A visual interactive device for geophysical data evaluation

Abstract

This invention discloses a visualization and interactive device for geophysical data evaluation, relating to the field of data processing technology. It includes an intent input parsing module for acquiring and parsing user input information to obtain preliminary geological intent features; an example feature extraction module for generating a high-dimensional feature vector representing the user's geological intent as the geological intent feature vector; multi-dimensional features including the internal physical property statistical features, external geometric morphology features, spatial context relationship features, and derived attribute features of the example area; a matching degree generation visualization module for calculating the similarity between the geological intent feature vector and each geophysical data unit within the target work area, generating a three-dimensional geological similarity volume; and an iterative optimization module for receiving user feedback based on the visualization results. This invention significantly improves the accuracy of target identification and effectively avoids the problems of missing key targets or interference from noisy data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a visualization and interactive device for geophysical data evaluation. Background Technology

[0002] In geophysical data interpretation and evaluation, the core task of operators (such as geologists and geophysicists) is to identify and evaluate potential favorable geological targets, such as anticlines, channel sand bodies, and lithological traps, from massive amounts of 3D seismic, well logging, and other data. This process is highly dependent on the operators' professional knowledge, experience, and understanding of subsurface geological patterns.

[0003] However, existing geophysical data assessment equipment has significant technical bottlenecks: on the one hand, the equipment only supports low-level, discrete query parameter input methods, requiring operators to manually convert abstract, patterned professional geological intentions into a series of specific operational instructions, such as manually delineating spatial ranges on seismic data volumes, setting threshold values ​​for attributes such as amplitude and curvature one by one, and manually selecting interpretation horizons; on the other hand, this conversion process is highly dependent on the operator's personal experience, professional proficiency, and mastery of the system operation, which not only makes the operation process cumbersome, time-consuming, and labor-intensive, resulting in low efficiency in conveying intentions, but also makes it very easy to miss key geological targets or introduce a large amount of noisy data due to parameter setting deviations. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a visualization and interactive device for geophysical data evaluation.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a visualization and interactive device for geophysical data evaluation, comprising: The intent input parsing module is used to acquire and parse the information input by the user to obtain preliminary geological intent features; The example feature extraction module is used to respond to the user's selection of an example region in the three-dimensional geophysical data volume, perform multi-dimensional feature extraction on the example region, and generate a high-dimensional feature vector representing the user's geological intent as the geological intent feature vector; the multi-dimensional features include the internal physical property statistical features, external geometric morphology features, spatial context relationship features, and derived attribute features of the example region. The matching degree generation visualization module is used to calculate the similarity between the geological intent feature vector and each geophysical data unit in the target work area, generate a three-dimensional geological similarity volume, and visualize the geological similarity volume. The iterative optimization module is used to receive feedback information from the user based on the visualization results, and adjust the geological intent feature vector and / or similarity calculation model according to the feedback information to update the geological similarity volume and its visualization.

[0006] As a preferred embodiment of the present invention, the intent input parsing module includes: The natural language processing unit is used to receive natural language information input by the user through voice or text, and to parse the natural language information based on a preset geophysical knowledge base, and to identify and extract geological entities, attribute requirements and spatial relationship information therein; The sketch recognition unit is used to receive sketch information input by the user in the sketch drawing area and extract the geometric features of the sketch information.

[0007] As a preferred embodiment of the present invention, the example feature extraction module includes: The example area selection interface is used to provide user interface interactive elements, allowing users to select the example area on a 3D geophysical data volume view or a 2D profile view by using box selection, polygon selection, or point selection. The feature extraction unit is configured to perform at least one of the following types of feature extraction operations on the selected example region: Internal physical property statistical feature extraction: Calculate the statistics of basic geophysical properties within the example area; External geometric feature extraction: Calculate at least one of the following: volume, surface area, aspect ratio, spatial azimuth, boundary curvature, and thickness variation rate of the example region; Spatial context feature extraction: Analyze the spatial relationship between the example area and preset geological elements; Derivative attribute feature extraction: Calculate at least one derived attribute, including but not limited to coherence, curvature, and texture attributes, on the example region, and analyze its spatial distribution pattern; The feature fusion unit is used to normalize the various features extracted by the feature extraction unit and fuse them to generate the high-dimensional feature vector.

[0008] As a preferred embodiment of the present invention, the matching degree generation visualization module includes: The similarity calculation unit is used to extract feature vectors with the same dimension as the geological intention feature vector for each geophysical data unit pre-divided within the target work area, and to calculate the similarity value between the feature vector of each data unit and the geological intention feature vector. The similarity body construction unit is used to arrange and store the calculated similarity values ​​of each data unit according to their corresponding spatial locations, and to construct a three-dimensional geological similarity body that is consistent with the spatial range of the target work area. The adaptive visualization unit is used to automatically select and apply the corresponding visualization scheme to render and display the geological similarity volume based on the data distribution characteristics of the geological similarity volume and the user's geological target type.

[0009] As a preferred embodiment of the present invention, the adaptive visualization unit is specifically used for: When the data in the geological similarity volume exhibits a continuous gradient distribution, a heatmap is used to overlay and render the geological similarity volume onto the original geophysical data volume. The color mapping relationship of the heatmap is set according to the similarity value. When there are discrete high-value regions in the geological similarity body that meet the preset similarity threshold conditions, the three-dimensional spatial morphology of the high-value regions is extracted using the isosurface extraction algorithm and rendered and displayed in the form of colored entities. The system automatically marks key information of target regions that meet specific similarity criteria in the visualization interface.

[0010] As a preferred embodiment of the present invention, the iterative optimization module includes: The feedback receiving unit is used to provide user interface interactive elements so that the user can mark specific areas on the visualization results of the matching degree generation visualization module to input feedback information. The feature weight adjustment unit is used to adjust the weight coefficients of different dimensions of features in the geological intent feature vector according to the feedback information received by the feedback receiving unit; the adjustment method includes adjusting based on the weight values ​​manually input by the user, or automatically adjusting based on the feature differences between positive and negative samples through a preset optimization algorithm; The model update unit is used to trigger the matching degree generation visualization module to recalculate the similarity and generate the similarity volume based on the adjusted geological intention feature vector, and update the visualization results.

[0011] As a preferred embodiment of the present invention, the visual interactive device further includes a knowledge base module for storing and managing at least one of the following types of information: A geophysical knowledge base, containing geological terminology and its semantic relationships, characteristic descriptions of typical geological models, and association rules between geological intent and key features; Example feature vector library, used to store example areas selected by users in historical interpretation tasks and their corresponding geological intent feature vectors; The case library is used to associate and store successful geological interpretation cases. The cases include a description of the geological intent input by the user, a selected example area, a generated geological similarity body, and information on the finally confirmed target area.

[0012] This invention provides a visualization and interactive method for geophysical data evaluation, comprising the following steps: Acquire and parse natural language information and / or sketch information input by the user to obtain preliminary geological intent features; In response to the user's selection of an example region in the three-dimensional geophysical data volume, multi-dimensional features are extracted from the example region to generate a high-dimensional feature vector representing the user's geological intent as the geological intent feature vector; the multi-dimensional features include the internal physical property statistical features, external geometric morphology features, spatial context relationship features, and derived attribute features of the example region. Based on the geological intent feature vector, the similarity between it and each geophysical data unit in the target work area is calculated, a three-dimensional geological similarity volume is generated, and the geological similarity volume is visualized. The system receives feedback from users based on the visualization results and adjusts the geological intent feature vector and / or similarity calculation model according to the feedback to update the geological similarity volume and its visualization.

[0013] The present invention provides an electronic device, characterized in that it includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs containing instructions for performing the methods described in any of the embodiments of the present invention.

[0014] The present invention provides a computer-readable storage medium, characterized in that, when the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is able to perform the method described in any of the embodiments of the present invention.

[0015] The beneficial effects of this invention are: 1. This invention, through a multimodal intent input and parsing module, supports a combination of input methods such as natural language and sketching, and combines a geophysical knowledge base to achieve accurate parsing of professional semantics. It eliminates the need for operators to adapt to a single parameter input mode, enabling a more comprehensive and accurate capture of initial geological intent. This significantly reduces the threshold and cognitive load for intent expression. By guiding users to select typical example areas, it automatically performs deep extraction and fusion of multi-dimensional features, generating a geological intent feature vector that quantitatively represents the user's true intent. This transforms abstract, patterned cognition into calculable feature indicators, completely eliminating reliance on manual parameter settings and ensuring the fidelity of intent transmission.

[0016] 2. This invention generates a three-dimensional geological similarity volume based on geological intent feature vectors. Through an adaptive visualization scheme, it intuitively presents the similarity distribution of the entire work area. Whether it is similarity information with continuous gradient distribution or discrete high-value target areas, it can be rendered and displayed in the optimal way, allowing operators to quickly locate potential target areas. At the same time, the interactive iterative optimization module supports the dynamic adjustment of feature weights or similarity calculation models through convenient feedback methods such as positive and negative sample labeling, so as to achieve rapid convergence of screening results, significantly improve the accuracy of target identification, and effectively avoid the problems of missing key targets or interference from noisy data. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the overall structure of the visualization and interactive device for geophysical data evaluation according to the present invention.

[0018] Figure 2 This is a schematic diagram of the workflow of the visualization and interactive device for geophysical data evaluation according to the present invention. Detailed Implementation

[0019] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] like Figures 1-2 As shown, a visualization and interactive device for geophysical data evaluation includes: The intent input parsing module is used to acquire and parse the natural language information and / or sketch information input by the user to obtain preliminary geological intent features; The example feature extraction module is used to respond to the user's selection of an example region in the three-dimensional geophysical data volume, perform multi-dimensional feature extraction on the example region, and generate a high-dimensional feature vector representing the user's geological intent as the geological intent feature vector; the multi-dimensional features include the internal physical property statistical features, external geometric morphology features, spatial context relationship features, and derived attribute features of the example region. The matching degree generation visualization module is used to calculate the similarity between the geological intent feature vector and each geophysical data unit in the target work area, generate a three-dimensional geological similarity volume, and visualize the geological similarity volume. The iterative optimization module is used to receive feedback information from the user based on the visualization results, and adjust the geological intent feature vector and / or similarity calculation model according to the feedback information to update the geological similarity volume and its visualization.

[0022] Among them, the intent input parsing module is the core entry point for the device to capture the user's initial geological intent. Its core structure is built around the logic of multimodal input reception, professional semantic parsing and feature fusion. It can simultaneously accommodate the input and parsing of natural language information and sketch information, without requiring the user to adopt a single input method, and maximizes the fit with the operator's intent expression habits in actual work. The example feature extraction module is the core module for realizing the quantitative transformation of geological intent. Its structure includes an example area interaction interface, a multi-dimensional feature extraction unit, and a feature fusion unit. Its core function is to respond to the user's selection of typical example areas in the three-dimensional geophysical data volume, and generate a geological intent feature vector that can quantitatively represent the user's true geological intent through full-dimensional feature scanning and fusion. The matching degree generation visualization module is a key module for realizing the spatial mapping and intuitive presentation of geological intent. Its structure consists of a similarity calculation unit, a similarity volume construction unit, and an adaptive visualization unit. Its core function is to use the geological intent feature vector as a query template, perform full-range similarity calculation within the target work area, generate a three-dimensional geological similarity volume, and transform it into spatial distribution information that can be intuitively understood by operators through an adaptive visualization scheme. Finally, the iterative optimization module is a closed-loop optimization module that ensures that the visualization results are highly consistent with the user's actual geological intentions. Its structure includes a feedback receiving unit, a feature weight adjustment unit, and a model update unit. Its core function is to receive feedback information from the user based on the visualization results, dynamically adjust the geological intention feature vector or similarity calculation model, and then update the geological similarity volume and its visualization results.

[0023] In detail, the above four modules form a closed-loop workflow of input-calibration-mapping-optimization, which enables operators to complete geological target identification tasks efficiently and accurately without having to memorize complex underlying parameters. They only need to use professional language descriptions and example annotations. In practical use cases, this method significantly reduces the difficulty of conveying professional intent, improves the work efficiency of novice and intermediate operators, and reduces the problem of target area omission or noise interference caused by parameter setting deviations.

[0024] Furthermore, the intent input parsing module includes: The natural language processing unit is used to receive natural language information input by the user through voice or text, and to parse the natural language information based on a preset geophysical knowledge base, and to identify and extract geological entities, attribute requirements and spatial relationship information therein; The natural language processing unit receives user input through a built-in voice acquisition device or text input interface.

[0025] The voice acquisition device uses a noise-canceling microphone array, which can ensure accurate capture of user voice commands in noisy geological interpretation environments. The text input interface supports a variety of input methods such as keyboard input and handwriting input conversion.

[0026] The natural language processing unit incorporates a geophysical knowledge base, which includes a terminology database, a geological concept knowledge graph, and an intent-feature association rule base covering fields such as structural geology, sedimentology, and petroleum geology. The terminology database stores specialized geological terms such as anticline structures, channel sand bodies, fault closure, and complex traps, along with their synonyms and near-synonyms. The geological concept knowledge graph constructs semantic associations between geological objects, geophysical attributes, and spatial relationships. The intent-feature association rule base stores the mapping relationships between common geological intents and their corresponding key features.

[0027] When a user inputs a description of a geological target via voice or text, the natural language processing unit first performs word segmentation and terminology recognition, filters out irrelevant information, and then extracts key information based on the geophysical knowledge base, including geological entities (such as geological objects, target strata, and regional extent), attribute requirements (such as strong amplitude, low curvature, and thick layers), and spatial relationships (such as cutting, overburden, and adjacency).

[0028] The sketch recognition unit is used to receive sketch information input by the user in the sketch drawing area and extract the geometric features of the sketch information; Specifically, the sketch recognition unit provides a sketch drawing area and uses a lightweight vector drawing engine to support users in quickly sketching the spatial morphological features of geological intentions using devices such as mice and touch screens.

[0029] The sketch recognition unit extracts features from the drawn lines based on a deep learning model (such as a convolutional neural network), including geometric features such as the curvature, aspect ratio, number of inflection points, and branching of the lines.

[0030] For example, when a user sketches the dome shape of an anticline structure, the sketch recognition unit extracts geometric features such as closure, symmetry, and wing dip angle; when a user sketches the bending direction of a channel sand body, the sketch recognition unit extracts geometric features such as curvature value, aspect ratio, and bending radius. These geometric features are parts that are difficult for users to accurately describe in words based on their professional knowledge, and are of great significance for fully expressing geological intentions.

[0031] The preliminary geological intent features are obtained by fusing the information parsed by the natural language processing unit and the geometric features extracted by the sketch recognition unit.

[0032] The fusion algorithm performs weighted fusion based on the confidence levels of the two types of information, where the confidence level is determined by the completeness and clarity of the information.

[0033] For example, when a user inputs "searching for anticline structures with significant amplitude anomalies and complete shapes" and simultaneously sketches the anticline shape, the natural language processing unit identifies information such as the anticline structure, significant amplitude anomalies, and complete shape. The sketch recognition unit extracts geometric features such as closure degree of 0.85, symmetry of 0.92, and wing tilt angle of 15-25 degrees. After fusion, a preliminary geological intent feature is generated, which includes the geological object (anticline structure), attribute requirements (significant amplitude anomalies), and morphological features (closure degree of 0.85, symmetry of 0.92, and wing tilt angle of 15-25 degrees).

[0034] This multimodal fusion mechanism enables the system to simultaneously ensure the accuracy of verbal expression and the intuitiveness of sketching, effectively capturing the professional intent of the operator.

[0035] Furthermore, the example feature extraction module includes: The example area selection interface is used to provide user interface interactive elements, allowing users to select the example area on a 3D geophysical data volume view or a 2D profile view by using box selection, polygon selection, or point selection. After the user completes the initial geological intent feature description, the selected interface for the example area guides the user into the example calibration mode.

[0036] When the system determines that the feature dimensions of the preliminary intention are insufficient or the feature description is vague, it will automatically trigger an example selection prompt. Users can also enter this mode through the manual trigger button on the interactive interface. The prompt information, combined with the features of the preliminary geological intention, guides users to select a typical example area, such as asking users to select a typical channel sand body area in the three-dimensional data volume that meets the characteristics of strong amplitude and long, curved strip shape as an example.

[0037] The example area selection interface provides multiple selection methods: box selection is suitable for regular shaped areas, polygon selection is suitable for irregular shaped areas, point selection with radius setting is suitable for circular areas, and multi-area merging selection is suitable for multiple scattered example areas. After selection, the system supports editing operations on the example area, including adding sub-areas, deleting irrelevant parts, adjusting boundaries, etc., to ensure that the selected example area is typical and representative.

[0038] The feature extraction unit is configured to perform at least one of the following types of feature extraction operations on the selected example region: Internal physical property statistical feature extraction: Calculate the statistics of basic geophysical properties in the example area. The basic geophysical properties include at least one of amplitude, frequency, phase, and wave impedance. The statistics include at least one of mean, variance, skewness, and kurtosis. The mean reflects the overall level of the property, the variance reflects the dispersion of the property, and the skewness and kurtosis reflect the asymmetry and steepness of the property distribution. External geometric morphological feature extraction: Calculate at least one of the following for the example region: volume, surface area, aspect ratio, spatial azimuth, boundary curvature, and thickness variation rate, comprehensively covering key morphological features such as the scale, shape, and orientation of the geological body; Spatial context relationship feature extraction: Analyze the spatial relationship between the example area and the preset geological elements. The preset geological elements include, but are not limited to, at least one of stratigraphic interfaces, faults, and adjacent anomalous areas. The spatial relationship includes, but is not limited to, at least one of distance, orientation, and contact relationship. The preset geological elements involved cover core related objects such as stratigraphic interfaces, faults, and adjacent anomalous areas. Derivative attribute feature extraction: Calculate at least one derived attribute, including but not limited to coherence, curvature, and texture attributes, on the example area, and analyze its spatial distribution pattern to provide in-depth basis for the accurate identification of geological targets; The feature fusion unit is used to normalize the various features extracted by the feature extraction unit and fuse them to generate the high-dimensional feature vector. For example, the Min-Max normalization method is used to map features of different dimensions to the [0,1] interval, eliminating the influence of dimensional differences on subsequent matching calculations. Through variance analysis and mutual information entropy calculation, features with high distinguishability of geological intent are selected, and redundant and noisy features are eliminated, such as constant features with variance less than 0.05 and highly correlated features with mutual information entropy greater than 0.9 with other features. Finally, dozens to hundreds of key features are retained to form a high-dimensional feature vector, that is, the geological intent feature vector.

[0039] This geological intent feature vector is a quantitative representation of the user's abstract geological intent. It covers multiple dimensions of features such as the physical attributes, geometric shape, spatial relationships and internal structure of the example area, and can comprehensively and accurately reflect the overall picture of the ideal target in the operator's mind.

[0040] For example, the geological intention feature vector of a riverbed sand body may contain 60 feature parameters, such as mean amplitude of 0.72, amplitude variance of 0.15, curvature of 0.53, aspect ratio of 7.2, coherence of 0.88, and distance from fault of 120m. Its information content is far greater than the textual descriptions such as strong amplitude and curved strip shape.

[0041] Furthermore, the matching degree generation visualization module includes: The similarity calculation unit is used to extract feature vectors with the same dimension as the geological intention feature vector for each geophysical data unit pre-divided within the target work area, and to calculate the similarity value between the feature vector of each data unit and the geological intention feature vector. Before performing a full-area matching scan, the similarity calculation unit first preprocesses the geophysical data of the target area, extracting the feature vector of each data unit to ensure that the feature dimensions are completely consistent with the geological intent feature vector. The preprocessing process includes data standardization, noise suppression, and feature enhancement to improve the accuracy and robustness of the similarity calculation. A hybrid metric method is used for similarity calculation, employing different weighting strategies for different geological target types.

[0042] The similarity calculation employs at least one of the following metrics: cosine similarity, Manhattan distance, and Mahalanobis distance. If geometric features constitute a high proportion of the feature vectors (such as the closure degree and wing tilt angle of an anticlinal structure), cosine similarity can be selected to emphasize the directional consistency of the feature vectors; if the numerical differences of physical properties (such as the mean amplitude and wave impedance) are crucial for identification, Manhattan distance can be selected to enhance the sensitivity to numerical deviations; if it is necessary to comprehensively consider the directional and numerical differences of features, Mahalanobis distance can be selected to optimize the weighting effect of different feature dimensions through the covariance matrix.

[0043] The similarity body construction unit is used to arrange and store the calculated similarity values ​​of each data unit according to their corresponding spatial locations, and to construct a three-dimensional geological similarity body that is consistent with the spatial range of the target work area. During the construction process, the system adopts an adaptive resolution adjustment mechanism: for regions with drastic changes in similarity values ​​(such as the boundary between the target region and non-target regions), the local resolution is automatically increased, and an interpolation algorithm is used to generate a more refined transition region; for regions with gradual similarity values, the resolution is appropriately reduced to reduce data storage and computational overhead.

[0044] This dynamic resolution strategy ensures accurate delineation of the target area boundary and optimizes the efficiency of system resource utilization. The value of each data point in the geological similarity volume ranges from 0 to 1. The higher the value, the higher the similarity between the location and the geological intention feature vector, that is, the more similar it is to the comprehensive state of the user example area. As a new type of decision support data volume, the geological similarity volume directly reflects the spatial distribution of similarity with expert cognition, providing basic data for subsequent visualization.

[0045] An adaptive visualization unit is used to automatically select and apply the corresponding visualization scheme to render and display the geological similarity body based on the data distribution characteristics of the geological similarity body (such as continuous gradient distribution, discrete high value distribution or spatial clustering distribution) and the user's geological target type (such as structural identification, lithology prediction, reservoir evaluation). For example, in the scenario of identifying sand bodies in basin channels, when the geological similarity volume exhibits a continuous gradient distribution, the adaptive visualization unit uses a semi-transparent heatmap overlay method to overlay the geological similarity volume onto the original seismic data volume in the form of a heatmap. Gradient color mapping is used (red represents 0.8-1.0 high similarity, orange represents 0.6-0.8 medium-high similarity, yellow represents 0.4-0.6 medium similarity, blue represents 0.2-0.4 low similarity, and gray represents 0-0.2 very low similarity). By adjusting the transparency of the heatmap (30% for high similarity areas and 80% for low similarity areas), it is ensured that the target area is highlighted while the geological details of the original data are preserved.

[0046] For high-point recognition scenarios, when the geological similarity volume exhibits discrete high-value distribution characteristics, the adaptive visualization unit uses an isosurface extraction algorithm to automatically extract regions with similarity values ​​greater than a preset threshold (default 0.6, which can be manually adjusted), generate three-dimensional isosurfaces, and render them in the form of semi-transparent colored entities. Users can perform interactive operations such as rotation, sectioning, and scaling on the target area to intuitively observe the spatial morphology of the target area and its relationship with the surrounding structure.

[0047] Furthermore, the adaptive visualization unit is specifically used for: When the data in the geological similarity volume has a continuous gradient distribution, the geological similarity volume is superimposed and rendered on the original geophysical data volume in the form of a semi-transparent heatmap. The color mapping relationship of the heatmap is set according to the similarity value. When the data in the geological similarity volume exhibits a continuous gradient distribution, the adaptive visualization unit employs a semi-transparent heatmap overlay rendering method. The heatmap uses a gradient color mapping rule: similarity values ​​of 0.8 to 1.0 are represented in red, 0.6 to 0.8 in orange, 0.4 to 0.6 in yellow, 0.2 to 0.4 in blue, and 0 to 0.2 in gray.

[0048] The transparency of the heatmap is adaptively adjusted based on the similarity value. The transparency of high similarity areas is set to 30%, and the transparency of low similarity areas is set to 80%, ensuring that the target area is highlighted without obscuring the key information of the original data.

[0049] For example, in the scenario of river sand body identification, geological similarity volumes with continuous gradient distribution are superimposed on the original seismic profile through a semi-transparent heat map, which clearly shows the spatial correlation between the bending direction of the sand body and the amplitude anomaly.

[0050] When there are discrete high-value regions in the geological similarity body that meet the preset similarity threshold conditions, the three-dimensional spatial morphology of the high-value regions is extracted using the isosurface extraction algorithm and rendered and displayed in the form of colored entities. The default similarity threshold is 0.6. Users can manually adjust it according to the geological target type. After the isosurface is extracted, it is rendered and displayed using a semi-transparent colored entity. The color saturation is positively correlated with the similarity value. The system supports interactive operations such as rotation, sectioning, and scaling of the target area to intuitively observe its spatial distribution characteristics.

[0051] For example, in the identification of anticline structures, the adaptive visualization unit automatically extracts discrete high-value regions with similarity values ​​greater than 0.6, generates semi-transparent red isosurfaces, and clearly displays the dome shape, closed area, and spatial relationship with surrounding faults of the structure.

[0052] The key information of the target region that meets specific similarity conditions is automatically marked in the visualization interface. The key information includes at least one of the following: the volume of the target region, the maximum similarity value, and the average attribute value. Specific similarity conditions include core target regions with similarity values ​​greater than 0.8 and potential target regions with similarity values ​​between 0.6 and 0.8. For example, target region 1 is labeled with a similarity of 0.92, a volume of 2.5 km³, and an average amplitude of 0.78, which helps operators quickly assess the effectiveness of the target region.

[0053] Furthermore, the iterative optimization module includes: The feedback receiving unit is used to provide user interface interactive elements so that the user can mark specific areas on the visualization results of the matching degree generation visualization module to input feedback information. The feedback receiving unit provides a variety of interactive elements in the visual interface, including a marker button, a selection box tool, and a point selection tool.

[0054] When operators evaluate the initial visualization results, they can select different marking modes (positive sample mode, negative sample mode, or sample mode to be verified) by clicking the marking button, and then use the box selection tool to mark continuous areas or the point selection tool to mark discrete points.

[0055] The system supports both batch and single labeling operations. For example, multiple consecutive interference areas can be selected and labeled as negative samples, or a single core target area can be selected and labeled as a positive sample. After labeling, the system automatically records the spatial location and feature vector of the sample area, and calculates the similarity deviation between the sample area and the feature vector of the current geological intent. This feedback information is stored in a temporary cache for use by the feature weight adjustment unit.

[0056] The feature weight adjustment unit is used to dynamically adjust the weight coefficients of different dimensions of features in the geological intent feature vector according to the feedback information received by the feedback receiving unit. The adjustment method includes adjusting based on the weight values ​​manually input by the user, or automatically adjusting based on the feature differences between positive and negative samples through a preset optimization algorithm. When users manually input data, the system provides a feature weight adjustment panel on the interactive interface. This panel displays all feature dimensions and their current weights in a list format. Operators can adjust the weight values ​​of each feature (range 0~1) using the slider. For example, when operators find that the matching results contain many non-target regions and the coherence features of these regions differ significantly from the example regions, they can increase the weight of the coherence features to enhance their contribution to the similarity calculation.

[0057] When making automatic adjustments, the system calculates the statistical differences between positive and negative sample regions across various feature dimensions and uses a gradient descent algorithm to automatically adjust the weights, thereby increasing the similarity of positive sample regions and decreasing the similarity of negative sample regions. For example, if the positive samples marked by the user generally have high curvature but little amplitude variation, the system automatically increases the weight of the curvature feature and decreases the weight of the amplitude feature; if the negative samples marked by the user are mostly located near faults, the system increases the weight of the fault distance feature.

[0058] These two adjustment methods can be used individually or in combination, providing users with flexible optimization options.

[0059] The model update unit is used to trigger the matching degree generation visualization module to recalculate the similarity and generate the similarity volume based on the adjusted geological intention feature vector, and update the visualization results. The model update unit passes the adjusted geological intent feature vector to the similarity calculation unit, instructing it to recalculate the similarity value of each geophysical data unit in the entire work area; passes the newly calculated similarity value to the similarity volume construction unit to generate the updated geological similarity volume; and finally passes the updated geological similarity volume to the adaptive visualization unit to generate new visualization results.

[0060] The entire update process employs a background parallel processing mechanism, utilizing GPU acceleration to ensure that the update time does not exceed 30 seconds, thus not affecting the smoothness of the operator's work. After the update is completed, the system automatically replaces the old results with the new visualization results and highlights the changed areas in the interface, allowing the operator to intuitively perceive the optimization effect. At the same time, the system records the changes in feature weights and matching results for each iteration, allowing the operator to view the effects of each iteration through the iteration history button and revert to previous iteration versions when necessary.

[0061] Furthermore, the visual interactive device also includes a knowledge base module for storing and managing at least one of the following types of information: A geophysical knowledge base, containing geological terminology and its semantic relationships, characteristic descriptions of typical geological models, and association rules between geological intent and key features; Example feature vector library, used to store example areas selected by users in historical interpretation tasks and their corresponding geological intent feature vectors; Each time a user completes an interpretation task, the system automatically saves the spatial location of the user-selected example area, feature extraction parameters, and the final generated geological intent feature vector to this library. This library supports retrieval by dimensions such as regional range, geological target type, and time range, providing a reference for novice operators. For example, when an operator needs to find channel sand bodies in a basin, the system can automatically retrieve historical examples of the same region and the same geological target in the example feature vector library and recommend the most matching geological intent feature vector as the initial parameter, greatly reducing the operational difficulty and learning curve for beginners.

[0062] A case library is used to associate and store successful geological interpretation cases. The cases include a description of the geological intent input by the user, a selected example area, a generated geological similarity body, and information on the finally confirmed target area. Each case record includes a description of the user's original geological intent, the spatial location and feature parameters of the selected example area, the generated geological similarity volume, the feature weight adjustment record during the iterative optimization process, the final confirmed target area information and its verification results. The case library supports operators in comparing cases and drawing on experience by establishing relationships between cases.

[0063] Furthermore, the present invention also provides a visualization and interactive method for geophysical data evaluation, characterized by comprising the following steps: Acquire and parse natural language information and / or sketch information input by the user to obtain preliminary geological intent features; In response to the user's selection of an example region in the three-dimensional geophysical data volume, multi-dimensional features are extracted from the example region to generate a high-dimensional feature vector representing the user's geological intent as the geological intent feature vector; the multi-dimensional features include the internal physical property statistical features, external geometric morphology features, spatial context relationship features, and derived attribute features of the example region. Based on the geological intent feature vector, the similarity between it and each geophysical data unit in the target work area is calculated, a three-dimensional geological similarity volume is generated, and the geological similarity volume is visualized. The system receives feedback from users based on the visualization results and adjusts the geological intent feature vector and / or similarity calculation model according to the feedback to update the geological similarity volume and its visualization.

[0064] This method bridges the semantic gap between professional geological concepts and underlying technical parameters through an example calibration process; it comprehensively captures the integrated features of geological targets through a multi-dimensional feature extraction mechanism; it constructs an intuitive intent-driven data browsing method through a geological similarity volume; and it integrates human-machine intelligence through an iterative optimization mechanism, enabling the system to adapt to the differences in professional cognition among different operators.

[0065] The entire methodology is well-designed with clear steps, achieving a precise mapping from professional geological intent to visual results, and significantly improving the efficiency and accuracy of geophysical data assessment.

[0066] Furthermore, the present invention provides an electronic device, characterized in that it includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs containing instructions for performing the methods described in any of the embodiments of the present invention.

[0067] The electronic device adopts a modular hardware architecture design, including a multi-core central processing unit (CPU), a graphics processing unit (GPU), a large-capacity memory, a high-speed solid-state storage, and an interactive touch display component.

[0068] The memory stores operating system programs and application software programs, including data access programs, core processing programs, interactive performance programs, and knowledge base management programs.

[0069] The data access program interfaces with various geophysical data formats (such as SEGY, SGY, HDF5, etc.), providing a unified data read / write interface. The core processing program includes a multimodal intent parsing program, a high-dimensional feature extraction program, a similarity measurement program, and an iterative optimization program, used to implement the core computational logic of the aforementioned methods. The interactive presentation program provides a user interface and visualization rendering capabilities, supporting simultaneous rendering of multiple views and displaying various data types such as raw seismic data, attribute volumes, and geological similarity volumes. The knowledge base management program manages geological expertise, a library of typical geological models, and historical interpretation cases, providing domain knowledge support for the system. When one or more processors execute these programs, the program modules communicate through standardized interfaces, ensuring system scalability and module independence.

[0070] Furthermore, the present invention provides a computer-readable storage medium, characterized in that, when the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is able to perform the method described in any of the embodiments of the present invention.

[0071] The computer-readable storage medium includes, but is not limited to, any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), and random access memory (RAM).

[0072] The instructions in the computer-readable storage medium adopt a layered architecture design, including data access layer instructions, core processing layer instructions, interactive presentation layer instructions, and knowledge base layer instructions.

[0073] The data access layer instructions are used to interface with various geophysical data formats; the core processing layer instructions include multimodal intent parsing instructions, high-dimensional feature extraction instructions, and similarity measurement instructions, which are used to implement the core computational logic of the above methods; the interactive presentation layer instructions are used to provide user interface and visualization rendering functions; the knowledge base layer instructions are used to manage geological expertise, typical geological model libraries, and historical interpretation cases.

[0074] When the electronic device loads instructions from the computer-readable storage medium, the instructions at each layer communicate through standardized interfaces to ensure the system's scalability and module independence.

[0075] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A visualization and interactive device for geophysical data evaluation, characterized in that, include: The intent input parsing module is used to acquire and parse the information input by the user to obtain preliminary geological intent features; The example feature extraction module is used to respond to the user's selection of an example region in the three-dimensional geophysical data volume, perform multi-dimensional feature extraction on the example region, and generate a high-dimensional feature vector representing the user's geological intent as the geological intent feature vector; the multi-dimensional features include the internal physical property statistical features, external geometric morphology features, spatial context relationship features, and derived attribute features of the example region. The matching degree generation visualization module is used to calculate the similarity between the geological intent feature vector and each geophysical data unit in the target work area, generate a three-dimensional geological similarity volume, and visualize the geological similarity volume. The iterative optimization module is used to receive feedback information from the user based on the visualization results, and adjust the geological intent feature vector and / or similarity calculation model according to the feedback information.

2. The visualization and interactive device for geophysical data evaluation according to claim 1, characterized in that, The intent input parsing module includes: The natural language processing unit is used to receive natural language information input by the user through voice or text, and to parse the natural language information based on a preset geophysical knowledge base, and to identify and extract geological entities, attribute requirements and spatial relationship information therein; The sketch recognition unit is used to receive sketch information input by the user in the sketch drawing area and extract the geometric features of the sketch information.

3. The visualization and interactive device for geophysical data evaluation according to claim 1, characterized in that, The example feature extraction module includes: The example area selection interface is used to provide user interface interactive elements, allowing users to select the example area on a 3D geophysical data volume view or a 2D profile view by using box selection, polygon selection, or point selection. The feature extraction unit is configured to perform at least one of the following types of feature extraction operations on the selected example region: Internal physical property statistical feature extraction: Calculate the statistics of basic geophysical properties within the example area; External geometric feature extraction: Calculate at least one of the following: volume, surface area, aspect ratio, spatial azimuth, boundary curvature, and thickness variation rate of the example region; Spatial context feature extraction: Analyze the spatial relationship between the example area and preset geological elements; Derivative attribute feature extraction: Calculate at least one derived attribute, including but not limited to coherence, curvature, and texture attributes, on the example region, and analyze its spatial distribution pattern; The feature fusion unit is used to normalize the various features extracted by the feature extraction unit and fuse them to generate the high-dimensional feature vector.

4. The visualization and interactive device for geophysical data evaluation according to claim 1, characterized in that, The matching degree generation visualization module includes: The similarity calculation unit is used to extract feature vectors with the same dimension as the geological intention feature vector for each geophysical data unit pre-divided within the target work area, and to calculate the similarity value between the feature vector of each data unit and the geological intention feature vector. The similarity body construction unit is used to arrange and store the calculated similarity values ​​of each data unit according to their corresponding spatial locations, and to construct a three-dimensional geological similarity body that is consistent with the spatial range of the target work area. The adaptive visualization unit is used to automatically select and apply the corresponding visualization scheme to render and display the geological similarity volume based on the data distribution characteristics of the geological similarity volume and the user's geological target type.

5. The visualization and interactive device for geophysical data evaluation according to claim 4, characterized in that, The adaptive visualization unit is specifically used for: When the data in the geological similarity volume exhibits a continuous gradient distribution, a heatmap is used to overlay and render the geological similarity volume onto the original geophysical data volume. The color mapping relationship of the heatmap is set according to the similarity value. When there are discrete high-value regions in the geological similarity body that meet the preset similarity threshold conditions, the three-dimensional spatial morphology of the high-value regions is extracted using the isosurface extraction algorithm and rendered and displayed in the form of colored entities. The system automatically marks key information of target regions that meet specific similarity criteria in the visualization interface.

6. The visualization and interactive device for geophysical data evaluation according to claim 1, characterized in that, The iterative optimization module includes: The feedback receiving unit is used to provide user interface interactive elements so that the user can mark specific areas on the visualization results of the matching degree generation visualization module to input feedback information. The feature weight adjustment unit is used to adjust the weight coefficients of different dimensions of features in the geological intent feature vector according to the feedback information received by the feedback receiving unit; the adjustment method includes adjusting based on the weight values ​​manually input by the user, or automatically adjusting based on the feature differences between positive and negative samples through a preset optimization algorithm; The model update unit is used to trigger the matching degree generation visualization module to recalculate the similarity and generate the similarity volume based on the adjusted geological intention feature vector, and update the visualization results.

7. The visualization and interactive device for geophysical data evaluation according to claim 1, characterized in that, The visual interactive device also includes a knowledge base module for storing and managing at least one of the following types of information: A geophysical knowledge base, containing geological terminology and its semantic relationships, characteristic descriptions of typical geological models, and association rules between geological intent and key features; Example feature vector library, used to store example areas selected by users in historical interpretation tasks and their corresponding geological intent feature vectors; The case library is used to associate and store successful geological interpretation cases. The cases include a description of the geological intent input by the user, a selected example area, a generated geological similarity body, and information on the finally confirmed target area.

8. A visualization and interactive method for geophysical data evaluation, characterized in that, Includes the following steps: Acquire and parse natural language information and / or sketch information input by the user to obtain preliminary geological intent features; In response to the user's selection of an example region in the three-dimensional geophysical data volume, multi-dimensional features are extracted from the example region to generate a high-dimensional feature vector representing the user's geological intent as the geological intent feature vector; the multi-dimensional features include the internal physical property statistical features, external geometric morphology features, spatial context relationship features, and derived attribute features of the example region. Based on the geological intent feature vector, the similarity between it and each geophysical data unit in the target work area is calculated, a three-dimensional geological similarity volume is generated, and the geological similarity volume is visualized. The system receives feedback from users based on the visualization results and adjusts the geological intent feature vector and / or similarity calculation model according to the feedback to update the geological similarity volume and its visualization.

9. An electronic device, characterized in that, It includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, and the one or more programs include instructions for performing the method as described in claim 8.

10. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method as described in claim 8.

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