Multi-source marine geological information fusion and 3D visualization modeling method

Abstract

The present invention relates to the technical field of information processing and three-dimensional modeling, and specifically to a method for multi-source marine geological information fusion and three-dimensional visualization modeling, comprising the following steps: S1: collecting marine geological data from multiple sources; S2: preprocessing the collected data; S3: extracting marine geological features from the preprocessed data; S4: multi-dimensionally fusing the features extracted in S3; S5: constructing a marine geological three-dimensional model based on the fused data in S4 using a three-dimensional grid construction algorithm; S6: optimizing the constructed three-dimensional model; S7: visualizing the optimized three-dimensional model to generate an interactive three-dimensional display interface. The present invention improves the accuracy, information integrity and user interactive analysis capabilities of the marine geological model through multi-source data fusion, refined three-dimensional model construction and efficient visualization processing, and provides reliable technical support for geological research and engineering applications.

Description

Technical Field

[0001] The present invention relates to the technical field of information processing and three-dimensional modeling, and in particular to a method for fusion of multi-source marine geological information and three-dimensional visualization modeling. Background Art

[0002] With the development of marine resources and the deepening of seabed geological research, building accurate marine geological three-dimensional models has become a crucial task in modern marine exploration, engineering design and geological analysis. Current technology obtains marine geological data by integrating multiple data sources, including satellite remote sensing, sonar measurement, seabed sampling, etc. However, these data are heterogeneous, temporally and spatially inconsistent and complex, making data fusion and processing very difficult. In addition, traditional three-dimensional modeling technology often cannot ensure the accuracy and computational efficiency of the model when processing massive multi-source data, especially in areas with complex geological structures. It is difficult to capture subtle geological changes and make accurate visualizations.

[0003] However, the existing technology still has obvious deficiencies in the following aspects: first, when facing multi-source heterogeneous marine geological data, data fusion technology is difficult to ensure the temporal and spatial consistency between data and the accurate integration of information; second, the existing three-dimensional modeling algorithm cannot take into account both model accuracy and processing efficiency, especially in areas with complex geological levels or drastic structural changes, the model often lacks sufficient detail processing, resulting in the loss of key information or fuzzy display; finally, the current visualization system has weak interactivity and cannot provide users with flexible analysis tools. It is difficult for users to efficiently view, operate and analyze the geological features in the model according to their needs. Therefore, it is of great technical significance and application value to develop a method that can effectively fuse multi-source marine geological data, improve model accuracy and provide efficient visualization operations. Summary of the invention

[0004] Based on the above objectives, the present invention provides a method for multi-source marine geological information fusion and three-dimensional visualization modeling.

[0005] The multi-source marine geological information fusion and three-dimensional visualization modeling method includes the following steps:

[0006] S1: Collect marine geological data from multiple sources, including satellite remote sensing data, seafloor topography data, sonar data, and marine geological sample data, each of which includes corresponding timestamps and geographic coordinates;

[0007] S2: preprocessing the collected data, including data format conversion, spatiotemporal alignment, and noise filtering;

[0008] S3: extracting marine geological features from the preprocessed data, wherein the features include seafloor topography changes, geological layer structures, and physical property information;

[0009] S4: Using a multi-source information fusion algorithm based on graph theory, by building a weighted graph model and combining the weights and confidence levels of each data source, the features extracted in S3 are fused in multiple dimensions.

[0010] S5: Based on the fused data from S4, a 3D mesh construction algorithm is used to construct a 3D marine geological model;

[0011] S6: Optimize the constructed 3D model, including simplifying data points and processing details;

[0012] S7: Visualize the optimized three-dimensional model to generate an interactive three-dimensional display interface.

[0013] Optionally, the S1 specifically includes:

[0014] S11: Collecting marine geological data from satellite remote sensing data sources, wherein the satellite remote sensing data includes marine surface image data taken by satellites within a predetermined time, and using a global positioning system to add corresponding timestamps and geographic coordinates to each data point to indicate the exact location and time of data collection;

[0015] S12: Collect seabed topography data through ship-borne sonar equipment, record the time and location coordinates of the measurement in combination with the ship-borne real-time navigation system, and attach the timestamp and geographic coordinates to the measurement data;

[0016] S13: Collect underwater detection data from a sonar data source, wherein the sonar data is collected in real time by a multi-beam sonar device, and the collected data is synchronized with the navigation system of the ship to obtain time and geographic coordinate information of the measurement point;

[0017] S14: Acquire data from a marine geological sample data source. The data is acquired through marine drilling equipment or seabed sampling equipment. The time of each sampling operation is recorded, and the geographical location of the sampling point is determined by GPS. The timestamp and geographical coordinates are added to the collected sample data.

[0018] Optionally, the S2 specifically includes:

[0019] S21: converting the collected marine geological data into a data format, converting data from different sources into a compatible file format, converting the satellite remote sensing data into a raster image format, converting the seabed topography data and sonar data into a gridded point cloud format, and converting the marine geological sample data into a vector format;

[0020] S22: Perform spatiotemporal alignment on the converted data, use the timestamp and geographic coordinates attached to each data point to match them in space and time, and use linear interpolation to adjust the temporal resolution of different data sources so that all data sources are aligned in the same temporal and spatial framework;

[0021] S23: Noise filtering is performed on the data after time and space alignment to remove random noise in satellite remote sensing data, scattered noise in seabed topography measurement data, and multipath interference noise in sonar data.

[0022] Optionally, the S3 specifically includes:

[0023] S31: Extract the seabed topography change characteristics from the pre-processed satellite remote sensing data, use edge detection algorithm to identify the significant change areas of the seabed topography, and analyze the seabed ups and downs through digital elevation model to extract the topography change characteristics;

[0024] S32: Extract geological layer structural information from seafloor topography data, decompose geological layers at different depths based on multi-scale morphological analysis algorithms, and identify the distribution and structural characteristics of different geological layers through curvature calculation and slope analysis;

[0025] S33: Extract physical property information from sonar data, use inversion algorithms to calculate the acoustic properties of seafloor sediments, and extract physical property parameters such as density, porosity, and sound velocity of seafloor materials;

[0026] S34: Extract geological composition and mineral content information from marine geological sample data.

[0027] Optionally, the S4 specifically includes:

[0028] S41: construct a weighted graph model of multi-source data based on graph theory, wherein the weighted graph model consists of nodes and edges, wherein the nodes represent different geological features extracted from S3, and the edges represent the correlation between different features;

[0029] S42: setting a weight factor and confidence level for each data source, wherein the higher the confidence level, the more important the information of the corresponding data source is in the fusion process;

[0030] S43: using a weighted graph model to fuse feature information from different data sources, the fusion process comprehensively considers the confidence and feature importance of each data source, performs weighted summation on the geological features extracted from different data sources, and generates a fused comprehensive feature;

[0031] S44: Perform consistency check on the fused features, adjust the weighting coefficients by comparing the features of each data source before and after fusion, and ensure that the final output fusion features can accurately reflect the multi-dimensional information of marine geology.

[0032] Optionally, the S5 specifically includes:

[0033] S51: Based on the multi-source geological feature data fused in S4, the data is spatially divided, and the seabed geological area is divided into a plurality of three-dimensional units, wherein each unit represents a grid node containing geological features, and the grid node has a certain coordinate in space, and is connected according to the correlation between adjacent nodes to form a three-dimensional grid structure;

[0034] S52: Assign geological characteristic attributes to each grid node, including information on terrain height, geological layer structure, and physical attributes, and gradually define the spatial position and connection relationship of each node according to changes in geological characteristics;

[0035] S53: A three-dimensional grid model is gradually generated based on the depth level of marine geology by using a layer-by-layer construction method.

[0036] Optionally, the S53 specifically includes:

[0037] S531: Based on the depth level of marine geology, first construct a three-dimensional grid for the surface layer, divide the surface marine geological data into several levels according to the depth, and each layer is represented by a three-dimensional grid node;

[0038] S532: After the surface layer grid is constructed, it is gradually extended downward to construct a deeper geological structure grid. The grid nodes of the deep layer are adjusted based on the geological characteristic data of each depth, and the interpolation algorithm is used to determine the coordinates of the deep layer nodes;

[0039] S533: After the three-dimensional grid of each layer is constructed, the density of each layer of grid is adjusted according to the characteristics of different geological layers. The layers with drastic geological changes use higher grid density to ensure that subtle geological structure changes can be captured;

[0040] S534: The final generated three-dimensional grid model should ensure that the grid nodes at different depth levels are interconnected, and the grid should be smoothed according to the changes in marine geological characteristics to ensure the continuity of the model.

[0041] Optionally, the S6 specifically includes:

[0042] S61: simplify the data points of the constructed three-dimensional grid model, analyze the distribution density of grid nodes, delete redundant grid nodes, reduce the data volume while ensuring the accuracy of the model, and give priority to retaining nodes related to geological features when simplifying nodes. For flat or slightly changing areas, reduce the node density to optimize the calculation efficiency;

[0043] S62: Refine the detailed areas of the model, first identify the areas with large terrain fluctuations and complex geological structures in the model, and then use a local grid encryption algorithm to make the grid finer to avoid information loss or blurred details in important areas;

[0044] S63: Perform a consistency check on the optimized three-dimensional model, and ensure that the geometric structure and attribute information of the model are not damaged by comparing the predetermined nodes and geological feature data before and after the optimization.

[0045] Optionally, the S62 specifically includes:

[0046] S621: Determine whether the region needs to be encrypted by calculating the curvature change rate of each grid node in the model;

[0047] S622: After determining the area to be encrypted, encrypt the grid, specifically using a local grid encryption algorithm to add grid nodes according to the gradient change between nodes;

[0048] S623: For the encrypted grid area, adjust the grid density, and determine the grid density of each area according to the change rate of the geological characteristics in the area;

[0049] S624: Locally optimize the mesh after the encryption process, adjust the coordinate positions of adjacent nodes through local smoothing process, and avoid mesh discontinuity or distortion caused by encryption.

[0050] Optionally, the S7 specifically includes:

[0051] S71: loading the optimized 3D mesh model into a 3D visualization system, and converting the geometric structure of the model into 3D image data through a GPU-based graphics rendering engine;

[0052] S72: Generate an interactive interface for the three-dimensional model, allowing users to interact with the three-dimensional model through a rendering framework based on virtual reality or augmented reality;

[0053] S73: According to different geological characteristic information, the model is layered and the user is allowed to select different display modes in the visualization interface, including selecting to display only terrain information, geological structure information or physical property information, or displaying characteristic information of multiple dimensions at the same time;

[0054] S74: Add geological feature annotation function in the 3D display interface, allowing automatic or manual addition of labels for geological structures, fault lines, and sediment distribution features to help users identify and understand important geological information.

[0055] Beneficial effects of the present invention:

[0056] The present invention effectively solves the problem of diverse sources and strong heterogeneity of marine geological data by introducing a multi-source information fusion algorithm based on graph theory. By constructing a weighted graph model and combining the weights and confidence levels of each data source, it can ensure the accurate integration of information from different data sources in the time and space dimensions. This method not only improves the accuracy of data fusion, but also reduces redundancy and inconsistency between data. At the same time, through a three-dimensional grid construction algorithm, a refined three-dimensional marine geological model is generated layer by layer to ensure that the model can accurately reflect the geological layers and characteristics of different depths, especially in complex geological areas, it achieves accurate capture and expression of details and avoids the loss of important information.

[0057] The present invention uses an interactive interface to view and analyze the characteristics of different geological layers in real time. Users can freely zoom and rotate the model as needed to view the geological details of a specific area, and through layered display, annotation functions and data analysis tools, they can understand the geological structure more intuitively and deeply. Overall, the present invention not only improves the accuracy and display effect of the three-dimensional model, but also provides an efficient and practical visualization and analysis platform for geological research and marine engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 A schematic diagram of an information fusion and three-dimensional visualization modeling method according to an embodiment of the present invention;

[0060] Figure 2 The figure is a schematic diagram of the multi-dimensional fusion process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0062] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0063] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0064] like Figure 1-Figure 2 As shown, the multi-source marine geological information fusion and three-dimensional visualization modeling method includes the following steps:

[0065] S1: Collect marine geological data from multiple sources, including satellite remote sensing data, seafloor topography data, sonar data, and marine geological sample data, each of which includes corresponding timestamps and geographic coordinates to ensure consistency in subsequent processing;

[0066] S2: Preprocess the collected data, including data format conversion, spatiotemporal alignment, and noise filtering to ensure data consistency;

[0067] S3: Extracting marine geological features from the preprocessed data, including seafloor topography changes, geological layer structure and physical property information;

[0068] S4: Using a multi-source information fusion algorithm based on graph theory, by building a weighted graph model and combining the weights and confidence levels of each data source, the features extracted in S3 are fused in multiple dimensions.

[0069] S5: Based on the fused data from S4, a 3D mesh construction algorithm is used to construct a 3D marine geological model. The model construction process takes into account the continuity of the geological structure and the resolution of data at each level to ensure the accuracy and completeness of the model;

[0070] S6: Optimize the constructed 3D model, including simplifying data points and detail processing, using triangular mesh simplification algorithm to reduce redundant data points, improve model rendering efficiency, and maintain the accuracy of geological features;

[0071] S7: The optimized 3D model is visualized to generate an interactive 3D display interface, which allows users to observe marine geological features from different angles through operations such as zooming and rotating, and provides a hierarchical separation display function to display geological structures at different depths.

[0072] S1 specifically includes:

[0073] S11: Collecting marine geological data from satellite remote sensing data sources. Satellite remote sensing data includes ocean surface image data taken by satellites within a predetermined time, and using the Global Positioning System (GPS) to add corresponding timestamps and geographic coordinates to each data point to indicate the exact location and time of data collection;

[0074] S12: Collect seabed topography data through ship-borne sonar equipment, and record the measured time and location coordinates in combination with the ship-borne real-time navigation system (such as differential GPS), and attach timestamps and geographic coordinates to the measured data to ensure the spatial accuracy of the data;

[0075] S13: Collect underwater detection data from sonar data sources. Sonar data is collected in real time by multi-beam sonar equipment. The collected data is synchronized with the ship's navigation system to obtain the time and geographic coordinate information of the measurement point to ensure that the timestamp and location coordinates corresponding to each sonar data point are correct;

[0076] S14: Acquire data from marine geological sample data sources. The data is acquired through marine drilling equipment or seabed sampling equipment. The time of each sampling operation is recorded, and the geographical location of the sampling point is determined by GPS. The timestamp and geographic coordinates are added to the collected sample data to ensure the temporal and spatial consistency of the data. Through further disclosure of the above steps, the consistency of marine geological data collected from multiple sources in the temporal and spatial dimensions can be ensured, avoiding time or position deviations between different data sources, thereby providing an accurate basis for subsequent data fusion and three-dimensional modeling.

[0077] S2 specifically includes:

[0078] S21: Convert the collected marine geological data into different formats, using standardized conversion tools to convert data from different sources into compatible file formats, convert satellite remote sensing data into raster image format, convert seafloor topography data and sonar data into gridded point cloud format, and convert marine geological sample data into vector format to ensure compatibility of subsequent processing;

[0079] S22: Perform spatiotemporal alignment on the converted data, use the timestamp and geographic coordinates attached to each data point for spatial and temporal matching, use linear interpolation to adjust the temporal resolution of different data sources, and unify the spatial resolution through coordinate-based interpolation methods, so that all data sources are aligned in the same temporal and spatial framework;

[0080] S23: Noise filtering is performed on the data after time-space alignment to remove random noise in satellite remote sensing data, scattering noise in seabed topography measurement data, and multipath interference noise in sonar data to ensure the cleanliness and accuracy of the data. Through further disclosure of the above steps, it can be ensured that marine geological data from different sources meet unified standards after format conversion, time-space alignment, and noise filtering, providing high-quality input data for subsequent data fusion and modeling processes. This precise preprocessing method greatly improves the consistency and reliability of the data.

[0081] S3 specifically includes:

[0082] S31: Extract the seabed topography change characteristics from the preprocessed satellite remote sensing data, use edge detection algorithms (such as Canny edge detection) to identify the significant change areas of the seabed topography, and analyze the seabed ups and downs through the digital elevation model (DEM) to extract the topography change characteristics; the Canny edge detection formula is:

[0083] ,in, Represents pixel The gradient value at is the gradient in the x direction, is the gradient in the y direction; combined with the digital elevation model, further analyze the changes in the seabed terrain and calculate the seabed height difference , the formula is: ,in, is the height difference, is the height of the highest point, is the height of the lowest point; by calculating the height difference, the area with significant changes in the terrain can be identified, providing accurate data for subsequent terrain models;

[0084] S32: Extract geological layer structural information from seabed topography data, decompose geological layers at different depths based on a multi-scale morphological analysis algorithm, and identify the distribution and structural characteristics of different geological layers through curvature calculation and slope analysis; the curvature calculation formula is: , where C represents the curvature, z represents the height of the seabed, and x and y represent the horizontal and vertical coordinates respectively; by calculating the curvature values ​​at different locations, changes in geological structures, such as faults or folds, can be identified; and combined with the calculation of the slope S, the inclination of the geological layer can be further analyzed. The formula is: , where S represents the slope, and are the height change rates in the x and y directions, respectively. The slope value S is used to determine the inclination of the stratum, so as to better identify the structure and boundary of the geological stratum;

[0085] S33: Extract physical property information from sonar data, use inversion algorithms (such as the seabed acoustic wave reflection coefficient method) to calculate the acoustic characteristics of seabed sediments, and extract the physical property parameters of seabed material such as density, porosity and sound velocity; the inversion algorithm formula is: , where R is the reflection coefficient, is the acoustic impedance of water, is the acoustic impedance of the sediment; the calculation formula of the acoustic impedance Z is: , where Z is the acoustic impedance, is the density of the material, and v is the propagation speed of the sound wave in the material; by measuring the sound wave reflection intensity R and combining it with the acoustic impedance formula, the density of the sediment can be inferred and the speed of sound v, thereby inferring the physical properties of the seafloor sediments;

[0086] S34: Extract geological composition and mineral content information from marine geological sample data, specifically by quantitatively analyzing the mineral components in the samples and calculating the mineral content using the following formula: ,in, is the mineral content, For the mass of a mineral, is the total mass of the sample. The formula helps to infer the mineral composition and distribution characteristics of the seabed geology by calculating the proportion of minerals in the sample.

[0087] The multi-dimensional fusion in S4 specifically includes:

[0088] S41: Construct a weighted graph model of multi-source data based on graph theory. The weighted graph model consists of nodes and edges, where nodes represent different geological features extracted from S3, and edges represent the correlation between different features. The weight of the edge reflects the similarity between different features. The more similar the features, the higher their weight, indicating that they are more correlated in space or attributes.

[0089] S42: Set a weight factor and confidence level for each data source. The quality of the data source is evaluated based on historical performance. Data sources with lower error rates receive higher scores. Confidence levels are assigned to each data source based on these quality scores to indicate the reliability of the data source. The higher the confidence level, the more important the information of the corresponding data source is in the fusion process.

[0090] S43: The feature information of different data sources is fused using a weighted graph model. The fusion process comprehensively considers the confidence and feature importance of each data source, and performs weighted summation on the geological features extracted from different data sources to generate fused comprehensive features. This process ensures that information from different sources is reasonably integrated to obtain a multi-dimensional and unified geological feature description;

[0091] S44: Perform consistency check on the fused features to ensure that the final fusion results are consistent with the actual marine geological conditions. By comparing the features of each data source before and after fusion, adjust the weighting coefficients to ensure that the final output fusion features can accurately reflect the multi-dimensional information of marine geology and maintain a high level of consistency. Through a weighted graph model based on graph theory, combined with the weights and confidence levels of each data source for multi-dimensional fusion, the heterogeneity problem of multi-source marine geological data can be effectively solved to ensure the accuracy and consistency of the fusion results. This fusion algorithm improves the overall reliability of the data and provides accurate basic data for subsequent three-dimensional modeling.

[0092] The specific steps of multi-dimensional fusion are as follows:

[0093] First, let the weighted graph model be G, expressed as: , the weighted graph model consists of nodes V and edges E. The nodes represent the geological features extracted from step S3, and each node corresponds to a feature data set; the edge E represents the correlation between different feature data sets, and the weight w of the edge is calculated by the similarity measurement of the data source. The specific calculation formula is as follows: ,in, is the edge weight between node i and node j, Represents the Euclidean distance between the features represented by node i and node j; weight The larger it is, the higher the similarity between the two features;

[0094] Then, weight factors are set based on the quality and credibility of different data sources. , the data quality of each data source is evaluated by the accuracy of historical data, and the confidence is calculated by the following formula: ,in, represents the confidence of data source s, Score the quality of data source s, The quality score Q of the other n data sources is determined based on their error rate and historical performance. The lower the error rate, the higher the score and the higher the confidence level. It is used to adjust the weights of the nodes related to the data source in the weighted graph model;

[0095] Finally, the feature information extracted from different data sources is integrated through a weighted graph fusion algorithm based on weights and confidence, and the final fused feature vector F is calculated by the following weighted summation formula: , where F is the fused feature vector, is the confidence of the i-th data source, is the feature vector of the i-th data source. The algorithm can comprehensively consider the characteristics and credibility of each data source to obtain the fused multi-dimensional geological characteristics.

[0096] The construction of marine geological 3D model in S5 specifically includes:

[0097] S51: Based on the multi-source geological feature data fused in S4, the data is spatially divided, and the seabed geological area is divided into a number of three-dimensional units, where each unit represents a grid node containing geological features. The grid nodes have certain coordinates in space and are connected according to the correlation between adjacent nodes to form a three-dimensional grid structure;

[0098] S52: Assign geological characteristic attributes to each grid node, including information on terrain height, geological layer structure, and physical properties. These attribute values ​​are derived from the fused multi-source data to ensure that each node contains rich marine geological information. According to the changes in geological characteristics, the spatial position and connection relationship of each node are gradually defined to ensure that the grid model can accurately reflect the continuity and complexity of the geological structure.

[0099] S53: A three-dimensional grid model is gradually generated according to the depth level of marine geology by adopting a layer-by-layer construction method; starting from the shallow layer, a three-dimensional grid model of the surface and near-surface is constructed according to the fused geological features, and then extended downward to the deeper geological structure. The grid nodes of each layer are position-corrected and feature-assigned according to the fused data to ensure the consistency and accuracy of the model at different depths; the above-mentioned three-dimensional grid construction method based on fused data can efficiently and accurately generate a three-dimensional model that conforms to the marine geological structure. This method ensures the accuracy, continuity and consistency of the model, and provides a reliable foundation for subsequent visualization and in-depth geological analysis.

[0100] S53 specifically includes:

[0101] S531: Based on the depth level of marine geology, a three-dimensional grid is constructed for the surface layer first, and the surface marine geological data is divided into several levels according to the depth. Each layer is represented by a three-dimensional grid node, and the spatial coordinates of each node are Determined according to the fused geological features, where x and y are horizontal coordinates and z is depth; the distance d between grid nodes is calculated according to the change rate of the marine geological structure, and the formula is: , where d represents the distance between two grid nodes, and are the coordinates of adjacent nodes respectively. According to this formula, the grid structure of the surface layer is generated to ensure that the surface features are accurately reflected in the grid model;

[0102] S532: After constructing the surface grid, it is gradually extended downward to construct a deeper geological structure grid. The grid nodes of the deep layer are adjusted based on the geological characteristic data of each depth, and the interpolation algorithm is used to determine the coordinates of the deep layer nodes; for the spatial position of the deep layer grid nodes, the linear interpolation method is used, and the formula is as follows: ,in, The depth coordinates of the deep nodes calculated by interpolation, and is the known depth of the adjacent nodes, and is the corresponding node spacing, is the location of the node to be calculated; the interpolation algorithm is used to ensure that the grid nodes at each depth level accurately cover each layer of geological features;

[0103] S533: After the three-dimensional grid of each layer is constructed, the density of each layer of grid is adjusted according to the characteristics of different geological layers. The layers with drastic geological changes use higher grid density to ensure that subtle geological structure changes can be captured; grid density The calculation is based on the gradient of geological changes in a specific area. To determine, the formula is as follows: ,in, represents the grid density, is the gradient of geological characteristic changes in the region; the larger the gradient, the more complex the geological changes in the region, and the higher the corresponding grid density;

[0104] S534: The final generated three-dimensional grid model should ensure that the grid nodes at different depth levels are interconnected, and the grid is smoothed according to the changes in marine geological characteristics to ensure the continuity of the model; specifically, the Laplace smoothing algorithm is used to adjust the coordinates of the grid nodes to make the model surface smoother. The formula is: ,in, are the smoothed node coordinates, is the coordinate of the adjacent node, and n is the number of nodes adjacent to the node. Through this algorithm, the transition of the grid model between different depth levels is ensured to be smooth, avoiding obvious faults or discontinuous areas. Through the above-mentioned layer-by-layer three-dimensional grid model method, the model can be accurately constructed according to the depth level of marine geology to ensure that the geological characteristics of each level are accurately expressed. Combined with the interpolation algorithm and grid density adjustment, the geological changes at different depths can be captured, the accuracy and completeness of the model can be improved, and reliable technical support can be provided for subsequent geological analysis and visualization.

[0105] The optimization process in S6 specifically includes:

[0106] S61: Simplify the data points of the constructed three-dimensional grid model by analyzing the distribution density of grid nodes and deleting redundant grid nodes, thereby reducing the amount of data while ensuring the accuracy of the model. When simplifying nodes, nodes related to geological features are retained first, and for flat or slightly changing areas, node density is reduced to optimize calculation efficiency. This process ensures that unnecessary data points are reduced without affecting the overall accuracy of the model.

[0107] S62: Refine the detailed areas of the model, first identify the areas with large terrain fluctuations and complex geological structures in the model, and then use a local grid encryption algorithm to make the grid finer to avoid information loss or blurred details in important areas;

[0108] S63: Perform consistency check on the optimized 3D model to ensure that the model remains consistent with the original geological features after simplification and detail processing. By comparing the predetermined nodes and geological feature data before and after optimization, ensure that the geometric structure and attribute information of the model are not damaged, and ultimately ensure that the optimized model is suitable for subsequent visualization and data analysis tasks; by optimizing the 3D model, simplifying data points and refining the processing, it is ensured that key geological features are accurately expressed while improving calculation efficiency. The optimized model not only has high accuracy, but also improves the clarity of the model display, providing an efficient and reliable basis for subsequent geological analysis.

[0109] The local grid encryption algorithm used in S62 specifically includes:

[0110] S621: First, identify a predetermined area in the three-dimensional model, that is, a part with complex geological structure or large terrain undulation, and determine whether the area needs to be encrypted by calculating the curvature change rate of each grid node in the model; assuming that the curvature is C, the calculation formula is: , where z is the height of the node, x and y are the horizontal and vertical coordinates respectively; the specific judgment basis is that when the absolute value of the curvature C is greater than the preset threshold When the curvature value is greater, the area is considered to be a region with more drastic changes and needs to be meshed; the larger the curvature value, the more complex the geological changes in the area, and it is suitable to increase the refinement of the model by increasing the node density. The specific preset threshold is determined according to the application scenario and geological characteristics;

[0111] S622: After determining the area to be encrypted, encrypt the grid. Specifically, a local grid encryption algorithm is used to add grid nodes according to the gradient change between nodes. The position of the new node is determined by interpolation. The interpolation formula is: ,in, is the new node to be inserted, and For two adjacent nodes, is the interpolation factor, which takes values ​​between 0 and 1. This interpolation method ensures that new grid nodes are inserted in areas with rich details to capture the fine changes of geological features;

[0112] S623: For the encrypted grid area, adjust the grid density, and determine the grid density of each area according to the change rate of the geological characteristics in the area. Set the local grid density to , the calculation formula is: ,in, is the grid density of the local area, R is the change rate of geological characteristics, the greater the change rate, the higher the grid density, so that the geological details of the area can be accurately reflected;

[0113] S624: Finally, the mesh after encryption is locally optimized to ensure that the connection relationship between the newly inserted nodes and the original nodes remains consistent. The coordinate positions of adjacent nodes are adjusted through local smoothing to avoid mesh discontinuity or distortion caused by encryption, and to ensure the smoothness and consistency of the model after detail processing. The above-mentioned local mesh encryption algorithm can effectively improve the mesh accuracy of key areas and avoid the loss or blurring of important geological details. While maintaining the overall efficiency of the model, the algorithm performs refined processing on complex areas to ensure the accuracy and continuity of the model, providing reliable data support for subsequent geological analysis.

[0114] S7 specifically includes:

[0115] S71: Loading the optimized 3D mesh model into a 3D visualization system, and converting the geometric structure of the model into 3D image data through a GPU-based graphics rendering engine; in this step, a real-time rendering-based technology, such as ray tracing or rasterization rendering, is used to ensure that the geometric shape and texture details of the model can be accurately presented in the 3D display interface;

[0116] S72: Generate an interactive interface for the three-dimensional model, and enable the user to interact with the three-dimensional model through a rendering framework based on virtual reality or augmented reality. The interactive operations include zooming, rotating, and translating. The user can adjust the perspective and position of the model in real time through input devices such as a mouse, keyboard, or touch screen, so as to view the details of different geological layers and regions;

[0117] S73: The model is layered according to different geological characteristic information, allowing the user to select different display modes in the visualization interface, including selecting to display only terrain information, geological structure information or physical property information, or displaying characteristic information of multiple dimensions at the same time; the layered processing distinguishes different geological layers and characteristic attributes by setting transparency, color coding, etc., making the display of the model more intuitive and easy to understand;

[0118] S74: Add a geological feature annotation function in the three-dimensional display interface, allowing automatic or manual addition of labels for geological structures, fault lines, and sediment distribution features to help users identify and understand important geological information; this function can be selectively enabled according to user needs, and supports key annotations at different levels and regions; through the above-mentioned visualization processing steps, the optimized three-dimensional model can be efficiently presented to the user, and multi-dimensional interactive functions are provided, allowing users to flexibly operate the model, view different geological features, and add annotations. This processing method improves the operability of the model and the richness of information expression.

[0119] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0120] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for fusion of multi-source marine geological information and three-dimensional visualization modeling, characterized in that: The following steps are involved: S1: Collect marine geological data from multiple sources, including satellite remote sensing data, seafloor topography data, sonar data, and marine geological sample data, each of which includes corresponding timestamps and geographic coordinates; S2: preprocessing the collected data, including data format conversion, spatiotemporal alignment, and noise filtering; S3: extracting marine geological features from the preprocessed data, wherein the features include seafloor topography changes, geological layer structures, and physical property information; The S3 specifically includes: S31: Extract the seabed topography change characteristics from the pre-processed satellite remote sensing data, use edge detection algorithm to identify the significant change areas of the seabed topography, and analyze the seabed ups and downs through digital elevation model to extract the topography change characteristics; S32: Extract geological layer structural information from seafloor topography data, decompose geological layers at different depths based on multi-scale morphological analysis algorithms, and identify the distribution and structural characteristics of different geological layers through curvature calculation and slope analysis; S33: Extract physical property information from sonar data, use inversion algorithms to calculate the acoustic properties of seafloor sediments, and extract physical property parameters such as density, porosity, and sound velocity of seafloor materials; S34: Extract geological composition and mineral content information from marine geological sample data; S4: Using a multi-source information fusion algorithm based on graph theory, by building a weighted graph model and combining the weights and confidence levels of each data source, the features extracted in S3 are fused in multiple dimensions. The S4 specifically includes: S41: construct a weighted graph model of multi-source data based on graph theory, wherein the weighted graph model consists of nodes and edges, wherein the nodes represent different geological features extracted from S3, and the edges represent the correlation between different features; S42: setting a weight factor and confidence level for each data source, wherein the higher the confidence level, the more important the information of the corresponding data source is in the fusion process; S43: using a weighted graph model to fuse feature information from different data sources, the fusion process comprehensively considers the confidence and feature importance of each data source, performs weighted summation on the geological features extracted from different data sources, and generates a fused comprehensive feature; S44: Perform consistency check on the fused features, and adjust the weighting coefficients by comparing the features of each data source before and after fusion to ensure that the fused features finally outputted can accurately reflect the multi-dimensional information of marine geology; S5: Based on the fused data from S4, a 3D mesh construction algorithm is used to construct a 3D marine geological model; The S5 specifically includes: S51: Based on the multi-source geological feature data fused in S4, the data is spatially divided, and the seabed geological area is divided into a plurality of three-dimensional units, wherein each unit represents a grid node containing geological features, and the grid node has a certain coordinate in space, and is connected according to the correlation between adjacent nodes to form a three-dimensional grid structure; S52: Assign geological characteristic attributes to each grid node, including information on terrain height, geological layer structure, and physical attributes, and gradually define the spatial position and connection relationship of each node according to changes in geological characteristics; S53: A three-dimensional grid model is gradually generated based on the depth level of marine geology by using a layer-by-layer construction method; The S53 specifically includes: S531: Based on the depth level of marine geology, first construct a three-dimensional grid for the surface layer, divide the surface marine geological data into several levels according to the depth, and each layer is represented by a three-dimensional grid node; S532: After the surface layer grid is constructed, it is gradually extended downward to construct a deeper geological structure grid. The grid nodes of the deep layer are adjusted based on the geological characteristic data of each depth, and the interpolation algorithm is used to determine the coordinates of the deep layer nodes; S533: After the three-dimensional grid of each layer is constructed, the density of each layer of grid is adjusted according to the characteristics of different geological layers. The layers with drastic geological changes use higher grid density to ensure that subtle geological structure changes can be captured; S534: The final generated 3D grid model should ensure that the grid nodes at different depth levels are interconnected, and the grid should be smoothed according to the changes in marine geological characteristics to ensure the continuity of the model; S6: Optimize the constructed 3D model, including simplifying data points and processing details; S7: Visualize the optimized three-dimensional model to generate an interactive three-dimensional display interface.

2. The multi-source marine geological information fusion and three-dimensional visualization modeling method according to claim 1 is characterized in that: The S1 specifically includes: S11: Collecting marine geological data from satellite remote sensing data sources, wherein the satellite remote sensing data includes marine surface image data taken by satellites within a predetermined time, and using a global positioning system to add corresponding timestamps and geographic coordinates to each data point to indicate the exact location and time of data collection; S12: Collect seabed topography data through ship-borne sonar equipment, record the time and location coordinates of the measurement in combination with the ship-borne real-time navigation system, and attach the timestamp and geographic coordinates to the measurement data; S13: Collect underwater detection data from a sonar data source, wherein the sonar data is collected in real time by a multi-beam sonar device, and the collected data is synchronized with the navigation system of the ship to obtain time and geographic coordinate information of the measurement point; S14: Acquire data from a marine geological sample data source. The data is acquired through marine drilling equipment or seabed sampling equipment. The time of each sampling operation is recorded, and the geographical location of the sampling point is determined by GPS. The timestamp and geographical coordinates are added to the collected sample data.

3. The multi-source marine geological information fusion and three-dimensional visualization modeling method according to claim 1 is characterized in that: The S2 specifically includes: S21: converting the collected marine geological data into a data format, converting data from different sources into a compatible file format, converting the satellite remote sensing data into a raster image format, converting the seabed topography data and sonar data into a gridded point cloud format, and converting the marine geological sample data into a vector format; S22: Perform spatiotemporal alignment on the converted data, use the timestamp and geographic coordinates attached to each data point to match them in space and time, and use linear interpolation to adjust the temporal resolution of different data sources so that all data sources are aligned in the same temporal and spatial framework; S23: Noise filtering is performed on the data after time and space alignment to remove random noise in satellite remote sensing data, scattered noise in seabed topography measurement data, and multipath interference noise in sonar data.

4. The multi-source marine geological information fusion and three-dimensional visualization modeling method according to claim 1 is characterized in that: The S6 specifically includes: S61: simplify the data points of the constructed three-dimensional grid model, analyze the distribution density of grid nodes, delete redundant grid nodes, reduce the data volume while ensuring the accuracy of the model, and give priority to retaining nodes related to geological features when simplifying nodes. For flat or slightly changing areas, reduce the node density to optimize the calculation efficiency; S62: Refine the detailed areas of the model, first identify the areas with large terrain fluctuations and complex geological structures in the model, and then use a local grid encryption algorithm to make the grid finer to avoid information loss or blurred details in important areas; S63: Perform a consistency check on the optimized three-dimensional model, and ensure that the geometric structure and attribute information of the model are not damaged by comparing the predetermined nodes and geological feature data before and after the optimization.

5. The multi-source marine geological information fusion and three-dimensional visualization modeling method according to claim 4 is characterized in that: The S62 specifically includes: S621: Determine whether the region needs to be encrypted by calculating the curvature change rate of each grid node in the model; S622: After determining the area to be encrypted, encrypt the grid, specifically using a local grid encryption algorithm to add grid nodes according to the gradient change between nodes; S623: For the encrypted grid area, adjust the grid density, and determine the grid density of each area according to the change rate of the geological characteristics in the area; S624: Locally optimize the mesh after the encryption process, adjust the coordinate positions of adjacent nodes through local smoothing process, and avoid mesh discontinuity or distortion caused by encryption.

6. The multi-source marine geological information fusion and three-dimensional visualization modeling method according to claim 1 is characterized in that: The S7 specifically includes: S71: loading the optimized 3D mesh model into a 3D visualization system, and converting the geometric structure of the model into 3D image data through a GPU-based graphics rendering engine; S72: Generate an interactive interface for the three-dimensional model, allowing users to interact with the three-dimensional model through a rendering framework based on virtual reality or augmented reality; S73: According to different geological characteristic information, the model is layered and the user is allowed to select different display modes in the visualization interface, including selecting to display only terrain information, geological structure information or physical property information, or displaying characteristic information of multiple dimensions at the same time; S74: Add geological feature annotation function in the 3D display interface, allowing automatic or manual addition of labels for geological structures, fault lines, and sediment distribution features to help users identify and understand important geological information.

Images