A method and system for three-dimensional visualization interaction of seismic data

Abstract

The application relates to the field of data processing, and provides a seismic data three-dimensional visualization interaction method and system, which comprises the following steps: acquiring multi-type data containing at least two data types and at least two data attribute dimensions; based on the correlation between the data attribute dimensions, at least two data attribute dimensions are jointly mapped into a visual attribute combination, so that a visual object with semantic correlation is generated in a unified visualization space; in response to a space region selection operation in a first view, based on the data space distribution characteristics of data objects in the region, the selection operation is mapped to a corresponding space range and data filtering condition of a second view, so that the continuity of cross-view analysis context is maintained. The application realizes unified visual expression of semantic correlation of multi-type data, maintains the continuity of cross-view analysis context, and improves the efficiency and coherence of multi-dimensional data analysis.

Description

Technical Field

[0001] This application relates to the field of data processing, and more specifically, to a three-dimensional visualization and interactive method and system for seismic data. Background Technology

[0002] Currently, in fields such as seismological data visualization and 3D graphics, the 3D visualization of multi-type heterogeneous data typically employs 2D planar displays or relies on specialized desktop software. Existing visualization solutions, when handling multidimensional data containing various data types (such as seismic events, fault lines, well trajectories, and monitoring stations), often perform independent visual mapping for each attribute dimension of each data type; for example, mapping magnitude only to object size or depth to color. This single-dimensional, independent mapping method severs the inherent connections between data attributes, resulting in a lack of unified visual expression with semantic relationships among heterogeneous multi-type data within the same visualization space. Users find it difficult to simultaneously read and understand the collaborative relationships between multiple data attribute dimensions from a single visual element.

[0003] Furthermore, in visualization systems that include two-dimensional and three-dimensional views, existing view linkage mechanisms mainly rely on the synchronization of view states (such as the synchronization of camera position or selected object ID). This view state-based linkage method fails to use the spatial distribution characteristics of the data itself as an anchor point, which makes it easy to lose the analysis context (such as the spatial distribution range of the data and the filtering conditions of the depth dimension) when users switch between different views or select spatial regions, and lacks the continuity of analysis context based on data characteristics.

[0004] Therefore, there is an urgent need for a visualization interaction method that can realize the semantic association expression of multiple types of data within a unified visualization space and maintain the continuity of cross-view analysis context. Summary of the Invention

[0005] The purpose of this application is to provide a three-dimensional visualization and interactive method and system for seismic data, in order to alleviate the problems of existing technologies, such as the lack of unified visual expression of semantic association for multi-type heterogeneous data and the lack of continuity of analytical context based on data features across views.

[0006] In a first aspect, the present invention provides a three-dimensional visualization and interactive method for seismic data, comprising: Acquire multi-type data, which contains at least two data types, and each data type contains at least two data attribute dimensions; Based on the relationships between various data attribute dimensions in multiple types of data, at least two data attribute dimensions are jointly mapped to a combination of visual attributes to generate semantically related visual objects in a unified visualization space. In response to a spatial region selection operation in the first view, the spatial region selection operation is mapped to the corresponding spatial range and data filtering conditions in the second view based on the spatial distribution characteristics of the data objects within the spatial region, so as to maintain the continuity of the cross-view analysis context.

[0007] In an optional implementation, at least two data types include event-type data, and at least two data attribute dimensions include the intensity attribute and the depth attribute of the event-type data. Jointly map at least two data attribute dimensions to a combination of visual attributes, including: The intensity and depth attributes are jointly mapped to the size and color gradient parameters of the geometry. The size and color gradient parameters change synergistically based on the intrinsic relationship between the intensity and depth attributes.

[0008] In an optional implementation, the intensity attribute and depth attribute are jointly mapped to the size parameters and color gradient parameters of the geometry, including: Normalize the original values ​​of the intensity attribute and the depth attribute respectively to obtain normalized intensity value and normalized depth value; Based on the weight coefficients configured by attribute features, a weighted calculation is performed on the normalized intensity value and the normalized depth value to generate a joint mapping intermediate value; Based on the joint mapping intermediate value, the size mapping function is called to calculate the size parameters of the geometry, and the color mapping function is called simultaneously to calculate the color gradient parameters. The size parameters and color gradient parameters are driven by the same joint mapping intermediate value.

[0009] In an optional implementation, the first view is a two-dimensional view and the second view is a three-dimensional view; Based on the spatial distribution characteristics of data objects within a spatial region, the spatial region selection operation is mapped to the corresponding spatial range and data filtering conditions in the second view, including: Based on the spatial distribution range and depth distribution characteristics of the data within the selected spatial region in the two-dimensional view, the spatial distribution range is mapped to the corresponding spatial range in the three-dimensional view, and the depth distribution characteristics are transformed into data filtering conditions for the three-dimensional view.

[0010] In an optional implementation, based on the spatial distribution range and depth distribution characteristics of the data within a selected spatial region in the two-dimensional view, the spatial distribution range is mapped to the corresponding spatial range in the three-dimensional view, and the depth distribution characteristics are converted into data filtering conditions for the three-dimensional view, including: Extract the set of boundary coordinate points of the selected spatial region in the two-dimensional view to represent the geometric contour of the selected spatial region; Using a coordinate system transformation matrix from two-dimensional to three-dimensional, the set of boundary coordinate points is mapped to a set of three-dimensional spatial coordinate points; Based on the extreme value range of the coordinates of the set of three-dimensional spatial coordinate points, a three-dimensional spatial bounding box is generated to define the corresponding spatial range in the three-dimensional view. The minimum, maximum, and distribution density parameters of data objects within a selected spatial region in the two-dimensional view are statistically analyzed. Based on the distribution density parameters, a filtering threshold range for the depth dimension is determined, which serves as the data filtering condition for the three-dimensional view.

[0011] In an optional implementation, the method further includes: In response to spatial picking operations on visualized objects, identify the data type of the visualized object and the attribute feature values ​​of each data attribute dimension; Based on data type and attribute feature values, it provides type-aware differentiated visual enhancement feedback, with different combinations of visual enhancement parameters corresponding to visual objects of different data types.

[0012] In optional implementations, data types include event-type data and structure-type data; Type-aware differential visual augmentation feedback includes: For visualization objects of event-type data, it provides size enhancement effects based on intensity attribute values ​​and displays detailed information including intensity and depth attribute values. The enhancement magnitude of the size enhancement effect is positively correlated with the intensity attribute value. For visualization objects of constructed data, a trajectory highlighting effect and a direction parameter label are provided. The trajectory highlighting effect renders an enhanced glowing style along the line segment path of the constructed data, and the direction parameter label displays the direction angle and tilt angle parameters at preset positions.

[0013] Secondly, the present invention provides a three-dimensional visualization and interactive device for seismic data, comprising: The data acquisition module is configured to acquire multiple types of data, which include at least two data types and each data type includes at least two data attribute dimensions. The joint mapping module is configured to jointly map at least two data attribute dimensions into a combination of visual attributes based on the relationship between the data attribute dimensions in multiple types of data, so as to generate a semantically related visualization object in a unified visualization space. The cross-view linkage module is configured to respond to a spatial region selection operation in the first view, and based on the spatial distribution characteristics of the data objects within the spatial region, map the spatial region selection operation to the corresponding spatial range and data filtering conditions in the second view, so as to maintain the continuity of the cross-view analysis context.

[0014] In an optional implementation, the joint mapping module includes: The attribute extraction unit is configured to parse the data attribute dimensions and attribute value relationships of multiple types of data. The visual parameter calculation unit is configured to perform joint mapping calculations based on attribute value relationships to generate visual attribute combinations. The cross-view linkage module includes: The region coordinate extraction unit is configured to extract the boundary coordinates and spatial distribution characteristics of the selected spatial region in the first view; The spatial extent mapping unit is configured to convert boundary coordinates into the corresponding spatial extent of the second view, and to convert the depth distribution features in the spatial distribution features of the data into data filtering conditions for the second view.

[0015] Thirdly, the present invention provides a computer-readable storage medium storing program instructions that, when executed by a processor, implement the method described in the foregoing embodiments.

[0016] Beneficial effects: This invention, by leveraging the relationships between various data attribute dimensions in multi-type data, jointly maps at least two data attribute dimensions into a visual attribute combination, thus changing the existing method of independently mapping each attribute dimension. Since the visual attribute combination is generated by the joint driving force of multiple data attribute dimensions, the coordinated changes between visual elements can reflect the inherent relationships between data attributes, thereby forming semantically related visual objects within a unified visualization space. This allows users to simultaneously obtain data feature information from multiple dimensions from a single visual expression, improving the semantic expression capability and data analysis efficiency of heterogeneous multi-type data.

[0017] This invention, in response to a spatial region selection operation in a first view, maps the selection operation to the corresponding spatial range and data filtering conditions in a second view based on the spatial distribution characteristics of data objects within that region. This changes the existing method of synchronization based solely on view status (such as camera position or object ID). Because the linkage anchor point is based on the spatial distribution characteristics of the data itself (such as spatial range and depth distribution characteristics), it can automatically transform the spatial selection operation into spatial range definition and depth dimension data filtering conditions in another view during view switching. This effectively maintains the continuity of the cross-view analysis context, preventing users from losing the data analysis context during view switching and improving the coherence and accuracy of multidimensional spatial data analysis.

[0018] This invention provides type-aware, differentiated visual enhancement feedback based on data type and attribute feature values ​​in response to spatial picking operations on visualized objects, thus enabling the interactive feedback itself to carry data semantic information. Visualized objects of different data types present different combinations of visual enhancement parameters according to their attribute features, allowing users to intuitively perceive the data type and core attribute features of the picked object during interactive operations, further improving the data analysis focus and information acquisition efficiency of the interactive operation. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This application provides a schematic flowchart of a three-dimensional visualization and interactive method for seismic data. Figure 2 A schematic diagram of another three-dimensional visualization and interactive method for seismic data provided in this application embodiment; Figure 3 A schematic diagram of another three-dimensional visualization and interactive method for seismic data provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of a three-dimensional visualization interactive device for seismic data provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0022] This embodiment provides a three-dimensional visualization and interactive method for seismic data. This method aims to address the problems in existing visualization tools, such as the lack of a unified visual representation of semantically related heterogeneous data of various types, and the lack of continuity of analytical context based on data features across views. For example... Figure 1 As shown, the method specifically includes the following steps: Step S110: Obtain multi-type data, which includes at least two data types and each data type includes at least two data attribute dimensions.

[0023] Specifically, multi-type data refers to a collection of heterogeneous data that needs to be displayed and interacted with simultaneously in the same analysis scenario. For example, in a geological analysis scenario, multi-type data can include point data representing discrete events and linear or area data representing continuous spatial structures, each carrying different attribute dimensions.

[0024] It should be understood that although subsequent embodiments will be elaborated in detail using specific data types such as earthquake events and active faults as examples, the core mechanism of this embodiment is not limited to data in a specific professional field. Any data set with multiple types, each containing multiple attribute dimensions, can be applied to the technical solution of this invention. The data acquisition process can be implemented through standard data interfaces or file parsing modules, transforming the raw data into structured records that can be processed internally by the system.

[0025] Step S120: Based on the correlation between the data attribute dimensions in the multi-type data, at least two data attribute dimensions are jointly mapped to a combination of visual attributes to generate a semantically related visual object in a unified visualization space.

[0026] Specifically, in existing conventional visualization solutions, each attribute dimension is typically mapped independently visually. For example, intensity is mapped to object size, or depth is mapped to object color. This independent mapping severs the inherent relationships between data attributes, making it difficult for users to simultaneously read and understand the collaborative relationships of multiple dimensions from a single visual element. In contrast, the joint mapping mechanism adopted in this embodiment determines a set of visual attribute combinations by having at least two inherently related data attribute dimensions work together.

[0027] For example, jointly mapping intensity and depth attributes to the size and color gradient parameters of a geometric object allows size and color to no longer be independently changing visual channels, but rather to produce a synergistic change. When the intensity attribute value is high and the depth attribute value is shallow, the size and color gradient parameters will simultaneously change significantly based on this inherent relationship, resulting in a visually large and striking expression; conversely, when the intensity is low and the depth is deep, the combination of visual attributes will synergistically present a small and dim expression. This synergistic change generated by the joint mapping gives the generated visualization object a semantic correlation that cannot be achieved by a single attribute mapping. Users can simultaneously perceive and interpret the coupling state between multiple data attribute dimensions from the visual appearance of a single visualization object.

[0028] It should be understood that there are multiple paths to implement the joint mapping algorithm. For example, intermediate driving values ​​can be generated by normalized weighted calculation, or direct mapping can be performed through multidimensional interpolation functions. This embodiment will not elaborate on the specific mapping algorithm details. As long as the mechanism of joint driving of visual attribute combination and coordinated change by multiple attribute dimensions is satisfied, it falls within the protection scope of this invention.

[0029] Step S130: In response to the spatial region selection operation in the first view, based on the data spatial distribution characteristics of the data objects within the spatial region, the spatial region selection operation is mapped to the corresponding spatial range and data filtering conditions in the second view to maintain the continuity of the cross-view analysis context.

[0030] Specifically, the core of this step lies in linking based on the spatial distribution characteristics of the data, rather than the traditional synchronization based on view state. Existing conventional view linking mechanisms mainly rely on the synchronization of view state, such as synchronizing the camera's viewpoint position or synchronizing the identifier of the selected object. This linking method fails to use the spatial distribution characteristics of the data itself as an anchor, which makes it easy to lose the analysis context when the user switches between different views.

[0031] The linkage mechanism in this embodiment uses the spatial distribution characteristics of data objects within a selected spatial region in the first view as the mapping source. When a user selects or clicks on an irregular spatial region in the first view (e.g., a two-dimensional planar view), not only are the geometric boundaries of the region extracted, but more importantly, the distribution range of data objects within that region in the spatial dimension and the distribution characteristics in the depth dimension (e.g., depth extrema and density distribution) are extracted. Subsequently, these data spatial distribution characteristics are mapped to the second view (e.g., a three-dimensional stereo view), transforming them into corresponding spatial range definitions (e.g., generating a three-dimensional bounding box) and data filtering conditions (e.g., depth dimension filtering threshold range) in the second view. This mapping method ensures that the anchor point for cross-view linkage is data characteristics rather than view state, enabling the second view to automatically focus on a three-dimensional spatial range containing the same data characteristics and filter only data objects that conform to the depth distribution characteristics. This effectively maintains the continuity of the cross-view analysis context and prevents users from losing track of data distribution characteristics when switching views.

[0032] It should be understood that although this embodiment uses the linkage of two-dimensional to three-dimensional views as an example to illustrate the principle, the combination of the first view and the second view is not limited to this. Any combination of views with different spatial dimensions or projection methods, as long as the range and filtering conditions are mapped based on the spatial distribution characteristics of data, should be regarded as an extension of the technical solution of this invention.

[0033] Through the solutions described in steps S110 to S130 above, this embodiment realizes the semantic association expression of multiple types of heterogeneous data in the same visualization space, and maintains the continuity of the analysis context through cross-view linkage based on the spatial distribution characteristics of data. This mechanism not only changes the visual semantic fragmentation caused by independent mapping of attribute dimensions in the prior art, but also overcomes the context loss problem caused by view state synchronization, thus improving the coherence and efficiency of multidimensional data analysis.

[0034] In some embodiments, the data types and attribute dimensions in the joint mapping step are specifically described.

[0035] Specifically, at least two data types include event-type data, and at least two data attribute dimensions include the intensity and depth attributes of the event-type data. The at least two data attribute dimensions are jointly mapped to a combination of visual attributes, including: jointly mapping the intensity and depth attributes to the size and color gradient parameters of a geometric object, where the size and color gradient parameters change synergistically based on the inherent relationship between the intensity and depth attributes.

[0036] It should be understood that although this embodiment uses the intensity and depth attributes of event-type data as an example for illustration, the specific attribute names of event-type data may be different in analysis scenarios in other professional fields. For example, in the earthquake monitoring scenario, the intensity attribute is specifically expressed as magnitude and the depth attribute is specifically expressed as focal depth. In the industrial fault monitoring scenario, the intensity attribute can be the fault intensity and the depth attribute can be the fault occurrence level. As long as there is an inherent correlation between these attribute dimensions that needs to be perceived simultaneously, the joint mapping mechanism of this embodiment can be applied.

[0037] In this embodiment, the size parameters and color gradient parameters of the geometric object are no longer visual channels that independently respond to single data attributes, but rather they change synergistically based on the intrinsic relationship between intensity and depth. For example, when the intensity attribute value is high and the depth attribute value is shallow, the size parameters and color gradient parameters will synergistically present a visual expression that is both large and has a high gradient, making it eye-catching; conversely, when the intensity is low and the depth is deep, the combination of visual attributes will synergistically present a small and low gradient, making it dim. This synergistic change allows users to simultaneously perceive and interpret the coupling state of the two dimensions of intensity and depth from the visual appearance of a geometric object, avoiding the visual semantic fragmentation and interpretation ambiguity caused by large size but dim color in traditional independent mapping methods.

[0038] Furthermore, the intensity and depth attributes are jointly mapped to the size parameters and color gradient parameters of the geometry, including: normalizing the original values ​​of the intensity and depth attributes respectively to obtain normalized intensity and depth values; performing weighted calculations on the normalized intensity and depth values ​​based on the weight coefficients configured for the attribute features to generate a joint mapping intermediate value; and using the joint mapping intermediate value, calling the size mapping function to calculate the size parameters of the geometry and simultaneously calling the color mapping function to calculate the color gradient parameters, with the size parameters and color gradient parameters being uniformly driven by the same joint mapping intermediate value.

[0039] This embodiment translates the joint mapping of multi-dimensional attributes into specific computational logic through the above algorithm steps.

[0040] The normalization process aims to eliminate the differences in the dimensions and numerical ranges of the original values ​​of different attribute dimensions, and to uniformly map them to the dimensionless interval of 0 to 1, so as to ensure the numerical stability of subsequent weighted calculations.

[0041] The weighting coefficients can be adjusted to emphasize different attributes for different analysis scenarios. For example, in an analysis scenario focusing on high-intensity events, the weighting coefficient for the intensity attribute can be configured to 0.7, and the weighting coefficient for the depth attribute can be configured to 0.3; while in an analysis scenario focusing on deep events, the weighting coefficients can be interchanged between 0.3 and 0.7. Typical values ​​are usually between 0.1 and 0.9, and the sum of the two is 1. If the weighting coefficients are configured to out-of-bounds values, such as an intensity weight of 1.0 and a depth weight of 0.0, the joint mapping will degenerate into a single mapping driven solely by the intensity attribute. The influence of the depth attribute on visual expression will be completely erased, leading to severe distortion and information loss in visual semantics. This demonstrates the necessity of reasonably configuring the weighting coefficients to maintain the joint influence of multi-dimensional attributes.

[0042] If the size and color gradient parameters are driven independently by different intermediate values ​​or different attribute dimensions, even if they appear to change simultaneously, under extreme data combinations (such as high-intensity, extremely deep events), the size may be very large while the color is very dim. This independent mapping leads to a visual semantic fragmentation that severely interferes with the user's judgment. However, by using the same joint mapping intermediate value to uniformly drive the size and color mapping functions, it is ensured that regardless of how the original input intensity and depth values ​​are combined, the final generated size and color will always be calculated based on the same comprehensive evaluation value. This guarantees absolute consistency in coordinated change in both mathematical logic and visual presentation, allowing the combination of visual attributes to truly carry the semantic information of the intrinsic relationship between intensity and depth.

[0043] It should be understood that the above-described normalization and weighted calculation are merely one preferred algorithmic path for joint mapping. In other embodiments, multidimensional interpolation functions or lookup table mappings can also be used to generate unified driving values. As long as the mechanism of multiple attribute dimensions jointly determining a single driving source and synchronously controlling multiple visual attributes is satisfied, it should be considered an equivalent replacement for this embodiment. The above description is only an explanatory illustration of the joint mapping mechanism in this embodiment, and not a restrictive definition.

[0044] In some embodiments, the specific dimensions of the first view and the second view, as well as the linkage algorithm, are described in detail.

[0045] Specifically, the first view is a two-dimensional view, and the second view is a three-dimensional view. Based on the spatial distribution characteristics of data objects within a spatial region, the spatial region selection operation is mapped to the corresponding spatial range and data filtering conditions in the second view. This includes: based on the spatial distribution range and depth distribution characteristics of the selected spatial region in the two-dimensional view, mapping the spatial distribution range to the corresponding spatial range in the three-dimensional view, and converting the depth distribution characteristics into data filtering conditions for the three-dimensional view.

[0046] It should be understood that the core mechanism of this embodiment lies in resolutely abandoning conventional synchronization methods based on view states (such as camera position, viewing direction, or selected object identifiers). Conventional view state synchronization merely moves the camera in the 3D view above the corresponding coordinates in the 2D view, or highlights the same object ID. This method is prone to causing the loss of analysis context when data distribution is uneven or when depth dimension information exists. For example, if the selected area in the 2D view contains a large number of shallow events and a very small number of deep events, the linkage based on camera position will only focus the 3D view on the surface of that area, causing the sparse deep events to be obscured or submerged by the dense shallow events, and the user cannot continue the analysis of deep features in the 3D view. In contrast, this embodiment maps based on the spatial distribution characteristics of the data, directly extracting the spatial distribution range and depth distribution characteristics of the data itself within the selected 2D area as the mapping source, mapping the spatial distribution range to the corresponding spatial range in the 3D view, and converting the depth distribution characteristics into data filtering conditions in the 3D view. This ensures that no matter how the view is switched, the core distribution characteristics of the data are always preserved and transmitted as the anchor point of the analysis context, thereby effectively maintaining the continuity of the cross-view analysis context.

[0047] Furthermore, based on the spatial distribution range and depth distribution characteristics of the data within the selected spatial region in the two-dimensional view, the spatial distribution range is mapped to the corresponding spatial range in the three-dimensional view, and the depth distribution characteristics are transformed into data filtering conditions in the three-dimensional view. This includes the following specific algorithmic steps: Step S210: Extract the set of boundary coordinate points of the selected spatial region in the two-dimensional view to represent the geometric contour of the selected spatial region.

[0048] When users select a spatial region in a 2D view, they often drag the mouse to draw irregular polygons or rectangles to select the area. This step extracts all the coordinates of the selected region's boundary to form a set, thus accurately representing the geometric contour of the user's intended selection, rather than simply approximating it with a rectangular bounding box. This lays the data foundation for subsequent processing of irregular selection regions.

[0049] Step S220: Using a coordinate system transformation matrix from two-dimensional to three-dimensional, the set of boundary coordinate points is mapped to a set of three-dimensional spatial coordinate points.

[0050] Because of the dimensional differences and projection relationships between the coordinate systems of the two-dimensional view and the three-dimensional view, this step uses a pre-constructed coordinate system transformation matrix to elevate the boundary coordinate points on the two-dimensional plane to the three-dimensional space, giving them depth dimension coordinate information, thereby achieving cross-dimensional mapping.

[0051] Step S230: Based on the extreme value range of the coordinates of the three-dimensional spatial coordinate point set, generate a three-dimensional spatial bounding box to define the corresponding spatial range in the three-dimensional view.

[0052] Because the selection areas in a 2D view are often irregular polygons, which, when mapped to 3D space, form irregular 3D point cloud contours, directly defining the spatial extent and determining intersections of these irregular contours is computationally extremely complex and unstable. Generating 3D bounding boxes based on coordinate extreme value ranges is a general and efficient solution for handling irregular selection areas. It transforms complex irregular spatial queries into simple bounding box intersection determination, accommodating selection areas of various shapes while ensuring the computational robustness of defining the spatial extent of the 3D view.

[0053] Step S240: Calculate the minimum, maximum and distribution density parameters of data objects in the selected spatial area of ​​the two-dimensional view in the depth dimension, and determine the filtering threshold range of the depth dimension based on the distribution density parameters, which will serve as the data filtering conditions for the three-dimensional view.

[0054] Because data distribution in real-world applications is often extremely uneven, using only the minimum and maximum depth values ​​as the filtering threshold range can lead to a significant widening of this range by a few outlier deep or shallow events. This results in a large amount of noisy data irrelevant to the core analysis objective being filtered out in the 3D view. Determining the filtering threshold based on distribution density, however, can adapt to uneven data distribution, avoiding the omission of sparse but important core data or the introduction of significant noise by using fixed or extreme thresholds. For example, a typical method for calculating the distribution density parameter is kernel density estimation or histogram statistics, dividing the depth dimension into several equally spaced statistical intervals and calculating the frequency or relative frequency of data objects within each interval. The logic for determining the threshold interval can be to expand outwards from the interval with the highest density until the cumulative coverage reaches a preset confidence level, or until a specific proportion of the density below the overall average density is encountered, at which point the threshold is truncated. This density-distribution-based dynamic truncation logic can automatically eliminate outlier noise in the depth dimension and accurately pinpoint the core depth range of densely distributed data. This ensures that the data filtering conditions in the 3D view neither miss sparse but core cluster data nor are disturbed by extreme outliers. As a result, while maintaining the continuity of the analysis context, it significantly improves the noise resistance and accuracy of cross-view linkage.

[0055] It should be understood that the specific parameters of the coordinate system transformation matrix, the specific geometric algorithm for generating bounding boxes, and the specific mathematical model for density estimation can all be adaptively adjusted according to the actual rendering engine and data analysis needs. As long as the core mechanism of mapping and density truncation based on the spatial distribution characteristics of data is satisfied, they should all be considered equivalent replacements in this embodiment. The above description is only an explanatory illustration of the cross-view linkage mechanism in this embodiment, and not a restrictive definition.

[0056] In some embodiments, the interactive feedback steps are further described in detail.

[0057] Specifically, in response to spatial picking operations on visualized objects, the system identifies the data type of the visualized object and the attribute feature values ​​of each data attribute dimension; based on the data type and attribute feature values, it provides type-aware differentiated visual enhancement feedback, with different combinations of visual enhancement parameters corresponding to visualized objects of different data types.

[0058] It should be understood that the core mechanism of this embodiment lies in resolutely abandoning the traditional uniform highlighting feedback mode. In multidimensional data analysis scenarios, interactive feedback itself must carry data semantics. If the same border highlighting or uniform magnification effect is used for all picked objects, this visual feedback, although indicating that the object has been selected, cannot distinguish the data type and core attribute characteristics of the object at all. Users still need to look up attributes through an additional text panel in the dense visualization space, resulting in extremely poor data analysis direction of interactive operations. However, the type-aware differentiated feedback of this embodiment allows the combination of visual enhancement parameters to directly map the data type and attribute characteristic values ​​of the object. At the moment of picking, users can intuitively perceive whether the picked object is a discrete event or a continuous structure, as well as the magnitude level of its core attributes, through the form of visual feedback. This elevates interactive feedback from a simple selection prompt to an instant expression channel of data semantics, significantly improving the information acquisition efficiency of interactive operations.

[0059] The pickup and feedback timing logic in this embodiment follows the following processing flow: First, the system responds to spatial picking operations (such as precise object localization based on ray casting algorithms). After detecting an intersection, it obtains the nearest intersection and performs object recognition, identifying the data type of the visualized object and the attribute feature values ​​of each data attribute dimension. Subsequently, based on the identified data type, it enters different visual enhancement parameter calculation paths, ultimately outputting differentiated visual enhancement feedback and updating the rendered screen. This sequential logic ensures the immediacy and semantic accuracy of the feedback.

[0060] Furthermore, the data types include event-type data and structure-type data; the type-aware differentiated visual enhancement feedback includes: for event-type data visualization objects, providing size enhancement effects based on intensity attribute values ​​and displaying detailed information including intensity and depth attribute values, with the enhancement magnitude of the size enhancement effect being positively correlated with the intensity attribute value; for structure-type data visualization objects, providing trajectory highlighting effects and direction parameter annotations, with the trajectory highlighting effect rendering enhanced glowing styles along the line segment paths of the structure-type data, and the direction parameter annotations displaying direction angle and tilt parameters at preset positions.

[0061] For the size enhancement effect of event-type data, the specific mapping logic that the enhancement magnitude is positively correlated with the intensity attribute value can be implemented defensively using various function forms.

[0062] For example, in one implementation of a linear mapping, the amplification calculation formula can be configured as: amplification factor = base factor + intensity normalization value * increment coefficient, where the base factor ensures that even the smallest event can be perceived (e.g., 1.2), and the increment coefficient controls the visual impact of high-intensity events (e.g., 0.8), so that the higher the intensity, the greater the amplification factor.

[0063] In another implementation of segmented mapping, the intensity normalized value can be divided into three intervals: low, medium, and high. The low interval is mapped to a fixed slight magnification (e.g., 1.1x), the medium interval is mapped to a moderate magnification (e.g., 1.5x), and the high interval is mapped to a significant magnification (e.g., 2.0x). This segmented mapping can more sharply highlight extreme high-intensity events and avoid the visual confusion of linear mapping in the middle intensity range.

[0064] It should be understood that regardless of whether linear mapping or piecewise mapping is used, as long as the mapping relationship that the enhancement magnitude monotonically increases with the intensity attribute value is satisfied, it falls within the protection scope of this embodiment. Meanwhile, detailed information including intensity and depth attribute values ​​is typically displayed as a floating panel or label next to the enhanced geometry, allowing users to obtain precise values ​​of the core dimensions without secondary queries.

[0065] The implementation logic for highlighting the trajectory of constructed data, where the glowing pattern is rendered along the line segment path, is completely different from the point-like enhancement of event-type data. Constructed data is represented in space as continuously extending line segment geometry; scaling up its overall size will lead to severe distortion of the spatial topology.

[0066] Therefore, this embodiment uses a trajectory highlighting effect, which enhances the luminous style by rendering along the line segment path to highlight the direction and extension range of the structure.

[0067] The rendering parameters of the glow pattern can also be mapped to attribute feature values ​​to deepen semantic expression. For example, the glow width can be mapped to the degree of deviation of the orientation angle: when the orientation angle is consistent with the orientation of the main structure of the region, the glow width is narrower to indicate normal continuation; when the orientation angle deviates significantly, the glow width increases to indicate structural variation points, making the glow width itself a visual indicator of orientation anomalies.

[0068] The orientation parameter annotation displays the orientation angle and inclination parameters at preset locations (such as the midpoint or inflection point of a line segment). These are usually text labels arranged along the tangent direction of the line segment, ensuring that the annotation does not obscure the trajectory itself and that the direction is consistent with the orientation of the structure. Thus, the core orientation and inclination semantic information of the structure data is directly conveyed in the visual feedback.

[0069] The above description is merely an explanatory note on the differentiated interactive feedback mechanism for data type perception in this embodiment, and not a limiting definition. In other embodiments, the differentiated visual enhancement parameter combination may also include visual channels such as transparency gradients, animation pulses, or material texture switching. As long as these visual enhancement parameter combinations are dynamically configured based on the data type and attribute feature values ​​of the picked object, they should be considered equivalent substitutions for this embodiment.

[0070] The above technical solutions are applied to specific scenarios of earthquake monitoring and analysis systems to demonstrate their operational effectiveness and performance advantages in real-world environments. It should be understood that the multidimensional visualization analysis of earthquake data is merely a typical application example of the technical solutions of this invention, and not a limiting definition. The same joint mapping and cross-view linkage mechanism can be applied to any data analysis scenario with multiple types of heterogeneous attribute characteristics.

[0071] In the earthquake monitoring and analysis system application scenario set in this embodiment, multiple types of data include earthquake events and active faults. Earthquake events belong to event-type data, with their intensity attribute specifically represented by magnitude and their depth attribute specifically represented by focal depth. Active faults belong to tectonic data, with their strike attribute specifically represented by strike angle and their dip attribute specifically represented by dip angle parameters. For example... Figure 3 As shown, after obtaining these various types of data, the following workflow is executed: Step S310: Joint mapping generates semantically related objects.

[0072] Based on the relationships between various data attribute dimensions in multiple types of data, at least two data attribute dimensions are jointly mapped to a combination of visual attributes to generate semantically related visual objects within a unified visualization space.

[0073] Specifically, for earthquake events, the system jointly maps magnitude and focal depth to the size parameters and color gradient parameters of a sphere. The size parameters and color gradient parameters change synergistically based on the inherent relationship between magnitude and focal depth.

[0074] In seismological semantics, high-magnitude, shallow-focus earthquakes typically exhibit the greatest destructive power. The system uses joint mapping to represent these events as large, high-gradient-color, prominent spheres in three-dimensional space, intuitively reflecting the semantic association of destructive earthquakes. Conversely, low-magnitude, deep-focus earthquakes are represented as small, low-gradient-color, muted spheres. This synergistic change in visual representation allows analysts to instantly perceive the coupled hazard status of earthquake events across the two core dimensions of magnitude and depth, without needing to consult numerical panels, significantly improving the intuition and efficiency of disaster assessment.

[0075] Step S320: Maintain context for cross-view linkage based on data features.

[0076] In response to a spatial region selection operation in the first view, the spatial region selection operation is mapped to the corresponding spatial range and data filtering conditions in the second view based on the spatial distribution characteristics of the data objects within the spatial region, so as to maintain the continuity of the cross-view analysis context.

[0077] Specifically, the first view is a two-dimensional map view, and the second view is a three-dimensional stereo view. When an analyst selects a key monitoring area in the two-dimensional map view, the system maps the spatial distribution range and depth distribution characteristics of the selected spatial area in the two-dimensional view to the corresponding spatial range in the three-dimensional view, and converts the depth distribution characteristics into data filtering conditions for the three-dimensional view.

[0078] At the underlying algorithm execution level, the system extracts the set of boundary coordinate points of the selected spatial region in the two-dimensional view, maps them to a set of three-dimensional spatial coordinate points using a two-dimensional to three-dimensional coordinate system transformation matrix, and generates a three-dimensional spatial bounding box based on the extreme value range of the three-dimensional spatial coordinate point set to define the corresponding spatial range in the three-dimensional view. At the same time, the system statistically analyzes the minimum, maximum and distribution density parameters of earthquake events in the focal depth dimension within the region, and determines the screening threshold range of the depth dimension based on the distribution density parameters, which serves as the data screening condition for the three-dimensional view.

[0079] For example, when a selected area contains a large number of shallow microseismic events and a very small number of deep strong earthquakes, the system automatically truncates outlier depth values ​​based on density distribution, generating a depth screening threshold that focuses on the core distribution cluster. The 3D view then automatically focuses on the bounding box space and filters out only earthquake events that meet the depth threshold conditions, thus effectively maintaining the continuity of the cross-view analysis context and avoiding the problem of key deep-focus events being overwhelmed by shallow-focus noise, which is a problem in traditional methods based on camera position synchronization.

[0080] Step S330: Differentiated interactive feedback based on data type awareness.

[0081] In response to spatial picking operations on visualized objects, the system identifies the data type of the visualized object and the attribute feature values ​​of each data attribute dimension; based on the data type and attribute feature values, it provides type-aware differentiated visual enhancement feedback.

[0082] When analysts pick up earthquake event spheres, they identify them as event-type data and provide a size enhancement effect based on magnitude, along with detailed information including magnitude and focal depth. The enhancement magnitude is positively correlated with the magnitude; the higher the magnitude, the more significant the magnification, ensuring that the visual impact of the destructive event matches its degree of danger. When analysts pick up active fault segments, they identify them as tectonic data and provide a trajectory highlighting effect and strike parameter annotations. The trajectory highlighting effect renders an enhanced glowing style along the segment path, and the strike parameter annotations display the strike angle and dip parameters at preset locations.

[0083] This differentiated feedback allows the interactive operation itself to carry the professional semantics of seismic data. Analysts can intuitively distinguish between discrete events and continuous structures at the moment of picking them up, and obtain core attribute features, which greatly improves the information acquisition efficiency of interactive analysis.

[0084] To verify the performance advantages of the technical solution of this invention in a real environment, a dataset containing approximately 50,000 seismic events provided by the China Earthquake Networks Center was used for testing. The test results show that the 3D display frame rate of the 50,000 seismic events is stable at over 60fps, the interactive response latency for rotation, scaling, and translation operations is less than 16ms, and the total memory usage is controlled within 512MB.

[0085] It should be clearly pointed out that these excellent performance figures are mainly due to the support of known optimization techniques such as instantiation rendering and frustum culling of the underlying graphics rendering interface (e.g., WebGL), which enable efficient batch rendering of large-scale geometry and culling of objects outside the field of view. These known optimization techniques are not the core innovation of this application. The core innovative contribution of this application lies in the fact that, on the high-performance foundation provided by the aforementioned known rendering techniques, a multi-dimensional attribute joint mapping mechanism is used to endow the visual objects with semantic relationships, and a cross-view linkage mechanism based on data spatial distribution characteristics is used to maintain the continuity of the analysis context, thereby achieving a qualitative improvement in the levels of interactive semantics and analysis logic.

[0086] Furthermore, in terms of the interactive experience of perspective switching and view linkage, when cross-view linkage triggers the 3D view to focus on the bounding box, or when the analyst triggers the switching of preset perspectives (such as a top-down view, a front-view view, and a side-view view) via keyboard shortcuts, the ViewManager receives the instruction and executes the target position and interpolation calculation algorithm through the smooth transition animation module. The camera smoothly transitions from the current position to the target position by updating animation frames, avoiding visual disorientation caused by sudden changes in perspective and further ensuring the continuity and immersion of cross-view interaction.

[0087] It should be understood that although this embodiment uses an earthquake monitoring scenario as an example to demonstrate the complete operation process and test data, the core mechanisms of joint mapping to generate semantic associations and maintaining context based on data feature linkage are also applicable to other professional visualization fields containing multi-type heterogeneous data, such as meteorological trajectory analysis and urban underground pipe network monitoring. These applications should all be considered equivalent extensions of the technical solution of this invention. The above description is only an explanatory illustration of the application scenario of multi-dimensional visualization analysis of earthquake data in this embodiment, and not a limiting definition.

[0088] This embodiment provides a device infrastructure corresponding to the method, such as... Figure 4 As shown in the figure, this embodiment provides a three-dimensional visualization interactive device for seismic data, including: a data acquisition module 401, a joint mapping module 402, and a cross-view linkage module 403.

[0089] The data acquisition module 401 is configured to acquire multiple types of data, which include at least two data types and each data type includes at least two data attribute dimensions.

[0090] Specifically, the data acquisition module 401, as the data source of the entire device, is responsible for reading and parsing heterogeneous data sets from external data sources or internal storage media. In terms of structural configuration, the output end of the data acquisition module 401 establishes a data flow connection with the input end of the joint mapping module 402, continuously transmitting the parsed structured multi-type data to the joint mapping module 402 for subsequent visualization processing.

[0091] It should be understood that although the architecture of the data acquisition module 401 as an independent front-end module is shown in this embodiment and the accompanying drawings, in other embodiments, the data acquisition module 401 can also be merged with the joint mapping module 402 into an integrated data processing engine, as long as it can provide multi-dimensional attribute input for joint mapping.

[0092] The joint mapping module 402 is configured to jointly map at least two data attribute dimensions into a combination of visual attributes based on the relationship between the data attribute dimensions in multiple types of data, so as to generate a semantically related visualization object in a unified visualization space.

[0093] Specifically, the joint mapping module 402 is the core structural unit for realizing semantic association expression in this device. In the data flow direction, the joint mapping module 402 receives multi-type data from the data acquisition module 401. After performing attribute association analysis and joint visual parameter calculation within itself, it outputs the generated visual attribute combinations and visualization object description data to the rendering engine for scene construction. Simultaneously, it outputs the spatial distribution feature data of the visualization objects to the cross-view linkage module 403, providing data feature anchors for subsequent cross-view analysis context maintenance. This modular structural configuration decouples the association logic of the data attribute dimension from the visual rendering logic, ensuring that upgrades or replacements of the joint mapping algorithm do not affect the stability of data acquisition and cross-view linkage.

[0094] The cross-view linkage module 403 is configured to respond to a spatial region selection operation in the first view, and based on the spatial distribution characteristics of the data objects within the spatial region, map the spatial region selection operation to the corresponding spatial range and data filtering conditions in the second view, so as to maintain the continuity of the cross-view analysis context.

[0095] Specifically, the cross-view linkage module 403 is the interactive hub for achieving continuity of the analysis context in this device. In terms of structural configuration and data flow, the cross-view linkage module 403 has a two-way interactive interface: on the one hand, its input end receives the spatial region selection operation event triggered by the user in the first view, as well as the data spatial distribution characteristics of the data objects in that region synchronously obtained from the joint mapping module 402 or the scene rendering unit; on the other hand, its output end sends the calculated spatial range parameters and data filtering condition instructions corresponding to the second view to the camera control system and data filtering engine of the second view, respectively, driving the second view to automatically perform spatial focusing and data filtering.

[0096] In some embodiments, the cross-view linkage module 403 works in conjunction with the view manager and camera control system to ensure a smooth transition during view linkage.

[0097] It should be understood that although this embodiment describes the interactive connection between the cross-view linkage module 403 and the external camera control system and data filtering engine, in a highly integrated system architecture, these external units can also exist as internal sub-units of the cross-view linkage module 403, as long as the mechanism of updating the view state and filtering conditions based on data features is satisfied.

[0098] Through the configuration relationship and data flow design of the data acquisition module 401, the joint mapping module 402, and the cross-view linkage module 403 described above, the device in this embodiment structurally achieves semantic association expression of multi-type heterogeneous data and maintains the continuity of cross-view analysis context. This modular architecture not only clearly delineates the functional boundaries of data processing, visual mapping, and interactive linkage, but also provides a solid structural support foundation for the refinement of sub-units within each module and the implementation of algorithms in subsequent embodiments. The above description is only an explanatory description of the device's basic architecture in this embodiment, and not a restrictive definition.

[0099] In some embodiments, the joint mapping module 402 includes: an attribute extraction unit configured to parse the data attribute dimensions and attribute value relationships of multiple types of data; and a visual parameter calculation unit configured to perform joint mapping calculations based on attribute value relationships to generate a combination of visual attributes.

[0100] In terms of data interaction, the attribute extraction unit, as the internal data entry point of the joint mapping module 402, receives structured multi-type data records from the data acquisition module 401. The attribute extraction unit is not only responsible for extracting the basic attribute dimensions of each data object (such as the intensity and depth of event-type data), but more importantly, it analyzes the inherent relationships between these attribute dimensions. For example, it identifies the coupling pattern between high intensity and a specific depth range, and outputs this relationship to the visual parameter calculation unit in the form of a weighted coefficient configuration table or a correlation function description file. After receiving the original attribute values ​​and the correlation description, the visual parameter calculation unit executes the joint mapping calculation logic detailed in the embodiment: first, it performs normalization processing on the original values, then performs weighted calculation based on the weighted coefficients configured for the correlation to generate intermediate joint mapping values, and finally, the same intermediate value uniformly drives the call to the size mapping function and the color mapping function to calculate and generate a combination of visual attributes. The visual parameter calculation unit outputs the generated visual attribute combination parameters to the rendering engine for geometry drawing, and simultaneously outputs the spatial coordinates of the data objects with attached attribute features to the cross-view linkage module 403.

[0101] Because the parsing logic of attribute dimensions and the numerical calculation logic of visual parameters are essentially different processing tasks at different levels, the attribute extraction unit focuses on understanding and configuring data semantics, while the visual parameter calculation unit focuses on specific mathematical operations and interpolation mapping. Decoupling the two into independent sub-units allows the device to significantly improve its adaptability and the independence of algorithm upgrades when the analysis scenario changes and the emphasis on attribute associations needs to be adjusted. This is because the attribute extraction unit's weight configuration needs to be modified without affecting the underlying computational logic of the visual parameter calculation unit.

[0102] It should be understood that although this embodiment describes a clear division between the attribute extraction unit and the visual parameter calculation unit, in embedded visualization devices with limited computing resources, these two sub-units can also be merged into an integrated parameter parsing and calculation engine, as long as the data flow order of parsing association first and then joint calculation is met.

[0103] Furthermore, the cross-view linkage module 403 includes: a region coordinate extraction unit, configured to extract the boundary coordinates and data spatial distribution features of the selected spatial region in the first view; and a spatial range mapping unit, configured to convert the boundary coordinates into the corresponding spatial range of the second view, and convert the depth distribution features in the data spatial distribution features into data filtering conditions for the second view.

[0104] In terms of data interaction, the region coordinate extraction unit, serving as the interactive data entry point for the cross-view linkage module 403, receives spatial region selection operation events triggered by the user in the first view. Simultaneously, it obtains the spatial coordinates and attribute values ​​of all data objects within the selected region from the joint mapping module 402 or the scene rendering unit. The core task of the region coordinate extraction unit is not only to extract the set of boundary coordinate points of the selected spatial region to represent its geometric contour, but more importantly, to statistically extract the spatial distribution characteristics of the data objects within that region, particularly the minimum, maximum, and distribution density parameters of the depth dimension. Subsequently, the region coordinate extraction unit packages the set of boundary coordinate points and the depth distribution characteristic parameters and outputs them to the spatial range mapping unit. Upon receiving this data, the spatial range mapping unit executes the cross-dimensional mapping logic detailed in the embodiment: first, it uses a coordinate system transformation matrix to map the two-dimensional boundary coordinate points into a set of three-dimensional spatial coordinate points; then, it generates a three-dimensional spatial bounding box based on the coordinate extrema to define the corresponding spatial range; and finally, it performs dynamic truncation based on the distribution density parameters to determine the depth filtering threshold range. The spatial range mapping unit ultimately outputs the calculated three-dimensional spatial bounding box parameters to the camera control system of the second view to drive the viewpoint focusing, while simultaneously outputting the depth filtering threshold range to the data filtering engine to drive data filtering.

[0105] Because the extraction of region coordinates and the statistical analysis of data features are operations at the level of interactive event response and data traversal, while coordinate system transformation and bounding box generation are operations at the level of spatial geometric mapping, there are differences in their computational complexity and execution frequency. Decoupling the two allows the region coordinate extraction unit to respond efficiently and perform real-time feature statistics when the user frequently adjusts the selected region, while the spatial range mapping unit only triggers recalculation when the feature parameters change significantly. This avoids frequent invalid execution of the 3D spatial mapping algorithm, thereby improving the device's response performance and resource utilization in real-time interactive scenarios. Similarly, it should be understood that in a highly integrated graphics processing architecture, the region coordinate extraction unit and the spatial range mapping unit can be further divided into finer-grained coordinate extraction sub-units, density statistics sub-units, and spatial mapping sub-units, or merged into a unified view linkage processing core. As long as the core mechanism of spatial range mapping and filtering condition transformation based on data spatial distribution characteristics is met, it should be considered an equivalent replacement of this embodiment.

[0106] This embodiment provides a computer-readable storage medium to meet the compliance requirements of the medium and expand the deployment forms of the technical solution of the present invention.

[0107] Specifically, this embodiment provides a computer-readable storage medium storing program instructions, which, when executed by a processor, implement the three-dimensional visualization and interactive method for seismic data described in the above method embodiment.

[0108] The specific steps of this method include: acquiring multi-type data, which contains at least two data types, and each data type contains at least two data attribute dimensions; based on the correlation between the data attribute dimensions in the multi-type data, jointly mapping at least two data attribute dimensions into a visual attribute combination to generate semantically related visual objects within a unified visualization space; and responding to a spatial region selection operation in the first view, mapping the spatial region selection operation to the corresponding spatial range and data filtering conditions in the second view based on the data spatial distribution characteristics of the data objects within the spatial region, so as to maintain the continuity of the cross-view analysis context.

[0109] It should be understood that the term "computer-readable storage medium" in this embodiment refers to any medium capable of containing, storing, transmitting, propagating, or transmitting program instructions for use by or in conjunction with an instruction execution system, apparatus, or device. This broader definition of such media can take many forms in its physical form: for example, it can be a solid-state non-volatile medium such as a portable computer floppy disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage device, or magnetic storage device; it can also be an electronic signal transmission medium implemented with one or more wires for electrical connection; and even in some distributed visual interaction scenarios, it can be a network transmission medium such as the Internet or intranet, allowing program instructions to be remotely downloaded from one node to another for execution. This embodiment does not limit the specific physical carrier type of the medium, as long as it can stably store and allow the processor to read and execute the aforementioned joint mapping and cross-view linkage program instructions.

[0110] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0111] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0112] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0113] It should be noted that if the function is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0114] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0115] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A three-dimensional visualization and interactive method for seismic data, characterized in that, include: Acquire multi-type data, which contains at least two data types, and each data type contains at least two data attribute dimensions; Based on the correlation between various data attribute dimensions in multiple types of data, at least two data attribute dimensions are jointly mapped to a combination of visual attributes to generate semantically related visual objects in a unified visualization space. In response to a spatial region selection operation in the first view, the spatial region selection operation is mapped to the corresponding spatial range and data filtering conditions in the second view based on the spatial distribution characteristics of the data objects within the spatial region, so as to maintain the continuity of the cross-view analysis context.

2. The method according to claim 1, characterized in that, At least two data types include event-type data, and at least two data attribute dimensions include the intensity attribute and the depth attribute of event-type data; Jointly map at least two data attribute dimensions to a combination of visual attributes, including: The intensity and depth attributes are jointly mapped to the size and color gradient parameters of the geometry. The size and color gradient parameters change synergistically based on the intrinsic relationship between the intensity and depth attributes.

3. The method according to claim 2, characterized in that, The intensity and depth attributes are jointly mapped to the geometry's size and color gradient parameters, including: Normalize the original values ​​of the intensity attribute and the depth attribute respectively to obtain normalized intensity value and normalized depth value; Based on the weight coefficients configured by attribute features, a weighted calculation is performed on the normalized intensity value and the normalized depth value to generate a joint mapping intermediate value; Based on the joint mapping intermediate value, the size mapping function is called to calculate the size parameters of the geometry, and the color mapping function is called simultaneously to calculate the color gradient parameters. The size parameters and color gradient parameters are driven by the same joint mapping intermediate value.

4. The method according to claim 1, characterized in that, The first view is a two-dimensional view, and the second view is a three-dimensional view.

5. The method according to claim 4, characterized in that, Based on the spatial distribution characteristics of data objects within a spatial region, the spatial region selection operation is mapped to the corresponding spatial range and data filtering conditions in the second view, including: Based on the spatial distribution range and depth distribution characteristics of the data within the selected spatial region in the two-dimensional view, the spatial distribution range is mapped to the corresponding spatial range in the three-dimensional view, and the depth distribution characteristics are transformed into data filtering conditions for the three-dimensional view.

6. The method according to claim 5, characterized in that, Based on the spatial distribution range and depth distribution characteristics of data within a selected spatial region in a 2D view, the spatial distribution range is mapped to the corresponding spatial range in a 3D view, and the depth distribution characteristics are transformed into data filtering conditions for the 3D view, including: Extract the set of boundary coordinate points of the selected spatial region in the two-dimensional view to represent the geometric contour of the selected spatial region; Using a coordinate system transformation matrix from two-dimensional to three-dimensional, the set of boundary coordinate points is mapped to a set of three-dimensional spatial coordinate points; Based on the extreme value range of the coordinates of the set of three-dimensional spatial coordinate points, a three-dimensional spatial bounding box is generated to define the corresponding spatial range in the three-dimensional view. The minimum, maximum, and distribution density parameters of data objects within a selected spatial region in the two-dimensional view are statistically analyzed. Based on the distribution density parameters, a filtering threshold range for the depth dimension is determined, which serves as the data filtering condition for the three-dimensional view.

7. The method according to claim 1, characterized in that, The method also includes: In response to spatial picking operations on visualized objects, identify the data type of the visualized object and the attribute feature values ​​of each data attribute dimension; Based on data type and attribute feature values, it provides type-aware differentiated visual enhancement feedback, with different combinations of visual enhancement parameters corresponding to visual objects of different data types.

8. The method according to claim 7, characterized in that, Data types include event-type data and structure-type data.

9. The method according to claim 8, characterized in that, Type-aware differential visual augmentation feedback includes: For visualization objects of event-type data, it provides size enhancement effects based on intensity attribute values ​​and displays detailed information including intensity and depth attribute values. The enhancement magnitude of the size enhancement effect is positively correlated with the intensity attribute value. For visualization objects of constructed data, a trajectory highlighting effect and a direction parameter label are provided. The trajectory highlighting effect renders an enhanced glowing style along the line segment path of the constructed data, and the direction parameter label displays the direction angle and tilt angle parameters at preset positions.

10. A computer-readable storage medium, characterized in that, The system stores program instructions that, when executed by a processor, implement the method according to any one of claims 1-9.

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