A method, device and storage medium for estimating the magnetotelluric vector space

Abstract

The present invention discloses a method for estimating the magnetotelluric vector space, a computer device, and a computer-readable storage medium. The method realizes the rapid indexing of 4D space points by constructing a 4D space index tree structure; then, according to the obtained magnetotelluric data block, it traverses the frequency points of the magnetotelluric apparent resistivity data of the magnetotelluric data block, obtains each frequency point, and adds the 4D vector data of each obtained frequency point to the vector space in the order of four dimensions A, B, C, and D; then, it performs point density space filtering on the scattered points in the 4D space, and searches for the maximum value of the point density after filtering as the central estimated value. The algorithm is simple and efficient, and has a small deviation after 4D space estimation.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing in geological exploration, and particularly to a method for estimating the magnetotelluric vector space, a computer device, and a computer-readable storage medium. Background Art

[0002] Electrical anisotropy has a great influence on electromagnetic observation data. Magnetotellurics is a geophysical method widely used in the fields of deep geological structure exploration, oil and gas, and mineral resource exploration. Due to the great influence of electrical anisotropy on electromagnetic observation data, according to the principle that electromagnetic waves of different frequencies have different skin depths in conductive media during observation, it is necessary to observe the apparent resistivity in two directions.

[0003] Since the magnetotelluric apparent resistivity vector data is divided into two directions (X and Y), and the data in each direction is a complex structure, the magnetotelluric apparent resistivity vector data is distributed in a four-dimensional space. When estimating the central value for a large number of scattered point data in a four-dimensional space, it is difficult to establish a fast search algorithm for the four-dimensional space, and a global traversal algorithm needs to be used for search traversal, resulting in a huge amount of calculation. Therefore, the existing approach usually estimates the central value on two-dimensional data in the XY directions respectively, and then forms the result value with the estimated central values in the two directions. Although such an algorithm reduces the computational complexity, there is a certain deviation between the estimated values in the XY directions and the global estimated value in the four-dimensional space.

[0004] Therefore, it is necessary to provide a new method for estimating the magnetotelluric vector space. Summary of the Invention

[0005] The main purpose of the present invention is to provide a new method for estimating the magnetotelluric vector space, a computer device, and a computer-readable storage medium to solve the above technical problems.

[0006] To achieve the above object, a method for estimating the magnetotelluric vector space provided by the present invention includes the steps of:

[0007] S1, setting the four dimensions of the four-dimensional vector space as four dimensions A, B, C, and D respectively, each dimension being a vector and having a positive direction and a negative direction;

[0008] S2, traversing the frequency points of the magnetotelluric apparent resistivity data of the magnetotelluric data block according to the obtained magnetotelluric data block, obtaining each frequency point, and adding the four-dimensional vector data of each obtained frequency point as node data to the vector space in the order of the four dimensions A, B, C, and D; wherein, each frame of magnetotelluric data block contains 4 - 6 channels of 2 10 to 2 16Point raw data and calculated magnetotelluric apparent resistivity data;

[0009] S3. Sequentially perform the balance degree calculation and balance correction of the balanced binary tree on the tree spaces of the four dimensions A, B, C, and D respectively;

[0010] S4. Search for adjacent nodes in each dimension of A, B, C, and D for each data node in the 4D vector space, perform spatial filtering by inverse distance weighting of the distances between the nodes, and store the weighted cumulative value of the node and its adjacent nodes as the density value in this data node;

[0011] S5. Search all data nodes in the 4D vector space again, search for the data node with the largest density value, and use the spatial position of the data node with the largest density value as the central evaluation value of the overall space.

[0012] Preferably, in the 4D vector space, it is defined that: in the 4D vector space, ANodeL is used to identify the node smaller than this node in the A vector direction, and ANodeR points to the node larger than this node in the A vector direction; BNodeL points to the node smaller than this node in the B vector direction, and BNodeR points to the node larger than this node in the B vector direction; CNodeL points to the node smaller than this node in the C vector direction, and CNodeR points to the node larger than this node in the C vector direction; DNodeL points to the node smaller than this node in the D vector direction, and DNodeR points to the node larger than this node in the D vector direction; Density represents the density value of this node after spatial filtering by density; there is a root node RootA, RootB, RootC, RootD respectively established in the four dimension directions of A, B, C, and D of the 4D vector space.

[0013] For example, the 4D vector data of each frequency point

[0014] Preferably, in step S4, when searching for adjacent nodes for each data node in the 4D vector space, a specified maximum search distance is set. When the absolute value of the distance between nodes is greater than this maximum search distance, the search in this dimension direction stops.

[0015] Preferably, the maximum search distance is set to 100, or the calculation stops when the inverse distance weighting coefficient < 0.01.

[0016] Preferably, step S2 includes:

[0017] S21. According to the currently obtained magnetotelluric data block, traverse the frequency points of the magnetotelluric apparent resistivity data of the magnetotelluric data block to obtain the first frequency point;

[0018] S22. Add the 4 - dimensional vector data of the first frequency point to the vector space in the order of the four dimensions A, B, C, and D, and proceed to step S3;

[0019] S23. Determine whether there is a next frequency point for the current frequency point of the current magnetotelluric data block. If so, return to step S21; if not, proceed to step S24;

[0020] S24. Determine whether there is a next magnetotelluric data block for the current magnetotelluric data block. If so, return to step S21; if not, proceed to step S24.

[0021] The present 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, the steps of the magnetotelluric vector space estimation method described in any one of the above are implemented.

[0022] The present invention also provides a computer - readable storage medium. The computer - readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the magnetotelluric vector space estimation method described in any one of the above are implemented.

[0023] The magnetotelluric vector space estimation method provided by the present invention first designs a 4 - dimensional space index tree structure through step S1. The four dimensions of the 4 - dimensional vector space are respectively set as the four dimensions A, B, C, and D. Each dimension is a vector and has a positive direction and a negative direction, realizing the rapid indexing of 4 - dimensional space points. Then, through step S2, according to the obtained magnetotelluric data block, the frequency points of the magnetotelluric apparent resistivity data of the magnetotelluric data block are traversed to obtain each frequency point, and the 4 - dimensional vector data of each obtained frequency point is added to the vector space in the order of the four dimensions A, B, C, and D. Among them, each frame of magnetotelluric data block contains 4 - 6 channels of 2 10 to 2 16Point original data and calculated magnetotelluric apparent resistivity data; S3, perform balance degree calculation and balance correction of the balanced binary tree on the tree spaces of the four dimensions A, B, C, and D in sequence; S4, search for adjacent nodes in each of the dimensions A, B, C, and D for each data node in the 4D vector space, perform spatial filtering by inverse distance weighting of the distances between the nodes, and store the weighted cumulative value of the node and its adjacent nodes as the density value in the data node; S5, re-search all data nodes in the 4D vector space, search for the data node with the maximum density value, and use the spatial position of the data node with the maximum density value as the central evaluation value of the overall space; for each magnetotelluric apparent resistivity data in all the data, add it to the 4D space index tree, perform point density spatial filtering on the scattered points in the 4D space, and search for the maximum value of the point density after filtering as the central estimation value. The algorithm is simple and efficient, and the deviation is small after the 4D space estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a schematic flowchart of the magnetotelluric vector space estimation method in an embodiment of the present invention;

[0025] Figure 2 is a schematic hardware structure diagram of a computer electronic device in an embodiment of the present invention.

[0026] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0028] In the following description, suffixes such as "module", "component", or "unit" used to represent elements are only for the convenience of describing the present invention and have no specific meaning in themselves. Therefore, "module", "component", or "unit" can be used interchangeably.

[0029] Please refer to Figure 1 , to achieve the above object, a magnetotelluric vector space estimation method provided in an embodiment of the present invention includes the steps:

[0030] S1, set the four dimensions of the 4D vector space as the four dimensions A, B, C, and D respectively. Each dimension is a vector and has a positive direction and a negative direction;

[0031] S2. Traverse the frequency points of the magnetotelluric apparent resistivity data in the acquired magnetotelluric data block, obtain each frequency point, and add the 4-dimensional vector data of each obtained frequency point as node data to the vector space in sequence according to the order of the four dimensions A, B, C, and D; where each frame of magnetotelluric data block contains 4-6 channels of 2 10 to 2 16 point raw data and the calculated magnetotelluric apparent resistivity data;

[0032] S3. Perform the balance degree calculation and balance correction of the balanced binary tree on the tree spaces of the four dimensions A, B, C, and D in sequence;

[0033] S4. Search for adjacent nodes in each of the A, B, C, and D dimensions for each data node in the 4D vector space, perform spatial filtering by inverse distance weighting of the distance between nodes, and store the weighted cumulative value of the node and its adjacent nodes as the density value in this data node;

[0034] S5. Search all data nodes in the 4D vector space again, search for the data node with the largest density value, and use the spatial position of this data node with the largest density value as the central evaluation value of the overall space.

[0035] In step S1, a 4D vector space data structure is constructed. Specifically, set ABCD for the four dimensions of the 4D vector space, and each dimension uses a scalar expression. Therefore, each dimension has two directions, one positive direction and one negative direction.

[0036] Represented using the SpatialNode structure

[0037] struct SpatialNode{

[0038] struct SpatialNode * ANodeL;

[0039] struct SpatialNode * ANodeR;

[0040] struct SpatialNode * BNodeL;

[0041] struct SpatialNode * BNodeR;

[0042] struct SpatialNode * CNodeL;

[0043] struct SpatialNode * CNodeR;

[0044] struct SpatialNode * DNodeL;

[0045] struct SpatialNode * DNodeR;

[0046] double Density;

[0047] void * PSD;

[0048] };

[0049] Among them, it is defined in the 4D vector space:

[0050] ANodeL identifies and points to the node smaller than this node in the direction of the A vector, and ANodeR points to the node larger than this node in the direction of the A vector;

[0051] BNodeL points to the node smaller than this node in the direction of the B vector, and BNodeR points to the node larger than this node in the direction of the B vector;

[0052] CNodeL points to the node smaller than this node in the direction of the C vector, and CNodeR points to the node larger than this node in the direction of the C vector;

[0053] DNodeL points to the node smaller than this node in the direction of the D vector, and DNodeR points to the node larger than this node in the direction of the D vector;

[0054] Density represents the density value of this node after filtering by the density space;

[0055] There is a root node RootA, RootB, RootC, RootD established in each of the four dimension directions A, B, C, D of the 4D vector space respectively.

[0056] Preferably, in step S4, when searching for adjacent nodes for each data node in the 4D vector space, a specified maximum search distance is set. When the absolute value of the distance between nodes is greater than this maximum search distance, the search in this dimension direction is stopped.

[0057] Preferably, the maximum search distance is set to 100, or the calculation stops when the inverse distance weighting coefficient < 0.01.

[0058] Preferably, step S2 includes:

[0059] S21, according to the currently obtained magnetotelluric data block, traverse the magnetotelluric apparent resistivity data frequency points of the magnetotelluric data block to obtain the first frequency point;

[0060] S22. Add the 4-dimensional vector data of the first frequency point to the vector space in the order of the four dimensions A, B, C, and D, and proceed to step S3;

[0061] S23. Determine whether there is a next frequency point for the current frequency point of the current magnetotelluric data block. If so, return to step S21; if not, proceed to step S24;

[0062] S24. Determine whether there is a next magnetotelluric data block for the current magnetotelluric data block. If so, return to step S21; if not, proceed to step S24.

[0063] For example, the first data frequency point D0 is obtained, and the vector data of D0 is (1, 1, 1, 1);

[0064] The vector data of the 4 dimensions of this frequency point D0 are respectively represented as the values 1 / 1 / 1 / 1 on the 4-dimensional vectors ABCD, and are added to the corresponding vector spaces of the four dimensions A, B, C, and D to form the node D0. The root nodes RootA, RootB, RootC, and RootD all point to this node D0;

[0065] Then add the second data (0, -1, 2, 3) node D1. The ANodeL of node D0 points to D1, the BNodeL points to D1, the CNodeR points to D1, and the DNodeR points to D1;

[0066] Add the Nth data node D N When, find the maximum node with the A vector direction less than the D N node A data value and the minimum node greater than the A value, insert this node, and adjust the pointing relationships of the surrounding nodes, that is, the ANodeL of node D N-1 points to D N . Insert the three vectors B, C, and D in the same way in turn.

[0067] In step S3, after the addition is completed, it is possible to judge whether the left and right of the binary tree in the directions of the four vectors A, B, C, and D are balanced. If not, adjust the position of the root node and point the root node to the middle position node in the vector direction to obtain a balanced binary tree.

[0068] The present 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, the steps of the magnetotelluric vector space estimation method described in any one of the above are implemented.

[0069] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the magnetotelluric vector space estimation method as described in any one of the above.

[0070] For the magnetotelluric vector space estimation method provided by the present invention, first, through step S1, a 4D space index tree structure is designed. The four dimensions of the 4D vector space are respectively set as four dimensions A, B, C, and D. Each dimension is a vector and has a positive direction and a negative direction, realizing the fast indexing of 4D space points. Then, through step S2, according to the obtained magnetotelluric data block, the frequency points of the magnetotelluric apparent resistivity data of the magnetotelluric data block are traversed to obtain each frequency point, and the 4D vector data of each obtained frequency point are sequentially added to the vector space in the order of the four dimensions A, B, C, and D. Among them, each frame of magnetotelluric data block contains 2 10 to 2 16 point raw data and the calculated magnetotelluric apparent resistivity data; S3, calculate and correct the balance degree of the balanced binary tree for the tree spaces of the four dimensions A, B, C, and D in sequence; S4, search for the adjacent nodes in each dimension A, B, C, and D for each data node in the 4D vector space, perform spatial filtering by inverse distance weighting of the distances between the nodes, and store the weighted cumulative value of the node and its adjacent nodes as the density value in this data node; S5, re-search all data nodes in the 4D vector space, search for the data node with the largest density value, and use the spatial position of the data node with the largest density value as the central evaluation value of the overall space; for each addition of the vector magnetotelluric apparent resistivity data in all the data to the 4D space index tree, perform point density spatial filtering on the scattered points in the 4D space, and search for the maximum value of the point density after filtering as the central estimation value. The algorithm is simple and efficient, and has a small deviation after 4D space estimation.

[0071] In a specific embodiment of the present invention, when the computer program of the computer-readable storage medium is executed by a processor, it implements the steps of the above magnetotelluric vector space estimation method, and can also achieve the corresponding technical effects.

[0072] Exemplarily, the computer program of the computer-readable storage medium includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0073] In the description of this specification, the description with reference to terms such as "one embodiment", "another embodiment", "other embodiments", or "the first embodiment to the Xth embodiment", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, method steps, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples.

[0074] The serial numbers of the above embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.

[0075] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for estimating the magnetotelluric vector space, characterized in that, Including the steps: S1. Set the four dimensions of the four-dimensional vector space as four dimensions A, B, C, and D respectively. Each dimension is a vector and has a positive direction and a negative direction; S2. According to the acquired magnetotelluric data block, traverse the magnetotelluric apparent resistivity data frequency points of the magnetotelluric data block to obtain each frequency point, and add the 4-dimensional vector data of each obtained frequency point as node data to the vector space in sequence according to the order of the four dimensions A, B, C, and D; wherein, each frame of magnetotelluric data block contains 4-6 channels of 2 10 to 2 16 point raw data and the calculated magnetotelluric apparent resistivity data; S3. Perform the balance degree calculation and balance correction of the balanced binary tree on the tree spaces of the four dimensions A, B, C, and D in sequence; S4. Search for the adjacent nodes of each data node in the four-dimensional vector space in the A, B, C, and D dimensions respectively, perform spatial filtering by inverse distance weighting of the distance between nodes, and store the weighted cumulative value of the node and its adjacent nodes as the density value into the data node; S5. Search all data nodes in the four-dimensional vector space again, search for the data node with the largest density value, and use the spatial position of the data node with the largest density value as the central evaluation value of the overall space.

2. The method according to claim 1, wherein In the four-dimensional vector space, it is defined that: in the four-dimensional vector space, ANodeL identifies the node smaller than this node in the A vector direction, and ANodeR points to the node larger than this node in the A vector direction; BNodeL points to the node smaller than this node in the B vector direction, and BNodeR points to the node larger than this node in the B vector direction; CNodeL points to the node smaller than this node in the C vector direction, and CNodeR points to the node larger than this node in the C vector direction; DNodeL points to the node smaller than this node in the D vector direction, and DNodeR points to the node larger than this node in the D vector direction; Density represents the density value of the node after spatial filtering by density; there is a root node RootA, RootB, RootC, RootD established in the four dimensions A, B, C, and D of the four-dimensional vector space respectively.

3. The method according to claim 1, wherein In step S4, when searching for adjacent nodes of each data node in the four-dimensional vector space, a specified maximum search distance is set. When the absolute value of the distance between nodes is greater than the maximum search distance, the search in this dimension direction is stopped.

4. The method according to claim 3, wherein The maximum search distance is set to 100, or the calculation stops when the inverse distance weighting coefficient <0.

01.

5. The method according to claim 1, wherein The step S2 includes: S21. According to the acquired current magnetotelluric data block, traverse the magnetotelluric apparent resistivity data frequency points of the magnetotelluric data block to obtain the first frequency point; S22. Add the four-dimensional vector data of the first frequency point to the vector space in the order of the four dimensions A, B, C, and D, and enter step S3; S23. Determine whether there is a next frequency point for the current frequency point of the current magnetotelluric data block. If so, return to step S21. If not, enter step S24; S24. Determine whether there is a next magnetotelluric data block for the current magnetotelluric data block. If so, return to step S21. If not, enter step S24.

6. 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 steps of the magnetotelluric vector space estimation method according to any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the magnetotelluric vector space estimation method according to any one of claims 1 to 5.

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