid-altitude vegetation-covered areas according to claim 1, characterized in that: The specific process of the overlay analysis is as follows: (3.1) Superimpose the acquired fold structure and mineralized alteration anomaly points; (3.2) Delineate the area where the fold structure and the mineralized alteration anomaly points overlap, and analyze the spatial and temporal relationship between the fold structure and the mineralized alteration anomaly points; (3.3) The distribution of mineralized alteration anomalies along the folded rock layers. If there is a certain continuity and the information of mineralized alteration anomalies in the parts with strong rock deformation is significantly different from that in other parts, it indicates that the folds were formed after mineralization; (3.4) The mineralized alteration anomalies are distributed along the rock layers of the folds. If they have poor continuity and are concentrated in the fold fracture zone, it indicates that the folds were formed before mineralization.

4. The remote sensing identification method for ore-controlling folds in mid-altitude vegetation-covered areas as claimed in claim 3, characterized in that: The abnormal mineralized alteration points mentioned in (3.4) are distributed along the rock layers of the folds. If they have poor continuity and are concentrated in the fold fracture zone, it can also indicate that the folds and mineralization are synchronous.

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