An AI-based prospecting analysis method and system for multi-source heterogeneous geological data fusion

Abstract

This application relates to the field of geological exploration technology, and in particular, to a method and system for prospecting analysis of multi-source heterogeneous geological data based on AI. The method includes: obtaining remote sensing images; obtaining sampling data; analyzing the remote sensing images to determine the three-dimensional coordinates of the area to be measured; analyzing the sampling data to determine the geological growth context between any adjacent sampling columns in the area to be measured; determining whether there is a vein in the area to be measured according to the geological growth context between any adjacent sampling columns; if there is a vein, determining the vein trend based on the three-dimensional coordinates. Through this solution, multiple data sources are integrated to obtain more comprehensive geological information, which helps to deeply understand the geological characteristics of the area to be measured; reduces the time and workload of manual operations; through analyzing the geological growth context and vein characteristics, more accurately predicts the trend and distribution of veins, provides an important basis for mineral resource exploration; improves the accuracy of vein identification; helps to improve the efficiency and success rate of mineral resource exploration.

Description

Technical Field

[0001] This application relates to the technical field of geological exploration, and particularly to a method and system for prospecting analysis based on the fusion of multi-source heterogeneous geological data using AI. Background Art

[0002] With the continuous development of technology, geological exploration technology plays an increasingly important role in mineral resource exploration. Especially in the field of mineral resource exploration, the method of prospecting analysis based on the fusion of multi-source heterogeneous geological data, as an emerging technical means, has gradually received wide attention. This method analyzes geological features by combining various data sources such as remote sensing images and sampling data, thereby improving the success rate and accuracy of prospecting.

[0003] However, traditional geological prospecting methods mainly rely on the experience and intuition of geological personnel, which have certain limitations. Existing methods for prospecting analysis based on the fusion of multi-source heterogeneous geological data have certain deficiencies in the implementation process, such as the processing and analysis of sampling data being relatively complex, and there being certain limitations in the mining of a large amount of multi-source heterogeneous data. Therefore, it is necessary to study a new method for prospecting analysis based on the fusion of multi-source heterogeneous geological data to improve the efficiency and accuracy of prospecting. Summary of the Invention

[0004] This application provides a method and system for prospecting analysis based on the fusion of multi-source heterogeneous geological data using AI to solve the above problems.

[0005] In the first aspect, this application provides a method for prospecting analysis based on the fusion of multi-source heterogeneous geological data using AI, the method comprising:

[0006] Obtain a remote sensing image; obtain sampling data; analyze the remote sensing image to determine the three-dimensional coordinates of the area to be measured;

[0007] Analyze the sampling data to determine the geological growth context between any adjacent sampling columns in the area to be measured;

[0008] Based on the geological growth context between any adjacent sampling columns, determine whether there is a vein in the area to be measured;

[0009] If there is a vein, based on the three-dimensional coordinates, determine the vein trend.

[0010] Through this solution, by integrating multiple data sources such as remote sensing images and sampling data, more comprehensive geological information can be obtained, which helps to understand the geological characteristics of the area to be measured more deeply; by using AI technology to automatically process and analyze a large amount of remote sensing images and sampling data, the efficiency of ore prospecting can be significantly improved, and the time and workload of manual operations can be reduced; by analyzing the geological growth context and vein characteristics, the trend and distribution of veins can be predicted more accurately, providing an important basis for mineral resource exploration; the three-dimensional coordinates and geological growth context of the area to be measured can be determined more accurately, thereby improving the accuracy of vein identification; more scientific decision-making support can be provided for the field of geological exploration, helping to improve the efficiency and success rate of mineral resource exploration.

[0011] Optionally, the obtaining of the sampling data includes:

[0012] Obtaining cross-sectional images and column images of a number of sampling columns;

[0013] Extracting the image features of the cross-sectional images, and determining the initial sampling direction according to the image features;

[0014] Based on the initial sampling direction, analyzing the cross-sectional images to determine the cross-sectional change situation;

[0015] Based on the initial sampling direction, analyzing the column images to determine the column change situation;

[0016] Taking the cross-sectional change situation and the column change situation as the sampling data.

[0017] Through this solution, by directly observing the cross-sections and column images of rocks, the structure and properties of rocks can be intuitively understood; the features extracted by using image processing technology are more objective and not affected by the subjective judgment of geological personnel; through accurate feature extraction, the sampling direction can be determined more accurately, improving the sampling efficiency; systematically analyzing the cross-sectional images can comprehensively understand the evolution process of the geological structure. By identifying the change patterns, the future change trends of the geological structure can be predicted. The column images provide a three-dimensional perspective, helping to more comprehensively understand the three-dimensional shape of the geological structure and enabling a more detailed analysis of the physical and chemical properties of rocks, such as mineral content, porosity, etc.

[0018] Optionally, the determining of the initial sampling direction according to the image features includes:

[0019] Obtaining the ground image before sampling;

[0020] Determining the geographical location of each sampling column before sampling according to the image features of each sampling column;

[0021] Filtering the ground images according to the geographical location to determine the exclusive image corresponding to each sampling column;

[0022] For each sampling column, analyze the exclusive image, determine the original ground features, and select the directional features based on a preset direction.

[0023] Compare the directional features with the image features, and determine the initial sampling direction according to the comparison result.

[0024] Through this solution, by using image processing and pattern recognition technologies, features can be objectively extracted from images, avoiding subjective errors and making the analysis results more reliable. Automated data processing of large amounts and analysis processes can significantly improve work efficiency, saving a large amount of time and manpower. Through positioning technologies such as GPS, the geographical location of the sampling column can be accurately determined. Combining ground images and sampling column images can achieve the fusion of multi-source data, providing more comprehensive geological information. By analyzing image features and directional features, the geological structure and the distribution of ore veins can be predicted more accurately, improving the success rate of prospecting.

[0025] Optionally, the analyzing the sampling data to determine the geological growth context between any adjacent sampling columns in the area to be measured includes:

[0026] Determine the geological surface structure and surface rock types according to the cross-section change situation;

[0027] Determine the rock layer features and the geological changes of each rock layer according to the column change situation;

[0028] For each sampling column, determine the geological structure sequence of the rock layer corresponding to each sampling column according to the rock layer features and the geological changes of each rock layer;

[0029] Determine the geological growth context between any adjacent sampling columns in the area to be measured according to the geological structure sequence.

[0030] Through this solution, relying on objective data and image processing technologies, the influence of personal experience and intuition is reduced, improving the objectivity and repeatability of the analysis; through high-resolution images and accurate geographical location information, the geological structure and rock types can be determined more accurately, thereby improving the accuracy of prospecting; combining cross-section and column image analysis can provide a comprehensive geological perspective, including geological surface structure, rock layer features, and geological changes, thus understanding the geological growth context more systematically; automated data processing and analysis processes can process a large amount of data, improving work efficiency and shortening the prospecting cycle; automated image analysis and feature extraction reduce errors caused by human operations, improving the reliability of data; by analyzing the geological structure sequence, the geological structure and the distribution of ore veins can be predicted more accurately, improving the success rate of prospecting; the collected data can be used to construct a three-dimensional geological model to more intuitively display the geological growth context, facilitating analysis and decision-making.

[0031] Optionally, determining the geological growth context between any adjacent sampling columns in the area to be measured according to the geological structure sequence includes:

[0032] For each sampling column, based on the initial sampling direction, taking any sampling column as the reference column, and selecting any other sampling column as the reference object; the reference object is the reference object of any reference column, and the reference object is not any reference column;

[0033] According to the three-dimensional coordinates of the area to be measured, determine the reference column coordinates and the reference object coordinates;

[0034] Taking the initial sampling direction as the connection direction, based on the reference column coordinates and the reference object coordinates, determine the surface to be measured between the reference column and the reference object;

[0035] According to the geological structure sequence of the rock formation corresponding to each sampling column, predict the geological changes of the surface to be measured;

[0036] According to the geological changes of the surface to be measured, determine the geological growth context between any adjacent sampling columns in the area to be measured.

[0037] Through this solution, by analyzing the geological structure sequence, the geological changes in the area to be measured can be predicted more accurately, thereby improving the prediction accuracy of the vein distribution and geological characteristics; providing detailed information about the geological structure and development trend, and more accurate analysis of the geological growth context helps to reduce exploration risks; by understanding the geological growth context, the economic value of mineral resources can be evaluated more effectively, thereby optimizing resource development decisions; through precise exploration, environmental damage can be reduced because they can guide more targeted exploration activities; the automated data processing and analysis process can process a large amount of data, improve work efficiency, and shorten the prospecting cycle; automated image analysis and feature extraction reduce errors caused by manual operations and improve the reliability of data.

[0038] Optionally, determining the surface to be measured between the reference column and the reference object based on the reference column coordinates and the reference object coordinates includes:

[0039] Taking the preset layer height as the interval standard, traverse the center coordinates of the reference column at each layer height and the center coordinates of the reference object at each layer,

[0040] Connect the center coordinates of the reference column at each layer height with the center coordinates of the reference object at the corresponding layer height to obtain a number of connecting lines until there is a connection relationship for the center of each layer height of all reference columns;

[0041] For each reference column, perform interpolation processing on the reference column to determine whether there is a possible layer gap outside the interval standard;

[0042] If it exists, determine the reference height of the reference column according to the layer gap height, and connect the center coordinates of the position where the layer gap height is located to obtain a layer gap connection line;

[0043] Determine the surface to be measured between the reference column and the reference column according to the several connection lines and the layer gap connection line.

[0044] Through this solution, by performing interpolation processing and connection line analysis on the coordinates of the reference column and the reference column, the geological structure can be more intuitively understood, especially the geological sequence and the contact relationship between strata; the interpolation processing can help identify layer gaps, which may indicate important geological features such as faults, sedimentary breaks or magma intrusions; based on accurate coordinate data and interpolation results, a more accurate geological model can be constructed, which is crucial for geological exploration and resource assessment; by determining the surface to be measured, the exploration strategy can be optimized and resources can be concentrated in the most potential areas for exploration; by analyzing the layer gaps and connection lines, potential changes between strata can be predicted, and trends of geological changes such as tilting, folding or fracturing of strata can be identified, enhancing the prediction ability of the geological growth context; the determined surface to be measured can be used to predict the trend and location of ore veins, which has direct significance for ore deposit exploration.

[0045] Optionally, after determining the geological growth context between any adjacent sampling columns in the area to be measured according to the geological changes of the surface to be measured, it further includes:

[0046] For each surface to be measured, connect any three or more surfaces to be measured to form a three-dimensional area;

[0047] Predict the geological growth changes of the three-dimensional area according to the geological changes of the surface to be measured;

[0048] Traverse all three-dimensional areas, and determine the geological growth context of the area to be measured according to the geological growth changes of the three-dimensional areas;

[0049] The determination of whether there is an ore vein in the area to be measured according to the geological growth context between any adjacent sampling columns includes:

[0050] Determine whether there is an ore vein in the area to be measured according to the geological growth context of the area to be measured.

[0051] Through this solution, by connecting the surfaces to be measured into a three-dimensional region, a more accurate geological model can be constructed, thereby understanding the geological structure and predicting geological changes. Analyzing the geological changes of the surfaces to be measured can help predict the geological growth changes of the three-dimensional region. Traversing all three-dimensional regions and analyzing their geological growth changes can more comprehensively determine the geological growth context of the region to be measured, providing more accurate guidance for geological exploration. According to the geological growth context between any adjacent sampling columns, it is possible to more accurately predict whether there is a vein in the region to be measured, thereby increasing the success rate of ore prospecting. The collected data can be used to construct a three-dimensional geological model, which can more intuitively display the geological growth context, facilitating analysis and decision-making.

[0052] Optionally, determining whether there is a vein in the region to be measured according to the geological growth context between any adjacent sampling columns includes:

[0053] Determining the rock types existing in each rock stratum according to the characteristics of the rock stratum;

[0054] Obtaining the regional location of the region to be measured, and determining the possible types of veins according to the regional location;

[0055] Based on the possible types of veins, rock types, and the geological growth context between any adjacent sampling columns, determining whether there is a vein in the region to be measured.

[0056] Through this solution, through detailed analysis of the characteristics of the rock stratum, since different rock types may indicate different geological environments and mineralization potentials, the rock types of each rock stratum can be accurately determined, providing basic data for subsequent vein prediction, helping to construct a more accurate geological model. According to the regional location, the possible types of veins matching the geological conditions of the area can be determined, improving the pertinence of ore prospecting, avoiding ineffective exploration in areas without potential vein types, and reducing risks and costs. By comprehensively considering the possible types of veins, rock types, and geological growth context, the comprehensiveness and accuracy of the analysis are improved; based on multi-factor analysis, it is possible to more accurately predict whether there is a vein in the region to be measured.

[0057] Optionally, after determining whether there is a vein in the region to be measured based on the possible types of veins, rock types, and the geological growth context between any adjacent sampling columns, it further includes:

[0058] If it is determined that there is no vein in the region to be measured, then determining whether there is a possibility of vein development in the region to be measured according to the geological growth context between any adjacent sampling columns;

[0059] If there is, predicting the geological change situation of the region to be measured within a preset distance range according to the geological growth context between any adjacent sampling columns;

[0060] Based on the geological changes in the area to be measured within a preset distance range, infer the location where the vein appears, and determine whether there is mining value based on the location where the vein appears.

[0061] Through this solution, unnecessary exploration and mining activities in areas without veins are avoided, saving human, material, and financial resources; reducing risk investment in areas without minerals and protecting the financial security of enterprises; deepening the understanding of geological structures and ore-forming environments and providing a theoretical basis for subsequent ore prospecting; integrating data from different sampling columns to form a continuous geological information chain, which helps to comprehensively analyze the geological characteristics of the area; clarifying the ore prospecting goals and directions in the area to be measured and improving the exploration efficiency; predicting the possible location of the vein through geological changes to guide exploration activities; increasing the success rate of exploration at possible vein locations.

[0062] In a second aspect, the present application provides an AI-based multi-source heterogeneous geological data fusion ore prospecting analysis system, including:

[0063] A coordinate analysis module, configured to obtain a remote sensing image; obtain sampling data; analyze the remote sensing image to determine the three-dimensional coordinates of the area to be measured;

[0064] A vein analysis module, configured to analyze the sampling data to determine the geological growth veins between any adjacent sampling columns in the area to be measured;

[0065] A vein judgment module, configured to determine whether there is a vein in the area to be measured according to the geological growth veins between any adjacent sampling columns;

[0066] A strike analysis module, configured to, if there is a vein, determine the vein strike based on the three-dimensional coordinates.

[0067] Optionally, when obtaining the sampling data, the coordinate analysis module is configured to:

[0068] Obtain cross-sectional images and column images of a number of sampling columns;

[0069] Extract the image features of the cross-sectional image, and determine the initial sampling direction according to the image features;

[0070] Based on the initial sampling direction, analyze the cross-sectional image to determine the cross-sectional change situation;

[0071] Based on the initial sampling direction, analyze the column image to determine the column change situation;

[0072] Use the cross-sectional change situation and the column change situation as the sampling data.

[0073] Optionally, when determining the initial sampling direction according to the image features, the coordinate analysis module is configured to:

[0074] Obtain the ground image before sampling;

[0075] Determine the geographical location of each sampling column before sampling according to the image features of each sampling column;

[0076] Filter the ground image according to the geographical location, and determine the exclusive image corresponding to each sampling column;

[0077] For each sampling column, analyze the exclusive image, determine the original ground features, and select the direction features based on a preset direction as a reference;

[0078] Compare the direction features with the image features, and determine the initial sampling direction according to the comparison result.

[0079] Optionally, when analyzing the sampling data to determine the geological growth context between any adjacent sampling columns in the area to be measured, the context analysis module is used for:

[0080] Determine the geological surface structure and surface rock type according to the cross-section change situation;

[0081] Determine the rock layer features and the geological changes of each rock layer according to the column change situation;

[0082] For each sampling column, determine the geological structure sequence of the rock layer corresponding to each sampling column according to the rock layer features and the geological changes of each rock layer;

[0083] Determine the geological growth context between any adjacent sampling columns in the area to be measured according to the geological structure sequence.

[0084] Optionally, when determining the geological growth context between any adjacent sampling columns in the area to be measured according to the geological structure sequence, the context analysis module is used for:

[0085] For each sampling column, based on the initial sampling direction, select any other sampling column as a reference column with any sampling column as a reference column; the reference column is the reference object of any reference column, and the reference column is not any reference column;

[0086] Determine the reference column coordinates and the reference column coordinates according to the three-dimensional coordinates of the area to be measured;

[0087] Taking the initial sampling direction as the connection direction, based on the reference column coordinates and the reference column coordinates, determine the surface to be measured between the reference column and the reference column;

[0088] Predict the geological changes of the surface to be measured according to the geological structure sequence of the rock layer corresponding to each sampling column;

[0089] Determine the geological growth context between any adjacent sampling columns in the area to be measured according to the geological changes of the surface to be measured.

[0090] Optionally, when determining the surface to be measured between the reference column and the reference column based on the reference column coordinates and the reference column coordinates, the context analysis module is used for:

[0091] Taking the preset layer height as the interval standard, traverse the center coordinates of the reference column at each layer height and the center coordinates of the reference column at each layer,

[0092] Connect the center coordinates of the reference column at each layer height with the center coordinates of the corresponding reference column at the same layer height to obtain a number of connection lines until there is a connection relationship for the centers of all reference columns at each layer height;

[0093] For each reference column, perform interpolation processing on the reference column to determine whether there is a possible layer gap outside the interval standard;

[0094] If there is, determine the reference height of the reference column according to the layer gap height, and connect the center coordinates of the positions where the layer gap height is located to obtain a layer gap connection line;

[0095] Determine the surface to be measured between the reference column and the reference column according to the number of connection lines and the layer gap connection line.

[0096] Optionally, the multi - heterogeneous geological data fusion prospecting analysis system further includes a three - dimensional analysis module, which is used for:

[0097] For each surface to be measured, connect any three or more surfaces to be measured to form a three - dimensional area;

[0098] Predict the geological growth changes of the three - dimensional area according to the geological changes of the surface to be measured;

[0099] Traverse all three - dimensional areas, and determine the geological growth context of the area to be measured according to the geological growth changes of the three - dimensional areas;

[0100] Determining whether there is a vein in the area to be measured according to the geological growth context between any adjacent sampling columns includes:

[0101] Determine whether there is a vein in the area to be measured according to the geological growth context of the area to be measured.

[0102] Optionally, when determining whether there is a vein in the area to be measured according to the geological growth context between any adjacent sampling columns, the vein judgment module is used for:

[0103] Determine the rock types existing in each rock layer according to the rock layer characteristics;

[0104] Obtain the regional position of the area to be measured, and determine the possible types of ore veins according to the regional position;

[0105] Based on the possible types of ore veins, rock types, and the geological growth context between any adjacent sampling columns, determine whether there are ore veins in the area to be measured.

[0106] Optionally, the multi - heterogeneous geological data fusion prospecting analysis system further includes a value analysis module for:

[0107] If it is determined that there are no ore veins in the area to be measured, then determine whether there is a possibility of ore vein development in the area to be measured according to the geological growth context between any adjacent sampling columns;

[0108] If there is, then predict the geological changes in the area to be measured within a preset distance according to the geological growth context between any adjacent sampling columns;

[0109] Based on the geological changes in the area to be measured within a preset distance, infer the positions where ore veins may appear, and determine whether there is mining value according to the positions where ore veins appear. Description of the Drawings

[0110] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0111] Figure 1 A schematic diagram of an application scenario provided by an embodiment of the present application;

[0112] Figure 2 A flowchart of a multi - heterogeneous geological data fusion prospecting analysis method based on AI provided by an embodiment of the present application;

[0113] Figure 3 A schematic diagram of the structure of a multi - heterogeneous geological data fusion prospecting analysis system based on AI provided by an embodiment of the present application. Detailed Embodiments

[0114] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are some, but not all, of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of this application without creative efforts shall fall within the scope of protection of this application.

[0115] In addition, the term "and / or" in this document merely describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0116] The following further describes the embodiments of this application in detail with reference to the accompanying drawings of the specification.

[0117] Traditional geological prospecting methods mainly rely on the experience and intuition of geological personnel and have certain limitations. There are certain deficiencies in the implementation of existing multi-source heterogeneous geological data fusion prospecting analysis methods. For example, the processing and analysis of sampling data are relatively complex, and there are certain limitations in the mining of a large amount of multi-source heterogeneous data.

[0118] Based on this, this application provides an AI-based multi-source heterogeneous geological data fusion prospecting analysis method and system, which acquires remote sensing images; acquires sampling data; analyzes the remote sensing images to determine the three-dimensional coordinates of the area to be measured; analyzes the sampling data to determine the geological growth context between any adjacent sampling columns in the area to be measured; determines whether there is a vein in the area to be measured according to the geological growth context between any adjacent sampling columns; if there is a vein, determines the vein trend based on the three-dimensional coordinates. By fusing multiple data sources, such as remote sensing images, sampling data, etc., more comprehensive geological information can be obtained, which helps to understand the geological characteristics of the area to be measured more deeply; through AI technology, automatic processing and analysis of a large amount of remote sensing images and sampling data can significantly improve the efficiency of prospecting and reduce the time and workload of manual operations; by analyzing the geological growth context and vein characteristics, the trend and distribution of veins can be predicted more accurately, providing an important basis for mineral resource exploration; the three-dimensional coordinates and geological growth context of the area to be measured can be determined more accurately, thereby improving the accuracy of vein identification; it can provide more scientific decision-making support for the field of geological exploration, helping to improve the efficiency and success rate of mineral resource exploration.

[0119] Figure 1A schematic diagram of an application scenario provided for this application. When prospecting for minerals during geological exploration, the method provided for this application is applied. Specifically, the method provided for this application is applied to any server, and the server interacts with the remote sensing system and the sampling system. The remote sensing image can cover a large area and provide high-resolution geological information from an aerial or satellite perspective, which helps to quickly identify surface geological features and potential ore deposits. The sampling data obtained through means such as ground drilling and trenching can provide detailed information on the underground geological structure. The analysis of the remote sensing image combined with high-precision GPS positioning can determine the three-dimensional coordinates of the area to be measured, improving the accuracy of geological exploration. The analysis of the sampling data can deeply understand the subtle changes in the geological structure and determine the geological growth context. Through the analysis of the geological growth context, the extension direction and shape of the ore vein can be accurately traced. Based on the analysis of the geological growth context, the existence of the ore vein can be more scientifically predicted, reducing blind exploration. Through the analysis of the geological growth context, the resource waste and exploration risks caused by misjudgment can be reduced. Using the three-dimensional coordinate information, the trend of the ore vein can be accurately determined, providing an accurate guide for subsequent mining work. Based on the trend of the ore vein, the mine design and mining plan can be optimized, improving the resource utilization rate.

[0120] The specific implementation method can refer to the following embodiments.

[0121] Figure 2 A flowchart of a method for prospecting analysis based on AI-based multi-source heterogeneous geological data fusion provided for an embodiment of this application. The method of this embodiment can be applied to the server in the above scenario. As Figure 2 shown, the method includes:

[0122] S201. Obtain a remote sensing image; obtain sampling data; analyze the remote sensing image to determine the three-dimensional coordinates of the area to be measured.

[0123] The remote sensing image can be an image of the earth's surface obtained from a distance through remote sensing technology (such as satellite remote sensing, aerial photography, UAV photography, etc.), such as information on the physical characteristics, geographical distribution, vegetation cover, and hydrological conditions of the earth's surface.

[0124] The sampling data can be geological, environmental, ecological, etc. data collected from the field; including rock samples, soil samples, water samples, etc.

[0125] The area to be measured can be a specific geographical area that needs to be geologically surveyed, explored, or monitored.

[0126] The three-dimensional coordinates can be three sets of coordinate values required to determine the position of a point in three-dimensional space, represented by longitude, latitude, and altitude; the three-dimensional coordinates are used to locate geological structures such as geological bodies and ore veins.

[0127] Specifically, remote sensing technology is used to obtain remote sensing images of the area to be measured; cross-sectional images and column images of the sampling columns are obtained through ground drilling, trenching, etc.; the AI algorithm is used to process the remote sensing images, and the three-dimensional coordinates of the area to be measured are determined through image recognition and pattern recognition technologies.

[0128] S202. Analyze the sampling data to determine the geological growth context between any adjacent sampling columns in the area to be measured.

[0129] The geological growth context can be the spatial distribution and evolution characteristics of geological bodies formed during the development of the earth's crust due to geological processes (such as sedimentation, metamorphism, magmatic activity, etc.); including the distribution patterns, forms, scales, and mutual relationships of geological structures such as rock layers, faults, joints, mineralized zones, etc.

[0130] Specifically, feature extraction is performed on the cross-sectional images and column images of the sampling columns, and combined with the ground image and sampling column image features, the geographical location of each sampling column before sampling is determined, thereby determining the initial sampling direction; according to the sampling data, the geological structure sequence of each sampling column is analyzed to determine the geological growth context between any adjacent sampling columns.

[0131] S203. Determine whether there is a vein in the area to be measured according to the geological growth context between any adjacent sampling columns.

[0132] A vein can be composed of one or more minerals, which show continuous or discontinuous linear or banded distribution in space.

[0133] Specifically, according to the analysis result of the geological growth context, judge whether there is a vein in the area to be measured.

[0134] Extract key features from the geological growth context dataset, such as lithology changes, fault distributions, joint densities, tectonic styles, etc., and use machine learning algorithms such as support vector machines (SVM), random forests (RF), or deep learning (such as convolutional neural network CNN) to perform pattern recognition on the extracted features; according to geological knowledge and experience, set the geological feature thresholds for the existence of veins, for example, specific lithology combinations, fault distribution patterns, joint densities, etc.; analyze the geological growth context to find abnormal features that match the set thresholds, and these abnormal features may indicate the existence of veins; according to the analysis result, use logical rules to judge whether there is a vein in the area to be measured. For example: If there are multiple abnormal features in the geological growth context dataset and these features conform to the typical pattern of veins, then judge that there is a vein. If the abnormal features are insufficient or do not conform to the vein pattern, then judge that there is no vein.

[0135] S204. If there is a vein, determine the vein strike based on the three-dimensional coordinates.

[0136] The vein strike can be the extension direction of the vein in space.

[0137] Specifically, if there is a vein, the three-dimensional coordinates of the area to be measured and three-dimensional geological modeling software are used to convert geological features and sampling data into a three-dimensional geological model; trend surface analysis techniques, such as polynomial fitting, radial basis function (RBF), etc., are used to extract the trend surface of the vein strike from geological data; the contour lines of the vein cross-section image are analyzed to determine the strike direction of the vein; clustering algorithms, such as K-means or DBSCAN, are used to cluster points with similar characteristics to determine the vein strike.

[0138] Through this solution, by integrating multiple data sources, such as remote sensing images, sampling data, etc., more comprehensive geological information can be obtained, which helps to understand the geological features of the area to be measured more deeply; through AI technology to automatically process and analyze a large number of remote sensing images and sampling data, the prospecting efficiency can be significantly improved, and the time and workload of manual operations can be reduced; by analyzing the geological growth context and vein characteristics, the strike and distribution of veins can be predicted more accurately, providing an important basis for mineral resource exploration; the three-dimensional coordinates and geological growth context of the area to be measured can be determined more accurately, thereby improving the accuracy of vein identification; more scientific decision-making support can be provided for the field of geological exploration, which helps to improve the efficiency and success rate of mineral resource exploration.

[0139] In some embodiments, cross-section images and column images of several sampling columns are taken; the image features of the cross-section images are extracted, and based on the image features, the initial sampling direction is determined; based on the initial sampling direction, the cross-section images are analyzed to determine the cross-section change situation; based on the initial sampling direction, the column images are analyzed to determine the column change situation; the cross-section change situation and the column change situation are used as sampling data.

[0140] The cross-section image can be a longitudinal section image of rock or soil obtained from a sampling column generated by geological drilling or other geological sampling.

[0141] The column image can be an outer surface image of a sampling column generated by geological drilling or other geological sampling.

[0142] The image features can be extracted from the image and used to describe features such as bedding, fractures, and mineral distribution inside the rock in the image.

[0143] The initial sampling direction can be the sampling direction determined first during geological sampling or drilling, used to identify the orientation of the sampling column and facilitate the orientation of the sampling column in later data fusion.

[0144] The cross-section change situation can be the changes in rock or soil features observed on the cross-section image, including the inclination of bedding, the change in lithology, the occurrence of faults, etc.

[0145] The changes in the columnar body can be the changes in the rock or soil characteristics on the outer surface of the sampled column observed in the columnar body image, including the uplift, subsidence, erosion, magmatic activity, etc. of the strata.

[0146] The geological changes can be the changes in geological phenomena observed during geological exploration.

[0147] Specifically, according to the geological characteristics of the area to be measured and the analysis results of remote sensing images, appropriate sampling points are selected. At the selected sampling points, geological drilling equipment is used to obtain underground rock samples, and special imaging equipment is used to obtain cross-sectional images and columnar body images; preprocessing operations such as denoising, contrast enhancement, and grayscale conversion are performed on the cross-sectional images to improve the image quality. Image processing techniques such as edge detection, texture analysis, and morphological operations are applied to extract image features. According to the extracted image features, the dip and strike of the rock strata are analyzed to determine the initial sampling direction. By comparing the cross-sectional image features of multiple sampling points, the accuracy of the initial sampling direction is verified. Based on the initial sampling direction, the change trends of the rock strata and geological structures in the cross-sectional image are analyzed to identify the changes in the cross-sectional image; the columnar body image is analyzed to extract the geometric features and lithological features of the columnar body; the changes in the columnar body image are detected, such as the bending, fracture, and mineralization phenomena of the rock strata; the cross-sectional changes and columnar body changes obtained from the analysis of the cross-sectional image and the columnar body image are integrated to form recorded sampling data.

[0148] Through this solution, by directly observing the cross-section and columnar body images of the rock, the structure and properties of the rock can be intuitively understood; the features extracted by image processing technology are more objective and not affected by the subjective judgment of geological personnel; through precise feature extraction, the sampling direction can be determined more accurately, improving the sampling efficiency; by systematically analyzing the cross-sectional image, the evolution process of the geological structure can be comprehensively understood. By identifying the change patterns, the future change trends of the geological structure can be predicted. The columnar body image provides a three-dimensional perspective, which helps to more comprehensively understand the three-dimensional shape of the geological structure and can more carefully analyze the physical and chemical properties of the rock, such as mineral content, porosity, etc.

[0149] In some embodiments, a ground image before sampling is obtained; according to the image features of each sampling column, the geographical location of each sampling column before sampling is determined; according to the geographical location, the ground image is screened to determine the exclusive image corresponding to each sampling column; for each sampling column, the exclusive image is analyzed to determine the original ground features, and based on a preset direction, direction features are selected; the direction features are compared with the image features, and according to the comparison result, the initial sampling direction is determined.

[0150] The ground image can be a surface image obtained by remote sensing technology. It can display the features of the Earth's surface, including topography, vegetation, water bodies, and other surface cover types.

[0151] Geographical location can refer to the specific position of a certain location on the earth, usually represented by longitude, latitude and altitude.

[0152] Exclusive image can be an image area cropped from a ground image and specifically corresponding to a certain specific sampling column.

[0153] Preset direction can be a reference direction preset in geological exploration, used to establish a unified coordinate system for facilitating the analysis and comparison of data at different sampling points.

[0154] Direction features can be features associated with the preset direction in the exclusive image, such as the slope direction of the terrain, the flow direction of the river, the arrangement direction of the vegetation, etc.

[0155] Specifically, use drones, satellite remote sensing or ground photography equipment to obtain high-resolution ground images of the sampling area, ensure that the images cover the positions of all sampling columns, and have sufficient clarity for subsequent analysis; use GPS or other positioning technologies to record the precise geographical location of each sampling column. Match the geographical location of the sampling column with the corresponding pixel position in the ground image; according to the geographical location of the sampling column, crop the area around each sampling column from the ground image to form an exclusive image; ensure that the exclusive image contains sufficient surrounding environment information for analyzing ground features; apply image processing technologies, such as edge detection, texture analysis, etc., to extract the ground features in the exclusive image; identify the ground features, such as terrain, vegetation, soil type, etc.; in the exclusive image, select a set of direction features (such as north direction) based on the preset direction, such as the slope of the terrain, the distribution pattern of the vegetation, etc.; use these direction features as a reference for comparing with the image features; compare and analyze the extracted image features with the selected direction features, and methods such as correlation coefficient and angle difference can be used to analyze the similarity or difference between the features to determine the best match; according to the comparison result, determine the initial direction of the sampling column, that is, the direction to be taken during sampling.

[0156] Through this solution, by using image processing and pattern recognition technologies, features can be objectively extracted from the images, avoiding subjective errors and making the analysis results more reliable. The automated data processing of a large number and analysis process can significantly improve work efficiency and save a large amount of time and manpower. Through positioning technologies such as GPS, the geographical location of the sampling column can be accurately determined. Combining ground images and sampling column images can achieve the fusion of multi-source data, providing more comprehensive geological information. By analyzing image features and direction features, the geological structure and the distribution of ore veins can be predicted more accurately, improving the success rate of prospecting.

[0157] In some embodiments, the geological surface structure and the surface rock type are determined according to the cross-section change; the rock layer characteristics and the geological changes of each rock layer are determined according to the column change; for each sampling column, the geological structure sequence of the rock layer corresponding to each sampling column is determined according to the rock layer characteristics and the geological changes of each rock layer; and the geological growth context between any adjacent sampling columns in the area to be measured is determined according to the geological structure sequence.

[0158] The geological surface structure may be the overall layout of shallow rocks and geological features below the ground surface, including geological phenomena such as the inclination, fracture, fold, and joint of the strata.

[0159] The surface rock type may be the type of rock that composes the geological surface structure. The rock types include sedimentary rocks (such as sandstone and shale), igneous rocks (such as granite and basalt), and metamorphic rocks (such as gneiss and marble).

[0160] The rock layer characteristics may be the physical, chemical, and structural properties of the rocks that compose the strata, including the thickness, bedding, color, mineral composition, fissures, and joints of the rock layers.

[0161] The geological change may be the structural and morphological changes that occur to geological bodies in time and space, including crustal movement, magmatic activity, sedimentation, weathering, and erosion, etc., and can be determined based on the age, lithology, fossils, and structural characteristics of the rock layers.

[0162] The geological structure sequence may be the order in which different rock layers are deposited or formed during the formation of the strata.

[0163] Specifically, analyze the characteristics such as bedding, fractures, and mineral distribution on the cross-section image to determine the structural type of the geological surface; determine the type of surface rock according to the color, texture, and composition analysis in the cross-section image and the column image; analyze the rock layer characteristics on the column image, such as layer thickness, bedding direction, and interlayer type; observe the geological changes of each rock layer, such as erosion, sedimentation, and faults; assign a geological structure sequence to each rock layer of each sampling column according to the rock layer characteristics and geological changes, usually numbered from the bottom layer to the top layer; analyze the superimposed relationship and contact relationship of the rock layers to determine their relative time sequence of formation; compare the rock layer characteristics and geological structure sequences of adjacent sampling columns to determine their similarities and differences; analyze the continuity and discontinuity of the rock layers to determine the geological growth context, that is, the change and development trend of the geological structure over time.

[0164] Through this solution, relying on objective data and image processing technology, the influence of personal experience and intuition is reduced, and the objectivity and repeatability of the analysis are improved; through high-resolution images and precise geographic location information, geological structures and rock types can be determined more accurately, thereby improving the accuracy of prospecting; combined with cross-sectional and cylindrical image analysis, a comprehensive geological perspective can be provided, including geological surface structures, rock formation characteristics, and geological changes, so as to more systematically understand the geological growth context; automated data processing and analysis processes can process large amounts of data, improve work efficiency, and shorten the prospecting cycle; automated image analysis and feature extraction reduce errors caused by human operations and improve data reliability; by analyzing the sequence of geological structures, the distribution of geological structures and ore veins can be more accurately predicted, thereby improving the success rate of prospecting; the collected data can be used to construct a three-dimensional geological model to more intuitively display the geological growth context, facilitating analysis and decision-making.

[0165] In some embodiments, for each sampling column, based on the initial sampling direction, any sampling column is used as a base column, and any other sampling column is selected as a reference column; the reference column is a reference object of any base column, and the reference column is not any base column; according to the three-dimensional coordinates of the area to be measured, the base column coordinates and the reference column coordinates are determined; with the initial sampling direction as the connection direction, based on the base column coordinates and the reference column coordinates, the surface to be measured between the base column and the reference column is determined; according to the geological structural sequence of the rock formation corresponding to each sampling column, the geological changes of the surface to be measured are predicted; according to the geological changes of the surface to be measured, the geological growth veins between any adjacent sampling columns in the area to be measured are determined.

[0166] A reference column can be a sampling column used as a reference point in geological exploration.

[0167] A reference column can be another sampling column connected to a benchmark column in geological exploration.

[0168] The connection direction may be a straight line direction between the datum column and the reference column.

[0169] The surface to be measured may be an imaginary plane between the datum column and the reference column, the plane passing through the connection direction and being perpendicular to the ground.

[0170] Specifically, select one sampling column as the reference column, which will serve as the reference point; select any sampling column other than the reference column as the reference column, ensuring that the reference column is not the reference column itself; use GPS or ground measurement technology to obtain the three-dimensional coordinates of the reference column and the reference column; record and store these coordinates for subsequent analysis; based on the initial sampling direction, use the coordinates of the reference column and the reference column to determine the connection line between them; extend this connection line to create an imaginary plane, that is, the plane to be measured, which will be used to predict geological changes; according to the geological structure sequence of the rock layer corresponding to each sampling column, analyze the distribution and change trend of the rock layer; use the geological structure sequence information to predict the possible geological features and changes on the plane to be measured; analyze the geological changes between the reference column and the reference column to identify the pattern and direction of the geological growth vein; combine the geological change information of the plane to be measured to determine the geological growth vein between adjacent sampling columns.

[0171] Through this solution, by analyzing the geological structure sequence, the geological changes in the area to be measured can be predicted more accurately, thus improving the prediction accuracy of the distribution of ore veins and geological features; providing detailed information about the geological structure and development trend, more accurate analysis of the geological growth vein helps to reduce exploration risks; by understanding the geological growth vein, the economic value of mineral resources can be evaluated more effectively, thus optimizing the resource development decision-making; through precise exploration, the damage to the environment can be reduced because they can guide more targeted exploration activities; the automated data processing and analysis process can process a large amount of data, improving work efficiency and shortening the prospecting cycle; automated image analysis and feature extraction reduce the errors caused by human operation and improve the reliability of the data.

[0172] In some embodiments, with the preset layer height as the interval standard, traverse the center coordinates of the reference column at each layer height and the center coordinates of the reference column at each layer. Connect the center coordinates of the reference column at each layer height with the center coordinates of the corresponding reference column at the same layer height to obtain a number of connection lines until there is a connection relationship for the centers of all reference columns at each layer height; for each reference column, perform interpolation processing on the reference column to determine whether there is a possible layer gap outside the interval standard; if so, according to the layer gap height, determine the reference height of the reference column, and connect the center coordinates at the position of the layer gap height to obtain the layer gap connection line; according to a number of connection lines and the layer gap connection line, determine the plane to be measured between the reference column and the reference column.

[0173] The preset layer height can be in geological exploration. The preset layer height is used to determine the height standard for stratigraphic division and is stored in a preset database.

[0174] The interval standard can be the distance or time interval between two adjacent points (such as sampling columns, strata) within a certain range.

[0175] A bedding gap may be a void or discontinuity between strata in a geological sequence caused by geological processes (such as faults, unconformities).

[0176] The bedding gap height may be the vertical distance of the bedding gap, i.e., the thickness of the void or discontinuity between two adjacent strata.

[0177] The bedding gap connection line may be a straight or curved line connecting the strata at both ends of the bedding gap in a geological cross-section or three-dimensional model.

[0178] Specifically, collect the coordinate data of the reference column and the reference pillar, and use the preset layer height as the interval standard to traverse the center coordinates of the reference column and the reference pillar on each layer; connect the center coordinates of the reference column at each layer height with the center coordinates of the reference pillar at the corresponding layer height to generate a connection line; repeat the above steps until there is a connection relationship for the center coordinates of each layer height of all reference columns; for each reference column, perform interpolation processing to determine whether there may be a bedding gap outside the interval standard; interpolation processing can use linear interpolation, polynomial interpolation or more advanced interpolation methods such as Kriging interpolation, etc.; if the interpolation processing result shows the existence of a bedding gap, determine the reference height of the reference pillar according to the height of the bedding gap; connect the center coordinates of the reference column and the reference pillar at the position where the bedding gap height is located to generate a bedding gap connection line; according to the generated connection line and the bedding gap connection line, determine the surface to be measured between the reference column and the reference pillar.

[0179] Through this solution, by performing interpolation processing and connection line analysis on the coordinates of the reference column and the reference pillar, the geological structure can be more intuitively understood, especially the geological sequence and the contact relationship between strata; interpolation processing can help identify bedding gaps, which may indicate important geological features such as faults, sedimentary discontinuities or magmatic intrusions; based on accurate coordinate data and interpolation results, a more accurate geological model can be constructed, which is crucial for geological exploration and resource assessment; by determining the surface to be measured, the exploration strategy can be optimized and resources can be concentrated on the most promising areas for exploration; by analyzing the bedding gaps and connection lines, potential changes between strata can be predicted, and trends of geological changes such as tilting, folding or fracturing of strata can be identified, enhancing the ability to predict the geological growth context; the determined surface to be measured can be used to predict the strike and location of ore veins, which has direct significance for ore deposit exploration.

[0180] In some embodiments, for each surface to be measured, connect any three or more surfaces to be measured to form a three-dimensional region; predict the geological growth changes of the three-dimensional region according to the geological changes of the surface to be measured; traverse all three-dimensional regions, and determine the geological growth context of the region to be measured according to the geological growth changes of the three-dimensional region; determine whether there is an ore vein in the region to be measured according to the geological growth context between any adjacent sampling columns, including: determining whether there is an ore vein in the region to be measured according to the geological growth context of the region to be measured.

[0181] The three-dimensional region can be a three-dimensional region that is enclosed by connecting three surfaces to be measured and has a closed edge from a top-down perspective.

[0182] Specifically, connect any three or more surfaces to be measured to form a three-dimensional region; use geological modeling software or programming tools to construct a three-dimensional model to display the spatial relationships of the surfaces to be measured; analyze the geological changes on the surfaces to be measured, such as analyzing geological structure characteristics such as formation dip, faults, and folds; use the geological model and software tools to predict the geological growth changes in the three-dimensional region; traverse all the constructed three-dimensional regions, analyze and record the geological growth changes in each region; according to the traversal results, determine the geological growth context in the region to be measured, analyze the geological connections between different regions, identify the main directions and paths of geological growth; according to the geological growth context between any adjacent sampling columns, analyze the possible distribution of ore veins; combine the geological model and geological theory to predict whether there are ore veins in the region to be measured.

[0183] Through this solution, by connecting the surfaces to be measured into a three-dimensional region, a more accurate geological model can be constructed, thereby understanding the geological structure and predicting geological changes. Analyzing the geological changes on the surfaces to be measured can help predict the geological growth changes in the three-dimensional region; traversing all the three-dimensional regions and analyzing their geological growth changes can more comprehensively determine the geological growth context in the region to be measured, providing more accurate guidance for geological exploration; according to the geological growth context between any adjacent sampling columns, it is possible to more accurately predict whether there are ore veins in the region to be measured, thereby increasing the success rate of ore prospecting; the collected data can be used to construct a three-dimensional geological model to more intuitively display the geological growth context, facilitating analysis and decision-making.

[0184] In some embodiments, according to the rock formation characteristics, determine the rock types existing in each rock formation; obtain the regional location of the region to be measured, and according to the regional location, determine the possible types of ore veins; based on the possible types of ore veins, rock types, and the geological growth context between any adjacent sampling columns, determine whether there are ore veins in the region to be measured.

[0185] The possible types of ore veins can be ore vein categories classified according to characteristics such as the types, origins, and occurrence forms of minerals.

[0186] Specifically, conduct feature analysis on each rock stratum within the area to be measured, including rock type, lithology, structure, texture, etc., to determine the rock type of each rock stratum, such as sandstone, shale, limestone, etc.; obtain the regional location information of the area to be measured, including geographical coordinates, topography, etc.; based on the regional location, combined with the regional geological background and known geological laws, determine the possible types of ore veins, such as metal ore veins, non-metal ore veins, etc.; analyze the geological growth context between any adjacent sampling columns, including geological structure features such as stratigraphic contact relationships, faults, folds, etc.; determine the main direction and path of geological growth, as well as the influence of geological structures on the formation of ore veins; based on the possible types of ore veins, rock types, and the analysis results of the geological growth context, judge whether there are ore veins in the area to be measured.

[0187] Through this solution, through detailed rock stratum feature analysis, since different rock types may indicate different geological environments and mineralization potentials, the rock type of each rock stratum can be accurately determined, providing basic data for subsequent ore vein prediction, helping to construct a more accurate geological model. Based on the regional location, the possible types of ore veins that match the geological conditions of the area can be determined, improving the pertinence of prospecting, avoiding ineffective exploration in areas without potential ore vein types, and reducing risks and costs. By comprehensively considering the possible types of ore veins, rock types, and geological growth context, the comprehensiveness and accuracy of the analysis are improved; based on multi-factor analysis, it is possible to more accurately predict whether there are ore veins in the area to be measured.

[0188] In some embodiments, if it is determined that there are no ore veins in the area to be measured, then based on the geological growth context between any adjacent sampling columns, determine whether there is a possibility of ore vein development in the area to be measured; if there is, then based on the geological growth context between any adjacent sampling columns, predict the geological changes within a preset distance range in the area to be measured; based on the geological changes within the preset distance range in the area to be measured, speculate on the location where the ore vein may appear, and based on the location where the ore vein appears, determine whether there is mining value.

[0189] The possibility of ore vein development can be to evaluate the probability that a certain area may form an ore vein in the future based on geological conditions and historical data.

[0190] The preset distance range can be a distance range preset during the geological exploration process, used to evaluate and analyze geological changes, ore vein distribution, or resource potential.

[0191] The location where the ore vein appears can be the specific location where the ore vein may form based on geological surveys and predictive analysis.

[0192] Specifically, if it is determined that there is no vein in the area to be measured, collect and analyze the geological data between any adjacent sampling columns, and evaluate whether there are geological conditions for vein development in the area to be measured according to the geological growth context and known vein formation conditions; use geological models and geodynamic models to predict the geological changes in the area to be measured within a preset distance range; analyze the prediction results, such as stratum deformation, fault activities, etc.; based on the predicted geological changes, speculate on the possible geological locations where veins may appear; combine geological history and the formation locations of known veins to further refine the speculation; evaluate the relationship between the speculated vein emergence locations and current technologies, market demands, and mining costs; combine the scale, grade, and ore type of the veins to determine the mining value of the veins.

[0193] Through this solution, unnecessary exploration and mining activities in areas without veins are avoided, saving human, material, and financial resources; reducing risk investment in mineral - free areas and protecting the financial security of enterprises; deepening the understanding of geological structures and ore - forming environments, providing a theoretical basis for subsequent prospecting; integrating data from different sampling columns to form a continuous geological information chain, which helps to comprehensively analyze the regional geological characteristics; clarifying the prospecting objectives and directions in the area to be measured, improving the exploration efficiency; determining the possible locations of veins through geological change prediction to guide exploration activities; increasing the success rate of exploration at possible vein locations.

[0194] Figure 3 The following is a schematic structural diagram of a prospecting analysis system for multi - heterogeneous geological data fusion based on AI provided by an embodiment of the present application, as Figure 3 shown. The multi - heterogeneous geological data fusion prospecting analysis system 300 based on AI in this embodiment includes: a coordinate analysis module 301, a context analysis module 302, a vein judgment module 303, and a strike analysis module 304.

[0195] The coordinate analysis module 301 is used to obtain a remote - sensing image; obtain sampling data; analyze the remote - sensing image to determine the three - dimensional coordinates of the area to be measured;

[0196] The context analysis module 302 is used to analyze the sampling data to determine the geological growth context between any adjacent sampling columns in the area to be measured;

[0197] The vein judgment module 303 is used to determine whether there is a vein in the area to be measured according to the geological growth context between any adjacent sampling columns;

[0198] The strike analysis module 304 is used to, if there is a vein, determine the vein strike based on the three - dimensional coordinates.

[0199] Optionally, when obtaining sampling data, the coordinate analysis module 301 is used for:

[0200] Obtain cross-sectional images and column images of a number of sampling columns;

[0201] Extract the image features of the cross-sectional images, and determine the initial sampling direction according to the image features;

[0202] Based on the initial sampling direction, analyze the cross-sectional images to determine the cross-sectional change situation;

[0203] Based on the initial sampling direction, analyze the column images to determine the column change situation;

[0204] Take the cross-sectional change situation and the column change situation as the sampling data.

[0205] Optionally, when determining the initial sampling direction according to the image features, the coordinate analysis module 301 is used to:

[0206] Obtain the ground image before sampling;

[0207] According to the image features of each sampling column, determine the geographical location of each sampling column before sampling;

[0208] According to the geographical location, screen the ground image to determine the exclusive image corresponding to each sampling column;

[0209] For each sampling column, analyze the exclusive image to determine the original ground features, and select the direction features based on the preset direction;

[0210] Compare the direction features with the image features, and determine the initial sampling direction according to the comparison result.

[0211] Optionally, when analyzing the sampling data to determine the geological growth context between any adjacent sampling columns in the area to be measured, the context analysis module 302 is used to:

[0212] According to the cross-sectional change situation, determine the geological surface structure and surface rock types;

[0213] According to the column change situation, determine the rock layer features and the geological changes of each rock layer;

[0214] For each sampling column, determine the geological structure sequence of the rock layer corresponding to each sampling column according to the rock layer features and the geological changes of each rock layer;

[0215] According to the geological structure sequence, determine the geological growth context between any adjacent sampling columns in the area to be measured.

[0216] Optionally, when determining the geological growth context between any adjacent sampling columns in the area to be measured according to the geological structure sequence, the context analysis module 302 is used to:

[0217] For each sampling column, based on the initial sampling direction, taking any one sampling column as the reference column, select any other sampling column as the reference object; the reference object is the reference object of any reference column, and the reference object is not any reference column;

[0218] According to the three-dimensional coordinates of the area to be measured, determine the coordinates of the reference column and the coordinates of the reference object;

[0219] Taking the initial sampling direction as the connection direction, based on the coordinates of the reference column and the coordinates of the reference object, determine the surface to be measured between the reference column and the reference object;

[0220] According to the geological structure sequence of the rock formation corresponding to each sampling column, predict the geological changes of the surface to be measured;

[0221] According to the geological changes of the surface to be measured, determine the geological growth context between any adjacent sampling columns in the area to be measured.

[0222] Optionally, when the context analysis module 302 determines the surface to be measured between the reference column and the reference object based on the coordinates of the reference column and the coordinates of the reference object, it is used for:

[0223] Taking the preset layer height as the interval standard, traverse the center coordinates of the reference column at each layer height and the center coordinates of the reference object at each layer,

[0224] Connect the center coordinates of the reference column at each layer height with the center coordinates of the corresponding reference object at the same layer height to obtain a number of connecting lines until there is a connection relationship for the centers of all reference columns at each layer height;

[0225] For each reference column, perform interpolation processing on the reference column to determine whether there is a possible layer gap outside the interval standard;

[0226] If there is, then according to the layer gap height, determine the reference height of the reference object, and connect the center coordinates at the position of the layer gap height to obtain a layer gap connecting line;

[0227] According to the number of connecting lines and the layer gap connecting line, determine the surface to be measured between the reference column and the reference object.

[0228] Optionally, the multi-source heterogeneous geological data fusion prospecting analysis system 300 further includes a three-dimensional analysis module 305, which is used for:

[0229] For each surface to be measured, connect any three or more surfaces to be measured to form a three-dimensional area;

[0230] According to the geological changes of the surface to be measured, predict the geological growth changes of the three-dimensional area;

[0231] Traverse all three-dimensional regions, and determine the geological growth context of the area to be measured according to the geological growth changes of the three-dimensional regions;

[0232] Determining whether there is a vein in the area to be measured according to the geological growth context between any adjacent sampling columns includes:

[0233] Determine whether there is a vein in the area to be measured according to the geological growth context of the area to be measured.

[0234] Optionally, when determining whether there is a vein in the area to be measured according to the geological growth context between any adjacent sampling columns, the vein judgment module 303 is used for:

[0235] Determine the rock types existing in each rock layer according to the rock layer characteristics;

[0236] Obtain the regional location of the area to be measured, and determine the possible types of veins according to the regional location;

[0237] Based on the possible types of veins, rock types, and the geological growth context between any adjacent sampling columns, determine whether there is a vein in the area to be measured.

[0238] Optionally, the multi-source heterogeneous geological data fusion prospecting analysis system 300 further includes a value analysis module 306, which is used for:

[0239] If it is determined that there is no vein in the area to be measured, then determine whether there is a possibility of vein development in the area to be measured according to the geological growth context between any adjacent sampling columns;

[0240] If there is, then predict the geological changes in the area to be measured within a preset distance according to the geological growth context between any adjacent sampling columns;

[0241] Speculate the position where the vein appears according to the geological changes in the area to be measured within the preset distance, and determine whether there is mining value according to the position where the vein appears.

[0242] The system of this embodiment can be used to execute the method of any of the above embodiments, and its implementation principle and technical effects are similar, so details are not described here.

Claims

1. A multi-heterogeneous geological data fusion prospecting analysis method based on AI, characterized in that: include: Acquisition of remote sensing images; Get sampling data; Analyzing the remote sensing image to determine the three-dimensional coordinates of the area to be measured; Analyze the sampling data to determine the geological growth veins between any adjacent sampling columns in the area to be tested; Determine whether there is a mineral vein in the area to be tested based on the geological growth veins between any adjacent sampling columns; If a mineral vein exists, determining the direction of the mineral vein based on the three-dimensional coordinates; The acquiring of sampling data comprises: Acquire cross-sectional images and cylinder images of several sampling columns; Extracting image features of the cross-sectional image, and determining an initial sampling direction according to the image features; Based on the initial sampling direction, analyzing the cross-sectional image to determine the cross-sectional change; Based on the sampling initial direction, analyzing the cylinder image to determine the cylinder change; Using the cross-section change and the column change as the sampling data; The analyzing the sampling data to determine the geological growth veins between any adjacent sampling columns in the area to be tested includes: Determine the geological surface structure and surface rock type according to the cross-sectional changes; Determine the characteristics of the rock layers and the geological changes of each rock layer according to the changes in the column; For each sampling column, according to the rock formation characteristics and geological changes of each rock formation, determine the geological structural sequence of the rock formation corresponding to each sampling column; According to the geological structure sequence, determining the geological growth veins between any adjacent sampling columns in the area to be tested; The geological tectonic sequence is used to characterize the order in which different rock layers are deposited or formed during the formation of the strata; Geological growth veins are used to characterize the changes and development trends of geological structures over time.

2. The method according to claim 1, characterized in that The step of determining the initial sampling direction according to the image feature includes: Obtain ground images before sampling; According to the image features of each sampling column, the geographical location of each sampling column before sampling is determined; According to the geographical location, the ground image is screened to determine an exclusive image corresponding to each sampling column; For each sampling column, the exclusive image is analyzed to determine the original ground features, and the directional features are selected based on the preset direction; The directional feature is compared with the image feature, and an initial sampling direction is determined according to the comparison result.

3. The method according to claim 1, characterized in that Determining the geological growth veins between any adjacent sampling columns in the area to be tested according to the geological structure sequence includes: For each sampling column, based on the initial sampling direction, any sampling column is used as a base column, and any other sampling column is selected as a reference column; the reference column is a reference object of any base column, and the reference column is not any base column; Determine the reference cylindrical coordinates and the reference cylindrical coordinates according to the three-dimensional coordinates of the area to be measured; Taking the sampling initial direction as the connection direction, based on the reference column coordinates and the reference column coordinates, determining the surface to be measured between the reference column and the reference column; Predicting the geological changes of the surface to be measured according to the geological structural sequence of the rock layer corresponding to each sampling column; According to the geological changes of the surface to be tested, the geological growth veins between any adjacent sampling columns in the area to be tested are determined.

4. The method according to claim 3, characterized in that: The step of determining the surface to be measured between the reference column and the reference column based on the reference column coordinates and the reference column coordinates comprises: Taking the preset storey height as the interval standard, traverse the center coordinates of the reference column at each storey height and the center coordinates of the reference column at each storey, Connect the coordinates of the center of the circle of the reference column at each floor height with the coordinates of the center of the circle of the reference column at the corresponding floor height to obtain a number of connecting lines until the centers of all the reference columns at each floor height are connected; For each reference column, interpolation processing is performed on the reference column to determine whether there is a possibility of a layer gap outside the spacing standard; If it exists, the reference height of the reference column is determined according to the interlayer gap height, and the coordinates of the center of the circle at the interlayer gap height are connected to obtain an interlayer gap connection line; According to the plurality of connection lines and the layer gap connection lines, a surface to be measured between the benchmark column and the reference column is determined.

5. The method according to claim 3, characterized in that: After determining the geological growth veins between any adjacent sampling columns in the area to be tested according to the geological changes of the surface to be tested, the method further includes: For each surface to be tested, any three or more surfaces to be tested are connected to form a three-dimensional area; Predicting geological growth changes in the three-dimensional area according to geological changes in the surface to be measured; Traversing all three-dimensional regions, and determining the geological growth veins of the region to be measured according to the geological growth changes of the three-dimensional regions; Determining whether there is a mineral vein in the area to be tested based on the geological growth veins between any adjacent sampling columns includes: Determine whether there is a mineral vein in the area to be tested based on the geological growth veins in the area to be tested.

6. The method according to claim 1, characterized in that Determining whether there is a mineral vein in the area to be tested based on the geological growth veins between any adjacent sampling columns includes: Determine the rock type present in each rock layer based on the rock layer characteristics; Acquiring the regional position of the area to be tested, and determining the possible type of the mineral vein according to the regional position; Based on the possible types of the mineral veins, the rock types and the geological growth veins between any adjacent sampling columns, it is determined whether there are mineral veins in the area to be tested.

7. The method according to claim 6, characterized in that After determining whether there is a vein in the area to be tested based on the possible type of the vein, the rock type and the geological growth veins between any adjacent sampling columns, the method further includes: If it is determined that there is no mineral vein in the test area, determining whether there is a possibility of mineral vein development in the test area according to the geological growth veins between any adjacent sampling columns; If so, predicting the geological changes of the area to be tested within a preset distance range according to the geological growth veins between any adjacent sampling columns; According to the geological changes in the area to be measured within a preset distance range, the location of the mineral vein is inferred, and based on the location of the mineral vein, it is determined whether there is mining value.

8. An AI-based multi-heterogeneous geological data fusion prospecting analysis system, characterized in that: Applied to perform the method according to any one of claims 1 to 7, comprising: A coordinate analysis module is used to obtain remote sensing images; obtain sampling data; analyze the remote sensing images to determine the three-dimensional coordinates of the area to be measured; A vein analysis module, used to analyze the sampling data and determine the geological growth veins between any adjacent sampling columns in the area to be tested; A mineral vein judgment module, used to determine whether there is a mineral vein in the area to be tested based on the geological growth veins between any adjacent sampling columns; The trend analysis module is used to determine the direction of the vein based on the three-dimensional coordinates if a vein exists.

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