Geological occurrence data interpolation method and device

Abstract

The invention discloses a geological occurrence data interpolation method and device. The method comprises the following steps: acquiring original sampling point data of a target tectonic region; the original sampling point data comprises geographic coordinates, occurrence data and stratum identification; the original tendency and the original dip angle in the occurrence data are converted into normal vector components under a Cartesian coordinate system; dividing the sampling points into different geological unit groups and corresponding structural boundaries according to the stratum identifiers; performing spatial interpolation operation on the normal vector component in each geological unit group based on the construction boundary; the normal vector component obtained through interpolation is inversely calculated into target geological occurrence data; and generating diversified output data according to the target geological occurrence data, wherein the diversified output data comprises a contour map based on the target inclination or the target inclination angle, a raster data file containing space coordinates and the target geological occurrence data and / or occurrence table data of regular grid points. The method is used for improving the reliability, the processing efficiency and the practicability of geological occurrence data interpolation.

Description

Technical Field

[0001] This invention relates to the field of geological data processing and three-dimensional geological modeling technology, and in particular to a method and apparatus for interpolating geological occurrence data. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention described herein. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] Currently, the acquisition of geological attitude data relies on limited sampling methods such as field surveys or borehole measurements. Existing interpolation techniques and commercial software have significant drawbacks: direct interpolation of angle values ​​leads to distortion in the periodicity of dip (0° is equivalent to 360°), failing to accurately represent the abrupt changes in attitude at fold turning points or on both sides of faults; data grouping relies on manual preprocessing, resulting in low levels of intelligence and a high susceptibility to errors; and the output format is limited, making it difficult to meet the diverse application needs such as geological mapping and 3D modeling. Summary of the Invention

[0004] This invention provides a geological attitude data interpolation method to improve the reliability, processing efficiency, and practicality of geological attitude data interpolation. The method includes:

[0005] Obtain raw sampling point data of the target structural region; the raw sampling point data includes geographic coordinates, attitude data, and stratigraphic identifiers;

[0006] The original dip and original dip angle in the attitude data are converted into normal vector components in Cartesian coordinate system;

[0007] Based on the stratigraphic markers, the sampling points were divided into different geological unit groups and corresponding tectonic boundaries;

[0008] Based on the aforementioned structural boundary, spatial interpolation is performed on the normal vector components within each geological unit group; the spatial interpolation is used to generate a continuous component field using a pre-configured interpolation algorithm and predefined resolution parameters.

[0009] The interpolated normal vector components are then used to calculate the target geological attitude data; the target geological attitude data includes the target dip and the target dip angle.

[0010] Based on the target geological attitude data, diverse output data is generated; the diverse output data includes contour maps based on the target dip or dip angle, raster data files containing spatial coordinates and target geological attitude data, and / or attitude table data with regular grid points.

[0011] This invention also provides a geological occurrence data interpolation device to improve the reliability, processing efficiency, and practicality of geological occurrence data interpolation. The device includes:

[0012] The data acquisition module is used to acquire raw sampling point data of the target structural area; the raw sampling point data includes geographic coordinates, attitude data and stratigraphic identifiers;

[0013] The normal vector component conversion module is used to convert the original dip and original dip angle in the attitude data into normal vector components in the Cartesian coordinate system.

[0014] The geological unit group division module is used to divide the sampling points into different geological unit groups and corresponding structural boundaries according to the stratigraphic identifiers.

[0015] The spatial interpolation module is used to perform spatial interpolation on the normal vector components within each geological unit group based on the structural boundary; the spatial interpolation is used to generate a continuous component field using a pre-configured interpolation algorithm and predefined resolution parameters.

[0016] The normal vector component inverse calculation module is used to inversely calculate the interpolated normal vector components into target geological attitude data; the target geological attitude data includes target dip and target dip angle;

[0017] The output data generation module is used to generate diversified output data based on the target geological attitude data; the diversified output data includes contour maps based on the target dip or target dip angle, raster data files containing spatial coordinates and target geological attitude data, and / or attitude table data with regular grid points.

[0018] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described geological occurrence data interpolation method.

[0019] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described geological occurrence data interpolation method.

[0020] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described geological occurrence data interpolation method.

[0021] In this embodiment of the invention, original sampling point data of the target structural region is acquired; the original sampling point data includes geographic coordinates, attitude data, and stratigraphic identifiers; the original dip and dip angle in the attitude data are converted into normal vector components in a Cartesian coordinate system; the sampling points are divided into different geological unit groups and corresponding structural boundaries according to the stratigraphic identifiers; based on the structural boundaries, spatial interpolation is performed on the normal vector components within each geological unit group; the spatial interpolation is used to generate a continuous component field using a pre-configured interpolation algorithm and predefined resolution parameters; the interpolated normal vector components are then used to back-calculate the target geological attitude data; the target geological attitude data includes the target dip and target dip angle; based on the target geological attitude data, diversified output data is generated; the diversified output data includes contour maps based on the target dip or target dip angle, raster data files containing spatial coordinates and target geological attitude data, and / or attitude table data with regular grid points. This invention eliminates the distortion caused by periodic jumps in traditional angle interpolation by converting dip and dip angle into vector components; it automatically groups data based on stratigraphic markers and identifies structural boundaries such as faults, performs spatial interpolation on each group of data, preserves the geological abrupt changes at the boundaries, and rejects forced smoothing that does not conform to geological laws; it can construct strike contour maps, 3D modeling raster files, and quantitative analysis tables, covering the needs of the entire geological work process; thus, it can significantly improve the reliability, processing efficiency, and practicality of geological occurrence data interpolation. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0023] Figure 1 This is a flowchart illustrating a geological occurrence data interpolation method according to an embodiment of the present invention;

[0024] Figure 2 This is a specific example diagram of a geological occurrence data interpolation method in an embodiment of the present invention;

[0025] Figure 3 This is an example diagram of the interactive interface of a geological occurrence data interpolation system according to an embodiment of the present invention;

[0026] Figure 4 This is a specific example diagram of the planar distribution result of orientation interpolation in an embodiment of the present invention;

[0027] Figure 5This is a schematic diagram of the structure of a difference result table in an embodiment of the present invention;

[0028] Figure 6 This is a schematic diagram of a geological occurrence data interpolation device according to an embodiment of the present invention;

[0029] Figure 7 This is a schematic diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0031] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0032] In the description of this specification, the terms "comprising," "including," "having," and "containing" are open-ended terms, meaning that they include but are not limited to. The terms "an embodiment," "a specific embodiment," "some embodiments," and "for example," etc., refer to specific features, structures, or characteristics described in connection with that embodiment or example that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. The order of steps involved in the various embodiments is used to illustrate the implementation of this application, and the order of steps is not limited and can be adjusted appropriately as needed.

[0033] The acquisition, storage, use, and processing of data in this application comply with relevant regulations. The information collected in this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with relevant standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation interfaces are provided for users to choose to authorize or refuse.

[0034] It should be noted that in the embodiments of this application, certain existing solutions in the industry, such as software, components, and models, may be mentioned. For example, some existing software tools, components, algorithm models, or solutions well-known in other technical fields may be cited. These should be considered exemplary, and their purpose is only to illustrate the feasibility of implementing the technical solution of this application. These mentions should be understood as typical examples, and their core purpose is to illustrate and verify the rationality and feasibility of implementing the technical solution proposed in this application. However, this does not mean that the applicant has already used or necessarily used the solution. Such citations do not imply that the applicant has actually adopted these existing solutions, or that it will necessarily adopt these methods in its technical implementation process in the future. In other words, these mentions are only illustrative in nature, helping to understand the connection and transcendence of the innovation points of this application with the prior art, and do not constitute an endorsement or reliance statement on a specific prior art product.

[0035] Geological occurrence data (such as the dip and dip angle of strata, the strike, dip and dip angle of faults, and the occurrence of fold axial planes) are fundamental data in geological research and engineering construction. This data is typically obtained at limited sampling points through methods such as field geological surveys and borehole core measurements. However, when conducting geological mapping, 3D geological modeling, resource reserve assessment, and disaster prediction, it is often necessary to obtain continuous or high-density occurrence distribution information throughout the entire study area.

[0036] Existing geological data interpolation methods or general commercial software (such as Surfer, ArcGIS, etc.) have the following main problems when processing geological occurrence data:

[0037] 1) Insufficient consideration of geological specialization. Attitude data has its own unique characteristics, such as the periodicity of angles (0° dip is equivalent to 360°) and the abrupt changes in attitude within different geological units or tectonic domains. Traditional interpolation methods (such as inverse distance weighting, kriging, trend surface methods, etc.) directly interpolate angle values, often ignoring the continuity and abrupt changes in geological structures. This may lead to interpolation results that do not conform to geological laws, such as producing smooth transitions at fold inflection points or on both sides of faults instead of the expected abrupt changes or drastic variations.

[0038] 2) Data range and filtering limitations exist. Some software may impose restrictions on the geographical or numerical range of input data. Furthermore, geological attitudes are often associated with specific strata or lithological units. Ideally, interpolation should be able to group data based on strata, meaning that attitudes within the same stratum should have a certain continuity and similarity, while attitudes between different strata may differ significantly. Existing methods often require users to manually pre-filter and segment data, which is cumbersome and error-prone. The reasonableness of the interpolation results largely depends on the geologist's data preprocessing and parameter selection, lacking intelligence and automation.

[0039] 3) Users have different requirements for the resolution of the interpolation results, and existing methods may not be flexible enough in terms of resolution customization.

[0040] 4) Geologists have diverse needs for the presentation of interpolation results, including contour maps of specific attitude values ​​(such as isodipity lines and isopyridation lines), continuous attitude raster surfaces (which can be used for 3D visualization), and tabular data that can be directly used for subsequent analysis (such as attitude values ​​of regular grid points). Existing software may lack sufficient output options.

[0041] Therefore, there is an urgent need for a geological occurrence interpolation method and system that can fully consider geological characteristics, support intelligent grouping by stratigraphy or tectonic unit, flexibly control resolution, and provide diverse output formats.

[0042] To address the aforementioned problems, this invention provides a geological attitude data interpolation method. This invention belongs to the field of geological data processing and 3D geological modeling, and specifically relates to a method and system for intelligent interpolation based on sparse and irregularly distributed field geological survey points (such as dip and dip angle attitude data) to generate a high-resolution attitude data field that conforms to geological laws and supports diverse outputs. This embodiment aims to improve the reliability, processing efficiency, and practicality of geological attitude data interpolation. See [link to relevant documentation]. Figure 1 , Figure 1 This is a flowchart illustrating a geological occurrence data interpolation method according to an embodiment of the present invention. The method may include:

[0043] Step 101: Obtain the original sampling point data of the target structural area; the original sampling point data includes geographic coordinates, attitude data and stratigraphic identifiers;

[0044] Step 102: Convert the original dip and original dip angle in the attitude data into normal vector components in Cartesian coordinate system;

[0045] Step 103: Divide the sampling points into different geological unit groups and corresponding structural boundaries according to the stratigraphic markers;

[0046] Step 104: Based on the structural boundary, perform spatial interpolation on the normal vector components within each geological unit group; the spatial interpolation is used to generate a continuous component field using a pre-configured interpolation algorithm and predefined resolution parameters;

[0047] Step 105: Calculate the interpolated normal vector components back into target geological attitude data; the target geological attitude data includes the target dip and the target dip angle;

[0048] Step 106: Generate diversified output data based on the target geological attitude data; the diversified output data includes contour maps based on the target dip or target dip angle, raster data files containing spatial coordinates and target geological attitude data, and / or attitude table data with regular grid points.

[0049] In this embodiment of the invention, original sampling point data of the target structural region is acquired; the original sampling point data includes geographic coordinates, attitude data, and stratigraphic identifiers; the original dip and dip angle in the attitude data are converted into normal vector components in a Cartesian coordinate system; the sampling points are divided into different geological unit groups and corresponding structural boundaries according to the stratigraphic identifiers; based on the structural boundaries, spatial interpolation is performed on the normal vector components within each geological unit group; the spatial interpolation is used to generate a continuous component field using a pre-configured interpolation algorithm and predefined resolution parameters; the interpolated normal vector components are then used to back-calculate the target geological attitude data; the target geological attitude data includes the target dip and target dip angle; based on the target geological attitude data, diversified output data is generated; the diversified output data includes contour maps based on the target dip or target dip angle, raster data files containing spatial coordinates and target geological attitude data, and / or attitude table data with regular grid points. This invention eliminates the distortion caused by periodic jumps in traditional angle interpolation by converting dip and dip angle into vector components; it automatically groups data based on stratigraphic markers and identifies structural boundaries such as faults, performs spatial interpolation on each group of data, preserves the geological abrupt changes at the boundaries, and rejects forced smoothing that does not conform to geological laws; it can construct strike contour maps, 3D modeling raster files, and quantitative analysis tables, covering the needs of the entire geological work process; thus, it can significantly improve the reliability, processing efficiency, and practicality of geological occurrence data interpolation.

[0050] In specific implementation, the first step is to obtain the original sampling point data of the target structural area; the original sampling point data includes geographical coordinates, occurrence data and stratigraphic identifiers.

[0051] In this embodiment, the geographic coordinates are used to characterize the spatial location coordinates of each sampling point; the spatial location coordinates are expressed using a Cartesian coordinate system or a geographic coordinate system.

[0052] The attitude data consists of dip and dip angle data composed of geological surface attitude measurements;

[0053] The stratigraphic identifier is used to distinguish stratigraphic codes or lithological unit identifiers of different geological units; the stratigraphic identifier uses alphanumeric codes to mark geological age or lithological classification attributes.

[0054] In the above embodiments, the original sampling point data is stored in the form of electronic files, specifically containing three types of core information: geographic coordinates, which are used to accurately characterize the three-dimensional location of each sampling point in geographic space. The coordinates are expressed in two standardized forms: a plane rectangular coordinate system or a geographic coordinate system, to ensure the universality and accuracy of spatial positioning; attitude data, which are obtained by geologists through field measurements, include dip angle values ​​and dip angle values ​​describing the spatial morphology of rock strata or structural surfaces. These measured data directly reflect the three-dimensional spatial orientation of geological structures; and stratigraphic identifiers, as key fields for distinguishing different geological units, are marked using a standardized coding form of letters and numbers, such as geological age codes or lithological classification codes. These identifiers are used in the data processing stage to automatically classify sampling points to the corresponding stratigraphic group or lithological unit.

[0055] The data loading process is implemented through a graphical user interface, allowing users to import spreadsheet files containing specific field structures. The geographic coordinate field must include at least planar coordinate values, with vertical elevation values ​​as an optional field to accommodate different terrain conditions. The attitude data field must include dip and dip angle values ​​conforming to geological surveying standards, and their numerical range is constrained by fundamental geological principles. The stratigraphic identification field adopts industry-standard coding rules to ensure that different geological units have uniquely identifiable labels.

[0056] The original sampling point data are directly derived from field geological survey records or borehole core logging results. The spatial coordinates of each sampling point are collected using professional surveying equipment. The plane coordinate system adopts a standard coordinate system or a local independent coordinate system, and the geographic coordinate system adopts standard latitude and longitude. Dip and dip angle data are obtained by direct measurement on outcrops or cores using a geological compass or inclinometer, and the numerical records follow geological angle measurement standards. Stratigraphic identification is done by geologists based on regional geological maps or on-site lithological identification results. The coding rules usually adopt international stratigraphic classification standards or a lithological classification scheme uniformly formulated by the project.

[0057] The data loading process includes an automatic format verification function to ensure that the original sampling point data file contains the necessary fields and that the numerical format is valid. For data points that are missing key fields or have abnormal values, users can interactively correct or selectively remove them.

[0058] In specific implementation, after step 101: obtaining the original sampling point data of the target structural area; the original sampling point data includes geographic coordinates, attitude data and stratigraphic identifiers, step 102: converting the original dip and original dip angle in the attitude data into normal vector components in the Cartesian coordinate system.

[0059] In this embodiment, the original dip and original dip angle in the attitude data are converted into normal vector components in Cartesian coordinates, including:

[0060] By using trigonometric functions, the dip angle value is decomposed into planar direction components, and the dip angle value is converted into the vertical component of the spatial normal vector. The planar direction components are composed of dip sine and dip cosine values. The vertical component is generated by dip cosine value. The dip sine, dip cosine, and dip cosine values ​​together form the normal vector component reflecting the spatial orientation of the geological surface.

[0061] In the above embodiments, by automatically parsing the stratigraphic code field or lithological unit identifier field carried in the original data file, sampling points with the same coding value are classified into independent geological unit groups. For example, all sampling points labeled J1 are classified into the Early Jurassic stratigraphic group, and sampling points labeled K2 are classified into the Late Cretaceous stratigraphic group. The stratigraphic identifier adopts an alphanumeric coding system, where the letter part represents the geological age or lithological category, and the number part represents the specific stratigraphic sequence number. This coding rule ensures that different geological units have unique identifiers.

[0062] The system receives external structural line data input by the user through a graphical interface, including vector boundaries such as known fault lines and fold axial plane traces, and automatically converts them into hard segmentation boundaries for interpolation calculations. On the other hand, it analyzes the rate of change characteristics of attitude data from spatially distributed sampling points, automatically identifying the location and orientation of abrupt attitude change zones through gradient calculation algorithms. Regions with an attitude change rate exceeding a preset threshold are identified as structural boundaries, such as identifying a steep dip zone between the hanging wall and footwall of a reverse fault. Specifically, the spatial gradient characteristics of the attitude change rate manifest as continuous regions where the dip or dip angle undergoes a significant angular shift within a unit distance.

[0063] Data points within each unit group participate only in the spatial interpolation calculations of that group; data are not mixed between different unit groups or tectonic domains. The tectonic boundary serves as an isolation zone for the interpolation calculations, ensuring that the geological units on either side of the boundary independently generate attitude data fields, thus preserving the true geological characteristics of dip reversal on both sides of faults or abrupt dip angle changes at fold inflection points. This mechanism effectively avoids the attitude transition distortion problem caused by forced smoothing when crossing different strata or tectonic units in traditional methods.

[0064] In specific implementation, after step 102: converting the original dip and original dip angle in the occurrence data into normal vector components in the Cartesian coordinate system, step 103: dividing the sampling points into different geological unit groups and corresponding structural boundaries according to the stratigraphic identifiers.

[0065] In this embodiment, the sampling points are divided into different geological unit groups and corresponding structural boundaries based on the stratigraphic identifiers, including:

[0066] By automatically parsing the stratigraphic code or lithological unit identifier carried by the sampling points, data points with the same identifier are classified into independent geological unit groups;

[0067] The fault lines or fold axis traces received from the input are used as external structural boundaries, or the structural boundaries are automatically identified by the algorithm based on the spatial gradient characteristics of the attitude change rate of the sampling points.

[0068] In the above embodiments, the stratigraphic code field or lithological unit identifier field carried in the original sampling point data is first automatically parsed to classify sampling points with the same coding value into independent geological unit groups. The stratigraphic identifiers are marked using a standardized alphanumeric coding system. For example, the letter J represents Jurassic strata, and the number 1 represents early stratification, forming a stratigraphic code like J1; or the letter S represents sandstone lithology, and the number 2 represents medium-grained texture, forming a lithological unit identifier like S2. This coding rule ensures that different geological units have unique identifiable labels, and the sampling points are automatically grouped based on these identifiers.

[0069] On one hand, it receives known geological structural line data input by users through a graphical user interface, including vector boundary information such as fault traces and fold axial plane traces, and automatically converts them into hard segmentation boundaries for interpolation calculations. On the other hand, it analyzes the variation characteristics of spatially distributed sample point attitude data and automatically identifies the location and strike of attitude abrupt change zones through gradient calculation algorithms. Specifically, the identification process detects continuous areas where the dip or dip angle changes significantly within a unit distance, and identifies banded areas where the rate of attitude change exceeds a preset threshold as potential structural boundaries. For example, it identifies dip abrupt change zones between the hanging wall and footwall of reverse faults or dip reversal zones at the turning point of an anticline.

[0070] Data points within each unit group participate only in the spatial interpolation calculations of that group; data are not mixed between different unit groups or tectonic domains. The tectonic boundary serves as an isolation zone for the interpolation calculations, ensuring that the geological units on both sides of the boundary independently generate attitude data fields, thereby accurately preserving geological structural features such as dip reversal on both sides of faults and abrupt dip angle changes at fold inflection points.

[0071] The boundary recognition algorithm incorporates adaptive spatial analysis capabilities. It automatically expands the gradient calculation window in sparse areas of the sampling points to ensure boundary continuity, while shrinking the window in dense areas to improve positioning accuracy. The generated structural boundary lines are output as vector data with topological attributes, which can be directly used as input parameters for subsequent spatial interpolation modules. The entire grouping and boundary recognition process requires no manual intervention in stratigraphic unit division and structural line drawing; it only needs to ensure the integrity and validity of the stratigraphic identifier fields in the original data. It automatically completes the classification of geological units and the division of structural domains, laying the foundation for subsequent intelligent interpolation.

[0072] In specific implementation, after step 103: dividing the sampling points into different geological unit groups and corresponding structural boundaries according to the stratigraphic identifier, step 104: performing spatial interpolation operation on the normal vector components in each geological unit group based on the structural boundary; the spatial interpolation operation is used to generate a continuous component field by using a pre-configured interpolation algorithm and predefined resolution parameters.

[0073] In this embodiment, based on the structural boundary, spatial interpolation is performed on the normal vector components within each geological unit group, including:

[0074] Before the interpolation operation begins, the user selects the interpolation algorithm type and the user-defined grid resolution parameters through the interactive interface.

[0075] For each geological unit group, spatial interpolation calculations are performed on the normal vector components using the selected interpolation algorithm; the spatial interpolation calculations preserve the abrupt changes in attitude at different tectonic boundaries; the interpolation algorithm type includes at least one of the following: kriging, radial basis function, or a combination of trend surface analysis and residual interpolation; the resolution parameter is used to define the spatial sampling density of the output data field.

[0076] In one embodiment, before the interpolation operation begins, the user-configured interpolation algorithm type and custom resolution parameters are received via a graphical user interface. The user selects an interpolation method suitable for the current geological conditions from a preset algorithm library, including one or more of the following: kriging, radial basis function, or a combination of trend surface analysis and residual interpolation. These algorithms optimize the transformed normal vector components to improve the spatial autocorrelation fitting ability. At the same time, the user sets the resolution parameter to define the spatial sampling density of the output data field, i.e., the spacing of the regular grid cells. This parameter directly affects the precision and computational efficiency of the interpolation results.

[0077] For each independent geological unit group after division, the selected algorithm is used to perform spatial interpolation calculations on the normal vector components, ensuring that the abrupt changes in attitude at different structural boundaries are strictly preserved. For example, no data smoothing transition is performed on both sides of hard boundaries such as fault lines or fold traces; only continuously varying component fields are generated within the same unit group. The interpolation calculation process fully considers the spatial distribution patterns of geological structures. The Kriging method introduces a variogram model to analyze the correlation between components, the radial basis function method adapts to local variations in sparse data regions, and trend surface analysis combined with residual interpolation effectively separates regional tectonic background from local details. During algorithm execution, the algorithm automatically applies preset resolution parameters to generate continuous component field data on a regular grid. Each grid cell contains the interpolated normal vector component values, providing a basis for subsequent back-calculation.

[0078] In specific implementation, after step 104: based on the structural boundary, perform spatial interpolation on the normal vector components within each geological unit group; the spatial interpolation is used to generate a continuous component field using a pre-configured interpolation algorithm and predefined resolution parameters, and then proceed to step 105: back-calculate the interpolated normal vector components into target geological attitude data; the target geological attitude data includes target dip and target dip angle.

[0079] In this embodiment, the interpolated normal vector components are back-calculated into target geological occurrence data, including:

[0080] The plane projection direction angle of the interpolation normal vector component is calculated using inverse trigonometric functions and used as the target inclination.

[0081] The angle between the normal vector and the vertical direction is calculated as the target tilt angle;

[0082] The conversion results of the target dip are periodically range-corrected; the target geological attitude data are standardized attitude angle values; the target dip reflects the horizontal extension direction of the geological surface; the target dip angle reflects the spatial tilt of the geological surface.

[0083] In the above embodiment, the plane projection direction angle of the interpolated normal vector component is calculated using inverse trigonometric functions as the target dip. This calculation employs a four-quadrant arctangent function to handle the positive and negative value combinations of the plane direction component, ensuring the mathematical rigor of the azimuth calculation. Simultaneously, the angle between the normal vector and the vertical direction is calculated as the target dip angle, which directly reflects the spatial tilt of the geological surface. A periodic range correction operation is performed on the conversion result of the target dip angle, uniformly adjusting any negative angles or values ​​exceeding 360 degrees that may arise from the mathematical calculation to the standard geological azimuth range, i.e., a continuous interval from 0 degrees to 360 degrees. For example, a negative dip angle is converted to a positive dip angle.

[0084] The target geological attitude data is output as standardized attitude angle values, whose numerical expression strictly follows geological surveying standards. The target dip characterizes the horizontal extension direction of the geological structural surface, such as fault strike or stratigraphic extension direction; the target dip angle quantifies the spatial tilt state of the geological structural surface, such as the plunging angle of strata or the dip of fault planes. This back-calculation process is completely reversible and maintains data accuracy, ensuring that the interpolation results of the previous normal vector components are converted into attitude angle information that can be directly used by geologists without loss.

[0085] The inverse calculation operation is performed independently for each interpolation grid point, and the generated target dip and dip angle data automatically inherit the spatial coordinate attributes of the original interpolation grid. The entire conversion process requires no manual intervention in parameter settings, and the built-in angle range correction mechanism automatically handles the mapping relationship between mathematical calculations and geological specifications. Standardized attitude angle values ​​can be directly input into the geological map compilation module or 3D modeling, providing a seamless data foundation for subsequent analysis.

[0086] In one embodiment, the necessity of attitude data transformation stems from the inherent defects of directly interpolating angular data. Dip data exhibits significant periodicity, with its azimuth characteristics making 0° and 360° spatially equivalent. Traditional arithmetic interpolation produces severe distortion when processing adjacent dip values ​​that cross periodic boundaries. For example, a simple arithmetic average of spatially similar dips of 350° and 10° will yield an incorrect result of 180°, which is completely opposite to the actual azimuth indicated by the original data, causing a drastic, non-geological jump in the interpolation result at the 0° / 360° boundary.

[0087] While dip angle data does not exhibit strict periodicity, its 0° to 90° range presents limitations in representing three-dimensional spatial orientation. Directly interpolating dip angle values ​​fails to accurately reflect the spatial orientation variations of rock strata, especially when it is necessary to analyze three-dimensional structural morphology in conjunction with dip data. Converting the data into spatial vector components can significantly improve interpolation stability.

[0088] The complexity of geological structures further highlights the limitations of angular interpolation. In structurally stable areas such as the limbs of folds, the attitude exhibits a gentle transition; however, in tectonically active areas such as fold inflection points or fault zones, the attitude often shows abrupt changes. Traditional angular interpolation methods cannot simultaneously adapt to these two drastically different patterns of change, especially when field sampling points are sparse, making it difficult to accurately capture the abrupt changes in attitude at key structural locations.

[0089] The core value of converting attitude angles into vector components lies in: eliminating the interference of angle periodicity on the interpolation process through mathematical reconstruction, enabling data points with similar spatial orientations to obtain continuous mathematical representations; at the same time, enhancing the algorithm's compatibility with smooth transition regions and structural abrupt regions, providing a stable and reliable data foundation for subsequent spatial interpolation.

[0090] In one embodiment, it also includes:

[0091] After back-calculating the interpolated normal vector components into the target geological attitude data, boundary smoothing is performed on the interpolation results:

[0092] Based on the user-configured smoothing threshold parameter, local filtering operations are performed on the attitude data field within the geological unit group to preserve the attitude abrupt change characteristics of different address unit groups.

[0093] In the above embodiments, after generating the target geological attitude data through the inverse calculation of the normal vector components, the user-configured smoothing threshold parameter is received via an interactive interface. This parameter controls the intensity range of the filtering operation. Local filtering is performed only on the attitude data field within a single geological unit group, and the processing is strictly limited to the region within the unit group boundary to ensure that it does not cross tectonic boundaries and affect adjacent unit data. The filtering operation performs smoothing optimization on any jagged or irregular boundaries that may exist within the unit group, for example, by using spatial convolution kernels to eliminate local fluctuations in the attitude values ​​of grid points.

[0094] The smoothing process preserves the abrupt changes in attitude between different geological unit groups, especially leaving no modification to the attitude jumps on both sides of structural boundaries such as fault lines and fold traces. The user-configured smoothing threshold parameter directly determines the size of the filtering window and the number of iterations; setting the threshold to zero completely skips the smoothing operation. This selective processing mechanism ensures improved continuity within geological units while strictly maintaining the authenticity of abrupt changes at structural boundaries.

[0095] The filtering operation employs an isotropic processing approach, uniformly applying the smoothing algorithm within each element group. For structurally complex regions with drastic changes in attitude, users can lower the smoothing threshold or disable the smoothing function to prevent key geological details from being obscured. The system automatically records smoothing parameter configurations, and the output report indicates the area where smoothing operations have been performed and the corresponding threshold parameters.

[0096] In one embodiment, converting geological attitude data (dip and dip angle) into spatial vector components is a core strategy for improving interpolation robustness. Specifically, this is achieved by representing the attitude parameters as Cartesian coordinate components of the unit normal vector of the geological surface. The dip angle is decomposed into planar direction components, and the dip angle is converted into vertical direction components; together, these three constitute a mathematical expression describing the spatial orientation of the geological surface.

[0097] The most common robust form is to convert the attitude (dip and dip angle) into three components N of a unit normal vector that can represent the spatial orientation of the geological surface. x N y N z .

[0098] After converting the tendency α to Nx and Ny (or sin(α) and cos(α)), these components are values ​​that change continuously between -1 and +1, eliminating the problem of jumps between 0° and 360°.

[0099] For example, the sin and cos values ​​for 350° and 10° will be very close, and the interpolation results will transition smoothly.

[0100] After interpolating to N(x,"interp"), N(y,"interp"), N(z,"interp"), it can be easily converted back to the habitual tendency (α″interp″) and inclination angle β″interp″.

[0101] Example: Suppose there are two data points:

[0102] Point A: Inclined αA = 350°

[0103] Point B: Inclined αB = 10°

[0104] If we directly interpolate the tendency (e.g., to find the value of the midpoint):

[0105] αmid,direct = (350 + 10) / 2 = 180° (direction is due south)

[0106] This is clearly wrong, because both points A and B are close to due north.

[0107] Using the transformed method: Assume the dip angles of both points are β = 30° (the dip angle does not affect the calculation logic of the dip direction, but the normal vector component requires it). Interpolation calculation (calculation process omitted) yields αmid,converted = 0° (or 360°). This result (true north) is very reasonably located between 350° and 10°. The transformed plane direction component varies continuously in the interval [-1,1], completely eliminating the 0° / 360° boundary jump. For example, the plane component values ​​of dip directions of 350° and 10° are very close, and the interpolation result naturally transitions to the 0° azimuth (true north), rather than the 180° incorrect direction produced by the traditional arithmetic mean.

[0108] By converting angles (especially dip angles with significant periodicity) into vector components in a Cartesian coordinate system, these quantities exhibit good continuity and linearity during interpolation, thus avoiding the severe distortion that can occur with direct angle interpolation. This makes the interpolation results more consistent with geological realities and mathematical robustness. The continuous variation characteristics of the vector form can both characterize the gradual changes in the attitude of fold limbs and preserve the structural discontinuities of fault zones or fold inflection points through abrupt changes in the component fields, overcoming the limitations of angle interpolation in adapting to complex geological scenarios. The vertical component more stably reflects the dip state of rock strata, especially when co-analyzing three-dimensional structural morphology, significantly outperforming direct interpolation of dip angle values.

[0109] In specific implementation, after step 105: back-calculating the interpolated normal vector components into target geological attitude data; the target geological attitude data includes target dip and target dip angle, step 106: generating diversified output data based on the target geological attitude data; the diversified output data includes contour maps based on the target dip or target dip angle, raster data files containing spatial coordinates and target geological attitude data, and / or attitude table data of regular grid points.

[0110] In this embodiment, based on the target geological occurrence data, diverse output data is generated, including:

[0111] Based on the spatial distribution of the target's orientation, a contour map reflecting the changes in the geological structure's orientation is generated;

[0112] Based on the spatial distribution of the target dip angle, a dip angle contour map reflecting the degree of rock strata dip is generated; the interval and style of the dip angle contour map can be customized.

[0113] The spatial coordinates of regular grid points and their corresponding target dip and tilt angle values ​​are combined into a raster data file with georeferenced attributes; the raster data file is stored in GeoTIFF or ASCII Grid format;

[0114] Generate attitude table data containing the spatial coordinates of regular grid points and their corresponding attitude values, and provide the attitude data table for geological analysis in CSV or Excel format.

[0115] In the above embodiments, firstly, a dip contour map reflecting the change in geological structure trend is generated based on the spatial distribution data of the target dip. This map intuitively displays the regional tectonic pattern and fault distribution characteristics through the density of the contour lines. Simultaneously, a dip angle contour map reflecting the dip degree of the rock strata is generated based on the spatial distribution data of the target dip angle. The contour line interval can be customized by the user, for example, five degrees or ten degrees can be set as the interval unit. The line style supports the adjustment of visualization parameters such as color and line width, which meets the geological map compilation specifications.

[0116] The spatial coordinates of regular grid points and their corresponding target dip and tilt angle values ​​are combined into a raster data file with georeferenced attributes. This file is stored in the industry standard GeoTIFF or ASCII Grid format. Its internal data organization follows the raster data structure specification. Each cell value contains a precise geolocation code and attitude angle value, which can be directly input into 3D geological modeling to drive automated modeling processes.

[0117] Generate a table of attitude data containing the spatial coordinates of regular grid points and their corresponding attitude values. Output the data in CSV comma-separated text format or Excel spreadsheet format. Table fields include plane coordinates, elevation, dip angle, and dip angle, forming a standardized dataset that can be directly used for geostatistical analysis. All three types of output results are generated synchronously with a single click through a unified platform. Users can selectively export single or combined results.

[0118] Raster data files automatically embed geographic projection parameters to ensure spatial registration with digital geological maps; the contour map generation module supports overlaying geological boundary layers to enhance readability; and the tabular data format is compatible with mainstream statistical analysis software. The output process fully covers the entire workflow from geological map compilation and 3D model construction to quantitative data analysis.

[0119] Two specific embodiments are given below to illustrate the specific application of the method of the present invention.

[0120] First specific embodiment:

[0121] Figure 2 This is a specific example diagram of a geological occurrence data interpolation method in an embodiment of the present invention, as shown in the figure. Figure 2 The first specific embodiment shown proposes a high-precision remote sensing data outcrop attitude interpolation method, involving the following modules and steps:

[0122] 1. Data Input and Preprocessing Module:

[0123] Data loading:

[0124] Receive raw sampling point data containing geographic coordinates (X, Y, Z optional), attitude data (dip α, dip β), and stratigraphic / lithological unit identifiers (Stratum_ID).

[0125] Occurrence data conversion:

[0126] To avoid the periodicity and abrupt direction problems of angle interpolation, the inclination (α) and tilt (β) are converted into forms that allow for more robust interpolation.

[0127] For example: convert it into the component N of the plane normal vector. x N z N y :

[0128] N x =sin(β)·sin(α)

[0129] N y =sin(β)·cos(α)

[0130] N z =cos(β).

[0131] Alternatively, for the tendency, it can be decomposed into U = sin(α) and V = cos(α) components, interpolated for U and V respectively, and then expressed as α = αtan2(U i nterp,V i (nterp) inverse calculation. The tilt angle can be directly interpolated or first converted to tan(β) and then interpolated.

[0132] Data verification and cleaning:

[0133] Identify, mark, or remove abnormal data points.

[0134] 2. Geological Constraints and Grouping Interpolation Module:

[0135] Stratigraphic / Unit Identification and Grouping:

[0136] Based on the input Stratum_ID, the data points are automatically divided into different geological unit groups.

[0137] Optional construction domain partitioning:

[0138] Allow users to input or identify (e.g., based on attitude change rate) the main tectonic boundaries (such as fault lines, fold traces) to divide the study area into different tectonic domains.

[0139] The grouping interpolation strategy is as follows:

[0140] (1) Intra-unit interpolation: Spatial interpolation is performed on the data points (converted components) within each geological unit group.

[0141] (2) Cross-unit / tectonic domain processing: At the boundaries of different geological units or tectonic domains, discontinuous occurrences are allowed or transitions are made according to preset geological rules, rather than forced smoothing.

[0142] 3. Core interpolation engine module:

[0143] Interpolation algorithm selection:

[0144] Multiple interpolation algorithms are provided for users to choose from, or the system can automatically recommend one based on data characteristics, for example:

[0145] (1) Improved Kriging: Kriging interpolation is performed on the transformed attitude components, taking into account their spatial autocorrelation. Co-kriging can be introduced if there is correlation between different attitude components or different geological parameters.

[0146] (2) Radial Basis Function (RBF): It is suitable for sparse data and can fit local changes well.

[0147] (3) Combining trend surface analysis with residual interpolation: First, fit the regional attitude trend, and then perform local interpolation on the residuals to better reflect the macro-structural background and local details.

[0148] (4) Other methods: such as using geographically weighted regression (GWR), neural networks, etc., to learn the spatial distribution pattern of yield based on training data.

[0149] Interpolation parameter settings:

[0150] (1) Resolution control: Users can customize the resolution (grid spacing) of the output grid.

[0151] (2) Search neighborhood: Defines the number of neighboring data points or the search radius to be considered when interpolating.

[0152] (3) Anisotropy: Allows users to define the anisotropy parameters of the interpolation based on the orientation of the geological structure.

[0153] 4. Result Inverse Calculation and Post-processing Module:

[0154] Inverse attitude calculation: The normal vector components (N) obtained by interpolation are... x ,interp,N y ,interp),N z The U and V components are converted back to the tend (αinterp) and dip (βinterp) that geologists are accustomed to.

[0155] β i nterp=αcos(N z interp))

[0156] α i nterp=αtan2(N x ,interp,N y (Note: Adjust to the 0-360° range)

[0157] or α i nterp=αtan2(U i nterp,V i nterp)

[0158] Boundary handling and smoothing: The boundaries of the interpolation results should be handled reasonably. Slight smoothing can be selectively applied to eliminate jagged edges (care should be taken to avoid over-smoothing that obscures geological details).

[0159] 5. Diverse output modules: Figure 4 This is a specific example diagram of the orientation interpolation plane distribution result in an embodiment of the present invention, such as... Figure 4As shown, this figure illustrates the visual representation of the interpolation results, such as a preview of contour lines or a raster map, which is used to intuitively verify the accuracy of the interpolation method. Figure 4 The horizontal and vertical axes represent the geodetic coordinates x and y, respectively; the colors represent the dip of the grain.

[0160] (1) Contour map generation: Generate dip contour maps and angle contour maps. Users can customize the interval, color, and label of the contour lines.

[0161] (2) Raster data output: Outputs a regular grid attitude data file (such as GeoTIFF, ASCII Grid, etc.), containing the dip and dip angle values ​​of the center of each grid cell. This data can be directly used in 3D geological modeling software.

[0162] (3) Visualization of three-dimensional attitude surface (optional): Based on the interpolation results, the attitude distribution is displayed in three-dimensional space in the form of planes or symbols.

[0163] (4) Outputting table data: Figure 5 This is a schematic diagram of the structure of a difference result table in an embodiment of the present invention, as shown below. Figure 5 As shown, the difference results can be output as an interpolated attitude data table (such as CSV, Excel, etc.) of regular grid points or other specified locations. Figure 5 In the figure, x and y are the geodetic coordinates x and y, respectively, and dip represents the dip of the attitude.

[0164] (5) Report generation: Automatically generate a brief report containing interpolation parameters, methods, and result previews.

[0165] This invention also includes a system for implementing the above method, the system comprising:

[0166] Data input interface: used to load raw occurrence data and stratigraphic information.

[0167] Data processing unit: executes core algorithms such as occurrence data transformation, geological grouping, interpolation calculation, and result inverse calculation.

[0168] Parameter configuration interface: Allows users to select interpolation methods, set resolution, geological constraints and other parameters.

[0169] Results Display and Output Unit: Used to display contour maps and raster maps, and export data in various formats.

[0170] Second specific embodiment: This second specific embodiment involves the processing procedure of stratigraphic attitude interpolation in a certain structural region, as shown below:

[0171] 1) Data preparation

[0172] The input data is a CSV file containing the following fields: ID, X, Y, Z, DipDirection, DipAngle, and Stratum_Code. DipDirection represents the dip direction, DipAngle represents the dip angle, and Stratum_Code represents the stratigraphic designation (e.g., J1, J2, K1, etc.).

[0173] 2) System Operation Procedure

[0174] Step 1: Data Loading and Preprocessing

[0175] Users load CSV data through the interface.

[0176] The system reads the data and converts the dip and tilt angles into normal vector components N(x,interp), N(y,interp), and N(z,interp).

[0177] For example, if a point dips at 120° and has a dip angle of 30°, then:

[0178] N x =sin(〖30〗°)·sin(〖120〗°)=1 / 2·√3 / 2=√3 / 4≈0.433

[0179] N y =sin(〖30〗°)·cos(〖120〗°)=0.5·(-0.5)=-0.25

[0180] N z =cos(〖30)〗°)=√3 / 2≈0.866

[0181] Step Two: Geological Constraints and Grouping

[0182] The system automatically divides data points into different stratigraphic groups, such as J1, J2, and K1, based on the Stratum_Code field. Users can choose whether to include known fault line data as hard boundaries or influence domains during interpolation.

[0183] Step 3: Setting and Executing Interpolation Parameters

[0184] Users select an interpolation algorithm, such as "ordinary kriging".

[0185] Set the output grid resolution, for example, 50 meters in the X direction and 50 meters in the Y direction.

[0186] Set the parameters for the Kriging method (such as the variogram model, nugget value, sill value, and range; or have the system automatically fit them based on the data characteristics).

[0187] The system performs N tests within each stratigraphic group. x Ny N z Kriging interpolation is performed on each component to generate regular grid data for the three components. When dealing with the boundaries of different stratigraphic groups, continuity is not enforced, and abrupt changes in attitude are allowed.

[0188] Step 4: Calculate the results in reverse

[0189] The system will use the interpolated N(x,"interp"), N(y,"interp"), and N(z,"interp") grid data to inversely calculate the dip and tilt grid data point by point.

[0190] Step 5: Output of Results

[0191] The user selects to generate a dip contour map, setting the contour interval to 5°. The system draws the dip contour map and can overlay stratigraphic boundaries. Alternatively, the user selects to export dip and dip angle raster files in GeoTIFF format. Or, the user selects to export regular grid point attitude data in CSV format.

[0192] Figure 3 This is an example diagram of the interactive interface of a geological occurrence data interpolation system according to an embodiment of the present invention, such as... Figure 3 As shown, this second specific embodiment can be applied to a stratigraphic interpolation processing system in a structural region. The system interface is as follows:

[0193] The main interface includes a data import area, a parameter setting area (stratum selection, interpolation method selection, resolution setting, etc.), a preview area, and a result export area.

[0194] The parameter settings area offers advanced options, such as selecting the variogram model for the Kriging method and inputting anisotropic parameters.

[0195] The preview area can display the initial effect of the interpolation results in real time or quickly.

[0196] The method and system proposed in this invention, by comprehensively considering the special characteristics of geological data and the complexity of geological structures, can significantly improve the automation level, accuracy, and geological realism of geological occurrence data interpolation, providing reliable data support for subsequent geological research and engineering applications.

[0197] The necessity of attitude data transformation stems from the inherent defects of directly interpolating angular data. Dip data exhibits significant periodicity, and its azimuth characteristics make 0° and 360° spatially equivalent. When processing adjacent dip values ​​that cross periodic boundaries, traditional arithmetic interpolation produces severe distortion. For example, a simple arithmetic average of 350° and 10° dips with similar spatial azimuths will yield an incorrect result of 180°, which is completely opposite to the actual azimuth indicated by the original data, causing a drastic, non-geological jump in the interpolation result at the 0° / 360° boundary.

[0198] While dip angle data does not exhibit strict periodicity, its 0° to 90° range presents limitations in representing three-dimensional spatial orientation. Directly interpolating dip angle values ​​fails to accurately reflect the spatial orientation variations of rock strata, especially when it is necessary to analyze three-dimensional structural morphology in conjunction with dip data. Converting the data into spatial vector components can significantly improve interpolation stability.

[0199] The complexity of geological structures further highlights the limitations of angular interpolation. In structurally stable areas such as the limbs of folds, the attitude exhibits a gentle transition; however, in tectonically active areas such as fold inflection points or fault zones, the attitude often shows abrupt changes. Traditional angular interpolation methods cannot simultaneously adapt to these two drastically different patterns of change, especially when field sampling points are sparse, making it difficult to accurately capture the abrupt changes in attitude at key structural locations.

[0200] The core value of converting attitude angles into vector components lies in: eliminating the interference of angle periodicity on the interpolation process through mathematical reconstruction, enabling data points with similar spatial orientations to obtain continuous mathematical representations; at the same time, enhancing the algorithm's compatibility with smooth transition regions and structural abrupt regions, providing a stable and reliable data foundation for subsequent spatial interpolation.

[0201] Of course, it is understood that there may be other variations of the above detailed process, and all such variations should fall within the protection scope of this invention.

[0202] In this embodiment of the invention, original sampling point data of the target structural region is acquired; the original sampling point data includes geographic coordinates, attitude data, and stratigraphic identifiers; the original dip and dip angle in the attitude data are converted into normal vector components in a Cartesian coordinate system; the sampling points are divided into different geological unit groups and corresponding structural boundaries according to the stratigraphic identifiers; based on the structural boundaries, spatial interpolation is performed on the normal vector components within each geological unit group; the spatial interpolation is used to generate a continuous component field using a pre-configured interpolation algorithm and predefined resolution parameters; the interpolated normal vector components are then used to back-calculate the target geological attitude data; the target geological attitude data includes the target dip and target dip angle; based on the target geological attitude data, diversified output data is generated; the diversified output data includes contour maps based on the target dip or target dip angle, raster data files containing spatial coordinates and target geological attitude data, and / or attitude table data with regular grid points. This invention eliminates the distortion caused by periodic jumps in traditional angle interpolation by converting dip and dip angle into vector components; it automatically groups data based on stratigraphic markers and identifies structural boundaries such as faults, performs spatial interpolation on each group of data, preserves the geological abrupt changes at the boundaries, and rejects forced smoothing that does not conform to geological laws; it can construct strike contour maps, 3D modeling raster files, and quantitative analysis tables, covering the needs of the entire geological work process; thus, it can significantly improve the reliability, processing efficiency, and practicality of geological occurrence data interpolation.

[0203] As described above, the embodiments of the present invention have the following beneficial effects compared with the prior art:

[0204] 1. Improve the geological reliability of interpolation results: By converting the attitude into vector components for interpolation and combining it with stratigraphic / tectonic unit grouping, unreasonable results caused by direct angle interpolation are avoided, making the interpolation results more consistent with the continuity and abrupt changes of geological structures.

[0205] 2. Enhance the intelligence and automation of data processing: It can automatically group and process data according to stratigraphic identifiers, reducing manual intervention and improving processing efficiency and accuracy.

[0206] 3. Enhance the applicability and flexibility of interpolation methods: Provide a variety of interpolation algorithms and flexible parameter configurations (such as resolution and anisotropy) to adapt to different geological conditions and user needs.

[0207] 4. Diversify output formats: It can generate various output formats commonly used by geologists, such as contour maps, raster data, and tabular data, which facilitates subsequent geological analysis, map compilation, and 3D modeling.

[0208] 5. Reduced reliance on operator experience: Through built-in geological expertise and optimized algorithms, even non-professional interpolation personnel can obtain relatively reasonable geological occurrence interpolation results.

[0209] 6. Effective handling of data sparsity issues: By using optimized interpolation algorithms (such as Kriging and RBF) and reasonable parameter settings, a relatively reliable regional attitude distribution can be generated even when there are insufficient field sampling points.

[0210] This invention also provides a geological attitude data interpolation device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the geological attitude data interpolation method, the implementation of this device can refer to the implementation of the geological attitude data interpolation method; repeated details will not be elaborated further.

[0211] Figure 6 This is a schematic diagram of a geological occurrence data interpolation device according to an embodiment of the present invention. The present invention also provides a geological occurrence data interpolation device to improve the reliability, processing efficiency, and practicality of geological occurrence data interpolation, such as... Figure 6 As shown, the device includes:

[0212] Data acquisition module 601 is used to acquire raw sampling point data of the target structural area; the raw sampling point data includes geographic coordinates, attitude data and stratigraphic identifiers;

[0213] The normal vector component conversion module 602 is used to convert the original dip and original dip angle in the attitude data into normal vector components in the Cartesian coordinate system.

[0214] Geological unit group division module 603 is used to divide the sampling points into different geological unit groups and corresponding structural boundaries according to the stratigraphic identifier;

[0215] The spatial interpolation module 604 is used to perform spatial interpolation on the normal vector components within each geological unit group based on the structural boundary; the spatial interpolation is used to generate a continuous component field using a pre-configured interpolation algorithm and predefined resolution parameters.

[0216] The normal vector component inverse calculation module 605 is used to inversely calculate the interpolated normal vector components into target geological attitude data; the target geological attitude data includes target dip and target dip angle;

[0217] The output data generation module 606 is used to generate diversified output data based on the target geological attitude data; the diversified output data includes contour maps based on the target dip or target dip angle, raster data files containing spatial coordinates and target geological attitude data, and / or attitude table data with regular grid points.

[0218] In one embodiment, the geographic coordinates are used to characterize the spatial location coordinates of each sampling point; the spatial location coordinates are expressed using a Cartesian coordinate system or a geographic coordinate system.

[0219] The attitude data consists of dip and dip angle data composed of geological surface attitude measurements;

[0220] The stratigraphic identifier is used to distinguish stratigraphic codes or lithological unit identifiers of different geological units; the stratigraphic identifier uses alphanumeric codes to mark geological age or lithological classification attributes.

[0221] In one embodiment, the normal vector component transformation module is specifically used for:

[0222] By using trigonometric functions, the dip angle value is decomposed into planar direction components, and the dip angle value is converted into the vertical component of the spatial normal vector. The planar direction components are composed of dip sine and dip cosine values. The vertical component is generated by dip cosine value. The dip sine, dip cosine, and dip cosine values ​​together form the normal vector component reflecting the spatial orientation of the geological surface.

[0223] In one embodiment, the geological unit grouping module is specifically used for:

[0224] By automatically parsing the stratigraphic code or lithological unit identifier carried by the sampling points, data points with the same identifier are classified into independent geological unit groups;

[0225] The fault lines or fold axis traces received from the input are used as external structural boundaries, or the structural boundaries are automatically identified by the algorithm based on the spatial gradient characteristics of the attitude change rate of the sampling points.

[0226] In one embodiment, the spatial interpolation module is specifically used for:

[0227] Before the interpolation operation begins, the user selects the interpolation algorithm type and the user-defined grid resolution parameters through the interactive interface.

[0228] For each geological unit group, spatial interpolation calculations are performed on the normal vector components using the selected interpolation algorithm; the spatial interpolation calculations preserve the abrupt changes in attitude at different tectonic boundaries; the interpolation algorithm type includes at least one of the following: kriging, radial basis function, or a combination of trend surface analysis and residual interpolation; the resolution parameter is used to define the spatial sampling density of the output data field.

[0229] In one embodiment, the normal vector component inverse calculation module is specifically used for:

[0230] The plane projection direction angle of the interpolation normal vector component is calculated using inverse trigonometric functions and used as the target inclination.

[0231] The angle between the normal vector and the vertical direction is calculated as the target tilt angle;

[0232] The conversion results of the target dip are periodically range-corrected; the target geological attitude data are standardized attitude angle values; the target dip reflects the horizontal extension direction of the geological surface; the target dip angle reflects the spatial tilt of the geological surface.

[0233] In one embodiment, a smoothing module is further included, for:

[0234] After back-calculating the interpolated normal vector components into the target geological attitude data, boundary smoothing is performed on the interpolation results:

[0235] Based on the user-configured smoothing threshold parameter, local filtering operations are performed on the attitude data field within the geological unit group to preserve the attitude abrupt change characteristics of different address unit groups.

[0236] In one embodiment, the output data generation module is specifically used for:

[0237] Based on the spatial distribution of the target's orientation, a contour map reflecting the changes in the geological structure's orientation is generated;

[0238] Based on the spatial distribution of the target dip angle, a dip angle contour map reflecting the degree of rock strata dip is generated; the interval and style of the dip angle contour map can be customized.

[0239] The spatial coordinates of regular grid points and their corresponding target dip and tilt angle values ​​are combined into a raster data file with georeferenced attributes; the raster data file is stored in GeoTIFF or ASCII Grid format;

[0240] Generate attitude table data containing the spatial coordinates of regular grid points and their corresponding attitude values, and provide the attitude data table for geological analysis in CSV or Excel format.

[0241] This invention provides an embodiment of a computer device for implementing all or part of the above-described geological occurrence data interpolation method. The computer device specifically includes the following components:

[0242] The computer device comprises a processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between related devices; the computer device can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the computer device can be implemented with reference to the embodiments for implementing the geological occurrence data interpolation method and the embodiments for implementing the geological occurrence data interpolation device, the contents of which are incorporated herein by reference, and repeated details will not be described again.

[0243] Figure 7 This is a schematic diagram of a computer device provided in an embodiment of the present invention, which discloses a schematic block diagram of the system configuration of a computer device 1000 according to an embodiment of this application. Figure 7 As shown, the computer device 1000 may include a central processing unit 1001 and a memory 1002; the memory 1002 is coupled to the central processing unit 1001. It is worth noting that... Figure 7 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0244] In one embodiment, the geological occurrence data interpolation function can be integrated into the central processing unit 1001. The central processing unit 1001 can be configured to perform the following control:

[0245] Obtain raw sampling point data of the target structural region; the raw sampling point data includes geographic coordinates, attitude data, and stratigraphic identifiers;

[0246] The original dip and original dip angle in the attitude data are converted into normal vector components in Cartesian coordinate system;

[0247] Based on the stratigraphic markers, the sampling points were divided into different geological unit groups and corresponding tectonic boundaries;

[0248] Based on the aforementioned structural boundary, spatial interpolation is performed on the normal vector components within each geological unit group; the spatial interpolation is used to generate a continuous component field using a pre-configured interpolation algorithm and predefined resolution parameters.

[0249] The interpolated normal vector components are then used to calculate the target geological attitude data; the target geological attitude data includes the target dip and the target dip angle.

[0250] Based on the target geological attitude data, diverse output data is generated; the diverse output data includes contour maps based on the target dip or dip angle, raster data files containing spatial coordinates and target geological attitude data, and / or attitude table data with regular grid points.

[0251] In another embodiment, the geological occurrence data interpolation device can be configured separately from the central processing unit 1001. For example, the geological occurrence data interpolation device can be configured as a chip connected to the central processing unit 1001, and the geological occurrence data interpolation function can be realized through the control of the central processing unit.

[0252] like Figure 7 As shown, the computer device 1000 may further include: a communication module 1003, an input unit 1004, an audio processor 1005, a display 1006, and a power supply 1007. It is worth noting that the computer device 1000 does not necessarily need to include... Figure 7 All components shown; in addition, the computer device 1000 may also include Figure 7 For components not shown, please refer to existing technology.

[0253] like Figure 7 As shown, the central processing unit 1001, sometimes also referred to as a controller or operation control, may include a microprocessor or other processor device and / or logic device. The central processing unit 1001 receives input and controls the operation of various components of the computer device 1000.

[0254] The memory 1002 may be, for example, one or more of a cache, flash memory, hard drive, removable medium, volatile memory, non-volatile memory, or other suitable device. It can store the aforementioned device-related information, and may also store programs for executing that information. The central processing unit 1001 can execute the program stored in the memory 1002 to perform information storage or processing, etc.

[0255] Input unit 1004 provides input to central processing unit 1001. This input unit 1004 may be, for example, a keypad or touch input device. Power supply 1007 provides power to computer device 1000. Display 1006 displays images, text, and other display objects. This display may be, for example, an LCD display, but is not limited to this.

[0256] The memory 1002 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs, etc. The memory 1002 can also be some other type of device. The memory 1002 includes a buffer memory 1021 (sometimes referred to as a buffer). The memory 1002 may include an application / function storage unit 1022 for storing application programs and function programs or processes for executing operations of the computer device 1000 via the central processing unit 1001.

[0257] The memory 1002 may also include a data storage unit 1023 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the computer device. The driver storage unit 1024 of the memory 1002 may include various drivers for the computer device for communication functions and / or for performing other functions of the computer device (such as messaging applications, address book applications, etc.).

[0258] The communication module 1003 is a transmitter / receiver that transmits and receives signals via the antenna 1008. The communication module (transmitter / receiver) 1003 is coupled to the central processing unit 1001 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0259] Based on different communication technologies, multiple communication modules 1003 can be configured in the same computer device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module (transmitter / receiver) 1003 is also coupled to a speaker 1009 and a microphone 1010 via an audio processor 1005 to provide audio output via the speaker 1009 and receive audio input from the microphone 1010, thereby realizing typical telecommunications functions. The audio processor 1005 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 1005 is also coupled to a central processing unit 1001, enabling on-device recording via the microphone 1010 and on-device playback of stored sound via the speaker 1009.

[0260] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described geological occurrence data interpolation method.

[0261] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described geological occurrence data interpolation method.

[0262] In this embodiment of the invention, original sampling point data of the target structural region is acquired; the original sampling point data includes geographic coordinates, attitude data, and stratigraphic identifiers; the original dip and dip angle in the attitude data are converted into normal vector components in a Cartesian coordinate system; the sampling points are divided into different geological unit groups and corresponding structural boundaries according to the stratigraphic identifiers; based on the structural boundaries, spatial interpolation is performed on the normal vector components within each geological unit group; the spatial interpolation is used to generate a continuous component field using a pre-configured interpolation algorithm and predefined resolution parameters; the interpolated normal vector components are then used to back-calculate the target geological attitude data; the target geological attitude data includes the target dip and target dip angle; based on the target geological attitude data, diversified output data is generated; the diversified output data includes contour maps based on the target dip or target dip angle, raster data files containing spatial coordinates and target geological attitude data, and / or attitude table data with regular grid points. This invention eliminates the distortion caused by periodic jumps in traditional angle interpolation by converting dip and dip angle into vector components; it automatically groups data based on stratigraphic markers and identifies structural boundaries such as faults, performs spatial interpolation on each group of data, preserves the geological abrupt changes at the boundaries, and rejects forced smoothing that does not conform to geological laws; it can construct strike contour maps, 3D modeling raster files, and quantitative analysis tables, covering the needs of the entire geological work process; thus, it can significantly improve the reliability, processing efficiency, and practicality of geological occurrence data interpolation.

[0263] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0264] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0265] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0266] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0267] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for interpolating geological occurrence data, characterized in that, include: Obtain the original sampling point data of the target structural region; The original sampling point data includes geographic coordinates, attitude data, and stratigraphic identifiers; The original dip and original dip angle in the attitude data are converted into normal vector components in Cartesian coordinate system; Based on the stratigraphic markers, the sampling points were divided into different geological unit groups and corresponding tectonic boundaries; Based on the aforementioned structural boundary, spatial interpolation is performed on the normal vector components within each geological unit group; the spatial interpolation is used to generate a continuous component field using a pre-configured interpolation algorithm and predefined resolution parameters. The interpolated normal vector components are then used to calculate the target geological attitude data; the target geological attitude data includes the target dip and the target dip angle. Based on the target geological attitude data, diverse output data is generated; the diverse output data includes contour maps based on the target dip or dip angle, raster data files containing spatial coordinates and target geological attitude data, and / or attitude table data with regular grid points.

2. The method as described in claim 1, characterized in that, The geographic coordinates are used to characterize the spatial location coordinates of each sampling point; the spatial location coordinates are expressed using a Cartesian coordinate system or a geographic coordinate system. The attitude data consists of dip and dip angle data composed of geological surface attitude measurements; The stratigraphic identifier is used to distinguish stratigraphic codes or lithological unit identifiers of different geological units; the stratigraphic identifier uses alphanumeric codes to mark geological age or lithological classification attributes.

3. The method as described in claim 1, characterized in that, The original dip and original dip angle in the attitude data are converted into normal vector components in Cartesian coordinates, including: By using trigonometric functions, the dip angle value is decomposed into planar direction components, and the dip angle value is converted into the vertical component of the spatial normal vector. The planar direction components are composed of dip sine and dip cosine values. The vertical component is generated by dip cosine value. The dip sine, dip cosine, and dip cosine values ​​together form the normal vector component reflecting the spatial orientation of the geological surface.

4. The method as described in claim 1, characterized in that, Based on the stratigraphic markers, the sampling points are divided into different geological unit groups and corresponding structural boundaries, including: By automatically parsing the stratigraphic code or lithological unit identifier carried by the sampling points, data points with the same identifier are classified into independent geological unit groups; The fault lines or fold axis traces received from the input are used as external structural boundaries, or the structural boundaries are automatically identified by the algorithm based on the spatial gradient characteristics of the attitude change rate of the sampling points.

5. The method as described in claim 1, characterized in that, Based on the aforementioned structural boundary, spatial interpolation is performed on the normal vector components within each geological unit group, including: Before the interpolation operation begins, the user selects the interpolation algorithm type and the user-defined grid resolution parameters through the interactive interface. For each geological unit group, spatial interpolation calculations are performed on the normal vector components using the selected interpolation algorithm; the spatial interpolation calculations preserve the abrupt changes in attitude at different tectonic boundaries; the interpolation algorithm type includes at least one of the following: kriging, radial basis function, or a combination of trend surface analysis and residual interpolation; the resolution parameter is used to define the spatial sampling density of the output data field.

6. The method as described in claim 1, characterized in that, The interpolated normal vector components are then used to calculate the target geological attitude data, including: The plane projection direction angle of the interpolation normal vector component is calculated using inverse trigonometric functions and used as the target inclination. The angle between the normal vector and the vertical direction is calculated as the target tilt angle; The conversion results of the target dip are periodically range-corrected; the target geological attitude data are standardized attitude angle values; the target dip reflects the horizontal extension direction of the geological surface; the target dip angle reflects the spatial tilt of the geological surface.

7. The method as described in claim 1, characterized in that, Also includes: After back-calculating the interpolated normal vector components into the target geological attitude data, boundary smoothing is performed on the interpolation results: Based on the user-configured smoothing threshold parameter, local filtering operations are performed on the attitude data field within the geological unit group to preserve the attitude abrupt change characteristics of different address unit groups.

8. The method as described in claim 1, characterized in that, Based on the target geological occurrence data, diverse output data are generated, including: Based on the spatial distribution of the target trend, a trend contour map reflecting the changes in the geological structure trend is generated; Based on the spatial distribution of the target dip angle, a dip angle contour map reflecting the degree of rock strata dip is generated; the interval and style of the dip angle contour map can be customized. The spatial coordinates of regular grid points and their corresponding target dip and tilt angle values ​​are combined into a raster data file with georeferenced attributes; the raster data file is stored in GeoTIFF or ASCII Grid format; Generate attitude table data containing the spatial coordinates of regular grid points and their corresponding attitude values, and provide the attitude data table for geological analysis in CSV or Excel format.

9. A geological occurrence data interpolation device, characterized in that, include: The data acquisition module is used to acquire the original sampling point data of the target structural region; The original sampling point data includes geographic coordinates, attitude data, and stratigraphic identifiers; The normal vector component conversion module is used to convert the original dip and original dip angle in the attitude data into normal vector components in the Cartesian coordinate system. The geological unit group division module is used to divide the sampling points into different geological unit groups and corresponding structural boundaries according to the stratigraphic identifiers. The spatial interpolation module is used to perform spatial interpolation on the normal vector components within each geological unit group based on the structural boundary; the spatial interpolation is used to generate a continuous component field using a pre-configured interpolation algorithm and predefined resolution parameters. The normal vector component inverse calculation module is used to inversely calculate the interpolated normal vector components into target geological attitude data; the target geological attitude data includes target dip and target dip angle; The output data generation module is used to generate diversified output data based on the target geological attitude data; the diversified output data includes contour maps based on the target dip or target dip angle, raster data files containing spatial coordinates and target geological attitude data, and / or attitude table data with regular grid points.

10. The apparatus as claimed in claim 9, characterized in that, The output data generation module is specifically used for: Based on the spatial distribution of the target trend, a trend contour map reflecting the changes in the geological structure trend is generated; Based on the spatial distribution of the target dip angle, a dip angle contour map reflecting the degree of rock strata dip is generated; the interval and style of the dip angle contour map can be customized. The spatial coordinates of regular grid points and their corresponding target dip and tilt angle values ​​are combined into a raster data file with georeferenced attributes; the raster data file is stored in GeoTIFF or ASCII Grid format; Generate attitude table data containing the spatial coordinates of regular grid points and their corresponding attitude values, and provide the attitude data table for geological analysis in CSV or Excel format.

11. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 8.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 8.

13. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 8.

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