Method and system for identifying hydrothermal alteration zone in carbonate rock construction

Abstract

The invention relates to an identification method and system for a hydrothermal alteration zone in carbonate rock construction, and belongs to the field of remote sensing geological prospecting, and the identification method comprises the steps: obtaining and preprocessing a spaceflight hyperspectral remote sensing image of a to-be-identified region, and obtaining surface reflectance data; mineral component analysis is carried out based on the surface reflectance data, carbonate mineral dominant pixels are marked, and a binary mask array after assignment is generated; extracting hydrothermal alteration information and generating an information intensity array according to the assigned binary mask array; performing binarization processing on the information intensity array according to a preset threshold value to generate a binary logic array; and converting the binary logic array into a geographic space vector area file based on an original image geographic coordinate system, and taking the geographic space vector area file as a hydrothermal alteration zone identification result. According to the method, rapid and accurate identification of the hydrothermal alteration zone in the carbonate rock area can be facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of remote sensing geological exploration, in particular to a method and system for identifying a hydrothermal alteration zone in a carbonate rock formation. BACKGROUND

[0002] In the field of mineral resource exploration, remote sensing technology can quickly locate potential mineralization areas by capturing the spectral characteristics of surface minerals, but there are still many technical challenges in identifying hydrothermal alteration zones in carbonate rock formations. As an important metallogenic marker of metal deposits (such as gold, copper, lead and zinc), hydrothermal alteration zones are accompanied by chemical modification of hydrothermal fluids to surrounding rocks, which often appear as calcite / dolomite recrystallization, iron staining caused by pyrite oxidation, and the formation of layered silicate minerals such as chlorite and sericite in carbonate rock formations. These minerals exhibit diagnostic spectral fingerprints in specific wavebands, which can theoretically be quickly circled by remote sensing means.

[0003] Currently, common methods for identifying hydrothermal alteration zones are usually limited to the extraction of single spectral features, and cannot be combined with the geological background of carbonate rock formations, and cannot adapt to the complexity of carbonate rock formations, resulting in a large number of false positive results in non-target lithology areas (such as siliceous clastic rock areas), resulting in a decrease in the accuracy of the identification results. SUMMARY

[0004] In order to quickly and accurately identify hydrothermal alteration zones in carbonate rock areas, the present application provides a method and system for identifying a hydrothermal alteration zone in a carbonate rock formation.

[0005] In a first aspect, the present application provides a method for identifying a hydrothermal alteration zone in a carbonate rock formation, which adopts the following technical solution: A method for identifying a hydrothermal alteration zone in a carbonate rock formation, the method comprising: acquiring a space hyperspectral remote sensing image of a region to be identified and performing preprocessing to obtain surface reflectance data; performing mineral composition analysis based on the surface reflectance data, marking carbonate mineral dominant pixels, and generating an assigned binary mask array; extracting hydrothermal alteration information and generating an information intensity array according to the assigned binary mask array; performing binary processing on the information intensity array according to a preset threshold to generate a binary logic array; converting the binary logic array to a geographic spatial vector area file based on the original image geographic coordinate system as a hydrothermal alteration zone identification result.

[0006] By adopting the technical scheme, the efficiency and precision of the carbonatite hydrothermal alteration zone recognition are significantly improved, the high-precision correction in the preprocessing stage eliminates the influence of the atmosphere and sensor noise; by accurately locking the carbonatite dominant area, the interference of non-target objects is avoided; the multi-feature joint extraction mechanism fully utilizes the comprehensive spectral response of the hydrothermal alteration in the iron oxide, hydroxyl and carbonatite mineralization, greatly reduces the single feature misjudgment probability; and the adaptive threshold segmentation and vector output ensure that the technical results can directly serve the actual needs of the geological survey. The present application considers both the automation processing capability and the scientific rigor, and still maintains stable recognition performance under complex geological conditions, thereby providing a reliable technical scheme for the rapid evaluation of mineral resources.

[0007] Optionally, the preprocessing step comprises: detecting effective pixels and invalid pixels in the spaceborne hyperspectral remote sensing image; filling the invalid pixels by using a linear interpolation algorithm based on the radiation brightness values of adjacent effective pixels to obtain the spaceborne hyperspectral remote sensing image after bad line correction; performing radiation calibration based on the spaceborne hyperspectral remote sensing image after bad line correction to convert the original value into radiation brightness data; inputting the radiation brightness data into an atmospheric correction model to obtain surface reflectance data.

[0008] By adopting the technical scheme, the quality of the hyperspectral remote sensing data is fundamentally improved, and the accuracy and efficiency of subsequent mineral recognition and alteration analysis are ensured. The detection of invalid pixels isolates the sensor noise source, sets the target for data repair; the linear interpolation filling restores the continuity of the image, prevents bad line interference from the overall analysis; the radiation calibration converts the relative digital value into an absolute physical quantity, establishes a quantifiable radiation basis; and the atmospheric correction eliminates the atmospheric distortion, outputs the standard surface reflectance, and accurately reflects the spectral characteristics.

[0009] Optionally, the step of performing mineral composition analysis based on the surface reflectance data, marking the carbonatite mineral dominant pixels and generating the assigned binary mask array comprises: based on each pixel of the surface reflectance data, applying a spectral matching algorithm to analyze the dominant mineral composition to generate a mineral composition identifier; and based on the mineral composition identifier, marking the carbonatite mineral dominant pixels; constructing a binary mask array consistent with the spatial dimension of the surface reflectance data, and assigning the marked carbonatite mineral dominant pixels to the corresponding positions of the binary mask array to obtain the assigned binary mask array.

[0010] By adopting the technical scheme, the mask array is designed based on the mineral paragenetic rule (carbonate-muscovite combination and correlation with hydrothermal activity), the mask rule is designed based on the mineral paragenetic rule (carbonate-muscovite combination and correlation with hydrothermal activity), the mask array has geological rationality and high calculation efficiency, the spatial dimension consistency ensures the pixel-level processing accuracy, and the binary logic constructs a subsequent alteration information extraction "geographic fence", which fundamentally excludes non-target ground object interference. Compared with the traditional global processing method, the step concentrates the calculation resources in the area with clear geological significance, improves the signal-to-noise ratio of alteration recognition (such as avoiding vegetation spectrum confusion), and significantly shortens the processing time.

[0011] Optionally, the step of extracting hydrothermal alteration information and generating an information intensity array according to the assigned binary mask array comprises: According to the assigned binary mask array, an information intensity initial array with consistent dimensions is created, and all element values are initialized; each pixel position of the assigned binary mask array is traversed, and the iron alteration characteristic value, the hydroxyl alteration characteristic value and the carbonate alteration characteristic value of all carbonate mineral dominant pixels are extracted; The iron alteration characteristic value, the hydroxyl alteration characteristic value and the carbonate alteration characteristic value are multiplied to obtain a combined feature intensity; The combined feature intensity is assigned to the current pixel position of the information intensity initial array, and an information intensity array is output.

[0012] By adopting the technical scheme, the feature value calculation is targeted to capture the quantitative indicators of iron oxide absorption edge (0.775-1.2 μm), hydroxyl mineral absorption valley (2.182-2.242 μm) and carbonate characteristic peak (2.130-2.183 μm), and the spectral fingerprint of geological minerals is converted into a calculable parameter; secondly, the multiplication combination forces the coexistence of three types of alteration paragenetic modes, and significantly suppresses the false positives caused by single mineral interference; finally, the spatial assignment of continuous intensity values constructs an alteration intensity gradient field, which supports the flexible optimization of adaptive threshold segmentation, and retains weak abnormal information for geological depth analysis.

[0013] Optionally, the step of performing binary processing on the information intensity array according to a preset threshold to generate a binary logic array comprises: The preset threshold is dynamically determined according to the statistical distribution characteristics of the information intensity array; Each element in the information intensity array is traversed, and when the element value is greater than the preset threshold, the corresponding position in the binary logic array is assigned a value of 1, otherwise a value of 0.

[0014] By adopting the technical scheme, the threshold is dynamically set based on the statistical distribution characteristics of the information intensity array, the spatial and temporal variability of background noise in the actual geological environment (such as different rock weathering rates or vegetation coverage differences) is fully considered, and the risks of under-segmentation (noise misjudgment caused by too low threshold) and over-segmentation (missing weak alteration caused by too high threshold) are avoided; secondly, the binary array is generated by pixel-by-pixel logical assignment, and the hydrothermal alteration pixel cluster meeting the preset geological significance is accurately segmented. The final generated binary logical array not only has mathematical decision rigor (minimizing intra-class variance), but also realizes quantitative mapping of geological entities and pixels.

[0015] Optionally, based on the geographic coordinate system of the original image, the binary logical array is converted into a geographic spatial vector area file as the step of identifying the hydrothermal alteration zone result, which comprises: writing the binary logical array into a raster file with geographic reference based on the geographic coordinate system of the original image; converting the raster file into a geographic spatial vector area file by a spatial conversion tool as the hydrothermal alteration zone identification result.

[0016] By adopting the technical scheme, the array is written into a raster file with geographic reference, accurately embedded into the original image coordinate system, and the spatial position deviation problem is solved; and then the raster file is converted into a vector area file by a GIS tool, the discrete pixels are aggregated into continuous polygons, and the geological application value of the result is significantly improved.

[0017] Optionally, the identification method comprises: a preprocessing module configured to acquire a spaceborne hyperspectral remote sensing image of a region to be identified and perform preprocessing to obtain surface reflectance data; a mineral composition analysis module configured to perform mineral composition analysis based on the surface reflectance data, mark carbonate mineral dominant pixels, and generate an assigned binary mask array; a hydrothermal alteration information extraction module configured to extract hydrothermal alteration information according to the assigned binary mask array and generate an information intensity array; a binary processing module configured to perform binary processing on the information intensity array according to a preset threshold to generate a binary logical array; an array conversion module configured to convert the binary logical array into a geographic spatial vector area file based on the geographic coordinate system of the original image as the hydrothermal alteration zone identification result.

[0018] In a third aspect, the present application provides a computer device, which adopts the following technical scheme: A computer device comprises a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of the method according to the first aspect.

[0019] In a fourth aspect, the present application provides a computer readable storage medium, which adopts the technical scheme as follows: A computer readable storage medium, which stores a computer program capable of being loaded and executed by a processor to perform any one of the methods in the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 FIG. 1 is a first flowchart of a method for identifying a hydrothermal alteration zone in a carbonate rock formation according to an embodiment of the present application.

[0021] Figure 2 FIG. 2 is a second flowchart of a method for identifying a hydrothermal alteration zone in a carbonate rock formation according to an embodiment of the present application.

[0022] Figure 3 FIG. 3 is a third flowchart of a method for identifying a hydrothermal alteration zone in a carbonate rock formation according to an embodiment of the present application.

[0023] Figure 4 FIG. 4 is a fourth flowchart of a method for identifying a hydrothermal alteration zone in a carbonate rock formation according to an embodiment of the present application.

[0024] Figure 5 FIG. 5 is a fifth flowchart of a method for identifying a hydrothermal alteration zone in a carbonate rock formation according to an embodiment of the present application.

[0025] Figure 6 FIG. 6 is a sixth flowchart of a method for identifying a hydrothermal alteration zone in a carbonate rock formation according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the objects, technical schemes and advantages of the present application clearer, the following will combine the drawings and embodiments to further describe the present application in detail. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application. Figures 1-6 DETAILED DESCRIPTION

[0027] The embodiments of the present application disclose a method for identifying a hydrothermal alteration zone in a carbonate rock formation.

[0028] Referring to Figure 1 The method for identifying a hydrothermal alteration zone in a carbonate rock formation comprises: In step S101, a spaceborne hyperspectral remote sensing image of a region to be identified is acquired and preprocessed to obtain surface reflectance data. The original data of the spaceborne hyperspectral remote sensing image is affected by sensor noise, atmospheric interference and radiation dimension difference, and therefore must be physically corrected to reflect the true surface reflection characteristics. The preprocessing step mainly includes bad line correction, radiation calibration and atmospheric correction. Specifically, abnormal pixel lines caused by sensor hardware anomalies can be corrected by bad line correction, and spectral continuity can be restored by neighborhood pixel interpolation (linear or polynomial), thereby preventing single-pixel line noise from interfering with the mineral mapping process. Radiometric calibration converts digital quantization values (DN) into absolute radiometric units, and atmospheric correction further eliminates atmospheric scattering and absorption effects, and the true reflectance of the surface is retrieved through a radiative transfer model.

[0029] Step S102, mineral composition analysis is performed based on the surface reflectance data, carbonate mineral dominant pixels are marked, and an assigned binary mask array is generated; The purpose of mineral composition analysis is to screen carbonate rock regions related to hydrothermal alteration. The Tetracorder algorithm can be used for spectral matching of each pixel. The core principle is to compare the surface reflectance curve with the mineral standard spectrum library (such as the USGS spectrum library), and determine the dominant mineral type by the minimum spectral angle mapping or similarity threshold.

[0030] In the embodiments of the present application, only when the dominant mineral is calcite, dolomite and their mixture with muscovite is marked as carbonate dominant. This selection is based on the typical mineral combination characteristics of hydrothermal alteration in carbonate rock formation. The generated binary mask array (mask) is essentially a spatial dimension Boolean filter, and the assignment rule is that carbonate dominant pixels are set to 1 and non-dominant pixels are set to 0. This step significantly reduces the operation range of subsequent processing, avoids the interference of irrelevant ground objects, and at the same time ensures that the alteration information extraction is only for regions with clear geological significance.

[0031] Step S103, according to the assigned binary mask array, extract the hydrothermal alteration information and generate an information intensity array; In the carbonate dominant region defined by the mask array, the hydrothermal alteration intensity is quantified by joint operation of multi-band spectral features. This step includes three types of feature extraction: iron oxide alteration feature (Fe), hydroxyl alteration feature (OH), and carbonate mineralization intensity (CaL). The final intensity array is in the form of the product B = Fe x OH x CaL, which requires the simultaneous presence of three types of features, avoiding single feature misjudgment, thereby screening out regions that meet the comprehensive spectral response of hydrothermal alteration.

[0032] Step S104, binaryzation processing is performed on the information intensity array according to a preset threshold to generate a binary logic array; The binaryzation processing converts continuous intensity values into logical identifiers by setting an empirical or adaptive threshold (threshold). The selection of the threshold needs to balance sensitivity and specificity: a too low threshold will increase noise misjudgment, and a too high threshold will miss weak alteration regions.

[0033] In the embodiments of the present application, the threshold value can be dynamically determined according to the regional background statistical characteristics (such as the peak and valley positions of the intensity histogram). In the final generated binary logic array T, 1 represents that the pixel meets the hydrothermal alteration intensity standard, and 0 is excluded. This step converts the uncertainty of the geological model into a calculable decision boundary, providing an explicit classification basis for spatial vectorization.

[0034] In step S105, the binary logic array is converted into a geographic spatial vector area file based on the original image geographic coordinate system as the hydrothermal alteration zone identification result.

[0035] The geographic spatial vector area file is a standard data format for geological mapping and resource exploration. The conversion process first maps the binary logic array to a georeferenced raster according to the image metadata (such as projection parameters and pixel size), and then generates polygon vector areas through morphological operations (such as closing operation to eliminate small holes) and neighborhood analysis (such as boundary tracking algorithm). The advantage of vectorization is to support spatial overlay analysis (such as overlay with geological maps and mining right range) and area calculation, which can be directly used for field verification and exploration deployment. This step ensures seamless integration of technical output and industry application, realizing the transformation of remote sensing information into operational geological results.

[0036] In the above embodiments, the efficiency and accuracy of the carbonate hydrothermal alteration zone identification are significantly improved. The high-precision correction in the preprocessing stage eliminates the influence of atmospheric and sensor noise. By precisely locking the carbonate dominant area, interference from non-target features is avoided. The multi-feature joint extraction mechanism fully utilizes the comprehensive spectral response of hydrothermal alteration in iron oxides, hydroxyl, and carbonate mineralization, significantly reducing the single feature misjudgment probability. The adaptive threshold segmentation and vectorization output ensure that the technical results can directly serve the actual needs of geological investigation. The present application balances automation processing capability and scientific rigor, and still maintains stable identification performance under complex geological conditions, providing a reliable technical solution for rapid evaluation of mineral resources.

[0037] Reference Figure 2 As an embodiment of step S101, the preprocessing step includes: Step S201, detecting valid pixels and invalid pixels in the spaceborne hyperspectral remote sensing image; In the original data of the spaceborne hyperspectral remote sensing image, the detection of valid pixels and invalid pixels is the cornerstone of the entire preprocessing process. Valid pixels are spectral data captured by a normal working sensor, which represent the true ground radiation response; while invalid pixels are caused by sensor hardware failure or data transmission error, commonly known as "bad lines" phenomenon, i.e. abnormal values or zero values in the image due to the failure of detector elements (such as dead pixels or hot pixels).

[0038] Specifically, this detection process is based on statistical analysis and threshold setting of radiation brightness: first, by calculating the deviation of each pixel's radiation value from its adjacent pixels (for example, set a standard deviation threshold), identify the area where a large number of pixel values are out of the normal range (such as radiation brightness less than 0 or greater than the maximum possible value). Invalid pixels are usually represented by a sudden change in radiation value or a constant invalid value (such as NaN or 0), which is particularly evident in high-resolution images, as sensor noise or cosmic ray interference can cause local data loss. For example, when the image has a bad line, all pixels in that row may show 0 radiation value, which is marked as invalid by the program; conversely, the pixel value is within a reasonable dynamic range (e.g. 0.1 to 100 W / m 2 / sr / μm) and no adjacent abnormalities, it is considered valid.

[0039] It can be understood that the core logic of this step is to isolate noise sources and ensure that subsequent processing is based only on reliable data, avoiding the spread of errors to the mineral mapping stage.

[0040] Step S202, based on the radiation brightness values of adjacent valid pixels, using a linear interpolation algorithm to fill in the invalid pixels, to obtain the bad line corrected spaceborne hyperspectral remote sensing image; Once the invalid pixels are detected, the filling operation aims to restore the continuity and integrity of the image, which is achieved through a linear interpolation algorithm.

[0041] Specifically, the algorithm uses the radiation brightness values of the adjacent valid pixels around the invalid pixel to calculate, and the specific principle is based on the spatial proximity assumption: adjacent pixels have similar spectral responses under the same type of terrain. For a single invalid pixel, the algorithm scans its adjacent valid pixels (such as the pixels in the up, down, left, and right directions), and calculates the weighted average of the radiation brightness of these valid pixels; for a whole bad line, the radiation values of the up and down valid pixels are used for linear fitting.

[0042] Mathematically, linear interpolation is represented as: for invalid pixel position (i, j), its filling value L_{fill} is derived from the radiation brightness L of adjacent valid pixel positions (i-1, j), (i+1, j), etc. For example, if the 100th row of the image is all invalid pixels, the algorithm will use the valid pixel values of the 99th and 101st rows to calculate the estimated value of each pixel in the 100th row, thus eliminating the void caused by the "bad line".

[0043] Step S203, based on the bad line corrected spaceborne hyperspectral remote sensing image, perform radiation calibration to convert the original value to radiation brightness data; Wherein, the radiation calibration is to convert the corrected image digital value (Digital Number, DN) to physical radiation brightness (unit: W / m 2Radiometric calibration is a key step with a principle derived from radiative transfer theory and sensor calibration model. The pixel value (DN) of the original image is a relative quantized integer, which does not reflect the true physical quantity; radiometric calibration uses the pre-calibrated gain (Gain) and bias (Bias) parameters to convert the DN value of each pixel to an absolute radiance value L through a linear conversion formula L = G x DN + B. Among them, G and B are obtained by satellite manufacturers through ground or in-orbit calibration experiments, which ensures that the converted data represents the actual received radiant energy of the sensor.

[0044] Step S204, input the radiance data into the atmospheric correction model to obtain the surface reflectance data.

[0045] Among them, atmospheric correction is the core final step of preprocessing, which aims to remove the influence of atmospheric scattering and absorption, and convert the radiance data to surface reflectance, i.e. the proportion of actual surface reflection of solar radiation (dimensionless, range 0-1). This process relies on a radiative transfer model (such as 6S or MODTRAN), which simulates the attenuation and enhancement of the atmosphere on radiation, and the input parameters include radiance L, solar zenith angle θ, atmospheric transmittance T, aerosol optical thickness, etc. The final output of the surface reflectance data is the direct input of the mineral mapping, and its accuracy determines the reliability of the entire technical scheme.

[0046] In the above embodiment, the quality of hyperspectral remote sensing data is fundamentally improved, ensuring the accuracy and efficiency of subsequent mineral identification and alteration analysis. The detection of invalid pixels isolates the sensor noise source, setting the target for data repair; linear interpolation fills in the continuity of the image, preventing bad line interference from affecting the overall analysis; radiometric calibration converts relative digital values to absolute physical quantities, establishing a quantifiable radiation basis; atmospheric correction eliminates atmospheric distortion, outputting standard surface reflectance, accurately reflecting spectral characteristics.

[0047] Reference Figure 3 As an embodiment of step S102, the step of performing mineral composition analysis based on the surface reflectance data, marking the carbonate mineral dominant pixels and generating the assigned binary mask array includes: Step S301, based on each pixel of the surface reflectance data, applying a spectral matching algorithm to analyze the dominant mineral composition and generating a mineral composition identifier; Among them, the data dimension of the surface reflectance data is [number of rows x number of columns x number of bands], which represents the reflectance of each pixel in each band in the spaceborne hyperspectral remote sensing image.

[0048] Specifically, the ground reflectance data of each pixel can be analyzed by a spectral matching algorithm to determine its dominant mineral composition. The scientific principle is based on the fingerprint characteristics of mineral spectra: different minerals have unique reflectance curve patterns at specific wavelengths (such as the 2.16 μm absorption peak of carbonate minerals). When the algorithm is executed, the continuous spectral curve of each pixel is matched with the standard mineral spectral library (such as the USGS spectral library) by spectral angle mapping (SAM) or least squares matching.

[0049] In the embodiments of the present application, the pixel reflectance data is regarded as a high-dimensional space vector (dimension = number of bands), and the standard mineral spectrum is regarded as a reference vector. The similarity is calculated and judged by the vector angle θ, and the reference mineral with the smallest θ value is selected as the dominant component (the embodiments of the present application are limited to be effective only when the result is calcite, dolomite or a mixture thereof with muscovite).

[0050] Step S302, based on the mineral composition identification, marking the carbonate mineral dominant pixel; In the embodiments of the present application, if the dominant mineral composition is calcite, a mixture of calcite and muscovite, dolomite or a mixture of dolomite and muscovite, the pixel is marked as a carbonate mineral dominant pixel, otherwise it is marked as a non-dominant pixel.

[0051] Step S303, constructing a binary mask array consistent with the spatial dimension of the ground reflectance data, and assigning the marked carbonate mineral dominant pixel to the corresponding position of the binary mask array to obtain the assigned binary mask array.

[0052] Wherein, the dimension of the binary mask array is [number of rows x number of columns], if it is a carbonate mineral dominant pixel, it is assigned a value of 1; if it is a non-dominant pixel, it is assigned a value of 0.

[0053] It can be understood that the value 1 constitutes a spatial mask, and the pixel is located in a carbonate rock formation and the mineral combination meets the hydrothermal alteration background, which is a legal area for subsequent spectral feature extraction. The 0 value area is excluded from processing, which significantly improves the calculation efficiency.

[0054] In the above embodiments, the marking rule is designed based on the mineral paragenetic law (the correlation between carbonate-muscovite combination and hydrothermal activity), so that the mask array has both geological rationality and calculation efficiency; the spatial dimension consistency ensures the pixel-level processing accuracy; and the binary logic constructs a "geographical fence" for subsequent alteration information extraction, which fundamentally excludes non-target ground object interference. Compared with the traditional global processing method, this step concentrates the calculation resources in the area with clear geological significance, which not only improves the signal-to-noise ratio of alteration identification (such as avoiding the confusion of vegetation spectrum), but also significantly shortens the processing time.

[0055] Referring toFigure 4 As an embodiment of step S103, according to the assigned binary mask array, the step of extracting the hydrothermal alteration information and generating the information intensity array includes: Step S401, according to the assigned binary mask array, create an initial array of information intensity with consistent dimensions, and initialize all element values; Specifically, an initial array B is constructed which is completely consistent in spatial dimensions (same number of rows and columns) with the binary mask array (mask), and all its elements are initialized to 0. The core logic is to establish a blank container corresponding one-to-one to the original image pixels for storing the subsequent calculated alteration information intensity.

[0056] It should be noted that by ensuring that the alteration intensity value of each pixel position can be accurately mapped to the corresponding geographic coordinates, the intensity value of the pixel not marked by the mask (non-carbonate dominant area) is kept as 0 and is automatically excluded in subsequent calculations.

[0057] Step S402, traverse each pixel position of the assigned binary mask array, extract the iron alteration characteristic value, hydroxyl alteration characteristic value and carbonate alteration characteristic value of all carbonate mineral dominant pixels; Specifically, the traversal operation is only for the pixels in the mask array assigned as 1 (carbonate mineral dominant area), and three types of characteristic values are calculated based on the reflectance spectrum subset: (1) The extraction step of the iron alteration characteristic value includes: intercepting the reflectance subset of 0.775-1.2 microns; performing de-continuity processing on the reflectance subset to obtain a normalized reflectance sequence; performing a cubic polynomial fitting on the normalized reflectance sequence to generate fitting coefficients; calculating the iron alteration characteristic value based on the fitting coefficients.

[0058] In the embodiments of the present application, the specific implementation includes: intercepting the spectrum subset of the current pixel in the 0.775 micron-1.2 micron interval, denoted as the wavelength sequence X = [x1, x2,..., xn]T, and the corresponding reflectance sequence Y = [y1, y2,..., yn]T.

[0059] The de-continuity processing is performed as follows: The de-continuity processed reflectance sequence is obtained

[0060] The coefficient matrix A is constructed, Calculate Four cubic fitting coefficients are obtained, i.e. K = [a, b, c, d]T; represents the product operation of corresponding elements of the array, AT represents the transpose of A, -1 represents the inverse operation of the matrix, 1n represents an n-dimensional vector filled with 1, and sgn represents the sign function.

[0061] Perform the operation: wherein, when fe<=0, the current pixel operation is exited.

[0062] (2) The specific extraction steps of the hydroxyl alteration feature value include: intercepting a reflectance subset in the 2.182-2.242-micron waveband; performing quadratic polynomial fitting on the reflectance subset to generate fitting coefficients; and calculating the hydroxyl alteration feature value based on the fitting coefficients; in the embodiments of the present application, the specific implementation includes: intercepting a spectrum subset of the current pixel in the 2.182-micron-2.242-micron interval, denoted as X=[x1, x2,..., xn]T, and the corresponding reflectance sequence is Y=[y1, y2,..., yn]T.

[0063] A coefficient matrix A is constructed, A=[X⊙X, X, 1nT], K=(ATA)-1ATY is calculated, and three quadratic fitting coefficients, that is, K=[a, b, c]T, are obtained.

[0064] An operation is performed: oh=sgn(-b / (2a)-2.195)*sgn(2.22+b / (2a))*a. Wherein, when oh<=0, the current pixel operation is exited.

[0065] (3) The specific extraction steps of the carbonate alteration feature value include: intercepting a reflectance subset in the 2.130-2.183-micron waveband; performing de-continuity processing on the reflectance subset to obtain a normalized reflectance sequence; locating the wavelength position corresponding to the minimum value in the normalized reflectance sequence; and calculating the carbonate alteration feature value based on the minimum value wavelength position; In the embodiments of the present application, the specific implementation includes: intercepting a spectrum subset of the current pixel in the 2.130-micron-2.183-micron interval, denoted as X=[x1, x2,..., xn]T, and the corresponding reflectance sequence is Y=[y1, y2,..., yn]T. De-continuity processing is performed according to the following formula: to obtain the de-continuity reflectance sequence The minimum value index in K is calculated, that is, The minimum value index in K is calculated, that is, The minimum value index in K is calculated, that is, When cal>0, fe*oh*cal is assigned to the current pixel corresponding position in the information intensity array B; otherwise, the current pixel operation is exited.

[0066] Step S403, multiplying the iron alteration feature value, the hydroxyl alteration feature value, and the carbonate alteration feature value to obtain a combined feature intensity; Wherein, since carbonate hydrothermal alteration is often accompanied by the symbiotic combination of iron oxide impregnation, hydroxyl minerals (such as chloritization) and carbonate recrystallization, the three types of characteristic values are combined by multiplication operation to generate the combination strength.

[0067] It should be noted that the multiplication operation requires that the three are significantly greater than zero at the same time (if any condition is not met, the strength is zero), and the strength product amplifies weak abnormal signals (such as weak symbiotic alteration) to improve the identification sensitivity.

[0068] Step S404, assign the combination feature strength to the current pixel position of the initial information intensity array, output the information intensity array.

[0069] Wherein, the combination feature strength is assigned to the corresponding pixel position of the initial array B, and the core logic is the lossless transmission of spatial information, which strictly corresponds each pixel position with the original image latitude and longitude, and the pixel not marked by the mask retains the initial value 0, which effectively distinguishes the alteration zone from the non-alteration zone; the intensity value is continuously distributed (such as 0.01-2.5), which avoids information loss caused by binaryzation (convenient for subsequent threshold optimization).

[0070] In the above embodiment, the characteristic value calculation captures the quantitative indicators of iron oxide absorption edge (0.775-1.2 μm), hydroxyl mineral absorption valley (2.182-2.242 μm) and carbonate characteristic peak (2.130-2.183 μm), which converts the spectral fingerprint of geological minerals into a calculable parameter; secondly, the multiplication combination requires the coexistence of three types of alteration symbiotic modes, which significantly suppresses the false positive caused by single mineral interference; finally, the spatial assignment of continuous intensity value constructs the alteration intensity gradient field, which not only supports the flexible optimization of adaptive threshold segmentation, but also retains weak abnormal information for geological depth analysis.

[0071] Reference Figure 5 As an embodiment of step S104, the step of generating a binary logic array by binarizing the information intensity array according to a preset threshold value includes: Step S501, dynamically determine the preset threshold value according to the statistical distribution characteristics of the information intensity array; Wherein, the data distribution law of the information intensity array (array B) is used to adaptively set the binarization threshold value. The hydrothermal alteration zone is usually distributed in clusters in space, and its intensity value shows obvious bimodal or multimodal structure in the histogram—the alteration pixels are concentrated in the high value area (strong signal), and the background pixels are distributed in the low value area (weak noise).

[0072] Specifically, the histogram peak and valley are analyzed to find the local minimum point (valley bottom) of the intensity value distribution, and the position is the potential best segmentation point. The threshold candidate value is iterated by maximizing the inter-class variance (Otsu algorithm idea) to select the point that maximizes the variance difference between the alteration zone and the non-alteration zone. For example, if the intensity value is unimodal distribution (large homogeneous area), the threshold value is increased to avoid misjudgment; if the histogram has a long tail in the low value area (such as noise interference), the background mean value is stripped through Gaussian fitting.

[0073] Step S502, traverse each element in the information intensity array, and assign value 1 to the corresponding position of the binary logic array when the element value is greater than the preset threshold, otherwise assign value 0.

[0074] Among them, the continuous alteration intensity information is converted into spatial binary decision, based on the logical screening of spatial grid data, combined with the abnormal intensity grading criterion in geology: scan each pixel position (such as position (i, j)) in image row and column order, read the intensity value B(i, j) of the position in array B, and compare the intensity value with the dynamic threshold threshold.

[0075] Exemplarily, when B(i, j)>threshold, assign value 1 to the same position (i, j) of the binary logic array T, indicating that the pixel belongs to the hydrothermal alteration zone, and assign value 0 when the condition is not met, representing the geological background area.

[0076] In the above embodiment, the threshold value is dynamically set based on the statistical distribution characteristics of the information intensity array, which fully considers the spatiotemporal variability of background noise in the actual geological environment (such as different rock weathering rates or vegetation coverage differences), avoiding the risks of under-segmentation (noise misjudgment caused by too low threshold) and over-segmentation (missing weak alteration caused by too high threshold); secondly, the binary array is generated by pixel-by-pixel logical assignment, which accurately segments the hydrothermal alteration pixel cluster meeting the preset geological significance. The generated binary logic array not only has mathematical decision rigor (minimizing intra-class variance), but also realizes the quantitative mapping of geological entities and pixels.

[0077] Reference Figure 6 As one embodiment of step S105, based on the original image geographic coordinate system, the binary logic array is converted into a geographic spatial vector area file as the step of hydrothermal alteration zone recognition result, which includes: Step S601, based on the original image geographic coordinate system, write the binary logic array into a raster file with geographic reference; Specifically, a grid structure completely consistent with the spatial dimensions of the original image is constructed (such as the same number of rows and columns), which means that the position of each pixel in the array T (such as the i-th row and the j-th column) directly inherits the spatial index of the original image; secondly, the array T is written into a grid format (such as GeoTIFF) through a software program (such as the GDAL library of Python or a professional remote sensing tool), while the metadata (such as the coordinate reference system CRS parameter) of the original image is attached.

[0078] For example, assuming that the resolution of the original image is 30 meters and the coordinate system is UTM Zone 50N, when writing the grid file, the coordinates of the pixel (100, 200) are calculated as XX degrees north and YY degrees east, and stored as a georeferenced grid. The "geographic coordinate system" refers to a positioning system on the surface of the earth (such as a grid of latitude and longitude), which ensures that spatial data can be accurately overlaid in a map; and the "grid file" is a grid data structure that stores spatial information in the form of a pixel matrix (value 1 represents a hydrothermal alteration zone, and value 0 represents the background), and its georeferencing property allows the alteration area to be directly aligned with the field geological survey area, avoiding misjudgment caused by positional deviation.

[0079] Step S602, converting the grid file into a geographic spatial vector area file through a spatial conversion tool as the result of hydrothermal alteration zone identification.

[0080] Among them, based on the aggregation and vectorization theory of spatial data analysis: the grid file is composed of pixel units (each pixel represents a fixed size of the ground surface area), but the hydrothermal alteration zone is a continuous spatial entity (such as a polygon area) in the real geological environment, so it needs to be processed by a GIS spatial conversion tool (such as the "raster to vector" module of ArcGIS or QGIS) to perform vectorization. The specific logic is: the tool first identifies all pixels with value 1 in the grid file (representing the alteration area), and based on connectivity analysis (algorithm such as four-neighborhood or eight-neighborhood search), these discrete pixels are aggregated into continuous blocks; then, for each block, a polygon boundary (i.e. the outline of the vector area) is generated, while the original geographic coordinate system information is preserved, ensuring that the vector file is consistent with the position on the earth's surface. For example, if a group of adjacent pixels (such as a 10x10 pixel area) in the grid file have a value of 1, the GIS tool will generate a polygon vector object whose vertex coordinates are calculated from the pixel boundaries and stored in Shapefile or GeoJSON format.

[0081] In the above embodiment, the array is written into a grid file with geographic reference, accurately embedding the original image coordinate system, solving the problem of spatial position deviation; and then converted into a vector area file through a GIS tool, realizing the aggregation of discrete pixels into continuous polygons, significantly improving the geological application value of the result.

[0082] The application further discloses a system for identifying a hydrothermal alteration zone in a carbonate formation.

[0083] The application further discloses a system for identifying a hydrothermal alteration zone in a carbonate formation. The preprocessing module is configured to acquire a space high-spectrum remote sensing image of a region to be identified and perform preprocessing to obtain surface reflectivity data. The mineral composition analysis module is configured to perform mineral composition analysis based on the surface reflectivity data, mark carbonate mineral dominant pixels, and generate a valued binary mask array. The hydrothermal alteration information extraction module is configured to extract hydrothermal alteration information and generate an information intensity array according to the valued binary mask array. The binary processing module is configured to perform binary processing on the information intensity array according to a preset threshold to generate a binary logic array.

[0084] The system for identifying a hydrothermal alteration zone in a carbonate formation can implement any of the above-mentioned identification methods, and the specific working processes of the modules in the system can refer to the corresponding processes in the above-mentioned method embodiments.

[0085] In the several embodiments provided in the present application, it should be understood that the provided methods and systems can be implemented in other manners. For example, the system embodiments described above are merely schematic; for example, the division of a certain module is merely a logical function division, and other division manners can be adopted during actual implementation; for example, a plurality of modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0086] The application further discloses a computer device.

[0087] The computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the above-mentioned identification method for a hydrothermal alteration zone in a carbonate formation when executing the computer program.

[0088] The application further discloses a computer readable storage medium.

[0089] The computer readable storage medium stores a computer program capable of being loaded and executed by a processor to implement any of the above-mentioned identification methods for a hydrothermal alteration zone in a carbonate formation.

[0090] Wherein, the computer readable storage medium can be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device; program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire line, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0091] It should be noted that in the above embodiments, the description of each embodiment has its emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0092] The above are the preferred embodiments of the present application, not to limit the protection scope of the present application, any feature disclosed in the specification (including abstract and drawings) can be replaced by other equivalent or similar purpose alternative features, unless specifically described. That is, unless specifically described, each feature is only an example of a series of equivalent or similar features.

Claims

1. A method of identifying a hydrothermal alteration zone in a carbonate build-up, characterized by, The identification method comprises: acquiring a spaceborne hyperspectral remote sensing image of a to-be-identified area and performing preprocessing to obtain surface reflectivity data; performing mineral composition analysis based on the surface reflectivity data, marking carbonate mineral dominant pixels, and generating an assigned binary mask array; extracting hydrothermal alteration information and generating an information intensity array according to the assigned binary mask array; performing binaryzation processing on the information intensity array according to a preset threshold to generate a binary logic array; converting the binary logic array into a geographic spatial vector area file based on an original image geographic coordinate system, and taking the geographic spatial vector area file as a hydrothermal alteration zone identification result.

2. A method of identifying a hydrothermal alteration zone in a carbonate build-up according to claim 1, characterized in that, The preprocessing step comprises: detecting valid pixels and invalid pixels in the spaceborne hyperspectral remote sensing image; filling the invalid pixels by using a linear interpolation algorithm based on the radiation brightness values of adjacent valid pixels to obtain a spaceborne hyperspectral remote sensing image after bad line correction; performing radiation calibration based on the spaceborne hyperspectral remote sensing image after bad line correction to convert original values into radiation brightness data; inputting the radiation brightness data into an atmospheric correction model to obtain the surface reflectivity data.

3. A method of identifying a hydrothermal alteration zone in a carbonate build-up according to claim 2, characterised in that, The step of performing mineral composition analysis based on the surface reflectivity data, marking carbonate mineral dominant pixels, and generating an assigned binary mask array comprises: applying a spectral matching algorithm to analyze dominant mineral composition based on each pixel of the surface reflectivity data to generate a mineral composition identifier; marking carbonate mineral dominant pixels based on the mineral composition identifier; constructing a binary mask array consistent with the spatial dimension of the surface reflectivity data, and assigning the marked carbonate mineral dominant pixels to corresponding positions in the binary mask array to obtain an assigned binary mask array.

4. A method of identifying a hydrothermal alteration zone in a carbonate build-up according to claim 3, characterized in that, The step of extracting hydrothermal alteration information and generating an information intensity array according to the assigned binary mask array comprises: creating an information intensity initial array with consistent dimensions according to the assigned binary mask array, and initializing all element values; traversing each pixel position of the assigned binary mask array, extracting iron alteration characteristic values, hydroxyl alteration characteristic values, and carbonate alteration characteristic values of all carbonate mineral dominant pixels; multiplying the iron alteration characteristic values, the hydroxyl alteration characteristic values, and the carbonate alteration characteristic values to obtain a combined characteristic intensity; assigning the combined characteristic intensity to the current pixel position of the information intensity initial array to output an information intensity array.

5. A method of identifying a hydrothermal alteration zone in a carbonate build-up according to claim 1, characterized in that, The step of performing binaryzation processing on the information intensity array according to a preset threshold to generate a binary logic array comprises: dynamically determining a preset threshold according to the statistical distribution characteristics of the information intensity array; traversing each element in the information intensity array, and assigning 1 to the corresponding position in the binary logic array when the element value is greater than the preset threshold, or assigning 0 otherwise.

6. A method of identifying a hydrothermal alteration zone in a carbonate build-up according to any one of claims 1 to 5, characterised in that, The step of converting the binary logic array into a geographic spatial vector area file based on an original image geographic coordinate system, and taking the geographic spatial vector area file as a hydrothermal alteration zone identification result comprises: writing the binary logic array into a raster file with geographic reference based on the original image geographic coordinate system; converting the raster file into a geographic spatial vector area file through a spatial conversion tool, and taking the geographic spatial vector area file as a hydrothermal alteration zone identification result.

7. A system for identifying hydrothermal alteration zones in carbonate rock formations, characterized in that, The identification system comprises: A preprocessing module is configured to acquire a space high-spectrum remote sensing image of a region to be identified and perform preprocessing to obtain surface reflectivity data; A mineral composition analysis module is configured to perform mineral composition analysis based on the surface reflectivity data, mark carbonate mineral dominant pixels, and generate an assigned binary mask array; A hydrothermal alteration information extraction module is configured to extract hydrothermal alteration information and generate an information intensity array according to the assigned binary mask array; A binary processing module is configured to perform binary processing on the information intensity array according to a preset threshold to generate a binary logic array; An array conversion module is configured to convert the binary logic array into a geographic space vector area file based on an original image geographic coordinate system, as a hydrothermal alteration zone identification result.

8. A computer device, comprising: A computer program is stored in a memory and executable on a processor, and the processor executes the program to implement the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: A computer program is stored in a memory and executable on a processor, and the processor executes the program to implement the method of any one of claims 1 to 6.

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