CN-122018037-A - Mu-ion absorption imaging geological interpretation method, device, equipment, medium and product

CN122018037ACN 122018037 ACN122018037 ACN 122018037ACN-122018037-A

Abstract

The application discloses a muon absorption imaging geological interpretation method, device, equipment, medium and product, relating to the field of mineral resource exploration, wherein the method comprises the steps of acquiring muon absorption imaging data, geological data and geophysical prospecting data of a research area; according to muon absorption imaging data, a visual feature extraction method and a machine learning algorithm are adopted to extract features to obtain gradient features, continuity features, morphological features and clustering features, according to the gradient features, the continuity features, the morphological features and the clustering features, the ore-forming favorable fracture, lithology interfaces and the ore-controlling rock mass are interpreted to determine the space morphology and attribute parameters of geology in a research area, according to geological data and geophysical prospecting data, reliability verification is carried out on interpretation results of the ore-forming favorable fracture, the lithology interfaces and the ore-controlling rock mass, and according to the reliability verification results, the feature extraction process is regulated and then re-interpreted. The application has high degree of automation and strong adaptability, and improves the accuracy and reliability of geological interpretation.

Inventors

YANG LONGQUAN
ZHAO DAN
HUANG ZHIXIN
LIU HONGCHENG
ZHU WEI
LI BIHONG
YANG RUI
QIAO BAOQIANG
WU QUBO

Assignees

核工业北京地质研究院

Dates

Publication Date: 20260512
Application Date: 20260319

Claims (10)

1. A muon absorption imaging geologic interpretation method, characterized in that the muon absorption imaging geologic interpretation method comprises: Acquiring muon absorption imaging data, geological data and geophysical prospecting data of a research area; according to the muon absorption imaging data, performing feature extraction by adopting a visual feature extraction method and a machine learning algorithm to obtain gradient features, continuity features, morphological features and clustering features; Interpreting the favorable fracture, lithology interface and ore control rock mass of the ore formation according to the gradient characteristics, the continuity characteristics, the morphological characteristics and the clustering characteristics so as to determine the spatial morphology and attribute parameters of geology in a research area; And according to the geological data and the geophysical prospecting data, carrying out reliability verification on interpretation results of the favorable fracture, lithology interface and the rock mass of the controlled ore, and adjusting and re-interpreting the feature extraction process according to the reliability verification results.
2. The method of muon absorption imaging geologic interpretation according to claim 1, wherein obtaining muon absorption imaging data, geologic data, and geophysical prospecting data for the region of interest comprises: Acquiring muon absorption imaging original data, geological original data and geophysical original data of a research area; Establishing a unified space coordinate system, and performing space registration on the muon absorption imaging original data, the geological original data and the geophysical original data to obtain preliminary muon absorption imaging data, geological data and geophysical data; And sequentially carrying out noise rejection and geologic body density inversion on the preliminary muon absorption imaging data to obtain muon absorption imaging data.
3. The method of claim 1, wherein the muon absorption imaging data comprises muon flux data, density inversion profile data, and geologic volume density data, the geologic data comprises borehole data and geologic map data, and the geophysical data comprises geophysical data and regional geologic survey reports.
4. The method for geological interpretation of muon absorption imaging according to claim 1, wherein the step of extracting features by using a visual feature extraction method and a machine learning algorithm to obtain gradient features, continuity features, morphological features and clustering features comprises: According to the muon absorption imaging data, calculating a density gradient by adopting a Sobel operator, and identifying a density abrupt change band to obtain gradient characteristics; detecting the continuous extension length of the density change band by adopting Hough transformation according to the muon absorption imaging data to obtain a continuity characteristic; extracting morphological parameters with abnormal density by adopting a contour extraction algorithm according to the muon absorption imaging data to obtain morphological characteristics; And determining clustering characteristics by adopting a K-means clustering algorithm and a principal component analysis algorithm according to the muon absorption imaging data.
5. The method for geological interpretation of muon absorption imaging according to claim 4, wherein determining cluster features from said muon absorption imaging data using a K-means clustering algorithm and a principal component analysis algorithm comprises: Carrying out neighborhood analysis on the density inversion section in the muon absorption imaging data to construct a multidimensional feature vector of each pixel point; Performing principal component analysis on the multi-dimensional feature vector, calculating a feature covariance matrix, solving feature values and corresponding feature vectors of the feature covariance matrix, and selecting the first N principal components as core features to perform dimension reduction on the multi-dimensional feature vector to obtain a dimension-reduced feature vector, wherein N is the number of preset principal components; and clustering by adopting a K-means clustering algorithm according to the dimension reduction feature vector of each pixel point to obtain clustering features.
6. The muon absorption imaging geological interpretation method according to claim 1, wherein said gradient features comprise a density gradient for each pixel point, said continuity features comprise a continuous extension of a band of density variations, said cluster features comprise a plurality of clusters; Interpreting the ore-forming favorable fracture, lithology interface and ore-controlling rock mass according to the gradient feature, the continuity feature, the morphological feature and the clustering feature, comprising: Identifying an abnormal region meeting a first preset condition according to the gradient characteristics and the clustering characteristics, determining the horizontal position, the inclination angle and the spreading form of fracture abnormality in the abnormal region, and determining the favorable fracture of the ore formation according to the ore formation rule of the region, wherein the first preset condition is that the density gradient is more than or equal to 0.3 g/(cm 3 & m), and mutation boundaries exist among different clustering clusters; Identifying a characteristic band meeting a second preset condition according to the gradient characteristic, the continuity characteristic and the clustering characteristic to determine a lithology interface, wherein the second preset condition is that the density gradient is more than or equal to 0.3/(cm 3 m) and less than or equal to 0.8 g/(cm 3 m), the continuous extension length is more than or equal to 20m, and the standard deviation in a cluster is less than a set standard deviation threshold; And identifying abnormal bodies meeting a third preset condition according to the morphological characteristics and the clustering characteristics to determine the rock mass of the controlled ore, wherein the third preset condition is that the morphological characteristics are pulse-shaped or mound-shaped, the morphological characteristics extend from the deep part to the shallow part, the top interface is in a closed state or directly exposes out of the ground surface, the clustering clusters are independent clusters, and the difference between the density value and the surrounding rock is more than or equal to 0.2g/cm 3 .
7. A muon absorption imaging geological interpretation device, characterized in that it performs the muon absorption imaging geological interpretation method as claimed in any one of claims 1 to 6, comprising: The data acquisition module is used for acquiring muon absorption imaging data, geological data and geophysical prospecting data of the research area; The feature extraction module is used for carrying out feature extraction by adopting a visual feature extraction method and a machine learning algorithm according to the muon absorption imaging data to obtain gradient features, continuity features, morphological features and clustering features; The interpretation module is used for interpreting the favorable fracture, lithology interface and ore control rock mass of the ore formation according to the gradient characteristics, the continuity characteristics, the morphological characteristics and the clustering characteristics so as to determine the spatial morphology and attribute parameters of geology in a research area; And the verification module is used for carrying out reliability verification on interpretation results of the favorable fracture, lithology interface and the rock mass of the controlled ore according to the geological data and the geophysical prospecting data, and re-interpreting the characteristic extraction process after adjusting according to the reliability verification results.
8. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to perform the muon absorption imaging geological interpretation method as claimed in any one of claims 1 to 6.
9. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor performs a muon absorption imaging geological interpretation method as claimed in any one of claims 1 to 6.
10. A computer program product comprising a computer program, wherein the computer program when executed by a processor performs the method of muon absorption imaging geological interpretation of any of claims 1 to 6.

Description

Mu-ion absorption imaging geological interpretation method, device, equipment, medium and product Technical Field The application relates to the technical field of mineral resource exploration, in particular to a muon absorption imaging geological interpretation method, device, equipment, medium and product. Background Along with the improvement of the requirements of deep mineral resource exploration, the accurate detection of hidden geological targets (deep ore-forming fracture, hidden rock mass, lithology interfaces and the like) becomes a core difficult problem. The current main current deep geological detection technology (drilling exploration, traditional geophysical exploration and conventional geological interpretation technology) and result verification mechanism have obvious short plates, so that the requirements of high-precision exploration are difficult to meet, and the specific problems and the existing solutions are limited as follows: (1) The drilling exploration has the advantages of visual and reliable data, and has the core problems of high cost, long period and limited space coverage, and can not continuously detect a large-area hidden area. The existing solution of encrypting the drilling network improves coverage, but further increases cost, and the complicated construction area has a point-to-surface deducing error, so that the three-dimensional spreading form of the geological target is difficult to accurately describe. (2) The traditional geophysical exploration can realize large-area coverage by gravity, a magnetic method, an electric method and other technologies, but has the problems of strong polynaphrodisiac and insufficient resolution (such as difficulty in distinguishing rock bodies with similar density from stratum due to abnormal gravity and limited penetration of the electric method to high-resistance stratum). The existing multi-method joint inversion scheme lacks a unified data fusion standard, the data space matching error exceeds 1m, the inversion result is not reliable enough, and hidden fracture and lithology interfaces cannot be accurately positioned. (3) The conventional geological interpretation technology relies on personnel visual interpretation and experience accumulation, and has the problems of strong subjectivity and unquantized feature extraction. Most of imaging data such as density sections and the like are qualitatively described, accurate quantitative characterization such as fracture dip angles, lithology interface gradient thresholds and the like are lacked, and although research is introduced into simple algorithm auxiliary interpretation, a 'visual experience + intelligent algorithm' fusion system is not formed, objective standardized extraction of geological features is difficult to realize, and interpretation results are poor in consistency and low in accuracy. (4) The interpretation result verification mechanism is imperfect, and single data source verification (such as a small number of holes) is adopted, closed loops of multi-source data cross verification are lacked, and interpretation errors cannot be effectively corrected. When the interpretation result conflicts with the local drilling data, the error source (data or method error) is difficult to judge, the reliability of the result is insufficient, and the subsequent mineral exploration decision cannot be supported. Disclosure of Invention The application aims to provide a muon absorption imaging geological interpretation method, device, equipment, medium and product, which can improve the accuracy and reliability of geological interpretation. In order to achieve the above object, the present application provides the following solutions: in a first aspect, the present application provides a muon absorption imaging geological interpretation method, comprising: Acquiring muon absorption imaging data, geological data and geophysical prospecting data of a research area; according to the muon absorption imaging data, performing feature extraction by adopting a visual feature extraction method and a machine learning algorithm to obtain gradient features, continuity features, morphological features and clustering features; Interpreting the favorable fracture, lithology interface and ore control rock mass of the ore formation according to the gradient characteristics, the continuity characteristics, the morphological characteristics and the clustering characteristics so as to determine the spatial morphology and attribute parameters of geology in a research area; And according to the geological data and the geophysical prospecting data, carrying out reliability verification on interpretation results of the favorable fracture, lithology interface and the rock mass of the controlled ore, and adjusting and re-interpreting the feature extraction process according to the reliability verification results. In a second aspect, the present application provides a muon absorption imaging geological interpret