Gravity gradient tensor component joint probability imaging method, system and device based on correlation coefficient weight function and medium

Abstract

The invention discloses a gravity gradient tensor component joint probability imaging method, system and device based on a correlation coefficient weight function and a medium, and belongs to the technical field of gravity abnormal data processing, and the method comprises the steps: firstly carrying out the grid processing of each component of an actually measured gravity gradient tensor, and determining an independent component; then dispersing the underground space into regular three-dimensional grid units; the correlation coefficient of each independent component in a field source grid unit is calculated, weighted joint probability imaging calculation is carried out on the correlation coefficient, and finally an imaging result capable of equivalently representing underground three-dimensional density distribution is generated. According to the method, the weight function is introduced to calculate the joint probability imaging correlation coefficient of each component of the gravity gradient tensor, so that the limitation that single-component imaging is greatly influenced by directivity is effectively overcome, and the precision of an inversion result is improved; meanwhile, the method avoids a time-consuming multi-iteration process in conventional inversion, and greatly improves the calculation efficiency on the premise of ensuring the imaging accuracy.

Description

Technical Field

[0001] This invention relates to the field of gravity anomaly data processing technology, specifically to a method, system, device, and medium for joint probability imaging of gravity gradient tensor components based on correlation coefficient weighting functions. Background Technology

[0002] Gravity anomalies are a comprehensive reflection of the uneven density of geological bodies. Gravity exploration is a geophysical exploration method that uses the gravity changes caused by the density differences between geological bodies to conduct geological exploration. It has the advantages of being lightweight, fast, and efficient, and is widely used in the study of the Earth's internal structure and tectonics, environmental and engineering surveys, solid mineral exploration, and oil and gas resource detection.

[0003] Gravity anomaly inversion is the process of processing and analyzing measured gravity anomaly data to inversely determine the density information of underground space and ultimately obtain a geological interpretation of this data; it belongs to the inverse geophysical problem. Gravity anomaly inversion is a process of solving from low-dimensional data to high-dimensional data, and the calculation results are ill-posed, exhibiting instability and multiple solutions. Furthermore, the computational dimensionality is typically high during the inversion calculation, resulting in high computational costs.

[0004] To address the aforementioned issues, scholars both domestically and internationally have conducted extensive research. Probabilistic imaging methods based on potential field theory calculate the normalized cross-correlation coefficient between gravity anomalies and the scanning functions of the subsurface imaging units. By utilizing the correlation coefficient to represent the distribution of density in the equivalent subsurface geological body, the equivalent density information of the subsurface space can be quickly derived. Gravity gradient tensor data contains five independent components, each containing density information in different directions. Performing probabilistic imaging calculations on only a single component fails to effectively utilize the directional information contained in the other components. Therefore, jointly probabilistically imaging the components is crucial for improving the accuracy of the imaging results. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that existing gravity anomaly probability imaging methods usually only use single component data and cannot effectively fuse the density information contained in multiple independent components of the gravity gradient tensor pointing to different underground spatial directions, resulting in an incomplete characterization of underground geological structures, strong ambiguity in inversion results, and insufficient imaging accuracy.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a joint probabilistic imaging method for each component of the gravity gradient tensor based on a correlation coefficient weighting function, comprising, The measured gravity gradient tensor components are gridded to obtain individual geophysical field component data. The subsurface space of the target area is discretized into three-dimensional grid cells. Based on the individual geophysical field component data, the correlation coefficients of the field sources within the three-dimensional grid cells are calculated. Joint probabilistic imaging calculations are performed on the correlation coefficients of the individual geophysical field component data to obtain joint probabilistic imaging results of the individual geophysical field component data. The joint probabilistic imaging results of the individual geophysical field component data are equivalent to a three-dimensional density model of the subsurface space, and the distribution patterns of the three-dimensional density model of the subsurface space are analyzed for geological interpretation.

[0008] As a preferred embodiment of the joint probabilistic imaging method for each component of the gravity gradient tensor based on the correlation coefficient weight function described in this invention, wherein: the obtained single geophysical field component data includes, Obtain the gridded data of each component of the measured gravity gradient tensor. The gravity gradient tensor contains several components.

[0009] Based on the inherent constraint relationship of the gravity gradient tensor in the physical field, independent tensor components are determined from the plurality of components.

[0010] As a preferred embodiment of the joint probabilistic imaging method for each component of the gravity gradient tensor based on the correlation coefficient weight function described in this invention, wherein: the discretization of the underground space of the target area into three-dimensional mesh units includes, Define the start and end ranges of the target area to determine the modeling space.

[0011] Set the mesh parameters for the 3D mesh cells.

[0012] Based on the mesh parameters, the modeling space is divided in spatial dimensions to generate a set of regular three-dimensional mesh units.

[0013] As a preferred embodiment of the joint probabilistic imaging method for each component of the gravity gradient tensor based on the correlation coefficient weight function described in this invention, wherein: the correlation coefficient of the calculated field source within the three-dimensional mesh cell includes, For a three-dimensional mesh cell, the theoretical field values ​​generated by the three-dimensional mesh cell at all surface measurement points are calculated based on spatial geometric parameters.

[0014] The measured gravity gradient tensor data of a certain component after gridding are arranged in the order of measurement points with the same forward modeling kernel matrix to form the observation data vector.

[0015] For each grid cell, the correlation coefficient between the theoretical field value and the observed data vector is calculated.

[0016] As a preferred embodiment of the joint probabilistic imaging method for each component of the gravity gradient tensor based on the correlation coefficient weight function described in this invention, wherein: the joint probabilistic imaging calculation of the correlation coefficients of single geophysical field component data includes, By traversing all three-dimensional mesh elements, the correlation coefficient of each element with respect to the gravity gradient tensor component is calculated.

[0017] The correlation coefficients of all units are arranged in spatial order to form a correlation imaging coefficient matrix.

[0018] The beneficial effects of this preferred technical solution are as follows: by systematically traversing all three-dimensional mesh cells and calculating their correlation coefficients with each gravity gradient tensor component, a full-coverage and accurate scan of underground space is achieved; by arranging the correlation coefficients of each cell in spatial order to construct a correlation imaging coefficient matrix, the complex multidimensional inversion problem is transformed into a structured matrix operation problem, providing a standardized data foundation for subsequent multi-component data fusion, improving computational efficiency and stability, effectively reducing the curse of dimensionality problem in the inversion process, and laying a key technical foundation for rapidly obtaining a high-resolution underground density distribution model.

[0019] As a preferred embodiment of the joint probabilistic imaging method for gravity gradient tensor components based on correlation coefficient weighting function described in this invention, wherein: the obtained joint probabilistic imaging results of single geophysical field component data include, Calculate a weighting factor for joint imaging for each independent component of the gravity gradient tensor.

[0020] For each 3D mesh cell, the correlation coefficient of each component is multiplied by the weighting coefficient of the component to obtain the joint probability imaging correlation coefficient of the mesh cell.

[0021] The beneficial effect of this preferred technical solution is that by adaptively assigning weighting coefficients to each independent component of the gravity gradient tensor and calculating the joint probability imaging correlation coefficient of each grid cell based on the weighted fusion strategy, the limitation of insufficient information utilization in traditional single-component probability imaging methods is effectively overcome.

[0022] As a preferred embodiment of the joint probabilistic imaging method for each component of the gravity gradient tensor based on the correlation coefficient weight function described in this invention, the geological interpretation of analyzing the distribution pattern of the three-dimensional density model of underground space includes, The joint probability imaging correlation coefficients of all grid cells are combined according to their spatial location to form a three-dimensional density model of underground space.

[0023] By visualizing the three-dimensional density model of underground space in two or three dimensions, the spatial distribution pattern of the model in the target area can be analyzed, and the geological interpretation of the target area can be carried out.

[0024] The beneficial effects of this preferred technical solution are that by combining the correlation coefficients of joint probability imaging according to spatial location to construct a three-dimensional density model, the inversion results are transformed from abstract data to intuitive spatial structure; with the help of two-dimensional or three-dimensional visualization technology, the spatial morphology, distribution pattern and interrelationship of underground density anomalies can be clearly displayed.

[0025] This invention provides a system for a joint probabilistic imaging method for each component of the gravity gradient tensor based on a correlation coefficient weighting function.

[0026] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a system for joint probabilistic imaging of gravity gradient tensor components based on correlation coefficient weight function, comprising: a data preprocessing module, a three-dimensional modeling and meshing module, a forward modeling and single-component correlation coefficient calculation module, a joint probabilistic imaging fusion module, and a geological interpretation and visualization module.

[0027] The data preprocessing module is responsible for gridding the measured gravity gradient tensor components to obtain single geophysical field component data.

[0028] The 3D modeling and meshing module is responsible for discretizing the underground space of the target area into 3D mesh units.

[0029] The forward modeling and single-component correlation coefficient calculation module is responsible for calculating the correlation coefficient of the field source within a three-dimensional grid cell based on single geophysical field component data.

[0030] The joint probabilistic imaging fusion module is responsible for performing joint probabilistic imaging calculations on the correlation coefficients of single geophysical field component data to obtain joint probabilistic imaging results of single geophysical field component data.

[0031] The geological interpretation and visualization module transforms the combined probabilistic imaging results of single geophysical field component data into an equivalent three-dimensional density model of underground space, and analyzes the distribution pattern of the three-dimensional density model of underground space for geological interpretation.

[0032] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the aforementioned joint probability imaging method for each component of the gravity gradient tensor based on a correlation coefficient weight function.

[0033] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the aforementioned joint probability imaging method for each component of the gravity gradient tensor based on a correlation coefficient weighting function.

[0034] The beneficial effects of this invention are as follows: 1. This invention introduces a weighting function, and by calculating the joint probability imaging correlation coefficient of each component of the gravity gradient tensor based on the correlation coefficient weighting function, it overcomes the defect that single-component correlation imaging is greatly affected by the component correlation direction, reduces the error of single-component imaging results in non-correlated directions, and improves the accuracy of joint imaging results.

[0035] 2. The method of the present invention avoids the multiple iterative calculations of conventional gravity inversion methods, reduces the computational cost of inversion calculation, and ensures the computational efficiency of inversion. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0037] Figure 1 The above is a flowchart of a joint probability imaging method for each component of the gravity gradient tensor based on a correlation coefficient weighting function, provided as an embodiment of the present invention.

[0038] Figure 2 This is a schematic diagram of the model structure in a joint probabilistic imaging method for each component of the gravity gradient tensor based on a correlation coefficient weighting function, provided as an embodiment of the present invention.

[0039] Figure 3 This invention provides a plane contour map of each component of the gravity gradient tensor in a joint probabilistic imaging method based on a correlation coefficient weighting function, as an embodiment of the present invention.

[0040] Figure 4 The image shows the inversion result of the correlation imaging method of each single component of the gravity gradient tensor in a joint probability imaging method based on the correlation coefficient weight function of each component of the gravity gradient tensor provided in an embodiment of the present invention.

[0041] Figure 5 The image shows the inversion result of a joint probabilistic imaging method for gravity gradient tensor components based on correlation coefficient weighting function, provided in an embodiment of the present invention.

[0042] Figure 6 This is an overall framework diagram of a joint probability imaging system for each component of the gravity gradient tensor based on a correlation coefficient weighting function, provided as an embodiment of the present invention. Detailed Implementation

[0043] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0044] Example 1, referring to Figures 1-5 This is one embodiment of the present invention, which provides a joint probabilistic imaging method for each component of the gravity gradient tensor based on a correlation coefficient weighting function, including: S1. The measured gravity gradient tensor components are gridded to obtain single geophysical field component data.

[0045] S2. Discretize the underground space of the target area into three-dimensional grid units.

[0046] S3. Based on single geophysical field component data, calculate the correlation coefficient of the field source within the three-dimensional grid cell.

[0047] S4. Perform joint probability imaging calculation on the correlation coefficients of single geophysical field component data to obtain the joint probability imaging results of single geophysical field component data.

[0048] S5. Combine the results of probabilistic imaging of single geophysical field component data into an equivalent three-dimensional density model of underground space, and analyze the distribution pattern of the three-dimensional density model of underground space for geological interpretation.

[0049] This invention achieves coordinated constraint and weighted fusion of independent components of the gravity gradient tensor, significantly reducing the uncertainty in interpreting underground density structure; by combining probabilistic imaging theory with regular gridded modeling, computational efficiency is greatly improved while ensuring inversion resolution; the final generated three-dimensional probabilistic imaging model reveals the spatial distribution pattern of underground density anomalies in an intuitive and quantitative way.

[0050] Example 2, an embodiment of the present invention, provides a joint probabilistic imaging method for each component of the gravity gradient tensor based on a correlation coefficient weighting function, based on the previous embodiment, including: In this embodiment of the application, the single geophysical field component data in S1 refers to the gridded data of each component of the gravity gradient tensor. ,in It contains 9 components, namely , = .

[0051] The effectiveness of the joint probabilistic imaging inversion method in this patent is demonstrated using a combined model, which consists of two cuboid models, such as... Figure 2 As shown, the residual density of both cuboid models is 0.5 g / cm3. Forward modeling is performed on the combined model to obtain the simulated gravity gradient tensor components.

[0052] In the source-free space, the components of the gravity gradient tensor satisfy the potential field Laplace equation. As an irrotational field, the components of the gravitational gradient tensor exist. = , = , = The relationship, therefore the gravity gradient tensor Of the nine components, only five are independent, that is... = In this embodiment, the simulated gravity gradient tensor components are meshed, resulting in a 50x50 mesh. This embodiment only applies to... , , , , , These six components are subjected to forward modeling and meshing.

[0053] in, It is represented as a component in a certain spatial direction. It is expressed as the directional derivative in a certain spatial direction. These are represented as three mutually orthogonal spatial directions. These are represented by the nine components of the gravity gradient tensor. This represents the x-direction derivative of the gravitational field component. This represents the y-direction derivative of the gravitational field component in the x-direction. This represents the z-direction derivative of the gravitational field in the x-direction. This represents the y-direction derivative of the gravitational field component. This represents the x-direction derivative of the gravitational field component in the y-direction. This represents the z-direction derivative of the gravitational field component in the y-direction. This represents the x-direction derivative of the gravitational field component in the z-direction. This represents the directional derivative of the gravitational field in the z-direction. This represents the z-directional derivative of the gravitational field in the z-direction.

[0054] In one alternative implementation, the single geophysical field component data can be the five independent components of the gravity gradient tensor, i.e. The gridded data of these five independent components are used together as the input dataset for the subsequent probabilistic imaging algorithm. In steps S3 and S4, the correlation coefficient matrix of each of the five independent components is calculated, and in the joint probabilistic imaging calculation, each of the five components is assigned a weighting coefficient for fusion. This scheme utilizes all available information, can constrain the subsurface source from multiple directions, and theoretically can obtain the most robust and highest resolution inversion results.

[0055] In another alternative implementation, the single geophysical field component data can also be a specific combination of components selected from independent components based on actual data quality and geological task requirements, as follows: First, assess the quality of the measured data; if some independent components have a low signal-to-noise ratio due to severe noise interference, consider removing them. Simultaneously, based on prior geological information, select several components that are most sensitive to the target body (such as dikes or structures dipping in a certain direction).

[0056] Based on the analysis in S1, a customized dataset with non-independent components is constructed. For example, to highlight lateral resolution and suppress noise, a dataset including vertical components can be selected. And can provide information on changes in the horizontal direction. and This forms a three-component dataset. , , }.in, Through relational expressions =-( + It is calculated from other independent components.

[0057] In subsequent calculations, only the components in this customized dataset (such as { , , Correlation coefficients were calculated and joint imaging was performed.

[0058] Furthermore, obtaining single geophysical field component data in S1 includes the following steps A1-A2: A1. Obtain the gridded data of each component of the measured gravity gradient tensor. The gravity gradient tensor contains several components.

[0059] A2. Based on the inherent constraint relationship of the gravity gradient tensor in the physical field, determine the independent tensor components from the several components.

[0060] In this embodiment, the inherent constraint relationship of the physical field in A2 is the potential field Laplace equation. Using this equation, one diagonal component can be expressed using the other two. In this embodiment, it is chosen to... Represented as This relationship indicates that, The information contained in a component is not independent; it is entirely composed of… and Determined by linear combinations, and preserved by symmetry constraints. , , The constraints of the Laplace equation are preserved. , .

[0061] In one alternative implementation, the inherent constraints of the physical field can be the symmetry of the tensor. Based on the inherent symmetry properties of this tensor field, all mathematically non-redundant components are identified and filtered out. Specifically, by examining the inherent symmetry relationships between the components of the tensor, pairs of equal off-diagonal components are merged to ensure that in subsequent calculations, only one of each pair of equal components is retained as a representative, thereby constructing a smaller set of tensor components that has removed explicit redundant information.

[0062] In another alternative implementation, the inherent constraints of the physical field can be a simplified form of Maxwell's equations under static field conditions. First, the Laplace equation satisfied by the physical field in the source-free region is applied. This equation establishes the linear dependencies between a specific set of components in the tensor, allowing one component to be expressed as a linear combination of the remaining components and removed from the independent dataset. Then, the tensor symmetry conditions derived when the field is irrotational are applied to further merge all non-independent component pairs.

[0063] In the embodiments of this application, the three-dimensional mesh unit in S2 is a plurality of cuboid mesh units. Specifically, it includes, firstly, in a three-dimensional rectangular coordinate system, accurately delineating the horizontal boundaries of the target area in the east-west and north-south directions, as well as the depth exploration range from the ground surface downwards, thereby defining a three-dimensional rectangular space to be discretized.

[0064] Subsequently, based on the required resolution and computing resources, the cell sizes of the cuboid mesh were determined in the east-west, north-south, and vertical directions. Simultaneously, the number of meshes required in each of the three directions was calculated.

[0065] Next, regular grid partitioning is performed. Along the east-west direction, from the west boundary to the east boundary, the grid is divided at equal intervals according to the set cell size; along the north-south direction, from the south boundary to the north boundary, the same regular equal interval partitioning is performed; along the vertical direction, from the surface downwards to the maximum exploration depth, the grid is also divided at equal intervals.

[0066] Finally, through the rule-based division in the three directions mentioned above, the entire target underground space is discretized into a set of numerous uniformly sized, regularly arranged cuboid grid cells. Each cuboid cell is uniquely identified by its spatial location index and serves as the basic unit for subsequent forward modeling and probabilistic imaging.

[0067] In one alternative implementation, the three-dimensional mesh elements can be variable-sized hexahedral elements. First, based on prior geological information or preliminary inversion results, key areas requiring detailed characterization (such as known tectonic zones or mineralization anomaly areas) and background areas within the target region are identified. Subsequently, non-uniform meshing is performed: dense meshing with smaller mesh sizes is used in key areas to improve the resolution of those areas; sparse meshing with larger mesh sizes is used in background areas. Finally, a set of hexahedral elements with smoothly varying mesh sizes in three-dimensional space is generated.

[0068] In another alternative implementation, the three-dimensional mesh elements can also be locally refined hybrid hexahedral and tetrahedral elements. First, regular hexahedral elements are used to initially partition the underground space in most areas to maintain the regularity of the model and the efficiency of forward modeling. Subsequently, for specific geological targets (such as complex undulating interfaces or irregular ore body boundaries), the regular hexahedral elements are further subdivided into multiple tetrahedral elements in a localized area around them.

[0069] Furthermore, in S2, discretizing the underground space of the target area into three-dimensional mesh units includes the following steps B1-B3: B1. Define the start and end range of the target area to determine the modeling space.

[0070] B2. Set the mesh parameters for the three-dimensional mesh unit.

[0071] B3. Based on the mesh parameters, the modeling space is divided in spatial dimensions to generate a set of regular three-dimensional mesh units.

[0072] Furthermore, calculating the correlation coefficient of the field source within the three-dimensional mesh element in S3 includes the following steps C1-C3: C1. For a three-dimensional mesh element, calculate the theoretical field values ​​generated by the three-dimensional mesh element at all surface measurement points based on spatial geometric parameters.

[0073] C2. Arrange the measured gravity gradient tensor data of a certain component after gridding according to the measurement point order with the same forward modeling kernel matrix to form the observation data vector.

[0074] C3. For each grid cell, calculate the correlation coefficient between the theoretical field value and the observed data vector.

[0075] In the embodiments of this application, the theoretical field value in C1 is the correlation imaging coefficient. The total number of all surface measuring points within the survey area is determined to be N. For each specified underground cuboid grid cell (numbered q), the following calculation procedure is performed: Subsequently, for this grid cell q, the theoretical field value of a specific component of the gravity gradient tensor induced by it at each surface measuring point i is calculated. This theoretical value is calculated based on the geometric spatial parameters and uniform density assumption of the cuboid cell, and is obtained directly through its corresponding precise analytical formula.

[0076] Next, after completing the theoretical value calculations for all measurement points for this unit, these values ​​are arranged in the order of the measurement points to form a vector describing the theoretical response of this unit in the entire measurement area, which is the forward modeling kernel vector of this unit.

[0077] The specific calculation method is as follows: Where N is the total number of measurement points in the gridded measurement area. In order to be in Gravity gradient tensor component data of a point. For the q-th grid cell in The gravity gradient tensor component data generated by the point. Represents the components of the gravity gradient tensor. Represented as gravity gradient tensor components The relevant imaging coefficients at the q-th grid cell, Represented as the q-th grid cell in The gravity gradient tensor component generated by the point.

[0078] In one alternative implementation, the theoretical field values ​​can be analytical solutions based on the assumption of homogeneous physical properties, assuming that each 3D mesh cell has uniform physical property parameters (such as density). For regular geometries (such as cuboids), the theoretical physical field values ​​induced by the cell at all surface measurement points are calculated directly using its corresponding closed analytical expression. This method eliminates the need for numerical integration; an exact solution can be obtained directly by substituting the mesh corner coordinates and physical property parameters. In another alternative implementation, the theoretical field value can also be an approximate solution obtained based on numerical integration methods. Numerical integration is used when the mesh element shape is complex or irregular, making it impossible to obtain a simple analytical expression. Specifically, each three-dimensional mesh element is further subdivided into smaller volume elements, each of which is approximated as a point source. The field values ​​generated by all point sources at the field point are calculated and summed, and this summation approximates the total field generated by the entire mesh element.

[0079] Furthermore, the joint probabilistic imaging calculation of the correlation coefficients of single geophysical field component data in S4 includes the following steps D1-D2: D1. Traverse all three-dimensional mesh elements and calculate the correlation coefficient of each element with respect to the gravity gradient tensor component.

[0080] D2. Arrange the correlation coefficients of all units in spatial order to form a correlation imaging coefficient matrix.

[0081] For a cuboid mesh element q, assume that the coordinates of any two of its corner points are respectively and , its in Components of the gravitational gradient tensor at a point The expressions are as follows: in, express The distance between the point and any corner point of the cuboid mesh element q. The tensor component of the gravity gradient caused by the cuboid mesh element q represents the gravity gradient tensor component. kernel function, The tensor component of the gravity gradient caused by the cuboid mesh element q represents the gravity gradient tensor component. kernel function, The tensor component of the gravity gradient caused by the cuboid mesh element q represents the gravity gradient tensor component. kernel function, The tensor component of the gravity gradient caused by the cuboid mesh element q represents the gravity gradient tensor component. kernel function, The tensor component of the gravity gradient caused by the cuboid mesh element q represents the gravity gradient tensor component. kernel function, The tensor component of the gravity gradient caused by the cuboid mesh element q represents the gravity gradient tensor component. kernel function, This represents the x-coordinates of any two corner points of a cuboid mesh element. This represents the y-coordinate of any two corner points of a cuboid mesh element. This represents the z-coordinates of any two corner points of a cuboid mesh element. .

[0082] The correlation coefficient matrix is ​​formed by the correlation imaging coefficients of each component of the gravity gradient tensor across all grid cells. .

[0083] Furthermore, obtaining the joint probabilistic imaging results of single geophysical field component data in S4 includes the following steps E1-E2: E1 calculates a weighting coefficient for joint imaging for each independent component of the gravity gradient tensor.

[0084] E2. For each three-dimensional mesh cell, multiply the correlation coefficient of each component by the weighting coefficient of the component to obtain the joint probability imaging correlation coefficient of the mesh cell.

[0085] The formula for calculating the joint probability imaging of the q-th cuboid element is: in, The correlation coefficient results for joint probability imaging of cuboid mesh elements q are shown. The imaging coefficients are the components of the gravity gradient tensor corresponding to the cuboid mesh element q. The weighting coefficients.

[0086] Weighting coefficients The formula for calculating the weight coefficient is: in, Represented as gravity gradient tensor components The correlation coefficient at the cuboid mesh element q Represented as gravity gradient tensor components The correlation coefficient at the cuboid mesh element q Represented as gravity gradient tensor components The correlation coefficient at the cuboid mesh element q Represented as gravity gradient tensor components The correlation coefficient at the cuboid mesh element q Represented as gravity gradient tensor components The correlation coefficient at the cuboid mesh element q Represented as gravity gradient tensor components The correlation coefficient at cuboid mesh element q.

[0087] The joint probabilistic imaging correlation coefficients of all cuboid mesh elements constitute the underground space correlation coefficient model C, and each element in C... The value range of is [-1, 1], which represents the probability that the measured gravity gradient tensor is caused by the q-th cuboid grid cell, and can be used to characterize the distribution of underground density. A positive value indicates that the cuboid mesh element has excess mass; conversely, a negative value indicates an excess mass. A negative value indicates a mass deficit in the cuboid mesh cell. The closer the value is to 1, the greater the probability that the cuboid mesh element has a mass surplus; conversely, the closer the value is to 1, the greater the probability that the mass of the cuboid mesh element is surplus. The closer the value is to -1, the greater the probability that the cuboid mesh cell has a mass deficit.

[0088] The correlation coefficient matrix C obtained by applying the joint probabilistic imaging method of gravity gradient tensor components based on correlation coefficient weight function is a three-dimensional matrix of 50*50*20.

[0089] Furthermore, the geological interpretation of the distribution patterns of the three-dimensional density model of underground space in S5 includes the following steps F1-F2: F1. Combine the joint probability imaging correlation coefficients of all grid cells according to their spatial location to form a three-dimensional density model of underground space.

[0090] F2. By visualizing the three-dimensional density model of underground space in two or three dimensions, analyze the spatial distribution pattern of the model in the target area and provide geological interpretation of the target area.

[0091] Specifically, this includes performing forward modeling on the combined model to obtain the gravity gradient tensor. , , , , , Component results as follows Figure 3 As shown. Two sets of inversion comparison experiments were conducted on the model data. In the first set, the correlation imaging inversion method was applied to perform single-component correlation imaging calculations on each component of the gravity gradient tensor. The inversion results of each component of the gravity gradient tensor at the vertical profiles Z=110m and X=250m are shown below. Figure 4 As shown; the second group applies a joint probability imaging method based on the correlation coefficient weight function of the gravity gradient tensor to perform joint probability imaging calculations on the correlation imaging results of each component of the gravity gradient tensor. The joint probability imaging results obtained at the vertical profile Z=110m and X=250m are shown below. Figure 5 As shown in the figure. Both sets of figures are generated by plotting the inversion calculation results using Matlab and Surfer software. The resulting plots can display the spatial location and morphology of the model body, which can then be further interpreted geologically.

[0092] The comparison of the two sets of inversion results shows that the inversion calculation results of the joint probability imaging method of gravity gradient tensor components based on correlation coefficient weight function of the present invention can overcome the shortcomings of the inversion results of single component correlation imaging of gravity gradient tensor being greatly affected by the correlation direction, reduce the error of single component correlation imaging results in non-correlation direction, and improve the accuracy of inversion results.

[0093] Example 3, referring to Figure 6 This is one embodiment of the present invention, which provides a joint probabilistic imaging system for each component of the gravity gradient tensor based on the correlation coefficient weight function, including: a data preprocessing module, a three-dimensional modeling and meshing module, a forward modeling and single-component correlation coefficient calculation module, a joint probabilistic imaging fusion module, and a geological interpretation and visualization module.

[0094] The data preprocessing module is responsible for gridding the measured gravity gradient tensor components to obtain single geophysical field component data.

[0095] The 3D modeling and meshing module is responsible for discretizing the underground space of the target area into 3D mesh units.

[0096] The forward modeling and single-component correlation coefficient calculation module is responsible for calculating the correlation coefficient of the field source within a three-dimensional grid cell based on single geophysical field component data.

[0097] The joint probabilistic imaging fusion module is responsible for performing joint probabilistic imaging calculations on the correlation coefficients of single geophysical field component data to obtain joint probabilistic imaging results for single geophysical field component data.

[0098] The geological interpretation and visualization module transforms the combined probabilistic imaging results of single geophysical field component data into an equivalent three-dimensional density model of underground space, and analyzes the distribution patterns of the three-dimensional density model of underground space for geological interpretation.

[0099] This embodiment also provides an electronic device applicable to a joint probabilistic imaging method for each component of the gravity gradient tensor based on a correlation coefficient weighting function, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the joint probabilistic imaging method for each component of the gravity gradient tensor based on a correlation coefficient weighting function as proposed in the above embodiment.

[0100] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a joint probability imaging method for each component of the gravity gradient tensor based on a correlation coefficient weighting function as proposed in the above embodiment.

[0101] The storage medium proposed in this embodiment and the method for joint probabilistic imaging of gravity gradient tensor components based on correlation coefficient weight function proposed in the above embodiment belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0102] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for joint probability imaging of each component of gravity gradient tensor based on correlation coefficient weight function, characterized in that: The method comprises the steps of: The single geophysical field component data is obtained by grid processing of the measured gravity gradient tensor component data; The target area underground space is discretized into three-dimensional grid units; The correlation coefficient of the field source in the three-dimensional grid unit is calculated based on the single geophysical field component data; The correlation coefficient of the single geophysical field component data is calculated by joint probability imaging, and the single geophysical field component data joint probability imaging result is obtained; The single geophysical field component data joint probability imaging result is equivalent to the three-dimensional density model of the underground space, and the distribution rule of the three-dimensional density model of the underground space is analyzed for geological interpretation.

2. The method of claim 1, wherein the method is based on a correlation coefficient weight function. The single geophysical field component data is obtained by grid processing of the measured gravity gradient tensor component data; The independent tensor component is determined from the plurality of components based on the inherent constraint relationship of the gravity gradient tensor in the physical field. The target area underground space is discretized into three-dimensional grid units, which comprises the steps of:

3. The method of claim 2, wherein the method is based on a correlation coefficient weight function. The start and end range of the target area is determined to determine the modeling space; The grid parameters of the three-dimensional grid units are set; The modeling space is divided in the spatial dimension according to the grid parameters to generate a regular three-dimensional grid unit set. The correlation coefficient of the field source in the three-dimensional grid unit is calculated, which comprises the steps of:

4. The method of claim 3, wherein the method is characterized by: The theoretical field value of the three-dimensional grid unit at all surface measurement points is calculated based on the spatial geometric parameters; The measured gravity gradient tensor component data after grid processing is arranged in the same measurement point order of the forward kernel matrix to form an observation data vector; The correlation coefficient between the theoretical field value and the observation data vector is calculated for each grid unit. The correlation coefficient of the single geophysical field component data is calculated by joint probability imaging, which comprises the steps of:

5. The method of claim 4, wherein the method is based on a correlation coefficient weight function. All three-dimensional grid units are traversed to calculate the correlation coefficient of each unit for the gravity gradient tensor component; The correlation coefficients of all units are arranged in spatial order to form a correlation imaging coefficient matrix. The single geophysical field component data joint probability imaging result is obtained, which comprises the steps of:

6. The method of claim 4, wherein the method is based on a correlation coefficient weight function. A weighting coefficient for joint imaging is calculated for each independent component of the gravity gradient tensor; The correlation coefficient of each component is multiplied by the weighting coefficient of the component to obtain the joint probability imaging correlation coefficient of the grid unit for each three-dimensional grid unit. The distribution rule of the three-dimensional density model of the underground space is analyzed for geological interpretation, which comprises the steps of:

7. The method of claim 4, wherein the method is characterized by: The joint probability imaging correlation coefficients of all grid units are combined according to the spatial position to form a three-dimensional density model of the underground space; The model space distribution rule of the target area is analyzed by two-dimensional or three-dimensional visualization of the three-dimensional density model of the underground space for geological interpretation of the target area. The method comprises the steps of:

8. A system for joint probability imaging of each component of the gravity gradient tensor based on a correlation coefficient weight function, applying a method for improving the efficiency of short-term high-frequency energy storage according to any one of claims 1 to 7, characterized in that, The data preprocessing module, three-dimensional modeling and grid module, forward and single component correlation coefficient calculation module, joint probability imaging fusion module, and geological interpretation and visualization module are included. The data preprocessing module is responsible for grid processing of the measured gravity gradient tensor component data to obtain single geophysical field component data. ​ The three-dimensional modeling and gridding module is responsible for discretizing the underground space of a target area into three-dimensional grid units; The forward and single-component correlation coefficient calculation module is responsible for calculating the correlation coefficient of a field source in a three-dimensional grid unit based on single geophysical field component data; The joint probability imaging fusion module is responsible for calculating the joint probability imaging of single geophysical field component data based on the correlation coefficient of the single geophysical field component data; The geological interpretation and visualization module is equivalent to the joint probability imaging result of single geophysical field component data as a three-dimensional density model of the underground space, analyzes the distribution rule of the three-dimensional density model of the underground space, and performs geological interpretation. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the joint probability imaging method of each component of the gravity gradient tensor based on the correlation coefficient weight function in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the joint probability imaging method of each component of the gravity gradient tensor based on the correlation coefficient weight function in any one of claims 1 to 7.

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