Sampling inversion-based array surface wave imaging method and system, terminal and readable storage medium

Abstract

The invention relates to the technical field of data processing, and discloses an array surface wave imaging method and system based on sampling inversion, a terminal and a readable storage medium, and the method comprises the steps: extracting station pairs from all station pairs for multiple times to form a station pair combination; extracting single frequency dispersion curve data of each group of station pair combinations; and obtaining inversion frequency dispersion curve data according to the plurality of single frequency dispersion curve data, and establishing a spatial frequency dispersion curve according to the inversion frequency dispersion curve data. In order to solve the problem that three-dimensional velocity structure imaging cannot be carried out based on an array surface wave method under the condition that the number of stations is small, when the frequency dispersion curve is measured, all stations in an array are not used for frequency dispersion curve measurement any more, multiple random sampling is carried out, and the frequency dispersion curve measurement accuracy is improved. The phase velocity distribution of the space is inverted by using the difference of the frequency dispersion curves extracted by different station pair combinations, so that the phase velocity distribution in the arrays can be observed under the condition of fewer arrays.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a sampling inversion-based array surface wave imaging method, system, terminal and readable storage medium. Background Art

[0002] Surface wave imaging technology based on background noise is one of the important methods for detecting the internal structure of the earth. The core idea is to measure the phase velocity or group velocity dispersion curve of the surface wave when it passes through the underground structure from the noise cross-correlation function, and to invert the relationship between the underground structure and the dispersion curve to obtain the underground velocity structure.

[0003] Among them, the measurement methods of dispersion curves can be mainly divided into two categories: the ray-based dual-station dispersion curve measurement method and the array-based dispersion curve measurement method. Compared with the dual-station method, the array-based dispersion curve measurement method uses the cross-correlation function of all station pairs within the array to measure the dispersion curve. Its dispersion curve measurement accuracy is higher and it is easier to measure the high-order mode of the dispersion curve. In addition, under many conditions (such as when the station spacing is small), the dual-station method cannot observe the dispersion curve, but the array method is not limited by this. However, the array-based surface wave method can only obtain a dispersion curve representing the average underground structure under the array from an array, thereby obtaining the one-dimensional velocity structure under the array. When performing three-dimensional velocity structure imaging, the array surface wave method usually needs to divide the study area into multiple overlapping sub-arrays for imaging. However, in many cases, it is often limited by the detection area and cost, and the number of stations in the detection area is limited (for example, less than 100 stations), and it is difficult to obtain enough effective sub-arrays to obtain dispersion curves. In this case, this method can only detect one-dimensional velocity structure, which is difficult to meet the needs of underground three-dimensional structure detection. Summary of the invention

[0004] The purpose of the present invention is to provide a sampling inversion-based array surface wave imaging method, system, terminal and readable storage medium, aiming to solve the problem that the existing array-based dispersion curve measurement method is difficult to meet the needs of underground three-dimensional structure detection when the number of stations is small.

[0005] The technical solution adopted by the present invention to solve the technical problem is as follows: The present invention provides a sampling inversion-based array surface wave imaging method, the sampling inversion-based array surface wave imaging method comprising: Station pairs are repeatedly extracted from all station pairs to form station pair combinations; Extracting single dispersion curve data of each group of station pairs; Inversion dispersion curve data are acquired according to the plurality of single dispersion curve data, and a spatial dispersion curve is established according to the inversion dispersion curve data.

[0006] Further, the extracting of single dispersion curve data of each group of station pair combinations specifically includes: Divide the detection space into multiple grids; The weight corresponding to each grid and each station pair combination is extracted, and the average phase slowness corresponding to all grids and each station pair combination is calculated; the average phase slowness and weight corresponding to each station pair combination are used as the single dispersion curve data corresponding to the station pair combination.

[0007] Further, the extracting the weight corresponding to each grid and each station pair combination specifically includes: Obtaining dispersion curve rays corresponding to each station pair in each station pair combination; Obtaining the ray length of each dispersion curve ray and the length of the ray in each mesh passed through; The weight of each combination of the grid and the corresponding station pair is calculated according to the ray lengths and the passing lengths of all the dispersion curve rays included in each station pair combination.

[0008] Further, the obtaining of dispersion curve rays corresponding to each station pair in each station pair combination specifically includes: Acquire detection data of two stations of each station pair in each station pair combination, and calculate cross-correlation data corresponding to each station pair according to all the detection data; The corresponding dispersion curve rays are calculated according to the cross-correlation data.

[0009] Further, the calculating the cross-correlation data corresponding to each station pair according to all the detection data specifically includes: De-meaning, de-trending, de-instrument response and segmentation cutting are sequentially performed on all the detection data to obtain a plurality of segmentation data; Calculating the segmented cross-correlation data of each of the cutting data; All the segmented cross-correlation data corresponding to the station pair are superimposed to serve as the cross-correlation data of the corresponding station pair.

[0010] Furthermore, the step of acquiring inversion dispersion curve data according to the plurality of single dispersion curve data specifically includes: The inversion phase slowness of each grid is calculated according to the average phase slowness of each single dispersion curve and each weight: ; in, is the weight matrix, represents the vector composed of the inversion phase slowness of all grids, Represents the vector composed of each average phase slowness; The set of all the inverted phase slownesses is used as the inverted dispersion curve data.

[0011] Furthermore, the establishing of a spatial dispersion curve according to the inverted dispersion curve data specifically includes: Calculating a one-dimensional spatial dispersion curve of the entire detection space according to the inversion phase slowness of each of the grids; A three-dimensional spatial dispersion curve of the detection space is established according to the one-dimensional spatial dispersion curve.

[0012] In addition, to achieve the above-mentioned purpose, the present invention also provides a sampling inversion-based array surface wave imaging system, the sampling inversion-based array surface wave imaging system comprising: A combination extraction module is used to extract station pairs from all station pairs multiple times to form station pair combinations; A data extraction module, used for extracting single dispersion curve data of each group of station pairs; The data inversion module acquires inversion dispersion curve data according to the plurality of single dispersion curve data, and establishes a spatial dispersion curve according to the inversion dispersion curve data.

[0013] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal, which includes: a memory, a processor, and a sampling inversion-based array surface wave imaging program stored in the memory and executable on the processor, wherein the sampling inversion-based array surface wave imaging program, when executed by the processor, controls the terminal to implement the steps of the multi-objective filter comprehensive multi-solution acquisition method as described above.

[0014] In addition, to achieve the above-mentioned purpose, the present invention also provides a readable storage medium, which stores a sampling inversion-based array surface wave imaging program, and when the sampling inversion-based array surface wave imaging program is executed by a processor, the steps of the sampling inversion-based array surface wave imaging method as described above are implemented.

[0015] The present invention adopts the above technical solution to achieve the following effects: In order to solve the problem that the array surface wave-based method cannot perform three-dimensional velocity structure imaging when the number of stations is small, the present invention no longer uses all station pairs inside the array to measure the dispersion curve, but performs multiple random samplings, and uses the differences in dispersion curves extracted by different station pairs to invert the spatial phase velocity distribution, thereby achieving the observation of the phase velocity distribution inside the array with a small number of stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the steps of a method for array surface wave imaging based on sampling inversion in a preferred embodiment of the present invention; Figure 2 It is a detailed flow chart of a sampling inversion-based array surface wave imaging method in a preferred embodiment of the present invention; Figure 3 is a schematic diagram of sampling and extracting dispersion curves of stations in a preferred embodiment of the present invention; Figure 4 is a schematic diagram of grid division in a preferred embodiment of the present invention; Figure 5 is a schematic diagram of the results of a chessboard test in a preferred embodiment of the present invention; Figure 6 It is a structural schematic diagram of a sampling inversion-based array surface wave imaging system in a preferred embodiment of the present invention; Figure 7 A schematic diagram of an operating environment of a preferred embodiment of a terminal of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. 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.

[0018] Embodiment 1 Embodiment 1 of the present application is a surface wave imaging method based on sampling inversion. Specifically, see Figure 1 and Figure 2 A surface wave imaging method based on sampling inversion in this embodiment includes the following steps: S1. Station pairs are repeatedly extracted from all station pairs to form station pair combinations.

[0019] In order to solve the problem that the array surface wave-based method cannot perform three-dimensional velocity structure imaging when the number of stations is small, the present invention proposes a novel array three-dimensional velocity structure imaging method based on station team sampling within the array.

[0020] The core idea of ​​this method is that when measuring dispersion curves, instead of using all station pairs within the array to measure dispersion curves, multiple random samplings are performed, and the differences in dispersion curves extracted by different station pair combinations are used to invert the spatial phase velocity distribution, thereby realizing the observation of the phase velocity distribution within the array from a single array, and further inverting to obtain the three-dimensional velocity structure.

[0021] Please refer to Figure 3 As shown, this embodiment takes a dispersion curve observed by actual array data as an example. Figure 3 a in the figure shows the station pairs composed of all stations. Figure 3 Figure b shows the dispersion spectra extracted from all pairs of stations within the array. Figure 3 Figure c shows the station pairs consisting of 50% sampled stations. Figure 3 Panel d shows the dispersion spectra extracted from the station pairs consisting of 50% sampling stations.

[0022] from Figure 3 It can be seen that the quality of the two extracted dispersion spectra is comparable, but there is a slight difference in the position of the dispersion curves. This difference is mainly caused by different combinations of station pairs, which reflects the different underground structures that each station pair passes through.

[0023] It is not difficult to find that as long as the relationship between the structure and the observed dispersion curve phase velocity is quantified, the dispersion spectrum can be extracted by multiple random sampling of station pairs to invert the distribution of phase velocity inside the array, thereby further inverting the underground three-dimensional velocity structure.

[0024] To this end, this embodiment first extracts station pairs from all station pairs multiple times to form station pair combinations, so that in the subsequent steps, the dispersion curves of each station pair can be extracted respectively, and the difference in dispersion curves extracted from different station pair combinations can be used to invert the spatial phase velocity distribution, thereby realizing the observation of the phase velocity distribution inside the array from a single array, and further inverting to obtain the three-dimensional velocity structure.

[0025] It is worth noting that the ratio and number of stations extracted each time do not need to be consistent. In this embodiment, it is considered that the quality of the dispersion spectrum extracted from too few station pairs is too low, resulting in a large error in the dispersion curve. If there are too many station pairs, the dispersion spectrum extracted is too close to the dispersion spectrum extracted from all the data due to the averaging effect, and it is also difficult to distinguish the difference in structure in the inversion. Therefore, in this embodiment, 40%-70% of the total number of station pairs are extracted each time.

[0026] It is worth noting that the more times the extraction is performed, the more accurate the final inversion result will be, but the corresponding calculation will also be more cumbersome. The specific number of times is determined according to needs, but it must be at least greater than the number of spatial grids.

[0027] S2. Extracting single dispersion curve data of each group of station pair combinations.

[0028] Specifically, in this embodiment, data preprocessing is first performed to obtain the original noise data of each station, and then the original noise data is processed. The processing of the original noise data includes removing the mean, trend, instrument response and segmentation of the original noise data. The segmentation processing can be to cut 5 consecutive days of data into 1 hour each segment for storage.

[0029] Then, the segmented cross-correlation data of all station pairs in each segment are calculated according to the cut data of each segment. For each station pair, the segmented cross-correlation data of each segment are superimposed to obtain the cross-correlation data of the station pair.

[0030] In this embodiment, in order to control the quality of the cross-correlation, cross-correlation data screening is set. Specifically, the cross-correlation signal-to-noise ratio range can be set to discard the segmented cross-correlation data outside the range, or the segmented cross-correlation data with a set proportion of cross-correlation signal-to-noise ratios behind can be directly discarded.

[0031] Afterwards, for all pairs of stations, the dispersion curves are extracted by the array method according to the cross-correlation function to obtain the reference dispersion curves. This step is mainly to prepare for the subsequent orders, frequency bands of each order, and phase velocity range.

[0032] Then, for each station pair, the dispersion curve is extracted based on the cross-correlation data. When obtaining the dispersion curve, the frequency point of each extraction needs to be fixed. For example, if the dispersion curve frequency range is 1 to 5 Hz, then for each station pair, the dispersion curve phase velocities of 1, 1.1, 1.2 ... 4.9 and 5 Hz can be saved. At the same time, there are differences in surface waves of different orders, so the corresponding order needs to be marked at the same time, and the station number used by each sub-array is saved. The station number, order and phase velocity of different Hz will be included. Next, please refer to Figure 4 As shown, in this embodiment, the detection space is first divided into grid, and the The phase velocity of each grid is recorded as , where phase velocity refers to the velocity at a certain order and frequency under the dispersion curve. The phase slowness of a grid is recorded as , for example, the phase slowness of the first grid is recorded as , the phase slowness of the second grid is recorded as , No. The phase slowness of a grid is recorded as ,in , phase slowness, refers to the inverse of the velocity at a certain order and frequency of the dispersion curve. The phase velocity of each grid is formed into a vector , the phase slowness of each grid is formed into a vector .

[0033] Among them, the division of the grid can be in the form of average division, triangulation and Thiessen polygons. In this embodiment, the average division method is specifically adopted. It is worth mentioning that the grid after division is a two-dimensional grid of multiplication of rows and columns. The spacing between grids can refer to the approximate station spacing. Specifically, it can be estimated by calculating the distance between each station and the nearest station and averaging it. Among them, the division of the grid does not need to be very strict, as long as it roughly meets the requirements. In this embodiment, the two-dimensional grid is flattened into a one-dimensional form for calculation and explanation purposes.

[0034] For For each station pair, its ray length can be obtained And its ,in, Indicates The first grid that the ray of the station pair passes through, Indicates The second grid that the ray of the station pair passes through, Indicates The ray of the station pair passes through the grid, and record the The station pairs are located in each grid line Length in ,in, Indicates The length of the ray of the station pair in the first grid it passes through, Indicates The length of the ray of the station pair in the second grid it passes through, Indicates The ray of the station pair passes through the The length of the grid, then the average phase slowness of this station pair is: ; in, Indicates The average phase slowness of the station pair.

[0035] The above formula is actually equivalent to The average phase slowness of the ray of a station pair is written as the weighted average of the phase slowness of each grid, where the weight of the grid that the ray does not pass through is 0, and the weight of the grid that the ray passes through is the ray length of the passed part / total ray length. If there are a total of nr station pairs used to extract dispersion curves, then the final extracted dispersion curve data can be expressed as the average of the dispersion curves of these regions.

[0036] It is worth noting that, in this embodiment, the number of nr may not be fixed, as long as a dispersion curve with reliable quality can be extracted.

[0037] By substituting the phase slowness of each ray, the dispersion curve data extracted from nr station pairs can be expressed as A weighted average combination of dispersion curves on a grid: ; in, Indicates The weights of the grids are calculated from the rays of each station pair, It represents the phase slowness of the corresponding frequency and the corresponding order, and the weight of each grid and the average phase slowness of all grids are used as the single dispersion curve data.

[0038] S3. Acquire inversion dispersion curve data according to the plurality of single dispersion curve data, and establish a spatial dispersion curve according to the inversion dispersion curve data.

[0039] Specifically, if a total of The effective station pair combination that observed the dispersion curve for a certain frequency and a certain order of phase slowness observations constitutes a vector , where the vector Contains elements , ,…, , represents the first observed phase slowness, represents the second observed phase slowness, Indicates The phase slowness observed in each grid can be constructed And the relationship between each observed phase slowness vector: ; in, for OK The weight matrix of the columns, The line indicates that according to The weight vector calculated by combining the substation pairs , No. Column represents the The weight value corresponding to each grid.

[0040] It can be seen that in the forward step, the weight of each grid for each observation can be calculated, so the weight matrix can be calculated, is the observation of the phase slowness of each grid by each station pair combination, then the vector composed of the inverted phase slowness on each grid can be calculated by the inversion method .

[0041] After obtaining the inverted phase slowness on each grid, the dispersion curve of the entire detection space can be further established according to the inverted phase slowness of each grid. Specifically, in this embodiment, a one-dimensional dispersion curve of the entire detection space is first established, and the phase slowness of all grids can be regarded as the phase slowness of the grid points of the grid. In this way, for any point in the entire detection space, its phase slowness can be obtained by interpolation calculation through the inverted phase slowness of the four adjacent grids.

[0042] The technical solution of the present invention has an advantage that it can conveniently implement the checkerboard test to verify the inversion effect of the phase velocity dispersion curve, such as spatial resolution, recovery degree of different regions, etc. Please refer to Figure 5 , Figure 5 shows an example of a checkerboard test. Figure 5 a in the figure is the phase velocity distribution of the chessboard, where triangles are the locations of the stations. Figure 5 b in the figure is the inverted phase velocity distribution. By randomly selecting 5000 pairs of station pairs, this embodiment achieves the imaging of the phase velocity distribution of about 50 station pairs. Figure 5 It can be seen that the phase velocity distribution inside the array has been well restored.

[0043] The above method can be used to obtain the inverted phase velocity on each spatial grid at each frequency in each order dispersion curve. The inverted phase velocity on each grid is combined into a dispersion curve again. The one-dimensional velocity structure under each spatial grid can be obtained by the dispersion curve inversion method. These one-dimensional velocity structures can be further combined into a three-dimensional velocity structure.

[0044] It can be seen that in order to solve the problem that the array surface wave-based method cannot perform three-dimensional velocity structure imaging when the number of stations is small, the present invention no longer uses all station pairs inside the array to measure the dispersion curve, but performs multiple random samplings, and uses the differences in dispersion curves extracted from different station pairs to invert the spatial phase velocity distribution, thereby realizing the observation of the phase velocity distribution inside the array with a small number of arrays.

[0045] Embodiment 2 See also Figure 6 Based on the above method, the present invention also provides a sampling inversion-based array surface wave imaging system, the sampling inversion-based array surface wave imaging system comprising: A combination extraction module 51 is used to extract station pairs from all station pairs multiple times to form station pair combinations; A data extraction module 52, used to extract single dispersion curve data of each group of station pair combinations; The data inversion module 53 acquires inverted dispersion curve data according to the plurality of single dispersion curve data, and establishes a spatial dispersion curve according to the inverted dispersion curve data.

[0046] Further, the extracting of single dispersion curve data of each group of station pair combinations specifically includes: Divide the detection space into multiple grids; For each of the station pair combinations, extract the weight of each of the grids, and calculate the average phase slowness of all grids; For all station pairs, the average phase slowness is calculated by the array method.

[0047] Furthermore, for each station pair combination, the weight of each grid is extracted, and the average phase slowness of all grids is calculated, specifically including: For each of the station pairs, a dispersion curve ray is obtained, and a ray length and a length passed by the ray in each grid passed through are obtained according to the dispersion curve ray; The weight of each of the meshes is calculated according to the ray lengths and the passing lengths of all the dispersion curve rays.

[0048] Furthermore, the obtaining of dispersion curve rays specifically includes: Acquire detection data of the station to the two stations, and calculate cross-correlation data according to the detection data; The dispersion curve rays are calculated based on the cross-correlation data.

[0049] Further, the calculating the cross-correlation data according to the detection data specifically includes: The detection data is sequentially subjected to de-meaning, de-trending, de-instrument response and segmentation cutting to obtain a plurality of segmentation data; For each of the cut data, calculating the segmented cross-correlation data of the station pair; All the cross-correlation data are superimposed to form the cross-correlation data.

[0050] Furthermore, the step of acquiring inversion dispersion curve data according to the plurality of single dispersion curve data specifically includes: The inversion phase slowness of each grid is calculated according to the average phase slowness of each single dispersion curve and each weight: ; in, is the weight matrix, represents the vector composed of the inversion phase slowness of all grids, Represents the vector composed of each average phase slowness; The set of all the inverted phase slownesses is used as the inverted dispersion curve data.

[0051] Furthermore, the establishing of a spatial dispersion curve according to the inverted dispersion curve data specifically includes: Calculating a one-dimensional spatial dispersion curve of the entire detection space according to the inversion phase slowness of each of the grids; A three-dimensional spatial dispersion curve of the detection space is established according to the one-dimensional spatial dispersion curve.

[0052] It can be seen that in order to solve the problem that the array surface wave-based method cannot perform three-dimensional velocity structure imaging when the number of stations is small, the present invention no longer uses all station pairs inside the array to measure the dispersion curve, but performs multiple random samplings, and uses the differences in dispersion curves extracted from different station pairs to invert the spatial phase velocity distribution, thereby realizing the observation of the phase velocity distribution inside the array with a small number of arrays.

[0053] Embodiment 3 See also Figure 7 Based on the above method, the present invention further provides a terminal, which includes a processor 10, a memory 20 and a display 30. However, it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0054] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed in the terminal, such as the program code of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 20 stores a sampling inversion-based array surface wave imaging program 40, which can be executed by the processor 10, thereby realizing the terminal in the present application.

[0055] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 20, such as executing relevant programs of the array surface wave imaging method based on sampling inversion.

[0056] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light Emitting Diode) touch device, etc. The display 30 is used to display information on the terminal and to display a visual user interface.

[0057] In one embodiment, when the processor 10 executes the sampling inversion-based array surface wave imaging program 40 in the memory 20, the steps of the sampling inversion-based array surface wave imaging method described above are implemented.

[0058] Embodiment 3 This embodiment provides a storage medium, wherein the readable storage medium stores a sampling inversion-based array surface wave imaging program, and when the sampling inversion-based array surface wave imaging program is executed by a processor, the steps of the sampling inversion-based array surface wave imaging method as described above are implemented.

[0059] In summary, in order to solve the problem that the array surface wave-based method cannot perform three-dimensional velocity structure imaging when the number of stations is small, the present invention no longer uses all station pairs inside the array to measure the dispersion curve, but performs multiple random samplings, and uses the differences in dispersion curves extracted from different station pairs to invert the spatial phase velocity distribution, thereby realizing the observation of the phase velocity distribution inside the array with a small number of stations.

[0060] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or terminal including the element.

[0061] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer-readable storage medium, and the program can include the processes of the above-mentioned method embodiments when executed. The storage medium can be a memory, a disk, an optical disk, etc.

[0062] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A surface wave imaging method based on sampling inversion, characterized in that: The array surface wave imaging method based on sampling inversion includes: Station pairs are repeatedly extracted from all station pairs to form station pair combinations; Extracting single dispersion curve data of each group of station pairs; Inversion dispersion curve data are acquired according to the plurality of single dispersion curve data, and a spatial dispersion curve is established according to the inversion dispersion curve data.

2. The array surface wave imaging method based on sampling inversion according to claim 1, characterized in that: The extracting of single dispersion curve data of each group of station pairs specifically includes: Divide the detection space into multiple grids; The weight corresponding to each grid and each station pair combination is extracted, and the average phase slowness corresponding to all grids and each station pair combination is calculated; the average phase slowness and weight corresponding to each station pair combination are used as the single dispersion curve data corresponding to the station pair combination.

3. The array surface wave imaging method based on sampling inversion according to claim 2, characterized in that: The extracting the weight corresponding to each grid and each station pair combination specifically includes: Obtaining dispersion curve rays corresponding to each station pair in each station pair combination; Obtaining the ray length of each dispersion curve ray and the length of the ray in each mesh passed through; The weight of each combination of the grid and the corresponding station pair is calculated according to the ray lengths and the passing lengths of all the dispersion curve rays included in each station pair combination.

4. The array surface wave imaging method based on sampling inversion according to claim 3 is characterized in that: The obtaining of dispersion curve rays corresponding to each station pair in each station pair combination specifically includes: Acquire detection data of two stations of each station pair in each station pair combination, and calculate cross-correlation data corresponding to each station pair according to all the detection data; The corresponding dispersion curve rays are calculated according to the cross-correlation data.

5. The array surface wave imaging method based on sampling inversion according to claim 4, characterized in that: The step of calculating the cross-correlation data corresponding to each station pair according to all the detection data specifically includes: De-meaning, de-trending, de-instrument response and segmentation cutting are sequentially performed on all the detection data to obtain a plurality of segmentation data; Calculating the segmented cross-correlation data of each of the cutting data; All the segmented cross-correlation data corresponding to the station pair are superimposed to serve as the cross-correlation data of the corresponding station pair.

6. The array surface wave imaging method based on sampling inversion according to claim 3, characterized in that: The acquiring of inversion dispersion curve data according to the plurality of single dispersion curve data specifically comprises: The inversion phase slowness of each grid is calculated according to the average phase slowness of each single dispersion curve and each weight: ； in, is the weight matrix, represents the vector composed of the inversion phase slowness of all grids, Represents the vector composed of each average phase slowness; The set of all the inverted phase slownesses is used as the inverted dispersion curve data.

7. The array surface wave imaging method based on sampling inversion according to claim 6, characterized in that: The establishing of a spatial dispersion curve according to the inverted dispersion curve data specifically includes: Calculating a one-dimensional spatial dispersion curve of the entire detection space according to the inversion phase slowness of each of the grids; A three-dimensional spatial dispersion curve of the detection space is established according to the one-dimensional spatial dispersion curve.

8. A sampling inversion-based array surface wave imaging system, characterized in that: The array surface wave imaging system based on sampling inversion comprises: A combination extraction module is used to extract station pairs from all station pairs multiple times to form station pair combinations; A data extraction module, used for extracting single dispersion curve data of each group of station pairs; The data inversion module acquires inversion dispersion curve data according to the plurality of single dispersion curve data, and establishes a spatial dispersion curve according to the inversion dispersion curve data.

9. A terminal, characterized in that: The terminal comprises: a memory, a processor, and a sampling inversion-based array surface wave imaging program stored in the memory and executable on the processor. When the multi-objective-based filter integrated multi-solution acquisition program is executed by the processor, the terminal is controlled to implement the steps of the sampling inversion-based array surface wave imaging method according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores a sampling inversion-based array surface wave imaging program, which, when executed by a processor, implements the steps of the sampling inversion-based array surface wave imaging method according to any one of claims 1 to 7.

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