Hidden fracture geological structure feature extraction method, system, device and storage medium

Abstract

The application relates to the technical field of remote sensing geological exploration, and particularly relates to a concealed fault geological structure feature extraction method, system, device and storage medium; the method comprises the following steps: acquiring hyperspectral remote sensing data corresponding to a to-be-detected geological area; pre-processing the hyperspectral remote sensing data to obtain surface reflectivity data; based on the surface reflectivity of the pixel in the first set wave band range, extracting a muscovite diagnostic absorption characteristic wavelength corresponding to the pixel; grading the muscovite diagnostic absorption characteristic wavelengths corresponding to each pixel respectively; for the pixels corresponding to muscovite diagnostic absorption characteristic wavelengths of different grades, different color information is given according to a preset mapping relationship, and a muscovite grid image is obtained; and abnormal color bands or linear boundaries between different colors are extracted from the muscovite grid image as concealed fault geological structure features of the to-be-detected geological area. The application realizes accurate extraction of concealed fault geological structure features and is more efficient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing geological exploration, in particular to a hidden fault geological structure feature extraction method, system, device and storage medium. BACKGROUND

[0002] Geological fault structure is an important geological structure, which has important significance for mineral exploration, engineering construction, and earth science research.

[0003] Remote sensing geological exploration is a method of studying geological rules, conducting geological survey and resource exploration by comprehensively applying multi-source remote sensing data and other geological information on the basis of the research on the relationship between the spectrum, radiation characteristics and composition attributes of ground objects. Due to its technical advantages such as macroscopic, rapid and economical, it provides important technical support for geological prospecting work in geological survey work in China, especially in the central and western regions.

[0004] In the past conventional remote sensing work, the interpretation of linear structure is often displayed in an indirect way of controlling lithology, lithofacies, topography, and water system development characteristics, and is shown as different color tones and shapes on remote sensing images, so the interpretation of linear structure on remote sensing images mainly starts from color tone and shape. The above conventional method is mainly used for high-resolution and multispectral remote sensing images, but when facing ductile faults with inconspicuous structure traces or geological conditions such as erosion of topographic features, the above conventional method becomes very difficult to extract the features of hidden fault geological structure because the color tone and shape features of remote sensing image data are not obvious. SUMMARY

[0005] In order to solve the problem that the current conventional method is difficult to extract the features of hidden fault geological structure, the present application provides a hidden fault geological structure feature extraction method, system, device and storage medium.

[0006] In the first aspect, the hidden fault geological structure feature extraction method provided by the present application adopts the following technical scheme:

[0007] A hidden fault geological structure feature extraction method, comprising:

[0008] Obtaining hyperspectral remote sensing data corresponding to a to-be-measured geological area;

[0009] Pretreating the hyperspectral remote sensing data to obtain surface reflectivity data; the surface reflectivity data includes surface reflectivity of each pixel in a first set wavelength range;

[0010] Based on the surface reflectivity of the pixel in the first set wavelength range, extracting a diagnostic absorption characteristic wavelength of muscovite corresponding to the pixel;

[0011] The diagnostic absorption characteristic wavelengths of muscovite corresponding to each of the image elements are classified;

[0012] The image elements corresponding to the diagnostic absorption characteristic wavelengths of muscovite of different grades are given different color information according to a preset mapping relationship to obtain a muscovite grid image.

[0013] An abnormal color strip or a linear boundary between different colors is extracted from the muscovite grid image as a hidden fault geological structure feature of the to-be-measured geological region.

[0014] Through the above technical solution, based on the analysis and excavation of the inventors on the internal connection between the microscopic changes in the physical structure of muscovite caused by hidden faults in geological structures and the spectral drift of diagnostic absorption characteristic wavelengths of muscovite, and then using a related algorithm to extract the diagnostic absorption characteristic wavelengths of muscovite in a specific waveband range (i.e., a first set waveband range) from hyperspectral data, the calculated diagnostic absorption characteristic wavelengths of muscovite can more accurately reflect the spectral changes caused by hidden faults. Further, by classifying and coloring the diagnostic absorption characteristic wavelengths of muscovite, it is more conducive to accurately identifying and extracting hidden fault geological structure features. Compared with the conventional high-resolution or multispectral remote sensing image-based method, the hidden fault geological structure features are not obvious, which leads to the inability to accurately extract them. The present solution starts from a new angle (hidden fault → diagnostic absorption characteristic wavelength of muscovite → hyperspectral remote sensing), and realizes accurate extraction of hidden fault geological structure features, which is more efficient.

[0015] Optionally, the extraction of the diagnostic absorption characteristic wavelength of muscovite corresponding to the image element based on the surface reflectivity of the image element in the first set waveband range comprises:

[0016] Based on the surface reflectivity of the image element in the first set waveband range, a reflectivity value after continuum removal processing is calculated, and whether the image element has a diagnostic signal of muscovite is determined based on the reflectivity value.

[0017] If so, data point information corresponding to each of a plurality of small reflectivity values after continuum removal processing is obtained, a one-variable quadratic function is constructed with the wavelength of the data point as the dependent variable and the reflectivity as the independent variable, and the one-variable quadratic function is fitted.

[0018] The symmetry axis of the fitted one-variable quadratic function is taken as the diagnostic absorption characteristic wavelength of muscovite corresponding to the image element.

[0019] By adopting the technical scheme, the surface reflectivity data is extracted from the hyperspectral remote sensing data, and the continuum removal processing is adopted to determine whether the white mica diagnostic signal exists in each pixel, the data point information with the white mica diagnostic signal is used to screen the possible hidden fracture features, and the data point information without the white mica diagnostic signal cannot be used for the white mica diagnostic absorption characteristic wavelength, and thus the internal correlation between the hidden fracture features cannot be reflected. In this way, the data processing efficiency and the accuracy of the hidden fracture feature extraction are improved.

[0020] Optionally, the reflectivity value after the continuum removal processing includes adopting the following formula:

[0021]

[0022] wherein, R C (λ) represents the reflectivity value after the continuum removal processing; R O (λ) represents the surface reflectivity of the pixel at the wavelength of λ; CL_w represents the average wavelength corresponding to the wavelength range of the left end point of the continuum removal processing; CL_r represents the average reflectivity corresponding to the wavelength range of the left end point of the continuum removal processing; CR_w represents the average wavelength corresponding to the wavelength range of the right end point of the continuum removal processing; and CR_r represents the average reflectivity corresponding to the wavelength range of the right end point of the continuum removal processing.

[0023] The wavelength range of the left end point and the wavelength range of the right end point are located in the first set wavelength range, and the maximum wavelength of the wavelength range of the left end point is less than the minimum wavelength of the wavelength range of the right end point.

[0024] By adopting the technical scheme, the reflectivity value of the corresponding pixel after the continuum removal processing can be calculated based on the left end point data and the right end point data of the continuum removal processing and in combination with the surface reflectivity data in the first set wavelength range, and then whether the white mica diagnostic signal exists in the pixel can be determined based on the reflectivity value.

[0025] Optionally, the determination of whether the white mica diagnostic signal exists in the pixel based on the reflectivity value includes:

[0026] whether the following three conditions are all met:

[0027] 1-R C (λ)>threshold1;

[0028] R O (λ)(1-R C (λ))>threshold2;

[0029] (CL_r-RO (λ)) / (CR_r-R O (λ))>threshold3;

[0030] If yes, it is judged that there is a muscovite diagnostic signal; otherwise, it is judged that there is no muscovite diagnostic signal; wherein threshold1 is a first set threshold, threshold2 is a second set threshold, and threshold3 is a third set threshold.

[0031] By adopting the above technical solution, based on the judgment of the above three conditions, it can be accurately judged whether there is a muscovite diagnostic signal.

[0032] Optionally, the first set wavelength range includes 2120 nanometers to 2290 nanometers.

[0033] The left end point is in a wavelength range of 2120 nanometers to 2150 nanometers; and the right end point is in a wavelength range of 2260 nanometers to 2290 nanometers.

[0034] The first set threshold is 0.05; the second set threshold is 0.004; and the third set threshold is 1.2.

[0035] By adopting the above technical solution, based on the measured data, the accuracy of the hidden fracture feature extraction is higher.

[0036] Optionally, after extracting the muscovite diagnostic absorption characteristic wavelength corresponding to the pixel, the method further includes: judging whether the muscovite diagnostic absorption characteristic wavelength is located in a second set wavelength range; and the second set wavelength range is a subset of the first set wavelength range.

[0037] If yes, the step of classifying the muscovite diagnostic absorption characteristic wavelength corresponding to each pixel is performed;

[0038] If no, go to the extraction step of the muscovite diagnostic absorption characteristic wavelength corresponding to the next pixel, until the extraction of the muscovite diagnostic absorption characteristic wavelength corresponding to all pixels in the to-be-measured geological region is completed.

[0039] By adopting the above technical solution, the screened muscovite diagnostic absorption characteristic wavelength can more accurately reflect the spectral changes caused by the hidden fracture, thereby improving the accuracy of the hidden fracture feature extraction.

[0040] Optionally, the preset mapping relationship includes:

[0041] For muscovite diagnostic absorption characteristic wavelengths of different grades, different colors of a rainbow color system are sequentially assigned in order.

[0042] By adopting the technical scheme, the pixel corresponding to the diagnostic absorption characteristic wavelength of muscovite of different grades is assigned with a color tone with good contrast effect, so that the hidden fracture feature is more convenient to extract.

[0043] In a second aspect, the application provides a hidden fracture geological structure feature extraction system, which adopts the following technical scheme:

[0044] A hidden fracture geological structure feature extraction system comprises:

[0045] An acquisition module is configured to acquire hyperspectral remote sensing data corresponding to a geological region to be measured.

[0046] A preprocessing module is configured to preprocess the hyperspectral remote sensing data to obtain surface reflectivity data, wherein the surface reflectivity data comprises surface reflectivity of each pixel in a first set wavelength range.

[0047] A processing module is configured to extract a muscovite diagnostic absorption characteristic wavelength corresponding to each pixel based on the surface reflectivity of the pixel in the first set wavelength range.

[0048] A grading module is configured to grade the muscovite diagnostic absorption characteristic wavelengths corresponding to each pixel.

[0049] A color assignment module is configured to assign different color information to the pixels corresponding to the muscovite diagnostic absorption characteristic wavelengths of different grades according to a preset mapping relationship, to obtain a muscovite grid image.

[0050] A feature recognition and extraction module is configured to extract an abnormal color strip or a linear boundary between different colors from the muscovite grid image as a hidden fracture geological structure feature of the geological region to be measured.

[0051] In a third aspect, the application provides a device, which adopts the following technical scheme:

[0052] A device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the hidden fracture geological structure feature extraction method when executing the computer program.

[0053] In a fourth aspect, the application provides a computer readable storage medium, which adopts the following technical scheme:

[0054] A computer readable storage medium stores a computer program, and the computer program is executable on a processor to implement the hidden fracture geological structure feature extraction method.

[0055] By adopting the technical scheme, a carrier of a computer program of the hidden fracture geological structure feature extraction method is provided.

[0056] To sum up, the present application includes the following at least beneficial technical effects:

[0057] 1. The present scheme realizes efficient and accurate extraction of implicit fault geological structure features. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 is a flow chart of an implicit fault geological structure feature extraction method in an embodiment of the present application;

[0059] Figure 2 is a flow chart of a method for determining and acquiring muscovite diagnostic absorption characteristic wavelength in an embodiment of the present application;

[0060] Figure 3 is a value taking diagram of removing continuous processing left and right endpoints in an embodiment of the present application;

[0061] Figure 4 is a gray-scale muscovite raster image in an embodiment of the present application;

[0062] Figure 5 is a color-enhanced muscovite raster image in an embodiment of the present application;

[0063] Figure 6 is a structural block diagram of an implicit fault geological structure feature extraction system in an embodiment of the present application;

[0064] Figure 7 is a structural block diagram of an apparatus in an embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0066] An implicit fault geological structure feature extraction method is disclosed in an embodiment of the present application.

[0067] Reference Figure 1 The implicit fault geological structure feature extraction method mainly includes the following steps:

[0068] S101, acquiring hyperspectral remote sensing data corresponding to a to-be-measured geological region.

[0069] The to-be-measured geological region is a region that needs to be detected and identified whether there is an implicit fault geological structure feature and / or needs to extract an implicit fault geological structure feature.

[0070] The hyperspectral remote sensing data can be acquired in any existing manner, and embodiments of the present application do not limit this. This includes, but is not limited to, remote sensing data captured by hyperspectral land resource remote sensing satellites such as GF5, ZY1-02D, GF5-01A, ZY1-02E, and the like. Optionally, the hyperspectral remote sensing data includes spatial position data, radiation data, and spectral data, and the like.

[0071] It should be understood that the hyperspectral remote sensing data corresponding to the to-be-measured geological region, that is, the hyperspectral remote sensing data includes hyperspectral remote sensing data corresponding to or matching the spatial position of the to-be-measured geological region.

[0072] In optional embodiments of the present application, the recording form of the hyperspectral remote sensing data can be a data cube, each layer of which represents the recording result of electromagnetic wave energy in a specific waveband range, that is, the image recorded in a certain spectral waveband. For example, pixel S1 corresponds to waveband P1, and has a reflectivity R11; pixel S1 corresponds to waveband P2, and has a reflectivity R12; therefore, Rij can represent the reflectivity of pixel Si corresponding to waveband Pj, and the two-dimensional spatial position distribution is superimposed on the two-dimensional spectral reflectivity data, thereby forming a data cube.

[0073] Common natural images in life record energy information of three wavebands, namely red, green, and blue. Hyperspectral remote sensing data records more waveband information, breaking through the limitation of representing wavebands by colors. If the same pixel corresponding to each layer of image is taken out to form an observation vector, then this vector is arranged in order according to frequency (waveband), that is, the reflectivity data of one pixel in different wavebands. If a large number of small frequency intervals can be taken out within a certain frequency (waveband) range, a nearly continuous spectral curve will be obtained. According to spectral analysis technology, different spectral curves can reflect different properties of ground objects, which provides a theoretical basis for subsequent extraction of geological structure feature recognition.

[0074] However, the extraction of the implicit fracture geological structure feature cannot be realized only according to the theoretical basis that different spectral curves can reflect different properties of the ground object, because the internal correlation between the implicit fracture geological structure feature and the spectral curve or the spectral data is unknown, and the processing of the spectral data and the selection of the spectral band are also unknown. In this regard, the inventor analyzes and mines the internal correlation between the micro changes in the physical structure of muscovite caused by the implicit fracture in the geological structure and the spectral drift of the diagnostic absorption characteristic wavelength of muscovite, and then extracts the diagnostic absorption characteristic wavelength of muscovite in a specific band range (i.e., a first set band range) from the hyperspectral data by using a correlation algorithm, so that the calculated diagnostic absorption characteristic wavelength of muscovite can more accurately reflect the spectral changes caused by the implicit fracture. Further, the diagnostic absorption characteristic wavelength of muscovite is graded and colored, so as to be more conducive to accurately identifying and extracting the implicit fracture geological structure feature. Compared with the conventional high-resolution or multispectral remote sensing image-based mode, the implicit fracture geological structure feature is not obvious, which leads to the situation that the implicit fracture geological structure feature cannot be accurately extracted. The present scheme starts from a new angle (implicit fracture -> diagnostic absorption characteristic wavelength of muscovite -> hyperspectral remote sensing), and realizes accurate extraction of the implicit fracture geological structure feature, and is more efficient.

[0075] In S102, the hyperspectral remote sensing data is preprocessed to obtain surface reflectivity data; the surface reflectivity data includes surface reflectivity of each pixel in the first set band range.

[0076] In the optional embodiment of the present application, the preprocessing includes but is not limited to bad line correction, radiation calibration, and atmospheric correction, etc., so as to realize the screening and optimization of the hyperspectral remote sensing data as much as possible, reduce the influence of abnormal or missing data, and thus more accurate and comprehensive surface reflectivity data can be obtained.

[0077] The surface reflectivity is the reflectivity of the geological surface without the influence of the cloud layer and the atmospheric component. In general, the surface reflectivity is calculated from the radiation brightness image, and there are many calculation models, such as the radiation transfer model, and the essence is to remove the influence of the cloud layer, the atmospheric component, the adjacent ground object, etc. The atmospheric correction in ENVI is the MODTRAN4+ which adopts the radiation transfer model.

[0078] In the optional embodiment of the present application, the selected range of the surface reflectivity is the surface reflectivity in the first set band range, and the data outside the first set band range in the hyperspectral remote sensing data does not need to be obtained, so that the obtained data is more concise and effective for the extraction of the implicit fracture geological structure feature.

[0079] It should be understood that the band ranges of hyperspectral remote sensing data collected by different hyperspectral sensors are different, which is related to the performance parameters of the hyperspectral sensors. For example, the hyperspectral sensor OMIS developed by the Shanghai Institute of Technical Logistics of the Chinese Academy of Sciences supports a band range of 460-12500 nm, contains 128 bands, and has a spectral resolution of 10 nm; the hyperspectral sensor AISADual developed by Spectral Imaging supports a band range of 400-2450 nm, contains 500 bands, and has a spectral resolution of 2.9 nm.

[0080] In optional embodiments of the present application, the first set band range is set to be between 2120-2290 nm, so as to better extract the diagnostic absorption characteristic wavelength of muscovite.

[0081] S103, based on the ground reflectivity of the pixel in the first set band range, extracting the diagnostic absorption characteristic wavelength of muscovite corresponding to the pixel;

[0082] In optional embodiments of the present application, the ground reflectivity corresponding to each band in the first set band range of the pixel can be compared, and the band with the minimum ground reflectivity or multiple bands with smaller ground reflectivity are selected. The band with the minimum ground reflectivity is taken as the diagnostic absorption characteristic wavelength of muscovite, or the average wavelength of the multiple bands with smaller ground reflectivity is calculated and taken as the diagnostic absorption characteristic wavelength of muscovite; based on this, the subsequent extraction of the feature of the implicit fault geological structure is realized.

[0083] Although the above-mentioned manner can realize the extraction of the feature of the implicit fault geological structure to a certain extent, it is greatly limited by the influence of data anomalies / fluctuations, and since the muscovite diagnostic signal is not judged and recognized, the pixel data points without the muscovite diagnostic signal will be taken as the object of subsequent feature extraction, so there is a problem of inaccurate feature extraction, and even the feature cannot be extracted.

[0084] To this end, in optional embodiments of the present application, the diagnostic absorption characteristic wavelength of muscovite is determined and obtained by using the following manner, which is described with reference to Figure 2 , including:

[0085] S201, based on the ground reflectivity of the pixel in the first set band range, calculating the reflectivity value after removing the continuum processing; in optional embodiments of the present application, the reflectivity value after removing the continuum processing is calculated by using the following formula:

[0086]

[0087] wherein, R C (λ) represents the reflectivity value after removing the continuum processing;

[0088] R O(λ) represents the ground reflectivity of the pixel at the wavelength of the waveband λ;

[0089] CL_w represents the average wavelength corresponding to the waveband range of the left end point of the continuum removal processing;

[0090] CL_r represents the average reflectivity corresponding to the waveband range of the left end point of the continuum removal processing;

[0091] CR_w represents the average wavelength corresponding to the waveband range of the right end point of the continuum removal processing;

[0092] CR_r represents the average reflectivity corresponding to the waveband range of the right end point of the continuum removal processing;

[0093] wherein the waveband range of the left end point and the waveband range of the right end point are located in the first set waveband range, and the maximum wavelength of the waveband range of the left end point is less than the minimum wavelength of the waveband range of the right end point; as shown in the reference Figure 3 .

[0094] In optional embodiments of the present application, the waveband range of the left end point is 2120 nm to 2150 nm, and the waveband range of the right end point is 2260 nm to 2290 nm.

[0095] S202, judging whether the pixel has the diagnostic signal of muscovite based on the reflectivity value;

[0096] If yes, step S203 is executed;

[0097] If no, go to step S201, and process the next pixel until the processing of all pixels corresponding to the entire geological area to be measured is completed.

[0098] In optional embodiments of the present application, judging whether the pixel has the diagnostic signal of muscovite based on the reflectivity value includes judging whether the following three conditions are all true:

[0099] 1-R C (λ)>threshold1;

[0100] R O (λ)(1-R C (λ))>threshold2;

[0101] (CL_r-R O (λ)) / (CR_r-R O (λ))>threshold3;

[0102] If yes, it is judged that there is muscovite diagnostic signal; otherwise, it is judged that there is no muscovite diagnostic signal; wherein threshold1 is a first set threshold, threshold2 is a second set threshold, and threshold3 is a third set threshold. Wherein, threshold2 and threshold3 are determined according to the diagnostic spectral characteristics of muscovite minerals combined with comprehensive tests (experience).

[0103] In the optional embodiment of the present application, the first set threshold threshold1 = 0.05; the second set threshold threshold2 = 0.004; and the third set threshold threshold3 = 1.2. Based on the measured data, it is shown that the accuracy of the hidden fracture feature extraction is higher.

[0104] S203, obtaining data point information corresponding to each of the plurality of smaller reflectivity values after the continuum removal processing, constructing a one-dimensional quadratic function with wavelength as the dependent variable and reflectivity as the independent variable for fitting;

[0105] By bringing the ground reflectivity R O (λ) corresponding to the pixel at different wavebands λ into the continuum removal processing formula:

[0106]

[0107] The reflectivity value R O (λ) corresponding to the data point (λ, R C (λ)) after the continuum removal processing is obtained; each data point in the first set waveband range is brought into the above-mentioned continuum removal processing formula, so that the reflectivity value R C (λ) after the continuum removal processing of each data point is obtained; by comparing the sizes of the reflectivity values R C (λ) corresponding to each data point, the data point information corresponding to a plurality of (for example, at least 3) smaller reflectivity values (sorted from small to large, the first few are extracted) is selected, which are assumed to be (λ11, R O (λ11)), (λ21, R O (λ21)) and (λ31, R O (λ31)) respectively; a one-dimensional quadratic function with wavelength as the dependent variable and reflectivity as the independent variable is constructed, which is assumed to be y = ax 2 +bx+c; the above-mentioned plurality of data points are used to fit the quadratic function; and the fitting result (the specific values of a, b and c) is obtained. It should be understood that the fitting method can adopt any existing method, which is not described here.

[0108] S204, taking the symmetry axis of the fitted one-dimensional quadratic function as the muscovite diagnostic absorption characteristic wavelength corresponding to the pixel.

[0109] That is, the value of -b / 2a is calculated as the diagnostic absorption characteristic wavelength of muscovite.

[0110] In the optional embodiment of the present application, after the diagnostic absorption characteristic wavelength of muscovite corresponding to the pixel is extracted, it is further determined whether the diagnostic absorption characteristic wavelength of muscovite is located in the second set wavelength range; if yes, the step of S104 is executed; if no, the extraction step of the diagnostic absorption characteristic wavelength of muscovite corresponding to the next pixel is turned to, until the extraction of the diagnostic absorption characteristic wavelength of muscovite corresponding to all pixels in the to-be-measured geological region is completed.

[0111] The second set wavelength range is a subset of the first set wavelength range; the filtered diagnostic absorption characteristic wavelength of muscovite can more accurately reflect the spectral change caused by the implicit fracture, thereby improving the accuracy of the implicit fracture feature extraction.

[0112] In the optional embodiment of the present application, the second set wavelength range is between 2195 nm and 2226 nm.

[0113] S104, grading the diagnostic absorption characteristic wavelength of muscovite corresponding to each pixel respectively;

[0114] In the optional embodiment of the present application, the grading can be performed according to the set wavelength interval. For example, the set wavelength interval is set to 2 nm, and 16 wavelength bands (2195-2196, 2197-2198, 2199-2200, 2201-2202, 2203-2204, 2205-2206, 2207-2208, 2209-2210, 2211-2212, 2213-2214, 2215-2216, 2217-2218, 2219-2220, 2221-2222, 2223-2224, 2225-2226) are obtained.

[0115] S105, according to the preset mapping relationship, different color information is given to the pixels corresponding to the diagnostic absorption characteristic wavelength of muscovite of different grades, and a muscovite grid image is obtained;

[0116] In the optional embodiment of the present application, different colors of the rainbow color system are sequentially given to the diagnostic absorption characteristic wavelength of muscovite of different grades. The pixels corresponding to the diagnostic absorption characteristic wavelength of muscovite of different grades have large color scales and good contrast effects, and thus the implicit fracture feature can be extracted in the later stage.

[0117] S106, an abnormal color strip or a linear boundary between different colors is extracted from the muscovite grid image as the implicit fracture geological structure feature of the to-be-measured geological region.

[0118] Abnormal color strip, namely linear color strip (strip).

[0119] Linear boundary, namely the contact boundary between different colors extends in a linear manner in a certain scale, which is referred to as a linear boundary.

[0120] In optional embodiments of the present application, the linear color strip (strip) or linear boundary can be extracted in a human-computer interaction visual interpretation manner in a GIS platform as a hidden fault geological structure feature of a to-be-measured geological region.

[0121] Abnormal color strip refers to a narrow color strip that is completely different from the background tone;

[0122] The embodiments of the present application have important practical significance for extracting fault structure features with inconspicuous structure traces by fine spectral detection of hyperspectral remote sensing data, systematic spectral drift caused by the micro-influence of hydrothermal activity or thermal effect of muscovite structure caused by fault geological structure.

[0123] In order to better understand the technical solutions of the present application, the following takes the hyperspectral remote sensing EMIT (NASA’s Earth Surface Mineral Dust Source Investigation) image of the Altai region as an example for specific introduction.

[0124] Select one scene EMIT L2A level data (EMIT_L2A_RFL_001_20220823T043448_2223503_014.nc) as a hyperspectral data source, use netCDF4 (a Python toolkit) to read wavelength, wave width and image data, and obtain a surface reflectance image array containing 285 wave bands, wherein the wave band list is as follows:

[0125] [381.0,388.4,395.8,403.2,410.6,418.1,425.5,432.9,440.3,447.7,455.2,462.6,470.0,477.5,484.9,492.3,499.8,507.2,514.7,522.1,529.5,537.0,544.4,551.9,559.3,566.8,574.2,581.7,589.1,596.6,604.0,611.5,618.9,626.4,633.8,641.3,648.7,656.2,663.6,671.1,678.6,686.0,693.5,700.9,708.4,715.8,723.3,730.8,738.2,745.7,753.1,760.6,768.1,775.5,783.0,790.4,797.9,805.4,812.8,820.3,827.7,835.2,842.7,850.1,857.6,865.1,872.5,880.0,887.4,894.9,902.4,909.8,917.3,924.8,932.2,939.7,947.1,954.6,962.1,969.5,977.0,984.4,991.9,999.4,1006.8,1014.3,1021.8,1029.2,1036.7,1044.1,1051.6,1059.1,1066.5,1074.0,1081.4,1088.9,1096.4,1103.8,1111.3,1118.7,1126.2,1133.7,1141.1,1148.6,1156.0,1163.5,1170.9,1178.4,1185.9,1193.3,1200.8,1208.2,1215.7,1223.1,1230.6,1238.1,1245.5,1253.0,1260.4,1267.9,1275.3,1282.8,1290.3,1297.7,1305.2,1312.6,1320.1,1327.5,1335.0,1342.4,1349.9,1357.3,1364.8,1372.2,1379.7,1387.1,1394.6,1402.0,1409.5,1416.9,1424.4,1431.8,1439.3,1446.7,1454.2,1461.6,1469.1,1476.5,1484.0,1491.4,1498.9,1506.3,1513.8,1521.2,1528.7,1536.1,1543.5,1551.0,1558.4,1565.9,1573.3,1580.8,1588.2,1595.6,1603.1,1610.5,1618.0,1625.4,1632.9,1640.3,1647.7,1655.2,1662.6,1670.0,1677.5,1684.9,1692.4,1699.8,1707.2,1714.7,1722.1,1729.5,1737.0,1744.4,1751.8,1759.3,1766.7,17 74.1,1781.6,1789.0,1796.4,1803.9,1811.3,1818.7,1826.2,1833.6,1841.0,1848.4,1855.9,1863.3,1870.7,1878.2,1885.6,1893.0,1900.4,1907.9,1915.3,1922.7,1930.1,1937.6,1945.0,1952.4,1959.8,1967.3,1974.7,1982.1,1989.5,1996.9,2004.4,2011.8 ,2019.2,2026.6,2034.0,2041.4,2048.9,2056.3,2063.7,2071.1,2078.5,2085.9,2093.4,2100.8,2108.2,2115.6,2123.0,2130.4,2137.8,2145.2,2152.6,2160.1,2167.5,2174.9,2182.3,2189.7,2197.1,2204.5,2211.9,2219.3,2226.7,2234.1,2241.5,2248.9,225 6.3,2263.7,2271.1,2278.5,2285.9,2293.3,2300.7,2308.1,2315.5,2322.9,2330.3,2337.7,2345.1,2352.5,2359.9,2367.3,2374.7,2382.1,2389.5,2396.9,2404.3,2411.7,2419.1,2426.4,2433.8,2441.2,2448.6,2456.0,2463.4,2470.8,2478.2,2485.5,2492.9].

[0126] The beamwidth information is as follows:

[0127] [8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.4,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.5,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.6,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.7,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8,8.8].

[0128] Based on the spectral parameters of the EMIT hyperspectral remote sensing image, 24 bands (235 to 258, 2123.0, 2130.4, 2137.8, 2145.2, 2152.6, 2160.1, 2167.5, 2174.9, 2182.3, 2189.7, 2197.1, 2204.5, 2211.9, 2219.3, 2226.7, 2234.1, 2241.5, 2248.9, 2256.3, 2263.7, 2271.1, 2278.5, 2285.9, 2293.3) were selected as the spectral range for extracting the diagnostic absorption characteristics of muscovite. The following operations were performed on the spectrum of each pixel:

[0129]

[0130] The mean wavelength and mean reflectance of bands 235 to 239 are taken as the left endpoint of the continuum, denoted as (CL_w, CL_r); the mean wavelength and mean reflectance of bands 254 to 258 are taken as the right endpoint of the continuum, denoted as (CR_w, CR_r).

[0131] Take the average wavelength corresponding to bands 235 to 239, that is, calculate the average value of the five bands 2123.0, 2130.4, 2137.8, 2145.2, and 2152.6, which is 2137.8. The others are similar, and will not be repeated here.

[0132] Using the left and right endpoints of the continuum determined in the previous step, perform continuum removal processing on the ground object spectra between bands 235 and 258 according to the above formula.

[0133] In the formula, band 235 ≤ λ ≤ band 258, in nanometers, representing the wavelength of the band; R O (λ) represents the corresponding surface reflectance.

[0134] When 1-R C (λ)>0.05;

[0135] R O (λ)(1-R C (λ))>0.004;

[0136] (CL_r-R O(λ)) / (CR_r-R O (λ))>1.2;

[0137] If both conditions are met, a muscovite diagnostic signal is considered to exist, and the following steps are executed; otherwise, return.

[0138] Calculate R C The minimum value of (λ) R min and corresponding wavelength

[0139] Take R C The five spectral data points with the lowest reflectance in (λ) are subjected to a quadratic fitting, and the wavelength of the axis of symmetry of the quadratic function is calculated, which is the diagnostic absorption characteristic wavelength of muscovite at 2200 nm.

[0140] The calculated diagnostic absorption characteristic wavelength of muscovite is evaluated. If it falls between 2195 nm and 2226 nm, the value is returned; otherwise, a null value is returned. This criterion eliminates interference from alunite, pyrophyllite, chalcedony, and other minerals.

[0141] The absorption wavelengths of muscovite were divided into 10 levels with intervals of 2199, 2200, 2201, 2202, 2203, 2204, 2205, 2206, and 2207 nanometers, and then colored sequentially as follows: rgb(255,30,0), rgb(255,115,0), rgb(255,191,0), rgb(251,255,25), rgb(207,255,110), rgb(143,255,186), rgb(15,251,255), rgb(59,167,255), rgb(54,94,255), and rgb(8,8,255).

[0142] For example, pixels in the 2195 to 2199 nm band are colored with RGB(255,30,0), pixels in the 2200 nm band are colored with RGB(255,115,0), ..., pixels in the 2207 nm band are colored with RGB(54,94,255), and pixels in the 2208 to 2226 nm band are colored with RGB(8,8,255).

[0143] In an optional embodiment of this application, for pixels that do not have a muscovite diagnostic signal, the color can be directly assigned as rgb(0,0,0), that is, as pure black.

[0144] The aforementioned color-enhanced muscovite diagnostic absorption characteristic wavelength images were imported into a GIS (geographic information system) platform. Linear color bands (strips) or linear extension contact boundaries between different colors were then visually extracted using a human-computer interactive method, serving as potential fault geological structures. (Reference)Figure 4 As shown, to facilitate viewing of color information representing diagnostic absorption characteristic wavelengths of muscovite, a color bar is provided Figure 5 for reference.

[0145] Based on the same design concept, the embodiment also discloses a hidden fracture geological structure feature extraction system.

[0146] Referring to Figure 6 , the hidden fracture geological structure feature extraction system can be implemented in a smart terminal, a computer or the like in the form of an APP, a webpage or a mini program, and comprises:

[0147] An acquisition module 61 is configured to acquire hyperspectral remote sensing data corresponding to a geological region to be measured;

[0148] A preprocessing module 62 is configured to preprocess the hyperspectral remote sensing data to obtain surface reflectivity data; the surface reflectivity data comprises surface reflectivity of each pixel in a first set wavelength range;

[0149] A processing module 63 is configured to extract diagnostic absorption characteristic wavelengths of muscovite corresponding to each pixel based on the surface reflectivity of the pixel in the first set wavelength range;

[0150] A grading module 64 is configured to grade the diagnostic absorption characteristic wavelengths of muscovite corresponding to each pixel respectively;

[0151] A color assigning module 65 is configured to assign different color information to the pixels corresponding to the diagnostic absorption characteristic wavelengths of muscovite of different grades according to a preset mapping relationship, to obtain a muscovite grid image;

[0152] A feature recognition and extraction module 66 is configured to extract an abnormal color strip or a linear boundary between different colors from the muscovite grid image as a hidden fracture geological structure feature of the geological region to be measured.

[0153] The various variations and specific examples of the method provided in the above embodiments are also applicable to the hidden fracture geological structure feature extraction system of the present embodiment. Through the foregoing detailed description of the hidden fracture geological structure feature extraction method, those skilled in the art can clearly understand the functions and implementation processes / principles of the hidden fracture geological structure feature extraction system in the present embodiment. For the sake of brevity of the description, no further detailed description is given here.

[0154] In order to better execute the program of the above method, the embodiment of the present application further provides a device, as shown in the figure. Figure 7 The device comprises a processor and a memory.

[0155] The device can be implemented in various forms, including mobile phones, tablets, palmtop computers, notebook computers and desktop computers and the like.

[0156] The memory can be used to store instructions, programs, codes, code sets or instruction sets. The memory can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the implicit fault geological structure feature extraction method provided by the above embodiments, etc.; and the data storage area can store data related to the implicit fault geological structure feature extraction method provided by the above embodiments, etc.

[0157] The processor can include one or more processing cores. The processor invokes data stored in the memory by running or executing instructions, program code sets or instruction sets stored in the memory, and performs various functions and processes data of the present application. The processor can be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor. It can be understood that, for different devices, the electronic device used to implement the functions of the processor can also be other, and the embodiments of the present application do not make specific limitations.

[0158] The embodiments of the present application provide a computer readable storage medium, for example, including: a U disk, a mobile hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes. The computer readable storage medium stores a computer program capable of being loaded by the processor and executing the implicit fault geological structure feature extraction method of the above embodiments.

[0159] The above embodiments are only used to introduce the technical solutions of the present application in detail, but the above embodiment descriptions are only used to help understand the method and its core idea of the present application, and should not be understood as limiting the present application. Those skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for extracting features of a geological structure of a hidden fault, characterized by, The method comprises the following steps: acquiring hyperspectral remote sensing data corresponding to a to-be-tested geological region; preprocessing the hyperspectral remote sensing data to obtain surface reflectivity data; the surface reflectivity data comprises surface reflectivity of each pixel in a first set wavelength range; based on the surface reflectivity of the pixel in the first set wavelength range, extracting a muscovite diagnostic absorption characteristic wavelength corresponding to the pixel; grading the muscovite diagnostic absorption characteristic wavelength corresponding to each pixel; for the pixels corresponding to muscovite diagnostic absorption characteristic wavelengths of different grades, assigning different color information according to a preset mapping relationship to obtain a muscovite grid image; extracting abnormal color bands or linear boundaries between different colors from the muscovite grid image as hidden fault geological structure features of the to-be-tested geological region; the step of extracting a muscovite diagnostic absorption characteristic wavelength corresponding to the pixel based on the surface reflectivity of the pixel in the first set wavelength range comprises the following steps: based on the surface reflectivity of the pixel in the first set wavelength range, calculating a reflectivity value after continuum removal processing; and based on the reflectivity value, judging whether the pixel has a muscovite diagnostic signal; if yes, acquiring data point information corresponding to a plurality of smaller reflectivity values after continuum removal processing; constructing a one-dimensional quadratic function with the wavelength of the data point as the dependent variable and the reflectivity as the independent variable to perform fitting; taking the symmetry axis of the one-dimensional quadratic function obtained by fitting as the muscovite diagnostic absorption characteristic wavelength corresponding to the pixel.

2. The implicit fault geologic structure feature extraction method of claim 1, wherein, the step of calculating a reflectivity value after continuum removal processing comprises using the following formula: wherein R C (λ) represents reflectance value after removing continuum treatment; R O (λ) represents ground reflectance of the pixel at the wavelength of λ; CL_w represents the average wavelength corresponding to the wavelength range of the left end point of the continuum treatment; CL_r represents the average reflectance corresponding to the wavelength range of the left end point of the continuum treatment; CR_w represents the average wavelength corresponding to the wavelength range of the right end point of the continuum treatment; and CR_r represents the average reflectance corresponding to the wavelength range of the right end point of the continuum treatment. the wavelength range where the left end point is located and the wavelength range where the right end point is located are within the first set wavelength range, and the maximum wavelength of the wavelength range where the left end point is located is less than the minimum wavelength of the wavelength range where the right end point is located.

3. The implicit fault geologic structure feature extraction method of claim 2, wherein, the step of judging whether the pixel has a muscovite diagnostic signal based on the reflectivity value comprises the following steps: judging whether the following three conditions are all met: 1-R C (λ) > threshold1; R O (λ)(1-R C (λ))>threshold2; (CL_r - R O (λ)) / (CR_r - R O (λ)) > threshold3; if yes, it is judged that there is a muscovite diagnostic signal; otherwise, it is judged that there is no muscovite diagnostic signal; wherein threshold1 is a first set threshold, threshold2 is a second set threshold, and threshold3 is a third set threshold.

4. The implicit fault geologic structure feature extraction method of claim 3, wherein, the first set wavelength range comprises 2120 nm to 2290 nm; the wavelength range where the left end point is located comprises 2120 nm to 2150 nm; and the wavelength range where the right end point is located comprises 2260 nm to 2290 nm; the first set threshold is 0.05; the second set threshold is 0.004; and the third set threshold is 1.

2.

5. The implicit fault geologic feature extraction method of any of claims 1-4, wherein, after the step of extracting a muscovite diagnostic absorption characteristic wavelength corresponding to the pixel, the method further comprises the following steps: judging whether the muscovite diagnostic absorption characteristic wavelength is located in a second set wavelength range; the second set wavelength range is a subset of the first set wavelength range; if yes, performing the step of grading the muscovite diagnostic absorption characteristic wavelength corresponding to each pixel; If not, go to the step of extracting the diagnostic absorption characteristic wavelength of muscovite corresponding to the next pixel, until the extraction of the diagnostic absorption characteristic wavelength of muscovite corresponding to all pixels of the to-be-detected geological region is completed.

6. The implicit fault geologic feature extraction method of any of claims 1-4, wherein, The preset mapping relationship includes: Different colors of a rainbow color system are sequentially assigned to muscovite diagnostic absorption characteristic wavelengths of different grades.

7. A system for extracting features of a geological structure of a hidden break, characterized in that, It includes: The acquisition module is configured to acquire hyperspectral remote sensing data corresponding to a to-be-detected geological region. The preprocessing module is configured to preprocess the hyperspectral remote sensing data to obtain surface reflectance data, wherein the surface reflectance data includes surface reflectance of each pixel in a first set wavelength range. The processing module is configured to extract a diagnostic absorption characteristic wavelength of muscovite corresponding to the pixel based on the surface reflectance of the pixel in the first set wavelength range. The processing module is configured to extract a diagnostic absorption characteristic wavelength of muscovite corresponding to the pixel based on the surface reflectance of the pixel in the first set wavelength range. The processing module is configured to extract a diagnostic absorption characteristic wavelength of muscovite corresponding to the pixel based on the surface reflectance of the pixel in the first set wavelength range. The processing module is configured to extract a diagnostic absorption characteristic wavelength of muscovite corresponding to the pixel based on the surface reflectance of the pixel in the first set wavelength range. If so, the data point information corresponding to each of the plurality of small reflectance values after continuum removal processing is acquired, and a quadratic function is constructed with the wavelength of the data point as the dependent variable and the reflectance as the independent variable for fitting. The symmetry axis of the fitted quadratic function is taken as the diagnostic absorption characteristic wavelength of muscovite corresponding to the pixel. The grading module is configured to grade the diagnostic absorption characteristic wavelengths of muscovite corresponding to each pixel. The color assignment module is configured to assign different color information to pixels corresponding to muscovite diagnostic absorption characteristic wavelengths of different grades according to a preset mapping relationship, to obtain a muscovite grid image.

8. An apparatus, comprising: The feature recognition and extraction module is configured to extract an abnormal color strip or a linear boundary between different colors from the muscovite grid image as a hidden fault geological structure feature of the to-be-detected geological region.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program. The computer program is executed by the processor to implement the hidden fault geological structure feature extraction method according to any one of claims 1-7. The computer readable storage medium stores a computer program. The computer program is executed by the processor to implement the hidden fault geological structure feature extraction method according to any one of claims 1-7.

Images