A multi-source geological information linkage analysis method and system based on rock core scanning images

Abstract

The application relates to a multi-source geological information linkage analysis method and system based on rock core scanning images. The method comprises the following steps: obtaining a drilling rock core scanning image and corresponding data, associating and binding the image and the data; establishing a linear mapping relationship between a target point pixel position and depth interval data, constructing a unified depth reference of the image pixel and the drilling depth; calling a preset feature template library to perform segmented similarity matching on the rock core scanning image, generating a similarity curve, and screening a target horizon according to a preset threshold; identifying a mineral area and a geological structure of a depth window corresponding to the target horizon, counting geological feature parameters, and analyzing the relationship between mineralization characteristics and structural control; and outputting geological exploration analysis results based on the identification results and the statistical results of the completed depth correlation. The application realizes bidirectional mapping and linkage updating between rock core scanning image results and geological logging data, and improves the consistency and efficiency of rock core data management and exploration interpretation.

Description

Technical Field

[0001] This application relates to the field of geological exploration technology, and in particular to a method and system for multi-source geological information linkage analysis based on rock core scanning images. Background Technology

[0002] In mineral geological exploration, geological surveys and scientific research, borehole rock cores are important physical data for judging stratigraphic structure, mineralization characteristics and tectonic signs. However, existing methods for logging and analyzing rock and mineral cores have the following shortcomings: On the one hand, rock and mineral core images, textual descriptions, test data, and analysis charts are stored in a scattered manner, lacking a unified index of target point locations, making subsequent verification, comparison, and sharing extremely difficult. Moreover, although existing rock and mineral core scanning analysis systems have functions such as archiving, image acquisition and correction, database management, columnar section generation, and color-based mineral extraction, most of these are single functions, lacking an overall solution for joint analysis and dynamic updating of multi-source data. On the other hand, multi-hole borehole comparison, identification of similar rock strata, mineral distribution statistics, and joint and fracture occurrence statistics mainly rely on manual experience, resulting in poor consistency and a lack of quantitative indicators. Manually browsing a large number of rock and mineral core scanning images to locate target strata is inefficient and easily overlooks minute features such as weak mineralization, veinlets, fractures, or alteration boundaries. Furthermore, manually drawing comprehensive columnar sections is time-consuming, and images, lithological symbols, geological descriptions, and attribute data are difficult to update synchronously, easily leading to inconsistencies between the results and the original data.

[0003] Therefore, there is a need for an integrated method and system that can associate, bind, jointly analyze, and dynamically update multi-source information such as core scan images, geological logging information, and laboratory data to improve exploration efficiency and consistency of results. Summary of the Invention

[0004] To address or partially address the problems existing in related technologies, this application provides a method and system for multi-source geological information linkage analysis based on rock and mineral core scan images. By establishing a unified depth benchmark between rock and mineral core image pixels and borehole depth, and combining template matching to intelligently identify mineralization and structural features, the system can link and fuse multi-source geological data to carry out statistical analysis, thereby achieving bidirectional mapping and linkage updates between rock and mineral core scan image results and geological logging data.

[0005] The first aspect of this application provides a method for multi-source geological information linkage analysis based on rock core scanning images, including the following steps: Acquire scanned images of borehole rock cores and their corresponding borehole data, pixel parameters and depth range data, associate and bind the images with the data, and generate target numbers and binding association information for target points in the images; Establish a linear mapping relationship between the pixel position of the target point and the depth interval data, and construct a unified depth benchmark between image pixels and borehole depth; Based on a unified depth benchmark, a preset feature template library is called to perform segmented similarity matching on the core scan images to generate similarity curves, and the target layer is obtained by filtering according to a preset threshold. Mineral regions and geological structures are identified within the depth window corresponding to the target stratum, geological characteristic parameters are statistically analyzed, and the relationship between mineralization characteristics and tectonic control is analyzed. Based on the identification and statistical results of the completed deep correlation, geological exploration and analysis results are output.

[0006] The linear mapping relationship is expressed as follows: ; in, This represents the actual drilling depth. The pixel position of the target point within the image. The starting depth of the image. The image termination depth, This represents the total number of pixels along the length of the core scan image. .

[0007] The method also includes: after analyzing the relationship between mineralization characteristics and tectonic control, generating a multi-dimensional comprehensive columnar chart based on the target point binding results and depth mapping results, and filling it with various analytical data; when the target point position, lithological description, and statistical data in the image change, the entire map column is refreshed in real time.

[0008] The segmented similarity matching process involves dividing the core scan image into multiple continuous or overlapping depth windows, calculating the similarity between each depth window and the template, and simultaneously recording the template number and matching confidence level.

[0009] Among them, the mineral regions include iron-bearing mineral regions, alteration regions, and vein regions; the geological structures include joints, fissures, and veinlets.

[0010] Among them, the geological characteristic parameters include the proportion of mineral area, number of structures, strike angle, and degree of development. The statistical method of the geological characteristic parameters is to count the mineral proportion, vein density, and joint and fracture distribution characteristics in batches according to the target number or depth segment.

[0011] Among them, the geological exploration and analysis results are associated with borehole core scan images, target numbers, and depth location tracing information based on depth interval data, including a target stratum location list, mineralized section delineation suggestions, multi-hole comparison diagrams, and a comprehensive evaluation report.

[0012] The second aspect of this application provides a multi-source geological information linkage analysis system based on core scan images, applicable to the multi-source geological information linkage analysis method based on core scan images provided in the first aspect of this application, including a core scan result access unit, an image correction and measurement unit, a target point location binding unit, a core image-text linkage database unit, a feature template library unit, a template similarity positioning unit, a mineral and occurrence joint analysis unit, a comprehensive columnar chart update unit, and an exploration evaluation output unit.

[0013] The technical solution provided in this application may include the following beneficial effects: This application provides a method and system for multi-source geological information linkage analysis based on core scan images. It binds core scan images, target point depth locations, geological descriptions, laboratory data, similarity matching results, mineral identification results, and occurrence statistics to a unified target point index, facilitating data querying, verification, and comparative analysis. Under a unified depth location benchmark, it achieves bidirectional mapping between image pixel locations and borehole depths, allowing users to quickly locate the corresponding depth segment in a comprehensive columnar section from the core image, and vice versa, improving the efficiency and accuracy of stratigraphic correlation. The method rapidly identifies potential mineralized or altered strata in massive amounts of core images through template similarity matching, reducing the workload of manual image browsing and subjective judgment, improving target location efficiency and discovery rate, and enabling the quantitative representation of mineral-rich areas, vein-dense areas, and fracture-developed areas, enhancing the quantifiability and consistency of exploration interpretation. It provides traceable evidence for orebody strike judgment, resource evaluation, results archiving, and subsequent verification, improving the systematic nature and reliability of exploration work.

[0014] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0015] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0016] Figure 1 This is a schematic flowchart illustrating the multi-source geological information linkage analysis method based on rock core scanning images, as shown in the embodiments of this application. Figure 2 This is a schematic diagram illustrating the binding of a core scan image and a target point as shown in an embodiment of this application; Figure 3 This is a schematic diagram of the integrated bar chart linkage update shown in the embodiments of this application; Figure 4This is a schematic diagram illustrating the joint analysis of template similarity localization and mineral occurrence as shown in the embodiments of this application. Detailed Implementation

[0017] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application more thorough and complete, and to fully convey the scope of this application to those skilled in the art.

[0018] A multi-source geological information linkage analysis system based on core scan images is constructed using existing core scan images and geological logging data. It includes a core scan results access unit, an image correction and measurement unit, a target point location binding unit, a core image-text linkage database unit, a feature template library unit, a template similarity positioning unit, a mineral and occurrence joint analysis unit, a comprehensive columnar section update unit, and an exploration evaluation output unit. These units work collaboratively to achieve the following functions: Core Scan Results Access Unit: This unit receives and preprocesses acquired core images along with corresponding basic information such as borehole number, run number, box number, and start and end depths. It can also access existing image correction, measurement, or cropping results to provide effective image length, pixel coordinates, and image quality parameters for subsequent analysis.

[0019] Image correction and measurement unit: Corrects (e.g., distortion removal), measures size and evaluates quality of received rock core images, and outputs information such as the total number of pixels in the effective length direction of the image.

[0020] Target point location binding unit: Based on the corresponding borehole depth range and pixel length, each image is bound to the borehole number, number of passes, and depth range, and target numbers are generated for sampling points, annotation points, etc. in the image; each target number contains borehole number, pass number, image number, pixel coordinates, converted depth, target type, and related descriptive information for data association in subsequent analysis.

[0021] The core image-text linked database unit is used to store core scan images, image segmentation information, lithological logging descriptions, stratigraphic information, laboratory analysis data, target point locations, template matching results, mineral identification results, occurrence statistics, and drawing parameters, thereby enabling the associated management of multi-source information.

[0022] Feature Template Library Unit: This unit stores image templates of typical rock minerals and alteration features. Each template includes information such as the template image, name, applicable strata, and similarity threshold. Templates can be generated by users from typical areas selected from known rock and mineral core images and can be continuously expanded as exploration progresses, improving the system's ability to quickly locate and compare within the same area.

[0023] Template similarity localization unit: Divides the core image to be analyzed into multiple continuous or overlapping image windows along the depth direction, and matches each window with each template in the template library to obtain a similarity value; the system continuously calculates the similarity curve along the depth direction and determines the similarity value based on the set similarity threshold. Depth segments with similarity higher than a threshold are extracted as candidate regions for the target layer, and the corresponding template number and matching confidence are output.

[0024] The mineral and occurrence joint analysis unit performs mineral and occurrence analysis on the image window containing the identified target strata. This unit extracts mineral regions (such as iron oxides, copper-bearing minerals, etc.) and alteration regions based on color, texture, and morphological features; identifies and edits joints, fissures, and veinlets; calculates parameters such as mineral area ratio, vein density, fissure development degree, and ore-forming element content, and associates them with the target number; and performs statistical analysis on the changes in mineral regions and the distribution of joints and fissures in adjacent depth segments to help determine the mineralization trend and tectonic control relationships.

[0025] The integrated bar chart update unit generates linked charts by calling a predefined integrated bar chart template. This template includes columns for depth scale, lithology legend, core image, lithology description, similarity curve, mineral analysis, occurrence statistics, laboratory data curves, evaluation conclusions, and symbol explanations. Based on the target point binding results, the system automatically arranges the core image and target markers in the image columns; automatically draws lithology bar charts based on lithological stratification results and the lithology symbol library; and automatically generates corresponding curves and attribute columns based on similarity positioning results, mineral and occurrence analysis results, and laboratory data. When the user updates the lithology description, target location, mineral identification results, occurrence statistics results, or laboratory data, all chart columns are updated synchronously in real time. When the user clicks on a depth segment in the bar chart, the system highlights the corresponding core image, target number, geological description, similarity value, mineral region, and occurrence record, supporting bidirectional positioning.

[0026] Exploration and Evaluation Output Unit: Based on the analysis results, this unit generates the final output, including a target stratigraphic location list, mineralized section delineation suggestions, multi-hole correlation maps, a core image album, a joint mineral and occurrence analysis report, a comprehensive evaluation report, a review task list, and database interface files. All these outputs retain their association with the original core images, target point locations, and depth coordinates, facilitating project review, results archiving, and subsequent verification.

[0027] like Figure 1 As shown, a multi-source geological information linkage analysis method based on rock core scanning images, applied to the above system, includes the following steps: S1. Obtain the borehole core scan image and its corresponding borehole data, pixel parameters and depth range data, associate and bind the image with the data, and generate target number and binding association information for target points in the image.

[0028] The system first receives sampling records and scanned images containing information such as borehole number, run number, box number, core number, starting depth, and ending depth. This method does not specify the specific resolution or pixel size of the core scanned images (selectable ranges include 2000×2000 to 10000×10000 pixels).

[0029] For each unfolded image of a rock core, the system records the total number of pixels along its effective length direction. Depth range corresponding to each cycle Image segment number and segment depth range.

[0030] For scenarios involving multiple core image fragments within the same extraction, the system establishes a separate pixel-depth mapping table for each image fragment, enabling independent management and precise mapping of multiple image fragments within a single extraction. Furthermore, the system generates unique target numbers for all key points within the image, covering all analysis targets such as sampling points and manually labeled points.

[0031] The complete associated information for each target number includes borehole number, cycle number, image number, pixel coordinates, converted depth, target type, and data source information, forming a unified index that runs through the entire analysis process. This provides standardized index support for subsequent depth conversion, template matching, mineral occurrence statistics, and results tracing.

[0032] S2. Establish a linear mapping relationship between the pixel position of the target point and the depth interval data, and construct a unified depth benchmark for image pixels and borehole depth.

[0033] like Figure 2 As shown, for any unfolded core image, the corresponding borehole depth range is... The total number of pixels in the effective length direction of the image is Establish pixel positions With actual depth Linear mapping relationship between them: in, This represents the actual drilling depth. The pixel position of the target point within the image. The starting depth of the image. The image termination depth, This represents the total number of pixels along the length of the core scan image. .

[0034] This mapping formula automatically associates the location of target points in rock core images with geological depth information, achieving a unified depth benchmark for image recognition results, manual annotation results, and borehole depth information.

[0035] S3. Based on a unified depth benchmark, the preset feature template library is called to perform segmented similarity matching on the core scan image, generate a similarity curve, and select the target layer according to the preset threshold.

[0036] In this embodiment, a dedicated image-text linkage database and feature template library are pre-configured to provide data support for intelligent matching analysis. The core image-text linkage database uses borehole number, depth segment, and target number as core indexes to uniformly store a full range of multi-source data, including core scan images, image segmentation information, lithological descriptions, stratigraphic systems, run information, laboratory analysis data, target point locations, template matching results, mineral identification results, occurrence statistics, and drawing parameters. This enables centralized and correlated management of multi-source geological information.

[0037] The feature template library is manually generated by technicians from known rock and mineral core images of the study area, selecting typical rocks, minerals, or altered regions. Each template fully includes the template image, rock and mineral type name, applicable stratigraphic level, similarity threshold, and remarks. Furthermore, the template library supports dynamic iterative updates, continuously expanding the template sample as field exploration progresses, effectively improving the ability to quickly locate and laterally compare rock and mineral features within the same mining area and metallogenic belt. The method does not specify a similarity threshold for identifying target stratigraphic levels (e.g., a range of 0.7–0.9 can be selected, adjusted according to rock texture and image quality).

[0038] During the template matching process, the system divides the entire rock core image to be analyzed into multiple continuous or overlapping depth windows along the depth direction. The image content within each standardized depth window is compared with various templates in the feature template library to calculate the similarity value corresponding to each depth position, thereby generating a continuous similarity curve distributed along the depth.

[0039] Based on a pre-configured similarity threshold θ, the system automatically extracts continuously distributed high-similarity depth segments in the image and identifies them as candidate areas for the target stratigraphic layer. At the same time, it records the template number and matching confidence level corresponding to the segment, thus completing the intelligent preliminary delineation of the target stratigraphic layer and providing an accurate depth window range for subsequent fine mineral and occurrence analysis.

[0040] S4. Identify mineral regions and geological structures within the depth window corresponding to the target stratum, statistically analyze geological characteristic parameters, and analyze the relationship between mineralization characteristics and tectonic control.

[0041] Based on image color, texture, and morphological features, the system automatically identifies mineral development and alteration areas within the target stratigraphic window. It also accurately identifies, displays, and supports manual secondary editing of geological structures such as joints, fissures, and veinlets within the image. The image window size can be selected appropriately based on rock strata characteristics and computing resources, for example, from 100×100 pixels to 500×500 pixels.

[0042] Based on this, the system automatically calculates quantitative parameters for various geological features in batches, mainly including core parameters such as the proportion of mineral areas, the proportion of altered areas, vein density, number of structures, joint and fracture orientation angles, and degree of structural development. The system supports batch summarizing and statistically analyzing the mineral proportion, vein development density, and spatial distribution characteristics of joints and fractures in multiple segments of images from the entire borehole according to two statistical dimensions: target number or depth segment.

[0043] Among them, mineral identification algorithms can employ methods based on color thresholding, texture analysis, or machine learning. Joint and fracture identification rules can utilize edge detection, morphological processing, and other methods based on rock structure, while setting parameters such as minimum length / width.

[0044] By comprehensively comparing the spatial distribution patterns of depth segments with high mineral content, strong alteration, and dense development of fractures and veinlets, and combining the distribution characteristics of high-similarity strata with template matching, we can assist in judging the regional mineralization development trend, tectonic occurrence patterns, and the control relationship between tectonics and mineralization enrichment, thus achieving a progressive analysis from image feature recognition to geological pattern analysis.

[0045] After completing the analysis of the relationship between mineralization characteristics and tectonic control, this method also includes a step of linking text and graphics to form a map: based on the target point binding results and depth mapping results, a multi-dimensional comprehensive columnar chart is generated to realize the visualization and real-time updating of multi-source geological data.

[0046] Specifically, the integrated bar chart update unit calls a pre-configured integrated bar chart template, which includes columns for depth scale, lithology symbols, core images, lithology descriptions, similarity curves, mineral statistics, occurrence statistics, laboratory data, and evaluation conclusions. Based on the target point location binding results, the system automatically arranges the core images and target markers in the image columns; automatically draws lithological patterns based on lithological stratification results and the lithology symbol library; automatically fills the corresponding columns with template matching results, mineral analysis results, occurrence statistics results, and laboratory data, and synchronously refreshes the corresponding columns when these data are updated; different iterations can be distinguished by separator lines.

[0047] When a user modifies lithological descriptions, target locations, mineral identification results, occurrence statistics, or laboratory data, each column updates synchronously in real time. When a user clicks on a depth segment in the bar chart, the system highlights the original core image, target number, geological description, similarity value, mineral region, and occurrence record corresponding to that depth, achieving a two-way association between the bar chart and the image. Figure 3 As shown.

[0048] S5. Based on the identification and statistical results of the completed deep correlation, output the geological exploration analysis results.

[0049] like Figure 4 As shown, in the evaluation and output stage of exploration results, the system comprehensively identifies and delineates potential mineralization zones by combining high similarity value segments, segments with increased mineral proportions, segments with densely developed fractures and veinlets, segments with abnormal laboratory data, and key geological descriptive terms. For multi-bore data in the mining area, it supports multi-bore lateral comparative analysis based on unified strata, unified depth range, and unified index type to help determine the extension trend of the ore body and the continuity of mineralization.

[0050] The final system can output standardized exploration results, including a target stratigraphic location list, mineralized section delineation suggestions, multi-well correlation maps, core image albums, mineral and occurrence joint analysis reports, comprehensive evaluation reports, and a review task list. All output results fully retain the correlation and traceability information with the original core images, target numbers, and depth locations, meeting the practical application needs of project review, results archiving, post-review, and geological decision-making, completing the entire multi-source geological information linkage analysis process.

[0051] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0052] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0053] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0054] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for multi-source geological information linkage analysis based on rock core scanning images, characterized in that, Includes the following steps: Acquire scanned images of borehole rock cores and their corresponding borehole data, pixel parameters and depth range data, associate and bind the images with the data, and generate target numbers and binding association information for target points in the images; Establish a linear mapping relationship between the pixel position of the target point and the depth interval data, and construct a unified depth benchmark between image pixels and borehole depth; Based on the unified depth benchmark, a preset feature template library is called to perform segmented similarity matching on the core scan image, generate a similarity curve, and select the target layer according to a preset threshold. Mineral regions and geological structures are identified within the depth window corresponding to the target stratum, geological characteristic parameters are statistically analyzed, and the relationship between mineralization characteristics and tectonic control is analyzed. Based on the identification and statistical results of the completed deep correlation, geological exploration and analysis results are output.

2. The method for multi-source geological information linkage analysis based on core scanning images according to claim 1, characterized in that, The linear mapping relationship is expressed as follows: ； in, This represents the actual drilling depth. The pixel position of the target point within the image. The starting depth of the image. The image termination depth, This represents the total number of pixels along the length of the core scan image. .

3. The method for multi-source geological information linkage analysis based on core scanning images according to claim 1, characterized in that, The method also includes: after analyzing the relationship between mineralization characteristics and structural control, generating a multi-dimensional comprehensive columnar chart based on the target point binding results and depth mapping results, and filling it with various analytical data; when the target point position, lithological description, and statistical data in the image change, the entire map column is refreshed in real time.

4. The method for multi-source geological information linkage analysis based on core scanning images according to claim 1, characterized in that, The segmented similarity matching involves dividing the core scan image into multiple continuous or overlapping depth windows, calculating the similarity between each depth window and the template, and simultaneously recording the template number and matching confidence level corresponding to the match.

5. The method for multi-source geological information linkage analysis based on core scanning images according to claim 1, characterized in that, The mineral regions include iron-bearing mineral regions, altered regions, and vein regions; the geological structures include joints, fissures, and veinlets.

6. The method for multi-source geological information linkage analysis based on rock core scanning images according to claim 1, characterized in that, The geological feature parameters include the area ratio of mineral regions, the number of structures, the strike angle, and the degree of development. The statistical method for the geological feature parameters is to count the mineral proportion, vein density, and joint and fracture distribution characteristics in batches according to the target number or depth segment.

7. The method for multi-source geological information linkage analysis based on core scanning images according to claim 1, characterized in that, The geological exploration and analysis results are associated with the borehole core scan images, target numbers, and depth location tracing information based on depth interval data, including a target stratum location list, mineralized section delineation suggestions, multi-hole comparison diagrams, and a comprehensive evaluation report.

8. A multi-source geological information linkage analysis system based on core scan images, applicable to the multi-source geological information linkage analysis method based on core scan images as described in any one of claims 1-7, characterized in that, It includes a core scanning results access unit, an image correction and measurement unit, a target point location binding unit, a core image and text linkage database unit, a feature template library unit, a template similarity positioning unit, a mineral and occurrence joint analysis unit, a comprehensive columnar chart update unit, and an exploration evaluation output unit.

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