Broken basin sag identification method based on data discontinuity or multiple data sources

Abstract

The invention belongs to the technical field of geophysical exploration and sedimentary basin structure analysis, and particularly relates to a broken basin sag identification method based on data discontinuity or multiple data sources, which comprises the following steps: S1, acquiring and preprocessing multi-source gravity data; s2, gravitational field characteristic parameter calculation and primary anomaly extraction; s3, performing superposition analysis on the known sag boundary and the gravitational field features; s4, boundary identification index calibration based on a statistical model; and S5, delineating a recess boundary system of discontinuous data or multiple data sources. The method provided by the invention can solve the problem that the recess structure is difficult to identify due to discontinuous data and inconsistent benchmarks, and realizes continuous and accurate delineation of discontinuous data or recess boundaries of multiple data source construction units.

Description

Technical Field

[0001] This invention belongs to the field of geophysical exploration and sedimentary basin structural analysis technology, specifically relating to a method for identifying rift basin depressions based on discontinuous or multiple data sources. Background Technology

[0002] Rift basins (or rift basins) are an important type of sedimentary basin on Earth. They form in an extensional crustal setting, and the interior of the basin is typically filled with low-density sedimentary rocks hundreds to thousands of meters thick, while the exterior is covered with a denser crystalline basement or ancient metamorphic rocks. This significant density difference makes gravity exploration a key geophysical tool for identifying and studying the basement structure and depression units (negative tectonic units) of such basins. By inverting Bouguer gravity anomaly data, the undulating morphology of the top surface of the sedimentary basement can be effectively characterized, thereby identifying the depression structures that control sedimentation.

[0003] However, when conducting holistic tectonic studies of unified rift basin systems across large regions with discontinuous or multiple data sources, traditional analytical methods relying on gravity data from single regions face significant challenges. These challenges primarily include: 1) Data discontinuity: Varying levels of geophysical surveys within the study area result in a lack of systematic, high-precision gravity measurement data, creating "data gaps"; 2) Inconsistent data benchmarks and resolutions: Even when gravity data exists, measurements and processing by different institutions within the study area often lead to variations in gravity benchmarks, measurement techniques, data processing procedures, and spatial resolutions. Directly splicing these data together introduces systematic errors, affecting the continuity and accuracy of tectonic identification; 3) Lack of unified identification standards: Due to the complexity of geological structures and differences in data sources, establishing an objective and applicable quantitative identification standard for depression boundaries across the entire study area is a bottleneck in mapping tectonic systems with discontinuous or multiple data sources.

[0004] Currently, for such problems, manual experience comparison or simple data interpolation extrapolation are often used, resulting in highly subjective and low-precision results that are difficult to meet the needs of detailed resource exploration and tectonic evolution research. Therefore, there is an urgent need for a new method that can integrate and calibrate multi-source gravity data and establish a universal identification model based on known reliable information, thereby enabling continuous, objective, and rapid depression identification of unified tectonic units with discontinuous data or multiple data sources. Summary of the Invention

[0005] The purpose of this invention is to provide a method for identifying depressions in rift basins based on discontinuous or multiple data sources. This method can solve the difficulty in identifying depression structures caused by discontinuous data and inconsistent benchmarks, and achieve continuous and accurate delineation of depression boundaries of tectonic units with discontinuous or multiple data sources.

[0006] Technical solution to achieve the purpose of this invention: A method for identifying rift basin depressions based on discontinuous or multiple data sources includes: Step S1: Acquisition and preprocessing of multi-source gravity data; Step S2: Calculation of gravitational field characteristic parameters and primary anomaly extraction; Step S3: Overlay analysis of known concave boundary and gravitational field characteristics; Step S4: Boundary identification index calibration based on statistical model; Step S5: Delineate the concave boundary system for discontinuous data or multiple data sources.

[0007] Further, step S1 includes: Step S1.1: Obtain two types of gravity data covering the entire study area with discontinuous or multiple data sources; the two types of gravity data include: global satellite / airborne Bouguer gravity anomaly data, denoted as G_global data; and regional Bouguer gravity anomaly data, denoted as G_regional data; Step S1.2: Perform coordinate system processing and projection transformation on the G_global data and G_regional data.

[0008] Further, step S2 includes: Step S2.1: Extract contour lines from the G_global data to preliminarily delineate abnormal areas; Step S2.2: Calculate the horizontal gradient modulus of the G_global data, denoted as Gradient data; then extract the contour lines of the Gradient data.

[0009] Furthermore, the formula for calculating the Gradient data in step S2.2 is as follows: Gradient = sqrt( (dG / dx)^2 + (dG / dy)^2 ) Wherein, dG / dx and dG / dy are the first-order horizontal derivatives of global satellite / airborne Bouguer gravity anomaly data in the east-west and north-south directions, respectively.

[0010] Further, step S3 includes: Within the study area, the depression boundary vector data that has been reliably determined through fine seismic, drilling, or high-precision gravity inversion are collected and denoted as B_known data. In a geospatial data processing platform or data processing platform, the B_known data is spatially overlaid with the contour lines of G_global data, Gradient data, and G_regional data obtained in step S2.

[0011] Further, step S4 includes: Step S4.1, Sampling: Set sampling points at fixed intervals along the B_known data boundary line; Step S4.2, Feature extraction: At each sampling point, read its corresponding G_global value, Gradient value, and local statistical features of G_regional at that point; Step S4.3, Correlation Analysis and Threshold Determination: Perform statistical analysis on the multiple sets of feature values ​​extracted in step S4.2, find the feature value interval where the boundary points of B_known data are most concentrated, and determine the feature value interval as the best concave boundary identification index.

[0012] Further, step S4.3 includes: Plot a scatter plot of (G_global, Gradient) and color it with known boundary points; through cluster analysis, determine the feature value intervals where more than 85% of the sampling points of the known concave boundary simultaneously meet the following two conditions as the best concave boundary identification index: The G_global value is in the interval [T_g_min, T_g_max], and the Gradient value is in the interval [T_r_min, T_r_max]; where T_g_min represents the lower threshold of the Bouguer gravity anomaly corresponding to the concave structure, T_g_max represents the upper threshold of the Bouguer gravity anomaly corresponding to the concave structure, T_r_min represents the lower threshold of the Bouguer gravity anomaly gradient corresponding to the concave structure, and T_r_max represents the upper threshold of the Bouguer gravity anomaly gradient corresponding to the concave structure.

[0013] Further, step S5 includes: Step S5.1: Apply the optimal concave boundary identification index determined in step S4 to the G_global and Gradient data volumes of the entire study area; Step S5.2: Spatially delineate all regions that simultaneously meet the above-mentioned optimal indentation boundary identification criteria; extract and vectorize the boundaries of these regions, or the transition zones between these regions and the background region, to generate inferred indentation boundary vector data, denoted as B_inferred data; Step S5.3: Merge and smooth the known B_known data with the inferred B_inferred data to form a unified concave boundary map covering the entire study area of ​​discontinuous or multiple data sources.

[0014] The beneficial technical effects of this invention are as follows: 1. Solved the problem of data discontinuity: Creatively utilizes globally covered satellite / airborne gravity data as a "bridge" and "background field", effectively making up for the lack of high-precision gravity data in the study area and realizing continuous analysis of discontinuous data construction.

[0015] 2. Data fusion and calibration were achieved: By statistically correlating high-precision regional data (points / lines) with global data (areas) in known areas, a quantitative model was established to calibrate global data to a level suitable for fine local identification, rather than simply splicing them together.

[0016] 3. Improved objectivity and efficiency of identification: The use of statistical modeling methods to determine the identification threshold reduces human experience intervention, making the process of delineating the concave boundary more objective, repeatable, and scalable, and enabling rapid processing of large-area data.

[0017] 4. The results can directly support geological research: The final output of the continuous depression structure map can be directly used to reconstruct the basin prototype, analyze the sedimentary filling pattern, and guide the regional prediction of mineral resources such as oil, gas and uranium, which has important practical application value. Attached Figure Description

[0018] Figure 1 The flowchart is provided by the present invention for a method for identifying rift basin depressions based on discontinuous or multiple data sources. Figure 2 This is a global satellite / airborne Bouguer gravity anomaly gradient map in an embodiment of the present invention. Figure 3 This is a partial enlarged schematic diagram of the overlay analysis of the known depression boundary (B_known) and G_global in the Erlian Basin according to an embodiment of the present invention. Figure 4 This is a diagram showing the final identification results of the depression structure of the Central-Mongolian Jurassic-Cretaceous rift basin group in this embodiment of the invention. Detailed Implementation

[0019] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0020] like Figure 1 As shown, this invention provides a method for identifying rift basin depressions based on discontinuous or multiple data sources, specifically including the following steps: Step S1: Acquisition and preprocessing of multi-source gravity data.

[0021] Step S1.1: Acquire multi-source gravity data Two types of gravity data were acquired to cover the entire study area, which may have discontinuous or multiple data sources: 1) Global satellite / airborne Bouguer gravity anomaly data (denoted as G_global data) (which can be obtained directly from the Scripps Institution of Oceanography website in the United States). This data is characterized by spatial continuity, unified benchmark, and global coverage; 2) Regional high-precision Bouguer gravity anomaly data (denoted as G_regional data). The portion of this data within the study area usually comes from ground or airborne measurements. It is highly accurate and rich in detail, but some parts of the study area are missing or incomplete.

[0022] Step S1.2: Perform coordinate system processing and projection transformation on global satellite / airborne Bouguer gravity anomaly data and regional high-precision Bouguer gravity anomaly data.

[0023] Step S2: Calculation of gravity field characteristic parameters and primary anomaly extraction.

[0024] Step S2.1: Extract contour lines from global satellite / airborne Bouguer gravity anomaly data (G_global data) to preliminarily delineate the anomaly area.

[0025] In one specific implementation, contour lines are extracted at 1 mGal intervals, where contour lines close to zero or a specific threshold can initially reflect the variation zone of deposition thickness.

[0026] Step S2.2: Calculate the horizontal gradient modulus (Gradient data) of global satellite / airborne Bouguer gravity anomaly data (G_global data) to enhance anomaly boundary information; then extract the contour lines of the gradient data (with an isointerval of 1 mGal / km). The formula for calculating the horizontal gradient modulus is: Gradient = sqrt( (dG / dx)^2 + (dG / dy)^2 ) Wherein, dG / dx and dG / dy are the first-order horizontal derivatives of global satellite / airborne Bouguer gravity anomaly data in the east-west and north-south directions, respectively.

[0027] Step S3: Overlay analysis of known concave boundary and gravitational field characteristics.

[0028] Within the study area, depression boundary vector data (B_known data) that has been reliably determined through fine-grained seismic, drilling, or high-precision gravity inversion are collected. In a geospatial data processing platform (GIS) or data processing platform, the depression boundary vector data (B_known data) are spatially overlaid with the contour lines of G_global data, Gradient data, and G_regional data obtained in step S2.

[0029] Step S4: Boundary identification index calibration based on statistical model.

[0030] Establish a quantitative relationship model connecting known geological boundaries (B_known data) with continuously available field feature parameters (G_global data, Gradient data).

[0031] Step S4.1, Sampling. Set sampling points at fixed intervals (e.g., 1 km) along the boundary line of the concave boundary vector data (B_known data).

[0032] Step S4.2, Feature extraction. At each sampling point, read its corresponding G_global value, Gradient value, and local statistical features (such as the mean) of G_regional at that point.

[0033] Step S4.3: Correlation Analysis and Threshold Determination. Perform statistical analysis (such as scatter plot analysis, cluster analysis, or logistic regression modeling) on ​​the multiple sets of feature values ​​extracted in step S4.2 to find the feature value interval where the boundary points of B_known data are most concentrated, and determine this feature value interval as the optimal concave boundary identification index.

[0034] In one specific implementation, analysis revealed that over 85% of the sampling points at the known concave boundary simultaneously satisfy the following two conditions: The G_global value is in the range [T_g_min, T_g_max] (e.g., [-12 mGal, -5 mGal]); and the Gradient value is in the range [T_r_min, T_r_max] (e.g., [0.8 mGal / km, 1.5 mGal / km]). Where T_g_min represents the lower threshold of the Bouguer gravity anomaly corresponding to the depression structure, T_g_max represents the upper threshold of the Bouguer gravity anomaly corresponding to the depression structure, T_r_min represents the lower threshold of the Bouguer gravity anomaly gradient corresponding to the depression structure, and T_r_max represents the upper threshold of the Bouguer gravity anomaly gradient corresponding to the depression structure.

[0035] The optimal indentation boundary identification index is determined by the combination of the G_global value in the interval [T_g_min, T_g_max] and the Gradient value in the interval [T_r_min, T_r_max].

[0036] Step S5: Delineate the concave boundary system for discontinuous data or multiple data sources.

[0037] Step S5.1, Index Application. The optimal concave boundary identification index (the combined range of G_global and Gradient) determined in step S4 is applied to the G_global and Gradient data volumes of the entire study area.

[0038] Step S5.2, Boundary Extraction. Spatially delineate all regions that simultaneously satisfy the above G_global and Gradient interval conditions. Extract and vectorize the boundaries of these regions, or the transition zones between these regions and the background region, to generate inferred concave boundary vector data (B_inferred data).

[0039] Step S5.3, Optimization and Integration. The known concave boundary vector data (B_known data) and the inferred concave boundary vector data (B_inferred data) are fused and smoothed to form a unified concave boundary map covering the entire study area with discontinuous or multiple data sources.

[0040] Example 1 Taking the depression structure of the Jurassic-Cretaceous rift basin group in Inner Mongolia Autonomous Region, China and southeastern Mongolia as an example, the specific implementation of the present invention will be described in detail.

[0041] A method for identifying rift basin depressions based on discontinuous or multiple data sources specifically includes the following steps: Step S1: Data preparation.

[0042] Download global high-resolution satellite Bouguer gravity anomaly data (CryoSat-2 and Jason-1 global satellite gravity models) covering the China-Mongolia border region (E95°-122°, N38°-51°), with a grid spacing of 1′×1′, to obtain G_global. Collect regional Bouguer gravity anomaly data (G_regional) at a scale of 1:200,000 or higher for the Yin'e Basin, Erlian Basin, and Hailar Basin within China, as well as published seismically confirmed depression boundary vector data (B_known).

[0043] Step S2: Calculation of characteristic parameters.

[0044] Using GIS or specialized gravity processing software, calculate the horizontal gradient modulus of G_global and generate a gradient map, such as... Figure 2 As shown. Contour lines for G_global and Gradient were extracted separately, with intervals of 1 mGal and 0.1 mGal / km for preliminary analysis.

[0045] Step S3: Overlay analysis.

[0046] The B_known maps (taking multiple depression boundaries within the Erlian Basin as an example) are overlaid on the G_global and Gradient maps, as shown below. Figure 3 As shown, it can be observed that the known boundary does not completely coincide with a single contour line, but rather traverses a specific gravity anomaly and gradient change zone.

[0047] Step S4: Statistical modeling and indicator calibration.

[0048] Along the known depression boundary of the Erlian Basin, which is about 500 kilometers long, 1,000 sample points were collected at 1 km intervals.

[0049] Extract the G_global value and Gradient value for each sample point.

[0050] A scatter plot of (G_global, Gradient) was plotted and colored with known boundary points. Cluster analysis (such as K-means) revealed that over 90% of the known boundary points clustered within regions where G_global was [-11, -4] mGal and Gradient was [0.7, 1.4] mGal / km.

[0051] Therefore, the optimal identification index is defined as: G_global ∈ [-11, -4] mGal, and Gradient ∈ [0.7, 1.4] mGal / km.

[0052] Step S5: Delineation of the entire area and integration of results.

[0053] The above indicators were applied to the G_global and Gradient data of the entire China-Mongolia study area. The software then used a conditional query function to extract all spatial pixels that met the combined conditions.

[0054] These pixels are subjected to boundary extraction and vectorization operations to generate inferred depression boundary vector data B_inferred, covering a large area in southeastern Mongolia.

[0055] By topologically merging B_known within China and B_inferred within Mongolia and smoothing their boundaries, a complete distribution map of the depression structure of the China-Mongolia rift basin group is generated, as shown below. Figure 4 As shown in the figure, this map clearly illustrates the tectonic structure of the transboundary basin, providing a new foundational map for regional geological research.

[0056] The present invention has been described in detail above with reference to the accompanying drawings and embodiments. However, the present invention is not limited to the above embodiments, and various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention. All contents not described in detail in the present invention can be derived from existing technologies.

Claims

1. A method for identifying rift basin depressions based on discontinuous or multiple data sources, characterized in that, include: Step S1: Acquisition and preprocessing of multi-source gravity data; Step S2: Calculation of gravitational field characteristic parameters and primary anomaly extraction; Step S3: Overlay analysis of known concave boundary and gravitational field characteristics; Step S4: Boundary identification index calibration based on statistical model; Step S5: Delineate the concave boundary system for discontinuous data or multiple data sources.

2. The method for identifying rift basin depressions based on discontinuous or multiple data sources according to claim 1, characterized in that, Step S1 includes: Step S1.1: Obtain two types of gravity data covering the entire study area with discontinuous or multiple data sources; the two types of gravity data include: global satellite / airborne Bouguer gravity anomaly data, denoted as G_global data; and regional high-precision Bouguer gravity anomaly data, denoted as G_regional data; Step S1.2: Perform coordinate system processing and projection transformation on the G_global data and G_regional data.

3. The method for identifying rift basin depressions based on discontinuous or multiple data sources according to claim 2, characterized in that, Step S2 includes: Step S2.1: Extract contour lines from the G_global data to preliminarily delineate abnormal areas; Step S2.2: Calculate the horizontal gradient modulus of the G_global data, denoted as Gradient data; then extract the contour lines of the Gradient data.

4. The method for identifying rift basin depressions based on discontinuous or multiple data sources according to claim 3, characterized in that, The formula for calculating the Gradient data in step S2.2 is as follows: Gradient = sqrt( (dG / dx)^2 + (dG / dy)^2 ) Wherein, dG / dx and dG / dy are the first-order horizontal derivatives of global satellite / airborne Bouguer gravity anomaly data in the east-west and north-south directions, respectively.

5. The method for identifying rift basin depressions based on discontinuous or multiple data sources according to claim 3, characterized in that, Step S3 includes: Within the study area, the depression boundary vector data that has been reliably determined through fine seismic, drilling, or high-precision gravity inversion are collected and denoted as B_known data. In a geospatial data processing platform or data processing platform, the B_known data is spatially overlaid with the contour lines of G_global data, Gradient data, and G_regional data obtained in step S2.

6. The method for identifying rift basin depressions based on discontinuous or multiple data sources according to claim 5, characterized in that, Step S4 includes: Step S4.1, Sampling: Set sampling points at fixed intervals along the B_known data boundary line; Step S4.2, Feature extraction: At each sampling point, read its corresponding G_global value, Gradient value, and local statistical features of G_regional at that point; Step S4.3, Correlation Analysis and Threshold Determination: Perform statistical analysis on the multiple sets of feature values ​​extracted in step S4.2, find the feature value interval where the boundary points of B_known data are most concentrated, and determine the feature value interval as the best concave boundary identification index.

7. The method for identifying rift basin depressions based on discontinuous or multiple data sources according to claim 6, characterized in that, Step S4.3 includes: Plot a scatter plot of (G_global, Gradient) and color it with known boundary points; through cluster analysis, determine the feature value intervals where more than 85% of the sampling points of the known concave boundary simultaneously meet the following two conditions as the best concave boundary identification index: The G_global value is in the interval [T_g_min, T_g_max], and the Gradient value is in the interval [T_r_min, T_r_max]; where T_g_min represents the lower threshold of the Bouguer gravity anomaly corresponding to the concave structure, T_g_max represents the upper threshold of the Bouguer gravity anomaly corresponding to the concave structure, T_r_min represents the lower threshold of the Bouguer gravity anomaly gradient corresponding to the concave structure, and T_r_max represents the upper threshold of the Bouguer gravity anomaly gradient corresponding to the concave structure.

8. The method for identifying rift basin depressions based on discontinuous or multiple data sources according to claim 6, characterized in that, Step S5 includes: Step S5.1: Apply the optimal concave boundary identification index determined in step S4 to the G_global and Gradient data volumes of the entire study area; Step S5.2: Spatially delineate all regions that simultaneously meet the above-mentioned optimal indentation boundary identification criteria; extract and vectorize the boundaries of these regions, or the transition zones between these regions and the background region, to generate inferred indentation boundary vector data, denoted as B_inferred data; Step S5.3: Merge and smooth the known B_known data with the inferred B_inferred data to form a unified concave boundary map covering the entire study area of ​​discontinuous or multiple data sources.

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