Digital modeling method and system for basin and peripheral region based on big data analysis

Abstract

The application discloses a basin and peripheral region digital modeling method and system based on big data analysis, relates to the technical field of digital modeling, and comprises the following steps: connecting a GIS system to acquire geographical distribution information of a target basin region; building a digital space, performing grid division on the digital space, and determining a digital grid; collecting detection data of each exploration zone in the target basin region, respectively performing data interpretation and preprocessing, and determining geological feature information; based on the digital grid, combining the geological feature information, performing three-dimensional modeling on each digital grid in parallel, and respectively determining a grid model; splicing the grid models, determining a digital basin peripheral model, performing macro-quantization, and generating a macro-digital basin peripheral model. The application solves the technical problem of low modeling and model construction efficiency of the existing technology in the joint modeling of a basin and a peripheral orogenic belt, and achieves the technical effect of improving the efficiency and accuracy of exploration and analysis of a basin and a peripheral region.

Description

Technical Field

[0001] This invention relates to the field of digital modeling technology, specifically to a method and system for digital modeling basins and surrounding areas based on big data analysis. Background Technology

[0002] With the continuous development of geological exploration technology and data acquisition methods, traditional exploration theories and techniques for basins and surrounding areas are facing increasing challenges. Existing modeling methods for sedimentary basins, primarily based on the developmental patterns and mechanisms of sedimentary rocks, differ fundamentally from those for basin basement and surrounding orogenic belts, which are based on the developmental patterns and mechanisms of volcanic rocks. The geological information obtained from these two types also differs in the types of energy and mineral resources explored. However, sedimentary basins and surrounding orogenic belts, as interconnected entities, require integrated 3D modeling for in-depth understanding to guide the exploration of related resources. Current methods often rely on human experience and single data sources, making it difficult to accurately capture geological features inside and outside the basin in complex geological environments. This is especially true for large-scale, dynamically changing exploration areas, where the processing capabilities of traditional methods are insufficient. With the advancement of industrialization, particularly in the exploration of oil, natural gas, and mineral resources, the need for integrated digital modeling of basins and surrounding areas is becoming increasingly urgent. Traditional data processing methods in the present technology have problems such as slow data processing speed, inability to construct linkage models of basins and surrounding orogenic belts under large-scale conditions, and inaccurate identification of geological features, making it difficult to support the needs of modern exploration processes for efficient and real-time data processing and analysis. Summary of the Invention

[0003] This application provides a digital modeling method and system for basins and surrounding areas based on big data analysis, which is used to address the technical problems of insufficient data processing accuracy, low model building efficiency, and difficulty in accurately quantifying geological features in existing technologies for basin exploration.

[0004] In view of the above problems, this application provides a method and system for digital modeling of basins and surrounding areas based on big data analysis.

[0005] The first aspect of this application provides a method for digital modeling basins and surrounding areas based on big data analysis, the method comprising:

[0006] A GIS system is connected to obtain the geographical distribution information of the target basin area, which includes the sedimentary basin and its surrounding areas. Based on the geographical distribution information, a digital space is constructed, and the digital space is divided into grids to determine the digital grids, wherein the grids are divided according to exploration zones. Detection data from each exploration zone within the target basin are collected, and data interpretation and preprocessing are performed to determine geological feature information, wherein multi-source detection data fusion and calibration are the preprocessing objectives. Based on the digital grids and combined with the geological feature information, each digital grid is used for parallel 3D modeling to determine the grid model. The grid models are stitched together to determine the digital basin perimeter model and perform macroscopic quantification to generate a macroscopic digital basin perimeter model, and a mapping is established between the digital basin perimeter model and the macroscopic digital basin perimeter model.

[0007] A second aspect of this application provides a digital modeling system for basins and surrounding areas based on big data analysis, the system comprising:

[0008] The system comprises the following modules: a geographic distribution information acquisition module, which connects to a GIS system to acquire geographic distribution information of a target basin area, including a sedimentary basin and its surrounding areas; a digital grid determination module, which constructs a digital space based on the geographic distribution information, divides the digital space into grids, and determines the digital grids, wherein the grid division is based on exploration zones; a geological feature information determination module, which collects detection data from each exploration zone within the target basin, performs data interpretation and preprocessing respectively, and determines geological feature information, wherein multi-source detection data fusion and calibration are the preprocessing objectives; a 3D modeling module, which performs 3D modeling in parallel on each digital grid based on the digital grid and the geological feature information, and determines the grid model respectively; and a digital basin perimeter model establishment module, which stitches the grid models to determine the digital basin perimeter model and performs macroscopic quantification to generate a macroscopic digital basin perimeter model, and establishes a mapping between the digital basin perimeter model and the macroscopic digital basin perimeter model.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] This application connects to a GIS system to obtain the geographical distribution information of a target basin area, which includes a sedimentary basin and its surrounding areas. Based on the geographical distribution information, a digital space is constructed, and the digital space is divided into grids to determine the digital grids, wherein the grids are divided according to exploration zones. Detection data from each exploration zone within the target basin are collected, and data interpretation and preprocessing are performed to determine geological feature information, wherein multi-source detection data fusion and calibration are the preprocessing objectives. Based on the digital grids and combined with the geological feature information, each digital grid is used for parallel 3D modeling to determine a grid model. The grid models are stitched together to determine the digital basin perimeter model and perform macroscopic quantification to generate a macroscopic digital basin perimeter model, and a mapping is established between the digital basin perimeter model and the macroscopic digital basin perimeter model. This invention addresses the technical problems of insufficient data processing accuracy, low model building efficiency, and difficulty in accurately quantifying geological features in existing basin exploration technologies. By connecting to a GIS system to obtain geographic distribution information, constructing a digital space and dividing it into grids based on exploration zones, and combining multi-source exploration data for geological feature interpretation and preprocessing, the invention achieves 3D modeling and stitching of the digital grid, ultimately generating a macroscopic digital basin perimeter model. This enables the construction of a high-precision digital basin perimeter model, supports macroscopic quantification and precise mapping of geological features, and ultimately improves the efficiency and accuracy of basin exploration and analysis. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic diagram of the process for digital modeling of basins and surrounding areas based on big data analysis provided in this application embodiment;

[0013] Figure 2 A schematic diagram of the structure of a basin and surrounding area digital modeling system based on big data analysis provided in this application embodiment.

[0014] Figure labeling: Module 11 for obtaining geographic distribution information, Module 12 for determining digital grid, Module 13 for determining geological feature information, Module 14 for 3D modeling, and Module 15 for establishing digital basin perimeter model. Detailed Implementation

[0015] This application provides a method and system for digital modeling basins and surrounding areas based on big data analysis. It addresses the technical problems of insufficient data processing accuracy, low model building efficiency, and difficulty in accurately quantifying geological features in existing basin exploration technologies. By connecting to a GIS system to obtain geographic distribution information, constructing a digital space and dividing it into grids based on exploration zones, and combining multi-source exploration data for geological feature interpretation and preprocessing, it achieves 3D modeling and stitching of the digital grid, ultimately generating a macroscopic digital basin perimeter model. This enables the construction of a high-precision digital basin perimeter model, supports macroscopic quantification and precise mapping of geological features, and ultimately improves the efficiency and accuracy of basin exploration and analysis.

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0017] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0018] Example 1, as Figure 1 As shown, this application provides a method for digital modeling of basins and surrounding areas based on big data analysis, the method comprising:

[0019] Step S100: Connect to the GIS system to obtain the geographical distribution information of the target basin area, which includes the sedimentary basin and the surrounding area.

[0020] In this embodiment, a connection is first established with a GIS system. A GIS system, as a tool for capturing, storing, analyzing, managing, and presenting geographic data, can provide accurate spatial information, including various types of information such as geographic location, environment, and geology. Through the GIS connection, data at different geographic scales, such as regional boundaries, topography, and underground structures, can be accessed and obtained in real time.

[0021] Once connected to the GIS system, relevant geographic distribution information is extracted based on the predetermined area, namely the target basin region. The target basin region consists of a sedimentary basin and a surrounding area. The sedimentary basin typically refers to an area formed by crustal subsidence, characterized by relatively flat terrain and serving as the primary accumulation site for sediments. The geological structure within the basin is usually quite homogeneous, but the geological structure in the surrounding area is often more complex, potentially including geological phenomena such as mountains and faults.

[0022] Through the above process, the geographical distribution information of the target basin area is obtained, which includes basin information and surrounding area information of the target basin area.

[0023] Step S200: Based on the geographic distribution information, a digital space is constructed, and the digital space is divided into grids to determine the digital grids, wherein the grids are divided based on exploration zones.

[0024] In this embodiment, the acquired geographic distribution information (such as topography and geological features) is first used to construct a digital space using computer modeling tools (such as ArcGIS and QGIS). The construction of the digital space is based on detailed geographic data of the target basin area, integrating multi-dimensional information such as topography, geology, and soil. At this point, the digital space is constructed using a globally universal coordinate system, such as WGS84, to ensure the standardization and accurate positioning of spatial data worldwide.

[0025] Next, the digital space is gridded. This step differs from random gridding; instead, the digital space is divided according to pre-defined exploration zones. Each exploration zone is a specific area pre-defined based on geological exploration, detection data, and other factors. These areas share high similarities in geological characteristics, therefore each exploration zone is processed as an independent grid unit. By gridding the digital space, the digital grid is determined.

[0026] Step S300: Collect exploration data from each exploration zone within the target basin, perform data interpretation and preprocessing respectively, and determine geological feature information, with multi-source exploration data fusion and calibration as the preprocessing objective.

[0027] In this embodiment, detection data is first collected to obtain multi-source detection data within each exploration zone of the target basin. This detection data originates from different exploration technologies, such as seismic wave data, drilling data, geological survey data, and remote sensing imagery. Each data source provides geological information with different spatial resolutions and depth coverage; therefore, by combining multi-source data, more comprehensive and detailed geological features can be obtained.

[0028] Next, data interpretation is performed. The purpose of data interpretation is to transform these multi-source data into specific geological information. For example, for remote sensing images, image processing techniques can be used to identify geological features such as faults, rock strata, and mineral deposits. For seismic wave data, the structure and distribution of underground rock strata are determined by analyzing reflection wave curves. All interpreted data will form different geological feature information and provide raw data for subsequent fusion and analysis.

[0029] Data preprocessing is the next crucial step. First, multi-source exploration data from the interpreted exploration zone is acquired and mapped according to geographical distribution, placing this data into a unified spatial coordinate system to ensure spatial alignment between different data sources. Next, multiple sets of geological data are identified, each containing multi-source exploration data from the same location. For example, different data from seismic exploration, drilling, and remote sensing imagery may coexist at the same location, forming a single set of geological data. Then, the data fusion stage begins, traversing all the multiple sets of geological data and fusing the data within each set. The purpose of data fusion is to integrate data from different exploration sources to form unified geological feature information. During the fusion process, the characteristics and quality of each data source must be considered, and weights must be assigned based on the reliability and accuracy of each data source. For example, data from drilling typically has higher accuracy and reliability, while remote sensing data has a wider coverage but may have lower accuracy; therefore, weighted fusion is needed to optimize the contribution of each data source.

[0030] Ultimately, this fusion method yields geological characteristic information for each exploration zone, which may include stratigraphic distribution, fault location, lithological variations, etc.

[0031] Furthermore, the method provided in the application embodiments, in addition to data preprocessing, further includes:

[0032] After obtaining the interpreted multi-source exploration data of the first exploration zone, the data is mapped based on the geographical distribution to determine multiple sets of geological data, wherein each set of geological data contains multi-source exploration data from the same distribution location; the multiple sets of geological data are traversed, and the data within each set is fused to determine the geological characteristic information of the first exploration zone, wherein the data fusion weight is set based on the characteristics of the data source.

[0033] In this embodiment, the first step is to acquire interpreted multi-source survey data. This stage utilizes image interpretation technology to identify and extract geological feature information. Remote sensing data, especially satellite or aerial images, can provide information on a wide range of land cover. During interpretation, image recognition algorithms (such as object recognition and pixel classification techniques) are used to annotate surface features in the images, and these annotations are mapped to geological terms (such as rock type, fault, mineral composition, etc.), ultimately generating corresponding geological feature information.

[0034] Next, data mapping is performed based on geographic distribution. After data interpretation, all data needs to be spatially mapped, that is, data from different sources are unified into the same geographic coordinate system (such as WGS84). To achieve this, coordinate transformation methods are used. Specifically, by using coordinate transformation algorithms, geographic information from different data sources (such as remote sensing images, seismic data, drilling data, etc.) is transformed into the same spatial frame to ensure geographical consistency between different data sources.

[0035] Next, multiple sets of geological data are identified. After spatial mapping, the multiple sets of data are divided into several data groups based on their geographical locations. Each set of data includes multi-source data obtained at the same spatial location using different detection methods. For example, a set of geological data may include remote sensing images, seismic wave reflection data, drilling data, etc., from the same location. In this way, it can be ensured that the information contained in each set of geological data is completely corresponding geographically.

[0036] Next, multiple sets of geological data are traversed, and the data within each set is fused one by one. This process typically employs data fusion algorithms, such as the weighted average method, to integrate multi-source data from the same location. The weights are set based on the quality and reliability of the data sources. For example, remote sensing data may be affected by atmospheric noise and have lower accuracy, so it can be assigned a lower weight during data fusion; while drilling data usually has higher accuracy, thus its weight is higher. When setting weights, technical experts subjectively determine the weights based on the characteristics of different data sources and their contribution to geological features. For example, remote sensing data may have strong spatial coverage but lower accuracy, thus its weight is lower; while seismic wave data, although with limited coverage, provides more reliable information on underground structures, thus it can be assigned a higher weight.

[0037] Finally, by fusing data within the group, the geological characteristics of the first exploration zone were obtained.

[0038] Step S400: Based on the digital grid and combined with the geological feature information, each digital grid performs three-dimensional modeling in parallel, and the grid model is determined respectively.

[0039] In this embodiment, to determine the mesh model, the geological features are first matched with the digital mesh to determine the geological attributes of each mesh cell. Then, the matched meshes are used in parallel for 3D modeling to generate an initial mesh model. Since mesh division is usually based on independent zones, there may be misalignment errors at zone boundaries, affecting the overall stitching effect. Therefore, it is necessary to identify adjacent boundaries in the initial mesh model to identify the seams between adjacent meshes, and eliminate errors through smoothing processing to generate the final mesh model for each digital mesh.

[0040] Furthermore, in the method provided in the application embodiments, the step of performing three-dimensional modeling and determining the mesh model further includes:

[0041] For the geological feature information, matching is performed in a digital grid to determine the matching grid; each matching grid performs three-dimensional modeling of the geological feature information in parallel to determine the initial grid model; the initial grid model is subjected to adjacent boundary identification, and the adjacent boundaries are smoothed to generate the grid model.

[0042] In this embodiment, the geological feature information (such as rock strata, mineral distribution, fault location, etc.) is first used to perform a one-to-one matching within each digital grid. Each digital grid represents a spatial unit within the target area and has specific geographic coordinates and attribute data. By analyzing the spatial relationship between the geological feature information and the digital grid, specific geological attributes (such as lithology, groundwater level, fault strike, etc.) are associated with the corresponding grid units to determine the matching grid.

[0043] After matching is completed, each matched mesh will undergo 3D modeling in parallel to generate a preliminary mesh model. Specifically, each mesh cell will be 3D modeled based on its corresponding geological features (such as rock layer thickness, fault strike, etc.), and the 3D structure within the mesh will be generated using 3D modeling algorithms (such as the finite element method) to obtain the initial mesh model.

[0044] After the initial mesh model is generated, the adjacent boundary identification and smoothing stage begins. First, a boundary identification algorithm is used to identify the boundaries between adjacent mesh cells. Since mesh generation is typically based on relatively independent zones, boundary misalignment and data discontinuity may occur. To address this, smoothing methods, such as information-based reconstruction or linear smoothing, are employed to correct and smooth the adjacent boundaries. Smoothing methods eliminate inconsistencies caused by mesh generation by adjusting the position and shape of boundary points, resulting in a smoother and more seamless model. Finally, after smoothing, a complete and accurate mesh model is generated.

[0045] Furthermore, in the method provided in the application embodiments, before performing 3D modeling, it further includes:

[0046] Configure a data interface, wherein each exploration zone is configured with a data interface; establish a mapping connection between the digital grid and the data interface; based on the data interface, receive and process the exploration data of the exploration zone, determine the geological feature information and send it back to the digital grid; perform three-dimensional modeling based on the geological feature information in the digital grid.

[0047] In this embodiment, a dedicated data interface is first configured for each exploration zone. An exploration zone is an independent area defined in geological exploration, typically based on geological characteristics or sampling requirements. Each exploration zone is equipped with a data interface, enabling exploration equipment (such as remote sensing instruments, drilling equipment, seismic detection equipment, etc.) to transmit exploration data in real time via a data communication protocol (such as WebSocket). The purpose of these data interfaces is to acquire and transmit exploration data from the exploration zone in a timely and accurate manner, ensuring data reliability and efficient transmission.

[0048] Next, a mapping connection is established between the digital grid and the data interface. The digital grid is a spatially discretized representation that divides the target area into multiple small units. Through Geographic Information System (GIS) technology, a mapping relationship is established between the digital grid units and the data interface of each exploration zone, so that the exploration data of the exploration zone can correspond to each grid unit of the digital grid.

[0049] After receiving data from the exploration zone, the system receives and processes the exploration data based on the data interface. This process includes data interpretation and preprocessing. Specifically, data from sources such as remote sensing and geological exploration is interpreted to extract geologically relevant features, such as rock strata type, faults, and groundwater levels. These data are then calibrated and fused to ensure accuracy and consistency. This processed geological feature information is then fed back to the corresponding digital grid cells, providing an accurate geological basis for subsequent modeling.

[0050] Finally, by combining the processed geological feature information, three-dimensional modeling based on the geological feature information is carried out in the digital grid to generate the corresponding grid model.

[0051] Step S500: The grid model is stitched together to determine the digital basin perimeter model and perform macroscopic quantization to generate a macroscopic digital basin perimeter model, and a mapping is established between the digital basin perimeter model and the macroscopic digital basin perimeter model.

[0052] In this embodiment, the grid models are first stitched together. Each grid model represents an independent region. Through the stitching operation, the three-dimensional geological information of different digital grid units is integrated into a continuous geological model. During this process, boundary alignment algorithms, such as boundary matching or smooth alignment algorithms, are used to correct boundary misalignments between adjacent grid units, ensuring seamless connection of each grid model and avoiding information gaps or inconsistencies caused by errors or seams. After stitching, a complete digital basin perimeter model is formed, which integrates the geological features of the entire target area and represents the three-dimensional geological structure of the area.

[0053] Next, based on this digital basin perimeter model, standardized identifiers (such as maps used for geological feature visualization) are introduced to perform macroscopic quantification of the model. This involves extracting macroscopic geological information from the whole and forming a macroscopic digital basin perimeter model. This step, through standardization, unifies the analysis of geological features in different regions, enabling the macroscopic digital basin perimeter model to reflect the overall structural characteristics of the basin.

[0054] Finally, after completing macroscopic quantification, a mapping between the digital basin perimeter model and the macroscopic digital basin perimeter model is established. This mapping is achieved by defining scale conversion rules and feature alignment methods between the two. Specifically, local geological information from the digital basin perimeter model is mapped to the macroscopic model through scale factors, and a feature matching algorithm is used to ensure the accurate transfer of geological features. Ultimately, the established mapping model enables mutual conversion between the two, thus supporting geological analysis and visualization at different scales.

[0055] Furthermore, the method provided in the application embodiments, which performs macroscopic quantization to generate a macroscopic digital basin perimeter model, further includes:

[0056] A standardized identifier is introduced, wherein the standardized identifier is a map for visualizing geological features; based on the standardized identifier, the digital basin perimeter model is macroscopically processed to determine the macroscopic digital basin perimeter model; the macroscopic digital basin perimeter model is traversed to identify syn-sedimentary structures in the basin interior and perimeter areas, wherein the consistency of the standardized identifier is used to determine the syn-sedimentary structures.

[0057] In this application embodiment, standardized identifiers are first introduced, which are maps used for visualizing geological features. These maps represent geological structural features by using different lines, symbols, and colors. For example, specific lines can be used to represent different types of faults, fold belts, or tectonic zones, symbols can indicate veins or specific geological units, and colors are used to distinguish different types of rock strata or sediments.

[0058] Next, based on standardized identifiers, the digital basin perimeter model undergoes macroscopic processing. In this process, by mapping geological feature information from the maps into the digital basin perimeter model, the geological features of the entire region are unified and integrated, transforming local details into a macroscopic global layout. For example, macroscopic features such as the thickness distribution of sedimentary layers and the distribution of tectonic zones are extracted and integrated into the model, forming a macroscopic digital basin perimeter model describing the entire basin structure.

[0059] After macroscopic processing, the macroscopic digital basin perimeter model is traversed to identify syn-sedimentary structures, i.e., structural features within and around the basin. These syn-sedimentary structures include syn-sedimentary anticlines, syn-sedimentary synclines, and syn-sedimentary faults. The technique used here is a syn-sedimentary structure identification algorithm, which determines the spatial consistency of these structures by comparing geological features (such as sedimentary layer thickness and structural distribution) with the consistency of identifiers in the map. Typically, matching analysis (such as spatial similarity analysis) and statistical methods (such as geostatistical methods) are used to identify which areas have consistent geological features, thereby determining whether they belong to syn-sedimentary structures.

[0060] Furthermore, in the method provided in the application embodiments, after identifying the syn-sedimentary structures within and around the basin, the method further includes:

[0061] Identify the first syngenetic structure, and based on the relative geological characteristics of the basin structure and the surrounding area, explore the first geological relationship, wherein the first syngenetic structure is any group of syngenetic structures; explore the second geological relationship based on the relationship between geological characteristics and basin type; add the first geological relationship and the second geological relationship into the model extension layer; establish the mapping between the model extension layer and the macroscopic digital basin periphery model.

[0062] In this embodiment, the step of identifying the first syn-sedimentary structure mainly relies on the comprehensive analysis of seismic reflection data and geological exploration data. Two-dimensional seismic data obtained using seismic reflection profiling technology is used to identify the sedimentary characteristics of different structures within the basin through reflection wave analysis, especially those structures with similar sedimentary characteristics, such as syn-sedimentary anticlines, syn-sedimentary synclines, and syn-sedimentary faults. These syn-sedimentary structures indicate that specific tectonic movements occurred simultaneously within the basin and its surrounding areas during the same sedimentary period, exhibiting strong spatial consistency. Using three-dimensional geological modeling software (such as Petrel, Gocad, etc.), these syn-sedimentary structures are visualized spatially, and their distribution location and morphological characteristics are further identified through reflection wave analysis. This step identifies the first syn-sedimentary structure within the basin and its spatial distribution.

[0063] The next step in exploring the primary geological relationships is based on the relative geological characteristics of the basin's structure and its surrounding areas. Geological feature analysis techniques, such as sedimentary layer thickness analysis, lithological analysis, and tectonic deformation analysis, are used to investigate the relationship between the basin's internal geological features and its surrounding areas. Lithological analysis (e.g., analyzing rock mineral composition, porosity, density, etc.) further uncovers the interaction between tectonic activity and sedimentary evolution within and outside the basin, and sequence stratigraphy confirms the relationships between stratigraphic layers. During this process, geological data fusion techniques (e.g., comprehensive analysis of multi-source remote sensing imagery, drilling data, and seismic data) are utilized to reveal the primary geological relationships within and outside the basin, namely the interaction and spatial distribution characteristics of sedimentary layers and tectonic deformation.

[0064] Then, based on the relationship between geological features and basin type, a second geological relationship is explored. This step employs geological modeling techniques, combined with basin type classification (such as tectonic basins, rift basins, etc.), to comprehensively analyze sedimentation, tectonic deformation, and subsidence evolution within the basin. Using tectonic simulation software (such as Petrel, Move, etc.), the relationship between tectonic deformation patterns and sedimentary environments within the basin is further determined according to different basin types, thus revealing the second geological relationship. In this process, the correlation between basin type classification and geological features provides a theoretical basis for analyzing the formation mechanism of basins and their interaction with surrounding areas.

[0065] Finally, the first and second geological relationships are added to the model extension layer, establishing a mapping relationship between the model extension layer and the macroscopic digital basin perimeter model. In this process, a hierarchical modeling technique is employed, gradually integrating geological information into the digital basin perimeter model in a hierarchical manner. The extension layer contains all relevant geological information derived from the first and second geological relationships and is visualized using geological map identifiers (such as standardized sedimentary anticline and fault zone symbols). Through model mapping technology, these geological relationships are mapped to different levels and regions of the macroscopic digital basin perimeter model, thus forming a complete digital representation of the geological structure and sedimentary environment.

[0066] Furthermore, in the method provided in the application embodiments, after generating the macroscopic digital basin perimeter model, it further includes:

[0067] The macroscopic digital basin perimeter model is visualized on the terminal; based on the mapping between the digital basin perimeter model and the macroscopic digital basin perimeter model, the grid model is invoked and displayed on the terminal interface.

[0068] In this embodiment, after constructing the macroscopic digital basin perimeter model, the next step is to visualize the model on a terminal to intuitively display its geological features and spatial layout. First, using 3D modeling software (such as Petrel, Gocad, etc.), the macroscopic digital basin perimeter model is converted into a format suitable for visualization. During this process, spatial data visualization technology is employed to present various geological features in the model, such as sedimentary layers, fault zones, and tectonic zones, using different colors, textures, and lines. This visualization helps users better understand the overall structure and spatial distribution characteristics of the basin and enables them to quickly identify key areas and potential resource exploration areas.

[0069] Next, based on the mapping between the digital basin perimeter model and the macro-level digital basin perimeter model, the data of the two are connected and synchronized. Specifically, a data interface (such as a RESTful API) is first used to realize data exchange and mapping between the digital basin perimeter model and the macro-level model. Through data synchronization algorithms, it is ensured that each grid cell in the digital grid model corresponds to the geological features in the macro-level digital basin perimeter model. In this process, information such as various geological units, sedimentary layers, and fault zones in the digital grid model is accurately mapped to the macro-level model, ensuring a seamless connection between the data relationships between the two, thereby maintaining the consistency and integrity of the models.

[0070] Finally, the grid model is accessed and displayed on the terminal interface through a user interface (GUI). Users can interact with the model through the interface, such as rotating, zooming, or switching between different geological feature views. At this stage, all processed and mapped geological information will be displayed on the terminal, allowing users to view and analyze the data at each level of the model in real time.

[0071] In summary, the embodiments of this application have at least the following technical effects:

[0072] This application connects to a GIS system to obtain the geographical distribution information of a target basin area, which includes a sedimentary basin and its surrounding areas. Based on the geographical distribution information, a digital space is constructed, and the digital space is divided into grids to determine the digital grids, wherein the grids are divided according to exploration zones. Detection data from each exploration zone within the target basin are collected, and data interpretation and preprocessing are performed to determine geological feature information, wherein multi-source detection data fusion and calibration are the preprocessing objectives. Based on the digital grids and combined with the geological feature information, each digital grid is used for parallel 3D modeling to determine a grid model. The grid models are stitched together to determine the digital basin perimeter model and perform macroscopic quantification to generate a macroscopic digital basin perimeter model, and a mapping is established between the digital basin perimeter model and the macroscopic digital basin perimeter model. This invention addresses the technical problems of insufficient data processing accuracy, low model building efficiency, and difficulty in accurately quantifying geological features in existing basin exploration technologies. By connecting to a GIS system to obtain geographic distribution information, constructing a digital space and dividing it into grids based on exploration zones, and combining multi-source exploration data for geological feature interpretation and preprocessing, the invention achieves 3D modeling and stitching of the digital grid, ultimately generating a macroscopic digital basin perimeter model. This enables the construction of a high-precision digital basin perimeter model, supports macroscopic quantification and precise mapping of geological features, and ultimately improves the efficiency and accuracy of basin exploration and analysis.

[0073] Example 2, based on the same inventive concept as the basin and surrounding area digital modeling method based on big data analysis in the previous examples, such as... Figure 2 As shown, this application provides a digital modeling system for basins and surrounding areas based on big data analysis. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0074] The system includes: a geographic distribution information acquisition module 11, which connects to a GIS system to acquire geographic distribution information of a target basin area, including the basin itself and its surrounding areas; a digital grid determination module 12, which constructs a digital space based on the geographic distribution information, divides the digital space into grids, and determines the digital grid, wherein the grid division is based on exploration zones; and a geological feature information determination module 13, which collects detection data from each exploration zone within the target basin and interprets the data accordingly. The process includes preprocessing to determine geological feature information, with multi-source detection data fusion and calibration as the preprocessing objective; a 3D modeling module 14, which performs 3D modeling in parallel on each digital grid based on the digital grid and combined with the geological feature information, to determine the grid model respectively; and a digital basin perimeter model establishment module 15, which stitches the grid models to determine the digital basin perimeter model and performs macroscopic quantization to generate a macroscopic digital basin perimeter model, and establishes a mapping between the digital basin perimeter model and the macroscopic digital basin perimeter model.

[0075] Furthermore, the system is also used to implement the following functions:

[0076] A standardized identifier is introduced, wherein the standardized identifier is a map for visualizing geological features; based on the standardized identifier, the digital basin perimeter model is macroscopically processed to determine the macroscopic digital basin perimeter model; the macroscopic digital basin perimeter model is traversed to identify syn-sedimentary structures in the basin interior and perimeter areas, wherein the consistency of the standardized identifier is used to determine the syn-sedimentary structures.

[0077] Furthermore, the system is also used to implement the following functions:

[0078] Identify the first syngenetic structure, and based on the relative geological characteristics of the basin structure and the surrounding area, explore the first geological relationship, wherein the first syngenetic structure is any group of syngenetic structures; explore the second geological relationship based on the relationship between geological characteristics and basin type; add the first geological relationship and the second geological relationship into the model extension layer; establish the mapping between the model extension layer and the macroscopic digital basin periphery model.

[0079] Furthermore, the system is also used to implement the following functions:

[0080] After obtaining the interpreted multi-source exploration data of the first exploration zone, the data is mapped based on the geographical distribution to determine multiple sets of geological data, wherein each set of geological data contains multi-source exploration data from the same distribution location; the multiple sets of geological data are traversed, and the data within each set is fused to determine the geological characteristic information of the first exploration zone, wherein the data fusion weight is set based on the characteristics of the data source.

[0081] Furthermore, the system is also used to implement the following functions:

[0082] For the geological feature information, matching is performed in a digital grid to determine the matching grid; each matching grid performs three-dimensional modeling of the geological feature information in parallel to determine the initial grid model; the initial grid model is subjected to adjacent boundary identification, and the adjacent boundaries are smoothed to generate the grid model.

[0083] Furthermore, the system is also used to implement the following functions:

[0084] Configure a data interface, wherein each exploration zone is configured with a data interface; establish a mapping connection between the digital grid and the data interface; based on the data interface, receive and process the exploration data of the exploration zone, determine the geological feature information and send it back to the digital grid; perform three-dimensional modeling based on the geological feature information in the digital grid.

[0085] Furthermore, the system is also used to implement the following functions:

[0086] The macroscopic digital basin perimeter model is visualized on the terminal; based on the mapping between the digital basin perimeter model and the macroscopic digital basin perimeter model, the grid model is invoked and displayed on the terminal interface.

[0087] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0088] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0089] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A digital modeling method for basins and surrounding areas based on big data analysis, characterized in that, The method includes: Connect to a GIS system to obtain geographic distribution information of the target basin area, which includes the sedimentary basin and its surrounding area; Based on the aforementioned geographical distribution information, a digital space is constructed, and the digital space is divided into grids to determine the digital grids, wherein the grids are divided based on exploration zones; Detection data from each exploration zone within the target basin were collected, and the data were interpreted and preprocessed to determine geological feature information. The preprocessing objective was to fuse and calibrate multi-source detection data. Based on the digital grid and combined with the geological feature information, each digital grid is used for three-dimensional modeling in parallel to determine the grid model. The grid model is stitched together to determine the digital basin perimeter model and perform macroscopic quantization to generate a macroscopic digital basin perimeter model, and a mapping is established between the digital basin perimeter model and the macroscopic digital basin perimeter model. Macro-level quantification is performed to generate a macro-level digital basin perimeter model, including: A standardized identifier is introduced, wherein the standardized identifier is a map for visualizing geological features; Based on the standardized identifier, the digital basin perimeter model is macroscopically processed to determine the macroscopic digital basin perimeter model. Traversing the macroscopic digital basin perimeter model, identifying syn-sedimentary structures within and around the basin, wherein the consistency of the standardized identifiers is used to determine the syn-sedimentary structures; After identifying the syn-sedimentary structures within and around the basin, the following is included: Identify the first syngenetic structure, and based on the relative geological characteristics of the basin structure and the surrounding area, explore the first geological relationship, wherein the first syngenetic structure is any group of the syngenetic structures; Based on the relationship between geological features and basin types, explore secondary geological relationships; Add the first geological relationship and the second geological relationship to the model extension layer; Establish a mapping between the model extension layer and the macroscopic digital basin perimeter model.

2. The method for digital modeling basins and surrounding areas based on big data analysis as described in claim 1, characterized in that, Data preprocessing includes: After obtaining the interpreted multi-source detection data of the first exploration zone, the data is mapped based on the geographical distribution to determine multiple sets of geological data, where each set of geological data contains multi-source detection data from the same distribution location. The geological data of the first exploration zone is determined by traversing the multiple sets of geological data and fusing the data within each set. The data fusion weights are set based on the characteristics of the data source.

3. The method for digital modeling basins and surrounding areas based on big data analysis as described in claim 1, characterized in that, The process of performing 3D modeling, including determining the mesh model, includes: Based on the geological feature information, a matching grid is performed in the digital grid to determine the matching grid; Each matching grid performs 3D modeling of the geological feature information in parallel to determine the initial grid model; The initial mesh model is subjected to adjacent boundary identification, and the adjacent boundaries are smoothed to generate the mesh model.

4. The method for digital modeling basins and surrounding areas based on big data analysis as described in claim 1, characterized in that, Before performing 3D modeling, the following steps are required: Configure data interfaces, with one data interface configured for each exploration zone; Establish a mapping connection between the digital grid and the data interface; based on the data interface, receive and process the exploration data of the exploration zone, and determine the geological feature information to be transmitted back to the digital grid. Three-dimensional modeling based on geological feature information is performed in the digital grid.

5. The method for digital modeling basins and surrounding areas based on big data analysis as described in claim 1, characterized in that, After generating the macroscopic digital basin perimeter model, the following steps are included: The macroscopic digital basin perimeter model is visualized on the terminal. Based on the mapping between the digital basin perimeter model and the macro-digital basin perimeter model, the grid model is invoked and displayed on the terminal interface.

6. A basin and surrounding area digital modeling system based on big data analysis, applied to the basin and surrounding area digital modeling method based on big data analysis as described in any one of claims 1-5, characterized in that, The system includes: A geographic distribution information acquisition module is connected to a GIS system to acquire geographic distribution information of a target basin area, which includes the basin and its surrounding areas. A digital grid determination module, which constructs a digital space based on the geographic distribution information, divides the digital space into grids, and determines the digital grid, wherein the grid division is based on exploration zones; The geological feature information determination module collects exploration data from each exploration zone within the target basin, performs data interpretation and preprocessing, and determines geological feature information, with multi-source exploration data fusion and calibration as the preprocessing objective. A 3D modeling module, which, based on the digital grid and combined with the geological feature information, performs 3D modeling in parallel for each digital grid and determines the grid model respectively; The digital basin perimeter model establishment module stitches together the grid model to determine the digital basin perimeter model and performs macroscopic quantization to generate a macroscopic digital basin perimeter model, and establishes a mapping between the digital basin perimeter model and the macroscopic digital basin perimeter model.

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