Mineralization center determination method and device based on multi-source remote sensing data

Abstract

The invention discloses a mineralization center determination method and device based on multi-source remote sensing data, relates to the technical field of remote sensing image recognition, and aims to solve the problem that existing mineralization center determination is low in accuracy. Comprising the following steps: acquiring hyperspectral data of a target area and an actually measured rock and mine spectrum set; carrying out spectrum characteristic analysis according to the actually measured rock-mine spectrum set, and determining an absorption intensity abnormal region and an altered rock type corresponding to the absorption intensity abnormal region; according to the hyperspectral data and the alteration rock wave spectrum, alteration information is extracted by adopting a maximum noise ratio method and a hybrid modulation matched filtering method, and target alteration information is obtained; and determining a mineralization center of the target area based on the absorption intensity abnormal area, the altered rock type and the target alteration information. The method for determining the mineralization center based on the multi-source remote sensing data is used for improving the accuracy of alteration information in a mode of combining the actually measured rock and mine spectrum and the hyperspectral data, so that the determined mineralization center is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image recognition, and in particular to a method and device for determining a mineralization center based on multi-source remote sensing data. Background Art

[0002] The structurally altered rock type gold deposits are obviously controlled by the fault structure. There are intense structural-magmatic-fluid activities in the fault zone. Studying the alteration type and structural distribution characteristics are the main signs of prospecting. At present, the alteration anomaly information is usually extracted from remote sensing data. However, the resolution of remote sensing data is low and it may not be possible to clearly capture the alteration characteristics of the surface or underground. In addition, due to the complexity of the surface, in areas with more vegetation, the use of masking methods to remove vegetation may miss the geological information under the vegetation cover, making the extracted alteration information low in accuracy, thereby resulting in low accuracy of the determined mineralization center. Summary of the invention

[0003] The purpose of the present invention is to provide a method and device for determining the mineralization center based on multi-source remote sensing data, which improves the accuracy of alteration information extraction by combining the measured rock and mineral spectrum with hyperspectral data, thereby making the determined mineralization center more accurate.

[0004] In order to achieve the above object, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for determining a mineralization center based on multi-source remote sensing data, comprising: Acquire hyperspectral data of the target area and a measured rock and mineral spectrum set; the measured rock and mineral spectrum set includes altered rock spectrum; the measured rock and mineral spectrum set is collected by a full-spectrum spectrometer; Performing spectral characteristic analysis based on the measured rock and mineral spectrum set to determine the anomalous absorption intensity area and the altered rock type corresponding to the anomalous absorption intensity area; According to the hyperspectral data and the altered rock spectrum, the maximum noise ratio method and the hybrid modulation matching filter method are used to extract the alteration information to obtain the target alteration information; The mineralization center of the target area is determined based on the absorption intensity abnormal area, the altered rock type and the target alteration information.

[0005] Optionally, the step of acquiring the hyperspectral data of the target area and the measured rock and mineral spectrum set includes: Obtain hyperspectral data, multispectral data and DEM data of the target area; Based on the multi-spectral data, hyperspectral data and DEM data, alteration information extraction and line-loop structure interpretation are performed to obtain remote sensing alteration information and structural distribution characteristics; the remote sensing alteration information includes alteration areas; Determining a field verification route and verification points based on the structural distribution characteristics and remote sensing alteration information; the field verification route passes through the alteration zone; Rock and mineral samples are collected at verification points in the target area, and a full-spectrum spectrometer is used to scan the rock and mineral samples collected at each verification point to obtain a measured rock and mineral spectrum set.

[0006] Optionally, the multispectral data includes ASTER data and Landsat 8 OLI data; the remote sensing alteration information includes first alteration information and second alteration information; the alteration information extraction and line-loop structure interpretation based on the multispectral data, hyperspectral data and DEM data to obtain remote sensing alteration information and structural distribution characteristics include: Extracting alteration information from the ASTER data using a band ratio method, a principal component analysis method, and an energy spectrum area method to obtain first alteration information; Extracting alteration information from the hyperspectral data using a spectral angle mapping method to obtain second alteration information; Superimposing the DEM data with hillshade transformation to obtain superimposed data; A line-loop structural interpretation is established based on the superimposed data, Landsat 8 OLI data and ASTER data to obtain structural distribution characteristics.

[0007] Optionally, the first alteration information includes altered rock alteration information and silicification alteration information; the step of extracting alteration information from the ASTER data using a band ratio method, a principal component analysis method, and an energy spectrum area method to obtain the first alteration information includes: Performing principal component analysis on the target band of the ASTER data to obtain a first principal component analysis result; Using the formula: QI=(Band11*Band11) / (Band10*Band12) CI=Band13 / Band14 MI=Band12 / Band13 Extracting the thermal infrared band data of the ASTER data to obtain the quartz index, carbonate index and ferromagnesian index; Among them, QI is the quartz index, CI is the carbonate index, MI is the mafic index, Band10, Band11, Band12, Band13, Band14 are the 10th, 11th, 12th, 13th, and 14th bands of the ASTER data; Performing principal component analysis on the quartz index, carbonate index and ferromagnesian index to obtain a second principal component analysis result; The first target component is extracted by using the energy spectrum area method to obtain alteration information of altered rocks; the first target component is the target component in the first principal component analysis result; The energy spectrum area method is used to extract the second target component to obtain silicification alteration information; the second target component is the target component in the second principal component analysis result.

[0008] Optionally, the extracting the second target component by using the energy spectrum area method to obtain the silicification alteration information includes: Performing Fourier transform on the second target component to transform the second target component from the spatial domain to the frequency domain; Determine the frequency value of the abnormal component in the frequency domain as an energy spectrum, and determine the quantity corresponding to the frequency value of the abnormal component as an area; Performing a double logarithmic operation on the energy spectrum and the area to obtain a double logarithmic graph; Fitting two straight lines in the double logarithmic graph, and determining the energy spectrum density value corresponding to the intersection of the two straight lines as the fractal filter threshold for separating the alteration information from the background information; Performing mask processing on the fractal filter threshold to obtain a mask result; Perform inverse Fourier transform on the mask result to obtain silicification alteration information.

[0009] Optionally, the second alteration information includes first limonite alteration information and first clay alteration information; the second alteration information is obtained by extracting alteration information from the hyperspectral data using the spectral angle mapping method, including: Resampling the hyperspectral data according to the waveband of the standard mineral spectrum to obtain target hyperspectral data; The spectral angle mapping method is used to extract the alteration information of the band with a wavelength greater than 2000 nm in the target hyperspectral data to obtain the first clay alteration information; The spectral angle mapping method is used to extract the alteration information of the band with a wavelength less than 1300nm in the target hyperspectral data to obtain the first limonite alteration information.

[0010] Optionally, the performing spectral characteristic analysis according to the measured rock and mineral spectrum set to determine the abnormal absorption intensity area and the altered rock type corresponding to the abnormal absorption intensity area includes: Extract the absorption intensity and central wavelength of the absorption peak of each verification point spectrum at the target position in the measured rock and mineral spectrum set; the target position is the position of the absorption peak closest to the 960nm band, the position of the absorption peak closest to the 2200nm band, and the position of the absorption peak closest to the 2335nm band; Performing interpolation processing on the absorption intensity to obtain an absorption intensity distribution diagram corresponding to each absorption peak, and performing interpolation processing on the central wavelength to obtain an absorption center distribution diagram corresponding to each absorption peak; Determining an abnormal absorption intensity region in the absorption intensity distribution diagram according to a preset absorption intensity threshold; The central wavelength range characteristics of the absorption intensity abnormal area are analyzed according to the absorption center distribution map to determine the altered rock type corresponding to the absorption intensity abnormal area.

[0011] Optionally, the target alteration information includes second limonite alteration information and second clay alteration information; the target alteration information is extracted by using a maximum noise ratio method and a hybrid modulation matching filter method according to the hyperspectral data and the altered rock spectrum, and the target alteration information includes: Resampling the altered rock spectrum according to the band range of the hyperspectral data to obtain a resampled spectrum; the band range of the resampled spectrum is the same as the band range of the hyperspectral data; The spectrum before 1300 nm in the resampled spectrum is used as the first end member spectrum, and the spectrum after 2000 nm in the resampled spectrum is used as the second end member spectrum; Performing a minimum noise separation transformation on the hyperspectral data to obtain a hyperspectral image after MNF transformation; performing a minimum noise separation transformation on the first endmember spectrum to obtain a first endmember spectrum transformation result; performing a minimum noise separation transformation on the second endmember spectrum to obtain a second endmember spectrum transformation result; Performing hybrid modulation matched filtering decomposition on the hyperspectral image and the first endmember spectral transformation result to obtain a first multi-band image file including a matched filter fractional value band and an infeasible band of the first endmember; performing hybrid modulation matched filtering decomposition on the hyperspectral image and the second endmember spectral transformation result to obtain a second multi-band image file including a matched filter fractional value band and an infeasible band of the second endmember; Pixels with high MF values ​​and low unfeasibility in each end member are extracted from the first multi-band image file to obtain limonite alteration information; pixels with high MF values ​​and low unfeasibility in each end member are extracted from the second multi-band image file to obtain clay alteration information.

[0012] Optionally, determining the mineralization center of the target area based on the absorption intensity abnormal area, the altered rock type and the target alteration information includes: The absorption intensity abnormal area, the altered rock type corresponding to the absorption intensity abnormal area, the target alteration information, the geochemical anomaly, and the spectral absorption characteristics of each type of altered rock are superimposed to obtain a superimposed information map; Analyze the superimposed information map to determine the distribution of various types of altered rocks; The distribution is compared with the distribution of altered rocks in known mineralized centers to determine the mineralized centers.

[0013] Compared with the prior art, the present invention provides a method for determining the mineralization center based on multi-source remote sensing data, including: obtaining hyperspectral data of the target area and a measured rock and mineral spectrum set; performing spectral feature analysis based on the measured rock and mineral spectrum set to determine the absorption intensity abnormal area and the altered rock type corresponding to the absorption intensity abnormal area; extracting alteration information based on the hyperspectral data and the altered rock spectrum using the maximum noise ratio method and the hybrid modulation matching filter method to obtain the target alteration information; determining the mineralization center of the target area based on the absorption intensity abnormal area, the altered rock type and the target alteration information. Among them, the measured rock spectrum is obtained by scanning with a full spectrum scanner, and the measured rock spectrum has a higher resolution, which can distinguish different alteration intensities and mineral types that are difficult to distinguish with the naked eye. At the same time, the measured rock and mineral spectrum is used as a reference spectrum, and combined with the hyperspectral data to extract alteration information, which can reduce interference factors and can more accurately extract alteration information compared to the standard spectrum library. In addition, it is more accurate to determine the mineral center based on the absorption intensity abnormal area and the extracted alteration information.

[0014] In a second aspect, the present invention further provides a device for determining a mineralization center based on multi-source remote sensing data, comprising: A data acquisition module, used to acquire the hyperspectral data of the target area and a measured rock and mineral spectrum set; the measured rock and mineral spectrum set includes an altered rock spectrum; the measured rock and mineral spectrum set is collected by a full-spectrum spectrometer; An absorption intensity abnormal area determination module is used to perform spectral feature analysis based on the measured rock and mineral spectrum set to determine the absorption intensity abnormal area and the altered rock type corresponding to the absorption intensity abnormal area; A target alteration information extraction module is used to extract alteration information based on the hyperspectral data and the altered rock spectrum by using a maximum noise ratio method and a hybrid modulation matching filter method to obtain target alteration information; The mineralization center determination module is used to determine the mineralization center of the target area based on the absorption intensity abnormal area, the altered rock type and the target alteration information.

[0015] Compared with the prior art, the beneficial effects of the device for determining the mineralization center based on multi-source remote sensing data provided by the present invention are the same as the beneficial effects of the method for determining the mineralization center based on multi-source remote sensing data described in the above technical solution, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A flow chart of a method for determining a mineralization center based on multi-source remote sensing data provided by the present invention; Figure 2 The interpolation diagram of the measured spectrum characteristics of the Tuantian verification area corresponding to the aluminum hydroxyl absorption intensity provided by the present invention; Figure 3 The interpolation diagram of the measured spectrum characteristics of the Tuantian verification area corresponding to the aluminum hydroxyl absorption center provided by the present invention; Figure 4 The interpolation diagram of the spectrum characteristics measured in the Tuantian verification area corresponding to the magnesium hydroxyl absorption intensity provided by the present invention; Figure 5 The interpolation diagram of the measured spectrum characteristics of the Tuantian verification area corresponding to the magnesium hydroxyl absorption center provided by the present invention; Figure 6 The interpolation diagram of the measured spectrum characteristics of the Tuantian verification area corresponding to the iron dye absorption intensity provided by the present invention; Figure 7 The interpolation diagram of the measured spectrum characteristics of the Tuantian verification area corresponding to the iron-dyed absorption center provided by the present invention; Figure 8 A measured spectrum curve graph selected as an end member provided by the present invention; Fig. 9 The Tuantian verification area and spectral characteristics, geochemical anomalies, and remote sensing anomaly information map provided by the present invention; Fig.10 A schematic structural diagram of a device for determining a mineralization center based on multi-source remote sensing data provided by the present invention. DETAILED DESCRIPTION

[0017] In order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, words such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and effects. For example, the first threshold and the second threshold are only used to distinguish different thresholds, and their order is not limited. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0018] It should be noted that, in the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0019] In the present invention, "at least one" means one or more, and "plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, the combination of a and b, the combination of a and c, the combination of b and c, or the combination of a, b and c, where a, b, c can be single or multiple.

[0020] Before introducing the embodiments of the present invention, the following definitions are given for the relevant terms involved in the embodiments of the present invention: Spectral Angle Mapping Method: It is a spectral classification based on physics, which uses n-dimensional angles to match pixel spectra and reference spectra. The spectrum is regarded as a vector in a space with a dimension equal to the number of bands. The similarity between two spectra is determined by an algorithm that calculates the angle between the spectra. The endmember spectrum is obtained from an ASCII file or spectral database, or can be obtained directly from the image. SAM compares the angle of the endmember spectrum vector and the vector of each pixel in n-dimensional space. The smaller the angle, the closer the match with the reference spectrum. When the pixel is larger than the arc threshold of a specific maximum angle, it will not be classified.

[0021] Energy spectrum area method: English name: Spectrum-Area, referred to as SA, is a space-frequency integrated method for measuring anisotropy in Fourier frequency space. In Fourier frequency space, time-space complex patterns can be represented by signal combinations of different wavelengths, and spatial patterns can be described by energy spectrum density and phase information between different wavelengths. Therefore, wavelength and energy spectrum distribution can be used as indicators to measure changes in time-space patterns. The generalized self-similarity of time-space complex patterns can not only be measured by the SA model, but also frequency filters based on different generalized self-similarity can be obtained by the model. The obtained frequency filters can separate anomalies from background. This model has been widely used in the decomposition and identification of geophysical anomalies and geochemical anomalies.

[0022] As remote sensing technology is gradually mature in the field of geology, it is possible to obtain more accurate results by extracting information such as alteration and structure. However, due to the complexity of the surface, the alteration information extracted solely by remote sensing data cannot accurately determine the distribution of altered rocks and structures on the surface, and thus cannot accurately infer the mineralization center.

[0023] Based on the above problems, the present invention provides a method and device for determining a mineralization center based on multi-source remote sensing data, which will be described below with reference to the accompanying drawings.

[0024] See also Figure 1 The present invention provides a method for determining the mineralization center based on multi-source remote sensing data, such as Figure 1 As shown, the method comprises the following steps: Step S1: Acquire the hyperspectral data of the target area and the measured rock and mineral spectrum set; The measured rock and mineral spectrum set is a collection of preferred spectral curves corresponding to each verification point in the target area. The measured rock and mineral spectrum set includes altered rock spectra, which are a collection of preferred spectral curves corresponding to different types of altered rocks in the measured rock and mineral spectrum set; the measured rock and mineral spectrum set is collected by a full-spectrum spectrometer; the full-spectrum spectral scanner has a band range of 350nm~2500nm and a resolution of 2nm. Spectral scanning of rock and mineral samples can distinguish different alteration intensities and mineral types that are difficult to distinguish with the naked eye.

[0025] Hyperspectral data can be ZY1E and ZY1F data; hyperspectral data is data that has been preprocessed by radiation calibration, atmospheric correction, orthorectification, etc.

[0026] Step S2: performing spectral characteristic analysis according to the measured rock and mineral spectrum set to determine the abnormal absorption intensity area and the altered rock type corresponding to the abnormal absorption intensity area; The absorption intensity abnormal area is determined by determining the absorption intensity and central wavelength of the absorption peak. The absorption peak near 960nm is selected as the iron staining alteration absorption peak, the absorption peak near 2200nm is selected as the aluminum hydroxyl absorption peak, and the absorption peak near 2335nm is selected as the magnesium hydroxyl absorption peak.

[0027] Step S3: extracting alteration information using a maximum noise ratio method and a hybrid modulation matched filter method according to the hyperspectral data and the altered rock spectrum to obtain target alteration information; The target alteration information includes limonite alteration information and clay alteration information. The clay alteration information includes the alteration information of clay minerals such as montmorillonite, kaolinite, sericite, and chlorite.

[0028] Step S4: Determine the mineralization center of the target area based on the absorption intensity abnormal area, altered rock type and target alteration information.

[0029] Specifically, step S4 includes: superimposing the absorption intensity abnormal area, the altered rock type corresponding to the absorption intensity abnormal area, the target alteration information, the geochemical anomaly, and the spectral absorption characteristics of each type of altered rock to obtain a superimposed information map; Analyze the overlay information map to determine the distribution of each type of altered rock; Based on the distribution characteristics of the ore body, combined with the distribution of alteration, structure, and spectral characteristics, the distribution is compared with the distribution of altered rocks in known mineralized centers to determine the mineralized center.

[0030] Through the above method, it can be known that the measured rock spectrum of the present invention is obtained by scanning with a full spectrum scanner. The measured rock spectrum has a higher resolution and can distinguish different alteration intensities and mineral types that are difficult to distinguish with the naked eye. At the same time, the measured rock and mineral spectrum is used as a reference spectrum and combined with hyperspectral data to extract alteration information, which can reduce interference factors and can extract alteration information more accurately than the standard spectrum library. In addition, it is more accurate to determine the mineral center based on the abnormal absorption intensity area and the extracted alteration information.

[0031] Measured rock and mineral spectra are usually obtained by collecting rock samples through field verification and then scanning the rock samples. Due to the limitations of factors such as terrain, weather, and communication, the establishment of field verification points is inaccurate or the range is too large, and field geological surveys will take a lot of manpower and time. Moreover, the obtained rock samples are not representative. To this end, the present invention combines remote sensing methods with geological work to make geological surveys more efficient. Specifically, obtaining the hyperspectral data of the target area and the measured rock and mineral spectrum set in the above steps can be achieved by the following steps: Step S11: Acquire hyperspectral data, multispectral data and DEM data of the target area; Multispectral data includes ASTER data and Landsat 8 OLI data. Multispectral data is data that has been preprocessed by radiation calibration, atmospheric correction, normalization, etc.

[0032] Step S12: extracting alteration information and interpreting line-loop structures based on the multispectral data, hyperspectral data and DEM data to obtain remote sensing alteration information and structural distribution characteristics; the remote sensing alteration information includes alteration areas; the remote sensing alteration information includes first alteration information and second alteration information; Step S12 can be implemented based on the following steps: Step S121: extracting alteration information from the ASTER data using a band ratio method, a principal component analysis method, and an energy spectrum area method to obtain first alteration information; the first alteration information includes alteration information of altered rocks and silicification alteration information, and the altered rock alteration information includes iron staining alteration information and hydroxyl alteration information.

[0033] Among them, principal component analysis realizes data dimensionality reduction by projecting the original data into a new space composed of principal components, which helps to reduce computational complexity, especially when dealing with high-dimensional data. The dimensionality-reduced data retains the main information of the original data while removing redundancy and noise.

[0034] The spectral area method measures the generalized self-similarity of anisotropy in the Fourier spectral space, and can decompose the distribution of the spectrum into different filters by identifying different generalized self-similarities. Then, the Fourier inverse transform is used to separate anomalies and backgrounds. This self-similarity can be expressed by the following formula: A(>S)∝S^(-β) where S is the spectral density, A is the area greater than a certain critical value S0 of the spectral density, and different β values can be obtained on the logA(<S0)-log(S) graph. The abscissa of the double-logarithm graph is the spectral density value, and the ordinate is the area; usually on the log-log graph, all straight line segments obey this relationship. Different straight line segments in the double-logarithm graph represent different fractal relationships, and the abscissa value corresponding to the intersection of the two straight lines is the threshold of the fractal filter. According to the threshold of the fractal filter, various anomaly filters and background filters can be generated, and using these filters, the Fourier inverse transform can be used to transform them back into the spatial domain to achieve the separation of anomalies and backgrounds.

[0035] Step S122: Use the spectral angle mapping method to extract alteration information from the hyperspectral data to obtain second alteration information; The types of the second alteration information and the target alteration information are the same. To distinguish the two, the alteration information of the second alteration information is called the first limonitization alteration information and the first argillization alteration information; the alteration information of the target alteration information is called the second limonitization alteration information and the second argillization alteration information.

[0036] When using the spectral angle mapping method to extract alteration information, redundant information is easily generated. Therefore, according to the mineral spectral characteristics, the hyperspectral data is resampled according to the bands of the standard mineral spectrum to obtain target hyperspectral data; the spectral angle mapping method is used to extract the alteration information of the bands with wavelengths greater than 2000nm in the target hyperspectral data, and the argillization alteration information such as montmorillonite, kaolinite, sericite, and chlorite is extracted to obtain the first argillization alteration information; the spectral angle mapping method is used to extract the alteration information of the bands with wavelengths less than 1300nm in the target hyperspectral data to obtain the first limonitization alteration information.

[0037] The specific extraction process is to use the standard spectrum measured in the laboratory or the average spectrum of known points extracted from the image as a reference, calculate the generalized angle between each pixel vector in the hyperspectral data and the reference spectrum vector, and confirm the pixel vector corresponding to the angle within the preset angle range as alteration information.

[0038] Step S123: superimposing the DEM data with the hill shadow transformation to obtain superimposed data; Step S124: establishing a line-loop structural interpretation based on the superimposed data, Landsat 8 OLI data and ASTER data to obtain structural distribution characteristics.

[0039] The interpretation of line-ring structures includes the interpretation of linear structures and the interpretation of circular structures. The linear structures can be interpreted through the changes in the tones, shadow patterns, and patterns on the images corresponding to the Landsat 8 OLI data and the ASTER data, so as to understand the spatial distribution of geological structures, geological stability, mineral resource distribution, and other structural distribution characteristics. The circular structures are the annular anomalies of tones, the annular undulations of the terrain, the annularly distributed vegetation, the shadow patterns with annular characteristics, and other characteristics on the images corresponding to the Landsat 8 OLI data and the ASTER data.

[0040] S13: Determine a field verification route and verification points based on the structural distribution characteristics and remote sensing alteration information; The field verification route passes through the alteration zone; the verification points in the near-mine area can be encrypted to carry out field verification and rock mineral sample sampling.

[0041] Step S14: rock and mineral samples are collected at verification points in the target area, and the rock and mineral samples collected at each verification point are scanned using a full-spectrum spectrometer. According to the quality and characteristics of the spectral curve, one spectral curve is selected for each verification point in the scanned spectral curve to form a measured rock and mineral spectrum set.

[0042] The scanning steps include instrument connection, heat machine, background scanning, reference scanning and sample scanning. 4 to 6 surfaces are collected for each sample to obtain the measured spectrum of the rock and mineral samples and establish a measured rock and mineral spectrum library.

[0043] Remote sensing methods have fast processing speed, strong macroscopic observation capabilities, simple and efficient operation, and can provide rich information. By extracting alteration information from remote sensing data and verifying the route and points in the field, the accuracy of field verification can be improved and the workload of field investigation can be significantly reduced. In addition, the measured rock and mineral spectrum is obtained by spectral scanning in the verification area. By analyzing the spectrum of the verification area, the mineral distribution law can be discovered more accurately from the spectral perspective.

[0044] As an optional method, the above step S121 can be implemented based on the following steps: Step S1211: performing principal component analysis on the target band of the ASTER data to obtain a first principal component analysis result; Among them, the target band can be a suitable band selected according to needs, and different target bands are selected for different alteration types; for example: the absorption position of iron-stained minerals appears near 0.5μm and 0.9μm, corresponding to B1 and B3 of ASTER data, so the 1st, 2nd, 3rd, and 4th bands can be selected for principal component analysis; hydroxyl minerals have strong absorption in B6 of ASTER data and strong reflection in B7, so the data of 1st, 4th, 6th, and 7th bands of ASTER data can be selected for principal component analysis to enhance the difference between B6 and B7, and the contribution coefficients of B6 and B7 have opposite signs.

[0045] Step S1212: Use the formula: QI=(Band11*Band11) / (Band10*Band12) CI=Band13 / Band14 MI=Band12 / Band13 Extracting the thermal infrared band data of the ASTER data to obtain the quartz index, carbonate index and ferromagnesian index; Among them, QI is the quartz index, CI is the carbonate index, MI is the mafic index, Band10, Band11, Band12, Band13, Band14 are the 10th, 11th, 12th, 13th, and 14th bands of the ASTER data; Step S1213: performing principal component analysis on the three indices of quartz index, carbonate index and ferromagnesian index to obtain a second principal component analysis result; The second principal component analysis result includes principal component analysis results obtained by principal component analysis of quartz index, carbonate index and ferromagnesian index respectively.

[0046] Step S1214: extracting the first target component by using the energy spectrum area method to obtain alteration information of altered rocks; the first target component is the target component in the first principal component analysis result; Specifically, a Fourier transform is performed on the first target component to transform the first target component from the spatial domain to the frequency domain; the target component is the component that best matches the altered mineral.

[0047] The frequency value of the abnormal component in the frequency domain is determined as the energy spectrum, and the number corresponding to the frequency value of the abnormal component is determined as the area; a double logarithmic operation is performed on the energy spectrum and the area to obtain a double logarithmic graph; two straight lines are fitted in the double logarithmic graph, and the energy spectrum density value corresponding to the intersection of the two straight lines is determined as the fractal filter threshold for separating the alteration information from the background information; the fractal filter threshold is subjected to mask processing to obtain a mask result; Perform inverse Fourier transform on the mask results to obtain alteration information of altered rocks. When the first target component is the target component corresponding to iron-stained minerals, the alteration information of altered rocks obtained is iron-stained alteration information; when the first target component is the target component corresponding to hydroxyl minerals, the alteration information of altered rocks obtained is hydroxyl alteration information; the energy spectrum area method is used to extract alteration information, which can effectively enhance the weak anomaly information of mineralization in high vegetation coverage areas and improve the accuracy of alteration information.

[0048] Step S1215: extracting the second target component by using the energy spectrum area method to obtain silicification alteration information; the second target component is the target component in the second principal component analysis result.

[0049] Specifically, a Fourier transform is performed on the second target component to transform the second target component from the spatial domain to the frequency domain; the frequency value of the abnormal component in the frequency domain is determined as the energy spectrum, and the quantity corresponding to the frequency value of the abnormal component is determined as the area; a double logarithmic operation is performed on the energy spectrum and the area to obtain a double logarithmic graph; two straight lines are fitted in the double logarithmic graph, and the energy spectrum density value corresponding to the intersection of the two straight lines is determined as the fractal filter threshold for separating the alteration information from the background information; the fractal filter threshold is masked to obtain a mask result; and the mask result is inverse Fourier transformed to obtain silicification alteration information.

[0050] As an optional method, after acquiring the measured rock and mineral spectrum set, the above step S2 can be implemented based on the following steps: Based on the near-infrared mineral analysis expert system software, the absorption intensity and central wavelength of the absorption peaks of each verification point spectrum at the target position in the measured rock and mineral spectrum set are extracted; the target position is the position of the absorption peak closest to the 960nm band, the position of the absorption peak closest to the 2200nm band, and the position of the absorption peak closest to the 2335nm band; Based on the AreGIS platform, the absorption intensity of each absorption peak is interpolated to obtain the absorption intensity distribution map corresponding to each absorption peak, and the central wavelength of each absorption peak is interpolated to obtain the absorption center distribution map corresponding to each absorption peak; according to the distribution characteristics of the ore body and mineralized veins, the absorption intensity is selected with a suitable preset absorption intensity threshold, and the absorption intensity abnormal area in the absorption intensity distribution map is delineated according to the preset absorption intensity threshold; According to the absorption center distribution diagram, the central wavelength range characteristics of the absorption intensity abnormal area are analyzed to determine the altered rock type corresponding to the absorption intensity abnormal area.

[0051] As an optional method, the above step S3 can be implemented based on the following steps: The altered rock spectrum is resampled according to the band range of the hyperspectral data to obtain a resampled spectrum; the band range of the resampled spectrum is the same as the band range of the hyperspectral data; the altered rock spectrum is a spectral curve of the altered rock selected according to the characteristics and quality of the spectrum.

[0052] The spectrum before 1300nm in the resampled spectrum is used as the first end member spectrum, and the spectrum after 2000nm in the resampled spectrum is used as the second end member spectrum; the band before 1300nm is used to extract limonite alteration information, and the band after 2000nm is used to extract clay alteration information.

[0053] Performing minimum noise separation transformation on the hyperspectral data to obtain the hyperspectral image after MNF transformation; performing minimum noise separation transformation on the first endmember spectrum to obtain the first endmember spectrum transformation result; performing minimum noise separation transformation on the second endmember spectrum to obtain the second endmember spectrum transformation result; Performing hybrid modulation matched filtering decomposition on the hyperspectral image and the first endmember spectral transformation result to obtain a first multi-band image file including the matched filter fractional value band and the infeasibility band of the first endmember; performing hybrid modulation matched filtering decomposition on the hyperspectral image and the second endmember spectral transformation result to obtain a second multi-band image file including the matched filter fractional value band and the infeasibility band of the second endmember; The pixels with high MF values ​​and low unfeasibility in each end member are extracted from the first multi-band image file to obtain the second limonite alteration information; the pixels with high MF values ​​and low unfeasibility in each end member are extracted from the second multi-band image file to obtain the second clay alteration information. MF is the frequency modulation index.

[0054] The second limonitic alteration information and the second clayification alteration information are extracted by combining hyperspectral data and measured rock and mineral spectra, which are more accurate than the first limonitic alteration information and the first clayification information obtained by spectral mapping.

[0055] See also Figure 2-Figure 9 , taking the mineralization center detection of tectonic rock alteration type gold deposit in Ailaoshan orogenic belt as an example to illustrate. The specific steps include: Step 1: Geological background of the study area including investigation of tectonic rocks and minerals and alteration types; The Ailaoshan orogenic belt is an important part of the Sanjiang Tethys tectonic domain and has undergone a complete multi-cycle evolution process. In the middle and northern sections of the Ailaoshan orogenic belt, large tectonic altered rock-type gold deposits such as the Zhenyuan gold mine and the Mojiang gold mine are developed. The exposed strata in the study area include Proterozoic deep metamorphism, Paleozoic shallow metamorphism, and Mesozoic unmetamorphosis. Fold fault structures are strongly developed. The fault structural framework strikes northwest-southeast, and the northwest-southeast and secondary east-west faults are well developed. The Jiujia-Anding deep fault is a regional rock-controlling and ore-controlling fault, which belongs to the pre-ore-forming and ore-forming period faults, controlling the distribution of strata, igneous rocks, and tectonic altered rock-type gold deposits. The structurally altered rock type gold deposits are mainly produced at the intersection of the Jiujia-Anding Fault and the NW-trending Fault. The secondary NW-trending Fault is the main ore-guiding and ore-matching structure, the NW-W and near EW-trending Faults are ore-bearing structures, and the NE-trending Fault Group belongs to the post-metallogenic faults. The alteration of the surrounding rocks near the mines includes silicification, limonite, pyrite, sericite, kaolinite, chlorite, etc.

[0056] Step 2: Collect high-quality multispectral data and hyperspectral data in the target area of ​​the middle and northern sections of the Ailao Mountain orogenic belt. Collect 4 ASTER data from the USGS official website, with phases of 2001.03.08, 2003.02.19, 2005.02.08, and 2005.02.08, respectively. Collect 2 Landsat 8 OLI data, with phases of 2020.03.13 and 2020.03.13, respectively. Collect 4 ZY1E data from the China Resources Satellite Application Center, with phases of 2023.02.13, 2024.03.07, 2024.04.05, and 2024.04.12, and 1 ZY1F data, with a phase of 2023.01.08. Download DEM data from the Geospatial Data Cloud. The above data are all free of cloud and snow cover and have a high degree of interpretation. The remote sensing data needs to be preprocessed by radiometric calibration and atmospheric correction, and the hyperspectral data also needs to be geometrically corrected; After preprocessing, ASTER was subjected to band ratio method and PCA transformation. The characteristic matrix of silicification principal component transformation is shown in Table 1. The QI load of PC2 is the most different from CI and MI, so PC2 is selected as the characteristic vector of silicification alteration, and the sign is positive. PC2 is Fourier transformed, and the log-log graph is obtained after logarithmic operation. The DN value corresponding to the inflection point is -13.27165. After masking, the inverse Fourier transform is performed to extract the silicification anomaly information; Table 1 Principal component transformation characteristic matrix of silicification anomaly

[0057] The characteristic matrix of the main component transformation of hydroxyl alteration is shown in Table 2. The main component representing hydroxyl alteration minerals is band4 with a positive coefficient and band6 with a negative coefficient. The coefficients of band4 and band6 of PC3 and PC4 have opposite signs, and the load difference between band4 and band6 of PC4 is greater. Therefore, PC4 is the target component of hydroxyl alteration minerals. The sign is reversed. PC4 is Fourier transformed and a log-log graph is obtained after logarithmic operation. The DN value corresponding to its inflection point is -7.027003. After masking, an inverse Fourier transform is performed to extract hydroxyl alteration information. Table 2 Hydroxyl anomaly principal component transformation feature matrix

[0058] The characteristic matrix of the main component transformation of iron-stained alteration is shown in Table 3. The eigenvectors of the main components of iron-stained minerals have the same sign as the coefficients of band2 and band4 and are positive, and the same sign as the coefficients of band1 and band3 and are negative. Since there is no completely consistent eigenvector, and the signs of the coefficients of band1, band3, and band4 of PC3 are consistent, and the load difference between band3 and band4 is large, PC3 is selected as the target component of iron-stained alteration minerals, PC3 is Fourier transformed, and the logarithmic operation is performed to obtain the log-log graph. The DN value corresponding to the inflection point of the log-log graph is -3.834362. After masking, the inverse Fourier transform is performed to extract the iron-stained alteration information; Table 3. Principal component transformation characteristic matrix of iron staining anomaly

[0059] The standard mineral spectral library was resampled using the preprocessed ZY1E data, and the SAM method was used to extract limonite mineralization using the band before 1300nm, and kaolinite, montmorillonite, sericite, and chlorite using the band after 2000nm. The DEM data is processed with mountain shadows, combined with the Landsat 8 OLI image features, and combined with the image transformed by ASTER data to interpret the line loop structure: The signs of linear structure interpretation are: foothills, valleys, etc. extend in a straight line; the vegetation and landforms on both sides are inconsistent; the color tones and shadow patterns on both sides are inconsistent; the steep cliffs and fault triangles are arranged along a straight line; the direction of the water system or valley changes suddenly; The interpretation marks of circular structures are: circular ridges; circular uplifts and depressions caused by structures; there is an obvious center, and the joints and fissures around it radiate outward in an explosive manner, and the mountain direction radiates outward and extends for a long distance.

[0060] Step 3: Collect rock samples for field verification, obtain sample spectral curves using a ground full-spectrum spectral scanner, and establish a measured rock and mineral spectral library; According to the results of the alteration information extraction, a field verification route was set up in the Tuantian verification area for field verification, rock and mineral samples were collected, and the route was set up through the alteration area. There were a total of 4 field verification routes and 60 verification points. The verification points were encrypted on Route I according to the distribution characteristics of alteration and mineralized veins. Spectral scanning was performed using a CSD350-A full-spectrum spectrum scanner. After instrument connection, thermal machine, background scan, reference scan, and sample scan, 4 to 6 surfaces were collected for each sample.

[0061] Step 4: Perform diagnostic spectrum feature analysis to analyze the absorption intensity, central wavelength and other characteristics of the absorption valley; According to the quality and characteristics of the spectrum, a representative spectrum is selected at each point; The spectrum was analyzed using MSA software, and the absorption peak around 1900nm was selected as the water peak, and then the absorption peak around 2200nm was selected as the aluminum hydroxyl absorption peak, the absorption peak around 2335nm was selected as the magnesium hydroxyl absorption peak, and the absorption peak around 960nm was selected as the iron dye absorption peak, and the absorption intensity and central wavelength of the aluminum hydroxyl absorption peak, magnesium hydroxyl absorption peak, and iron dye absorption peak of each spectrum curve were obtained; The absorption intensity and central wavelength of each verification point are interpolated to obtain the absorption intensity and central wavelength distribution diagram after removing outliers; See also Figure 2-Figure 7 According to the distribution of mineralized veins, the aluminum hydroxyl absorption intensity above 0.08, the magnesium hydroxyl absorption intensity above 0.023, and the iron staining absorption intensity above 0.16 are regarded as the absorption intensity abnormal zone. The absorption center wavelength of the aluminum hydroxyl absorption intensity abnormal zone is 8nm right-shifted compared with 2200nm, which is consistent with the absorption center wavelength of minerals such as kaolinite and montmorillonite. The absorption center wavelength of the magnesium hydroxyl absorption intensity abnormal zone is 6nm right-shifted compared with 2335nm, which is consistent with the absorption position of chlorite. The central wavelength of the iron staining absorption intensity abnormal zone is 15nm left-shifted compared with 960nm, which is consistent with the absorption position of limonite.

[0062] Step 5: Correct the satellite-borne spectrum data based on the measured spectrum data to further extract remote sensing alteration information; According to the characteristics and quality of the spectrum, the spectrum curves of the altered rock are selected. A total of three spectrum curves are selected. The selected spectrum curves are as follows: Figure 8 As shown, a is the spectrum curve of aluminum hydroxyl alteration, b is the spectrum curve of magnesium hydroxyl alteration, and c is the spectrum curve of limonite mineralization; they are resampled to ZY1E and ZY1F data, which are used as end-member spectra, and the band before 1300nm is selected to extract limonite mineralization information, and the band after 2000nm is selected to extract clay mineralization information; MNF transformation was performed on the ZY1E and ZY1F hyperspectral images, and on the three measured spectra of altered rock minerals after resampling; The hyperspectral image results and endmember spectra after MNF transformation were selected, and MTMF decomposition was performed to obtain a multi-band image file containing the matched filter score band and infeasibility band of each endmember. The pixels with high MF score and low infeasibility value of each endmember were selected to obtain the information of limonite mineralization, aluminum hydroxyl alteration and magnesium hydroxyl alteration. The extraction results can provide a basis for further exploration of other tectonic altered rock-type gold mining areas in the middle and northern sections of the Ailao Mountain orogenic belt.

[0063] Step 6: Analyze the remote sensing alteration characteristics and structure and the relationship with known ore bodies to infer the mineralization center; The spectral absorption characteristics are superimposed with the ore body, geochemical anomalies, remote sensing anomaly information, etc. for analysis. The superimposed data are as follows: Fig. 9 As shown, the distribution of the absorption intensity anomaly area is consistent with the remote sensing alteration distribution. Iron-stained alteration is distributed near the ore body, aluminum-hydroxyl alteration is distributed on both sides of the ore body, and magnesium-hydroxyl alteration is distributed on the periphery of the ore body. According to the absorption intensity, absorption center wavelength distribution characteristics, and ore bodies, geochemical anomalies, remote sensing alteration, and remote sensing structural distribution, it is analyzed that the mineralization center of Tuantian area is the location of the known ore body, and continues to extend to the southeast of the ore body.

[0064] The above mainly introduces the solution provided by the embodiment of the present invention from the perspective of the interaction between various modules. It can be understood that in order to realize the above functions, it includes hardware structures and / or software modules corresponding to the execution of various functions. It should be easy for those skilled in the art to realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0065] In the case of dividing each functional module into corresponding functional modules, Fig.10 FIG. 2 shows a schematic diagram of a mineralization center determination device based on multi-source remote sensing data provided by the present invention. Fig.10 As shown, the device comprises: The data acquisition module 101 is used to acquire the hyperspectral data of the target area and a measured rock and mineral spectrum set; the measured rock and mineral spectrum set includes an altered rock spectrum; the measured rock and mineral spectrum set is collected by a full-spectrum spectrometer; The absorption intensity abnormal area determination module 102 is used to perform spectrum characteristic analysis based on the measured rock and mineral spectrum set to determine the absorption intensity abnormal area and the altered rock type corresponding to the absorption intensity abnormal area; The target alteration information extraction module 103 is used to extract the alteration information according to the hyperspectral data and the altered rock spectrum by using the maximum noise ratio method and the hybrid modulation matched filter method to obtain the target alteration information; The mineralization center determination module 104 is used to determine the mineralization center of the target area based on the absorption intensity abnormal area, the altered rock type and the target alteration information.

[0066] Optionally, the data acquisition module 101 may include: An acquisition unit, used to acquire hyperspectral data, multispectral data and DEM data of a target area; A remote sensing alteration information extraction unit is used to extract alteration information and interpret line-loop structures based on the multispectral data, hyperspectral data and DEM data to obtain remote sensing alteration information and structural distribution characteristics; the remote sensing alteration information includes alteration areas; A verification route designation unit, used to determine a field verification route and verification points based on the structural distribution characteristics and remote sensing alteration information; the field verification route passes through the alteration zone; The measured spectrum set acquisition unit is used to collect rock and mineral samples at the verification points in the target area, and use a full-spectrum spectrometer to scan the rock and mineral samples collected at each verification point to obtain a measured rock and mineral spectrum set.

[0067] Optionally, the multispectral data includes ASTER data and Landsat 8 OLI data; the remote sensing alteration information includes first alteration information and second alteration information; and the remote sensing alteration information extraction unit can be specifically used for: A first alteration information extraction node is used to extract alteration information from the ASTER data using a band ratio method, a principal component analysis method, and an energy spectrum area method to obtain first alteration information; A second alteration information extraction node is used to extract alteration information from the hyperspectral data using a spectral angle mapping method to obtain second alteration information; A DEM data overlay node is used to overlay the DEM data with the hillshade transformation to obtain overlay data; The structural distribution feature determination node is used to establish a line-loop structural interpretation based on the superimposed data, Landsat 8 OLI data and ASTER data to obtain structural distribution features.

[0068] Optionally, the first alteration information includes altered rock alteration information and silicification alteration information; the first alteration information extraction node can be specifically used for: Performing principal component analysis on the target band of the ASTER data to obtain a first principal component analysis result; Using the formula: QI=(Band11*Band11) / (Band10*Band12) CI=Band13 / Band14 MI=Band12 / Band13 Extracting the thermal infrared band data of the ASTER data to obtain the quartz index, carbonate index and ferromagnesian index; Among them, QI is the quartz index, CI is the carbonate index, MI is the mafic index, Band10, Band11, Band12, Band13, Band14 are the 10th, 11th, 12th, 13th, and 14th bands of the ASTER data; Performing principal component analysis on the quartz index, carbonate index and ferromagnesian index to obtain a second principal component analysis result; The first target component is extracted by using the energy spectrum area method to obtain alteration information of altered rocks; the first target component is the target component in the first principal component analysis result; The energy spectrum area method is used to extract the second target component to obtain silicification alteration information; the second target component is the target component in the second principal component analysis result.

[0069] Optionally, the extracting the second target component by using the energy spectrum area method to obtain the silicification alteration information includes: Performing Fourier transform on the second target component to transform the second target component from the spatial domain to the frequency domain; Determine the frequency value of the abnormal component in the frequency domain as an energy spectrum, and determine the quantity corresponding to the frequency value of the abnormal component as an area; Performing a double logarithmic operation on the energy spectrum and the area to obtain a double logarithmic graph; Fitting two straight lines in the double logarithmic graph, and determining the energy spectrum density value corresponding to the intersection of the two straight lines as the fractal filter threshold for separating the alteration information from the background information; Performing mask processing on the fractal filter threshold to obtain a mask result; Perform inverse Fourier transform on the mask result to obtain silicification alteration information.

[0070] Optionally, the second alteration information includes first limonite alteration information and first clay alteration information; the second alteration information extraction node can be specifically used for: Resampling the hyperspectral data according to the waveband of the standard mineral spectrum to obtain target hyperspectral data; The spectral angle mapping method is used to extract the alteration information of the band with a wavelength greater than 2000 nm in the target hyperspectral data to obtain the first clay alteration information; The spectral angle mapping method is used to extract the alteration information of the band with a wavelength less than 1300nm in the target hyperspectral data to obtain the first limonite alteration information.

[0071] Optionally, the absorption intensity abnormal area determination module 102 may include: An absorption intensity and central wavelength extraction unit is used to extract the absorption intensity and central wavelength of the absorption peak of each verification point spectrum in the measured rock and mineral spectrum set at the target position; the target position is the position of the absorption peak closest to the 960nm band, the position of the absorption peak closest to the 2200nm band, and the position of the absorption peak closest to the 2335nm band; An interpolation unit, configured to perform interpolation processing on the absorption intensity to obtain an absorption intensity distribution diagram corresponding to each absorption peak, and perform interpolation processing on the central wavelength to obtain an absorption center distribution diagram corresponding to each absorption peak; An absorption intensity abnormal area delineation unit, used to determine the absorption intensity abnormal area in the absorption intensity distribution diagram according to a preset absorption intensity threshold; The analysis unit is used to analyze the central wavelength range characteristics of the absorption intensity abnormal area according to the absorption center distribution map, and determine the altered rock type corresponding to the absorption intensity abnormal area.

[0072] Optionally, the target alteration information includes second limonite alteration information and second clay alteration information; the target alteration information extraction module 103 may include: A resampling unit, used for resampling the altered rock spectrum according to the band range of the hyperspectral data to obtain a resampled spectrum; the band range of the resampled spectrum is the same as the band range of the hyperspectral data; An end member spectrum selection unit, used for taking the spectrum before 1300nm in the resampled spectrum as the first end member spectrum, and taking the spectrum after 2000nm in the resampled spectrum as the second end member spectrum; A minimum noise separation transformation unit is used to perform a minimum noise separation transformation on the hyperspectral data to obtain a hyperspectral image after MNF transformation; perform a minimum noise separation transformation on the first endmember spectrum to obtain a first endmember spectrum transformation result; perform a minimum noise separation transformation on the second endmember spectrum to obtain a second endmember spectrum transformation result; A hybrid modulation matched filtering decomposition unit is used to perform hybrid modulation matched filtering decomposition on the hyperspectral image and the first endmember spectral transformation result to obtain a first multi-band image file including a matched filtering fractional value band and an infeasible band of the first endmember; and perform hybrid modulation matched filtering decomposition on the hyperspectral image and the second endmember spectral transformation result to obtain a second multi-band image file including a matched filtering fractional value band and an infeasible band of the second endmember; The target alteration information extraction unit is used to extract the pixels with high MF value and low infeasibility in each end member in the first multi-band image file to obtain the second limonite alteration information; and extract the pixels with high MF value and low infeasibility in each end member in the second multi-band image file to obtain the second clay alteration information.

[0073] Optionally, the mineralization center determination module 104 may specifically include: An information superposition unit is used to superimpose the absorption intensity abnormal area, the altered rock type corresponding to the absorption intensity abnormal area, the target alteration information, the geochemical anomaly, and the spectral absorption characteristics of each type of altered rock to obtain a superimposed information map; An altered rock distribution analysis unit, used to analyze the superimposed information map to determine the distribution of various types of altered rocks; The mineralization center prediction unit is used to compare the distribution with the distribution of altered rocks in known mineralization centers to determine the mineralization center.

[0074] The embodiment of the present invention can divide the functional modules according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0075] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instruction is loaded and executed on a computer, the process or function described in the embodiment of the present invention is executed in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device or other programmable device. The computer program or instruction may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program or instruction may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium, such as a floppy disk, a hard disk, or a tape; it may also be an optical medium, such as a digital video disc (DVD); it may also be a semiconductor medium, such as a solid state drive (SSD).

[0076] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in a claim. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0077] Although the present invention has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present invention. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present invention as defined by the appended claims and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present invention. Obviously, those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, the present invention is intended to include such modifications and variations if they fall within the scope of the claims of the present invention and their equivalents.

Claims

1. A method for determining mineralization centers based on multi-source remote sensing data, characterized in that: include: Acquire hyperspectral data of the target area and a measured rock and mineral spectrum set; the measured rock and mineral spectrum set includes altered rock spectrum; The measured rock and mineral spectrum set is collected by a full-spectrum spectrometer; Performing spectral characteristic analysis based on the measured rock and mineral spectrum set to determine the anomalous absorption intensity area and the altered rock type corresponding to the anomalous absorption intensity area; According to the hyperspectral data and the altered rock spectrum, the maximum noise ratio method and the hybrid modulation matching filter method are used to extract the alteration information to obtain the target alteration information; The mineralization center of the target area is determined based on the absorption intensity abnormal area, the altered rock type and the target alteration information.

2. The method for determining the mineralization center based on multi-source remote sensing data according to claim 1, characterized in that: The acquisition of the hyperspectral data of the target area and the measured rock and mineral spectrum set comprises: Obtain hyperspectral data, multispectral data and DEM data of the target area; Based on the multi-spectral data, hyperspectral data and DEM data, alteration information extraction and line-loop structure interpretation are performed to obtain remote sensing alteration information and structural distribution characteristics; the remote sensing alteration information includes alteration areas; Determining a field verification route and verification points based on the structural distribution characteristics and remote sensing alteration information; the field verification route passes through the alteration zone; Rock and mineral samples are collected at verification points in the target area, and a full-spectrum spectrometer is used to scan the rock and mineral samples collected at each verification point to obtain a measured rock and mineral spectrum set.

3. The method for determining the mineralization center based on multi-source remote sensing data according to claim 2, characterized in that: The multispectral data includes ASTER data and Landsat 8 OLI data; the remote sensing alteration information includes first alteration information and second alteration information; the alteration information extraction and line-loop structure interpretation based on the multispectral data, hyperspectral data and DEM data to obtain remote sensing alteration information and structural distribution characteristics include: Extracting alteration information from the ASTER data using a band ratio method, a principal component analysis method, and an energy spectrum area method to obtain first alteration information; Extracting alteration information from the hyperspectral data using a spectral angle mapping method to obtain second alteration information; Superimposing the DEM data with hillshade transformation to obtain superimposed data; A line-loop structural interpretation is established based on the superimposed data, Landsat 8 OLI data and ASTER data to obtain structural distribution characteristics.

4. The method for determining the mineralization center based on multi-source remote sensing data according to claim 3 is characterized in that: The first alteration information includes altered rock alteration information and silicification alteration information; the alteration information is extracted from the ASTER data using the band ratio method, the principal component analysis method, and the energy spectrum area method, and the first alteration information obtained includes: Performing principal component analysis on the target band of the ASTER data to obtain a first principal component analysis result; Using the formula: QI=(Band11*Band11) / (Band10*Band12) CI=Band13 / Band14 MI=Band12 / Band13 Extracting the thermal infrared band data of the ASTER data to obtain the quartz index, carbonate index and ferromagnesian index; Among them, QI is the quartz index, CI is the carbonate index, MI is the mafic index, Band10, Band11, Band12, Band13, Band14 are the 10th, 11th, 12th, 13th, and 14th bands of the ASTER data; Performing principal component analysis on the quartz index, carbonate index and ferromagnesian index to obtain a second principal component analysis result; The first target component is extracted by using the energy spectrum area method to obtain alteration information of altered rocks; the first target component is the target component in the first principal component analysis result; The energy spectrum area method is used to extract the second target component to obtain silicification alteration information; the second target component is the target component in the second principal component analysis result.

5. The method for determining the mineralization center based on multi-source remote sensing data according to claim 4, characterized in that: The extraction of the second target component by using the energy spectrum area method to obtain silicification alteration information includes: Performing Fourier transform on the second target component to transform the second target component from the spatial domain to the frequency domain; Determine the frequency value of the abnormal component in the frequency domain as an energy spectrum, and determine the quantity corresponding to the frequency value of the abnormal component as an area; Performing a double logarithmic operation on the energy spectrum and the area to obtain a double logarithmic graph; Fitting two straight lines in the double logarithmic graph, and determining the energy spectrum density value corresponding to the intersection of the two straight lines as the fractal filter threshold for separating the alteration information from the background information; Performing mask processing on the fractal filter threshold to obtain a mask result; Perform inverse Fourier transform on the mask result to obtain silicification alteration information.

6. The method for determining the mineralization center based on multi-source remote sensing data according to claim 3, characterized in that: The second alteration information includes first limonite alteration information and first clay alteration information; The method of extracting alteration information from the hyperspectral data by using the spectral angle mapping method to obtain the second alteration information includes: Resampling the hyperspectral data according to the waveband of the standard mineral spectrum to obtain target hyperspectral data; The spectral angle mapping method is used to extract the alteration information of the band with a wavelength greater than 2000 nm in the target hyperspectral data to obtain the first clay alteration information; The spectral angle mapping method is used to extract the alteration information of the band with a wavelength less than 1300nm in the target hyperspectral data to obtain the first limonite alteration information.

7. The method for determining the mineralization center based on multi-source remote sensing data according to claim 1, characterized in that: The spectral characteristic analysis is performed according to the measured rock and mineral spectrum set to determine the abnormal absorption intensity area and the altered rock type corresponding to the abnormal absorption intensity area, including: Extract the absorption intensity and central wavelength of the absorption peak of each verification point spectrum at the target position in the measured rock and mineral spectrum set; the target position is the position of the absorption peak closest to the 960nm band, the position of the absorption peak closest to the 2200nm band, and the position of the absorption peak closest to the 2335nm band; Performing interpolation processing on the absorption intensity to obtain an absorption intensity distribution diagram corresponding to each absorption peak, and performing interpolation processing on the central wavelength to obtain an absorption center distribution diagram corresponding to each absorption peak; Determining an abnormal absorption intensity region in the absorption intensity distribution diagram according to a preset absorption intensity threshold; The central wavelength range characteristics of the absorption intensity abnormal area are analyzed according to the absorption center distribution map to determine the altered rock type corresponding to the absorption intensity abnormal area.

8. The method for determining the mineralization center based on multi-source remote sensing data according to claim 1, characterized in that: The target alteration information includes second limonite alteration information and second clay alteration information; the target alteration information is extracted by using the maximum noise ratio method and the hybrid modulation matching filter method according to the hyperspectral data and the altered rock spectrum, and the target alteration information includes: Resampling the altered rock spectrum according to the band range of the hyperspectral data to obtain a resampled spectrum; the band range of the resampled spectrum is the same as the band range of the hyperspectral data; The spectrum before 1300 nm in the resampled spectrum is used as the first end member spectrum, and the spectrum after 2000 nm in the resampled spectrum is used as the second end member spectrum; Performing a minimum noise separation transformation on the hyperspectral data to obtain a hyperspectral image after MNF transformation; performing a minimum noise separation transformation on the first endmember spectrum to obtain a first endmember spectrum transformation result; performing a minimum noise separation transformation on the second endmember spectrum to obtain a second endmember spectrum transformation result; Performing hybrid modulation matched filtering decomposition on the hyperspectral image and the first endmember spectral transformation result to obtain a first multi-band image file including a matched filter fractional value band and an infeasible band of the first endmember; performing hybrid modulation matched filtering decomposition on the hyperspectral image and the second endmember spectral transformation result to obtain a second multi-band image file including a matched filter fractional value band and an infeasible band of the second endmember; In the first multi-band image file, pixels with high MF values ​​and low infeasibility in each end member are extracted to obtain the second limonite alteration information; in the second multi-band image file, pixels with high MF values ​​and low infeasibility in each end member are extracted to obtain the second clay alteration information.

9. The method for determining the mineralization center based on multi-source remote sensing data according to claim 1, characterized in that: Determining the mineralization center of the target area based on the absorption intensity abnormal area, the altered rock type and the target alteration information includes: The absorption intensity abnormal area, the altered rock type corresponding to the absorption intensity abnormal area, the target alteration information, the geochemical anomaly, and the spectral absorption characteristics of each type of altered rock are superimposed to obtain a superimposed information map; Analyze the superimposed information map to determine the distribution of various types of altered rocks; The distribution is compared with the distribution of altered rocks in known mineralized centers to determine the mineralized centers.

10. A device for determining mineralization center based on multi-source remote sensing data, characterized in that: include: A data acquisition module, used to acquire hyperspectral data of a target area and a measured rock and mineral spectrum set; the measured rock and mineral spectrum set includes an altered rock spectrum; The measured rock and mineral spectrum set is collected by a full-spectrum spectrometer; An absorption intensity abnormal area determination module is used to perform spectral feature analysis based on the measured rock and mineral spectrum set to determine the absorption intensity abnormal area and the altered rock type corresponding to the absorption intensity abnormal area; A target alteration information extraction module is used to extract alteration information based on the hyperspectral data and the altered rock spectrum by using a maximum noise ratio method and a hybrid modulation matching filter method to obtain target alteration information; The mineralization center determination module is used to determine the mineralization center of the target area based on the absorption intensity abnormal area, the altered rock type and the target alteration information.

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