Rock and outcrop cross-scale correlation method and system based on multi-modal data

Abstract

The application discloses a rock and outcrop cross-scale correlation method and system based on multi-modal data, belongs to the technical field of three-dimensional data platforms and geological learning, and comprises the following steps: S1, geological outcrop data acquisition and geological outcrop modeling are carried out, a three-dimensional geological outcrop model is obtained, multi-modal data are obtained by simultaneously collecting basic rock sample data, rock sample analysis and test data and rock sample explanation information; S2, the multi-modal data are managed by using a database and a file management mode, and the rock sample and the outcrop are correlated to obtain correlation data. Through the management of the multi-modal data, the deficiency of single expression mode is solved, and the fusion and integration of the multi-modal data are realized. Through the correlation mechanism of the rock and the outcrop, the deficiency of the existing system in the data correlation is solved.

Description

Technical Field

[0001] This invention relates to the fields of three-dimensional data platforms and geological learning technology, and in particular to a method and system for cross-scale correlation of rocks and outcrops based on multimodal data. Background Technology

[0002] With the rapid development of digital technology, 3D modeling, virtual simulation, and interactive learning technologies have been widely used in geological teaching and research. However, existing systems still have many shortcomings in data management, interactive display, and application scenarios, making it difficult to meet the growing needs of geological teaching and research.

[0003] Existing geological data management systems typically store and study rocks and landforms separately, lacking effective intelligent association mechanisms and hindering cross-scale learning from micro to macro levels. Specifically: Data isolation: Rock and landform data are usually stored in different databases or systems, making unified management and related queries difficult. Poor correlation: Existing systems often use simple manual association methods, making automated and intelligent matching of rocks and landforms difficult. Difficulty in cross-scale learning: Due to the lack of a rock-landform association mechanism, users find it difficult to expand from microscopic rock features such as mineral composition and structure to macroscopic landform features such as topography and geological environment, limiting the breadth and depth of learning. Poor learning experience: Existing systems are usually primarily static displays, lacking interactive functions, making it difficult for users to actively explore geological phenomena, thus limiting learning effectiveness.

[0004] Existing systems have shortcomings in multimodal data fusion and interactive display, making it difficult to meet the needs of geological teaching and research. Specifically: Limited presentation methods: Existing systems typically use only a single presentation method, such as reports or tables, lacking the fusion and integration of multimodal data. Weak data processing capabilities: Existing systems are inadequate in data processing, making it difficult to achieve high-precision data acquisition, resulting in insufficient model accuracy and completeness. Poor interactivity: Existing systems are usually primarily static displays, lacking interactive operation functions, making it difficult for users to actively explore geological phenomena and limiting learning effectiveness. Limited display effects: Existing systems are insufficient in 3D visualization, failing to provide an immersive display effect and impacting user experience.

[0005] Therefore, there is an urgent need for a method and system for cross-scale correlation of rocks and outcrops based on multimodal data that can effectively address the shortcomings of existing systems and meet the needs of geological teaching and research. Summary of the Invention

[0006] The purpose of this invention is to overcome the many shortcomings in data management, interactive display and application scenarios in the existing technology, and to provide a method and system for cross-scale correlation of rocks and outcrops based on multimodal data.

[0007] To achieve the above-mentioned objectives, this utility model provides the following technical solution:

[0008] A method for cross-scale correlation between rocks and outcrops based on multimodal data includes the following steps:

[0009] S1: Collect geological outcrop data and model geological outcrops to obtain a three-dimensional geological outcrop model. At the same time, collect basic data of rock specimens, rock specimen analysis and test data and rock specimen interpretation information to obtain multimodal data.

[0010] S2: The multimodal data is managed using a database and file management method, and the rock specimens are associated with the outcrops to obtain associated data.

[0011] By adopting the above technical solution, the shortcomings of the single expression method are solved by managing multimodal data, and the fusion and integration of multimodal data are realized; by using the correlation mechanism between rocks and outcrops, the shortcomings of the existing system in terms of data correlation are solved.

[0012] As a preferred embodiment of the present invention, the geological outcrop data acquisition in step S1 includes: acquiring three-dimensional laser point cloud data of the geological outcrop using a three-dimensional laser scanner; obtaining accurate spatial positioning data using a total station; and acquiring texture images of the outcrop texture from all directions using a high-definition camera.

[0013] As a preferred embodiment of the present invention, the geological outcrop modeling described in step S1 to obtain a three-dimensional geological outcrop model includes: firstly, based on the precise spatial positioning data from a total station, using RISCAN software to spatially locate and stitch the three-dimensional laser point cloud data to form a complete three-dimensional point cloud model; then, using Geomagic software to denoise the point cloud data while retaining key geological features; finally, using a professional texture mapping tool to map the texture image captured by a high-definition camera onto the three-dimensional skeleton model, giving the model realistic colors and textures.

[0014] As a preferred embodiment of the present invention, the collection of rock specimen analysis and testing data in step S1 includes: collecting rock specimens for analysis and testing, the analysis and testing including physical property analysis, hyperspectral mineral analysis, XRD whole-rock analysis and laser testing and analysis, to obtain multiple corresponding analysis reports, which contain information in the form of data, text and images.

[0015] As a preferred embodiment of the present invention, the management of the multimodal data in step S2 using a database and file management method includes: storing the multimodal data and three-dimensional geological outcrop model data in a unified structured manner according to their names, strata, locations, compositions, and corresponding types in a file management method;

[0016] Furthermore, entity tables were created for rock specimen analysis and testing data, geological outcrop models, basic rock specimen data, and rock specimen explanation information, and relationship tables were created for the relationships between entities.

[0017] As a preferred embodiment of the present invention, the step S2 of associating rock specimens with outcrops to obtain associated data includes: assigning a unique ID to each rock specimen to ensure the unique identification of the rock specimen in the system; extracting features such as the geographical location and landform type of the outcrop and assigning a unique ID to it; then associating the unique ID of the rock with the unique ID of the outcrop, storing the correspondence between the rock ID and the outcrop ID, and establishing the correspondence between the rock and the outcrop.

[0018] On the other hand, a cross-scale correlation system for rocks and outcrops based on multimodal data is provided, including a data processing layer for implementing the cross-scale correlation method for rocks and outcrops based on multimodal data as described in any of the above claims, and designing a functional architecture for the cross-scale correlation system according to the cross-scale correlation method for rocks and outcrops, including:

[0019] Rock and Outcrop Association Viewing Module: Used to enable interactive display of 3D geological outcrop models and rock specimens;

[0020] Rock specimen search module: used to search for rock specimens in various ways;

[0021] Geological outcrop search module: used to find 3D geological outcrops using various methods;

[0022] Rock analysis information viewing module: used to display the analytical and testing information of rock specimens;

[0023] The design method of the cross-scale correlation system functional architecture includes: designing data display logic to display the microscopic features of rock specimens: expanding from the microscopic features of rocks to mesoscopic features, and from mesoscopic features to macroscopic features;

[0024] The interactive display design includes operations such as zooming, rotating, panning, and clicking;

[0025] The design provides a display window and animation effects for dynamically loading multimodal resources.

[0026] As a preferred embodiment of the present invention, the rock and outcrop association viewing module includes: converting the coordinates of the three-dimensional geological outcrop model and the rock specimen using a seven-parameter coordinate transformation model, and loading the three-dimensional geological outcrop model using CesiumJS.

[0027] As a preferred embodiment of the present invention, the search methods of the rock specimen search module include: keyword search, rock category search, and main component search;

[0028] The geological outcrop search module includes the following search methods: keyword search, outcrop location search, and geological age search.

[0029] As a preferred embodiment of the present invention, the rock analysis information viewing module includes: displaying the analytical analysis information of the rock specimen;

[0030] The analytical and testing information is categorized as follows: physical property analysis, hyperspectral mineral analysis, laser testing and analysis, XRD whole-rock analysis, and XRF detection report.

[0031] Compared with the prior art, the beneficial effects of this application are:

[0032] This invention utilizes high-precision 3D modeling technology to visualize geological outcrops and rock specimens, overcoming the shortcomings of existing systems in data accuracy and improving the accuracy and completeness of outcrop models. Through the management of multimodal data, it addresses the limitation of a single representation method, achieving the fusion and integration of multimodal data. By establishing a correlation mechanism between rocks and outcrops, it overcomes the shortcomings of existing systems in data correlation, improving data retrieval efficiency and user experience. By constructing a cross-scale learning method, it enables the display of geological data from micro to macro levels, addressing the shortcomings of existing systems in cross-scale learning and improving learning effectiveness and research depth. Through interactive display and multimodal resource integration, it provides an immersive learning experience, overcoming the shortcomings of existing systems in interactivity and display effects, and increasing user learning interest and participation. By employing low-cost technologies and open-source tools, it reduces the development and maintenance costs of the system, addressing the shortcomings of existing systems in cost and scalability, and improving the system's accessibility and applicability. Attached Figure Description

[0033] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings:

[0034] Figure 1 The flowchart below shows a method for cross-scale correlation of rocks and outcrops based on multimodal data, as described in Example 1.

[0035] Figure 2 The flowchart of the multimodal data fusion method for cross-scale correlation of rocks and outcrops based on multimodal data described in Example 1 is shown below.

[0036] Figure 3 This is a schematic representation of the rock-outcrop association method based on multimodal data obtained in Example 1.

[0037] Figure 4 This is a system architecture diagram of a multi-scale correlation system between rocks and outcrops based on multimodal data, as described in Example 2.

[0038] Figure 5 This is a schematic diagram of the cross-scale correlation of a rock and outcrop cross-scale correlation system based on multimodal data as described in Example 2. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0040] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance, or suggesting any such actual relationship or order between these entities or operations. Additionally, the terms "connected," "linked," etc., can refer to a direct connection between components or an indirect connection via other components.

[0041] Example 1

[0042] A method for cross-scale correlation of rocks and outcrops based on multimodal data, such as Figure 1 As shown, it includes the following steps:

[0043] S1: Collect geological outcrop data and model geological outcrops to obtain a three-dimensional geological outcrop model. At the same time, collect basic data of rock specimens, rock specimen analysis and test data and rock specimen interpretation information to obtain multimodal data.

[0044] Specifically, in S1, the method for collecting geological outcrop data is as follows:

[0045] The Leica RTC360 high-speed 3D laser scanner was used to acquire laser point cloud data of geological outcrops at a scanning speed of 2 million points per second, with data resolution down to the millimeter level, for high-precision reconstruction of individual outcrop points. A total station was used to obtain precise spatial positioning data, and a Pentax 645D high-definition camera was used to acquire high-resolution images of the outcrop texture from all angles. The specific fusion process is as follows: Figure 2 As shown.

[0046] For example, a Leica RTC360 scanner is used to perform laser scanning and spatial positioning of the target outcrop. Scanning parameters: resolution 1 mm, acquisition rate of 2 million points per second; total station positioning: record the coordinates of the outcrop corner point (example: 39.2°N ± 0.001°, 116.4°E ± 0.001°).

[0047] Texture acquisition was performed using a Pentax 645D camera with a shooting interval of 50cm, an overlap rate of ≥80%, and total station positioning data was recorded simultaneously.

[0048] In S1, the method for modeling geological outcrops is as follows:

[0049] First, based on the precise spatial positioning data from the total station, RISCAN software is used to spatially locate and stitch the 3D laser point cloud data to form a complete 3D point cloud model. Then, Geomagic software is used to denoise the point cloud data, removing unnecessary noise points and retaining key geological features. Finally, professional texture mapping tools such as Texture Master are used to map the texture images captured by the high-definition camera onto the 3D skeleton model, giving the model realistic colors and textures.

[0050] For example: using RISCAN software for point cloud processing, inputting total station coordinates, ICP algorithm registration, stitching error <3mm; Geomagic denoising: removing outliers (threshold: point cloud deviating from the main model >5mm); Texture mapping by Map Master: aligning photo textures with point cloud UV coordinates;

[0051] In S1, the method for collecting analytical test data is as follows:

[0052] Rock samples were collected for analysis and testing, resulting in various analytical reports containing information in data, text, and image formats. Physical property analysis included porosity (%) and permeability (mD); hyperspectral mineral analysis included peak and trough band ranges, λm (nm) and ρm data, and sample spectral curves; laser testing and analysis included intensity and amplitude; XRD whole-rock analysis included quartz, potassium feldspar, plagioclase, calcite, zeolite, and clay minerals; and XRF testing reports included composition and test values.

[0053] For example, rock specimen analysis

[0054] Physical property analysis: including porosity (%) and permeability (mD);

[0055] Laser Testing and Analysis: Intensity and Amplitude;

[0056] XRD whole-rock analysis: mineral content including quartz, potassium feldspar, plagioclase, calcite, zeolite, clay minerals, etc.

[0057] XRF test report: components and test values;

[0058] Hyperspectral analysis: recorded peak λm = 1450 nm, reflectance ρm = 0.72.

[0059] In S1, the method for audio acquisition is explained as follows:

[0060] Standardized explanation templates are established based on the types of rock specimens, including geological age, lithological characteristics, and genetic type. Audio and video recordings are made during the explanations and stored in mp3, mp4, avi and other formats.

[0061] S2: The multimodal data is managed using a database and file management method, and the rock specimens are associated with the outcrops to obtain associated data.

[0062] Specifically, in S2, the methods for managing multimodal data and geological outcrop models are as follows:

[0063] Multimodal data and geological outcrop model data are stored in a unified structured manner using file management and classified according to their names, strata, locations, compositions, and corresponding types. In addition, entity tables are created for analytical test data, geological outcrop models, rock specimen data, and audio explanation data, and relationship tables are created for the relationships between entities.

[0064] For example, rock specimen table

[0065]

[0066]

[0067] Geological outcrops

[0068]

[0069] Rock-outcrop correlation table

[0070] In S2, the method for associating rock specimens with outcrops is as follows:

[0071] like Figure 3As shown, a unique ID is assigned to each rock specimen to ensure its unique identification within the system. Simultaneously, features such as the outcrop's geographical location and geomorphological type are extracted and assigned unique IDs. Then, the unique IDs of the rocks are associated with the unique IDs of the outcrops, storing the correspondence between rock IDs and outcrop IDs to establish the relationship between rocks and outcrops. This is achieved by creating an association table in the database, such as RockOutcrop, where one record is: Rock A (ID: 3a7bd3e2); Outcrop B (ID: 5c6d7e8f); Mineral composition similarity: 0.87; Association degree: 0.89. This means that the mineral composition similarity between rock A (ID: 3a7bd3e2) and outcrop B (ID: 5c6d7e8f) is 0.87, and their association degree is 0.89.

[0072] For example, rock-outcrop association

[0073] --Establish a relationship between rock A and outcrop B

[0074] INSERT INTO RockOutcrop VALUES(

[0075] '3a7bd3e2',

[0076] '5c6d7e8f',

[0077] 0.87, 0.89

[0079] ).

[0080] Example 2

[0081] A multi-scale correlation system for rocks and outcrops based on multimodal data, such as Figure 4 As shown, it includes a data processing layer for implementing the rock and outcrop cross-scale correlation method based on multimodal data as described in Implementation 1, and designing the cross-scale correlation system functional architecture according to the rock and outcrop cross-scale correlation method. It also includes a user interaction layer and a functional module layer.

[0082] The user interaction layer is a visual interface;

[0083] The functional module layer includes:

[0084] Rock and Outcrop Association Viewing Module: Used to enable interactive display of 3D outcrop models and rock specimens;

[0085] Specifically, the module for viewing the association between rocks and outcrops includes:

[0086] The coordinates of the geological outcrop model and rock specimens were transformed to ensure spatial consistency. CesiumJS was used to load the 3D digital outcrop model and rock specimens to ensure that the model could be accurately placed in the corresponding geographical location. Interactive methods were designed for the association, enabling the 3D digital outcrop model and rock specimens to perform various operations such as scaling, rotation, translation, and clicking. A display window and animation effects were designed to provide dynamic loading of multimodal resources such as text, images, and videos.

[0087] The coordinates of the geological outcrop model and rock specimens were transformed using a seven-parameter coordinate transformation model to ensure spatial consistency. The three-dimensional digital outcrop model was loaded using CesiumJS to ensure that the model could be accurately placed in the corresponding geographical location.

[0088] It enables interactive functions such as scaling, rotation, and translation, allowing users to freely explore 3D scenes;

[0089] This allows for the dynamic loading and display of multimedia resources such as text, images, and videos, providing comprehensive and specific learning resources.

[0090] Rock specimen search module: used to search for rock specimens in various ways;

[0091] Specifically, the rock specimen search module offers the following search methods: keyword search, rock category search, and main component search.

[0092] Geological outcrop search module: used to find 3D geological outcrops using various methods;

[0093] Specifically, the outcrop search module offers the following search methods: keyword search, outcrop location search, and geological age search.

[0094] Rock analysis information viewing module: Displays analytical and testing information of rock specimens;

[0095] Specifically, the rock analysis information viewing module includes:

[0096] This system displays analytical and laboratory information for rock specimens, presented in the form of data, text, images, audio, and video. The analytical and laboratory information is categorized as follows: physical property analysis, hyperspectral mineral analysis, laser testing and analysis, XRD whole-rock analysis, and XRF detection reports.

[0097] The design method of the cross-scale correlation system functional architecture includes: designing data display logic to display the microscopic features of rock specimens: expanding from the microscopic features of rocks to mesoscopic features, and from mesoscopic features to macroscopic features;

[0098] The interactive display design includes operations such as zooming, rotating, panning, and clicking;

[0099] The design provides a display window and animation effects for dynamically loading multimodal resources.

[0100] The methodology for cross-scale system functional architecture design is as follows:

[0101] The design logic for data display showcases the microscopic features of rock specimens, such as mineral composition, pore structure, and spectral curves. It expands from the microscopic features of rocks to mesoscopic features, such as how rock specimens are presented in geological outcrops; and from mesoscopic features to macroscopic features, such as three-dimensional models of geological outcrops and geological environments.

[0102] For example: Figure 5 As shown, Microscopic → Mesoscopic: Click on the rock mineral composition table (quartz 32%) to jump to the outcrop partial view of the rock location.

[0103] Meso-to-Macro: Click on the outcrop model to expand to a global view showing the geological environment.

[0104] The design incorporates various interactive operations such as zooming, rotating, panning, and clicking. It also provides a display window and animation effects for dynamically loading multimodal resources such as text, images, and videos. The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and all such simple modifications fall within the protection scope of this disclosure.

[0105] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0106] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A method for cross-scale correlation of rocks and outcrops based on multimodal data, characterized in that, Includes the following steps: S1: Collect geological outcrop data and model geological outcrops to obtain a three-dimensional geological outcrop model. At the same time, collect basic data of rock specimens, rock specimen analysis and test data and rock specimen interpretation information to obtain multimodal data. S2: The multimodal data is managed using a database and file management method, and the rock specimens are associated with outcrops to obtain associated data; Step S2 involves managing the multimodal data using a database and file management approach, including storing the multimodal data and three-dimensional geological outcrop model data in a unified structured manner according to their names, strata, locations, compositions, and corresponding types. Furthermore, entity tables were created for rock specimen analysis and testing data, geological outcrop models, basic data of rock specimens, and explanatory information of rock specimens, and relationship tables were created for the relationships between entities. The process of associating rock specimens with outcrops in step S2 to obtain associated data includes: assigning a unique ID to each rock specimen to ensure the unique identification of the rock specimen in the system; extracting the geographical location and landform type characteristics of the outcrops and assigning them a unique ID; associating the unique ID of the rock with the unique ID of the outcrop; storing the correspondence between the rock ID and the outcrop ID; and establishing the correspondence between the rock and the outcrop. The system interactively displays 3D geological outcrop models and rock specimens. Based on the obtained correlation data, it uses various methods to locate rock specimens and 3D geological outcrops. The data display logic includes: Showcasing the microscopic features of rock specimens: expanding from microscopic features to mesoscopic features, and from mesoscopic features to macroscopic features; The interactive display includes zooming, rotating, panning, and clicking, and uses a display window and animation effects for dynamic loading.

2. The method for cross-scale correlation of rocks and outcrops based on multimodal data according to claim 1, characterized in that, The geological outcrop data acquisition described in step S1 includes: acquiring three-dimensional laser point cloud data of the geological outcrop using a three-dimensional laser scanner; obtaining accurate spatial positioning data using a total station; and acquiring texture images of the outcrop texture from all angles using a high-definition camera.

3. The method for cross-scale correlation of rocks and outcrops based on multimodal data according to claim 1, characterized in that, The geological outcrop modeling described in step S1, to obtain a three-dimensional geological outcrop model, includes: first, based on the precise spatial positioning data from a total station, using RISCAN software to spatially locate and stitch together the three-dimensional laser point cloud data to form a complete three-dimensional point cloud model; then, using Geomagic software to denoise the point cloud data while preserving key geological features; finally, using a professional texture mapping tool to map the texture images captured by a high-definition camera onto the three-dimensional skeleton model, giving the model realistic colors and textures.

4. The method for cross-scale correlation of rocks and outcrops based on multimodal data according to claim 1, characterized in that, The rock specimen analysis and testing data mentioned in step S1 include: collecting rock specimens for analysis and testing, the analysis and testing including physical property analysis, hyperspectral mineral analysis, XRD whole-rock analysis and laser testing and analysis, and obtaining multiple corresponding analysis reports, which contain information in the form of data, text and images.

5. The method for cross-scale correlation of rocks and outcrops based on multimodal data according to claim 1, characterized in that, The coordinates of the three-dimensional geological outcrop model and the rock specimen were transformed using a seven-parameter coordinate transformation model, and the three-dimensional geological outcrop model was loaded using CesiumJS.

6. The method for cross-scale correlation of rocks and outcrops based on multimodal data according to claim 1, characterized in that, The methods for finding rock specimens include: keyword search, rock category search, and main component search; The methods for finding geological outcrops include: keyword search, outcrop location search, and geological age search.

7. The method for cross-scale correlation of rocks and outcrops based on multimodal data according to claim 1, characterized in that, The interactive display of the rock specimen includes: displaying the analytical and testing information of the rock specimen; The analytical and testing information is categorized as follows: physical property analysis, hyperspectral mineral analysis, laser testing and analysis, XRD whole-rock analysis, and XRF detection report.

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