Ground droop structure identification method and device, computer equipment and program product

Abstract

The invention provides an underground vertical structure identification method and device, computer equipment and a program product, and the method comprises the steps: determining a to-be-detected target hot melt lake and an underground target detection region corresponding to the target hot melt lake from hot melt lakes in a plateau permanent frozen soil region; performing data acquisition on the target detection area by using a ground penetrating radar to obtain initial radar data; the frequency of the ground penetrating radar is smaller than or equal to the target frequency, and the target frequency is related to the frozen soil depth of the plateau permanent frozen soil area; performing data encryption and interception processing on the initial radar data according to the target frequency and the time window length of the ground penetrating radar to obtain intermediate radar data; performing signal gain processing on the intermediate radar data in the vertical direction, and performing multiple signal filtering processing on the processed intermediate radar data in the horizontal direction to obtain target radar data; and identifying a vertical structure in the target detection area according to the target radar data.

Description

Technical Field

[0001] The present disclosure relates to the field of geological engineering technology, and in particular to an underground vertical structure identification method, device, computer equipment and program product. Background Art

[0002] In the permafrost areas formed on the plateau, there may be a large amount of underground gas (such as source rock gas), such as methane gas, ethane gas and the like. By detecting such gases, it plays an important guiding role in detecting whether there are coal resources in the frozen soil area. As for the detection of such gases, since such gases will form special underground vertical structures in the frozen soil area when they evaporate underground, if the underground vertical structures can be accurately identified, the corresponding gases can be effectively detected.

[0003] Therefore, detecting the underground vertical structure under the permafrost area becomes a technical issue worthy of attention. Summary of the invention

[0004] The embodiments of the present disclosure at least provide a method, apparatus, computer device and program product for identifying underground vertical structures.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for identifying an underground vertical structure, comprising: Determine a target thaw lake to be detected and an underground target detection area corresponding to the target thaw lake from the thaw lakes in the permafrost region of the plateau; Using a ground penetrating radar to collect data on the target detection area to obtain initial radar data; the frequency of the ground penetrating radar is less than or equal to the target frequency, and the target frequency is related to the frozen soil depth of the plateau permafrost area; According to the target frequency and the time window length of the ground penetrating radar, the initial radar data is encrypted and intercepted to obtain intermediate radar data; Performing signal gain processing on the intermediate radar data in the vertical direction, and performing multiple signal filtering processes on the processed intermediate radar data in the horizontal direction to obtain target radar data; Based on the target radar data, vertical structures in the target detection area are identified.

[0006] In a possible implementation manner, after identifying the vertical structure in the target detection area according to the target radar data, the method further includes: If the vertical structure included in the target detection area is located below the periphery of the target thaw lake, it is determined that the formation cause of the target thaw lake is: underground gas eruption.

[0007] In a possible implementation manner, after identifying the vertical structure in the target detection area according to the target radar data, the method further includes: Determining a predicted diameter of a vertical structure corresponding to below the target thaw lake according to a lake surface diameter of the target thaw lake and a first conversion coefficient; Determining a predicted depth of a vertical structure corresponding to below the target thaw lake according to the frozen soil depth, the lake surface diameter, and a second conversion coefficient; determining whether the first conversion coefficient needs to be adjusted according to the target diameter of the vertical structure in the target detection area and the predicted diameter, and It is determined whether the second conversion coefficient needs to be adjusted according to the target depth of the vertical structure in the target detection area and the predicted depth.

[0008] In a possible implementation, determining a target thaw lake to be detected and an underground target detection area corresponding to the target thaw lake from a thaw lake in a plateau permafrost region includes: Determining the target thaw lake according to the shape of the thaw lake in the plateau permafrost region; Determining the measurement profile length of the ground penetrating radar according to the lake surface diameter of the target thermal melt lake and the third conversion coefficient; A target detection area corresponding to the target thermal melt lake is determined according to the measured section length and the preset detection shape.

[0009] In a possible implementation, encrypting and intercepting the initial radar data according to the target frequency and the time window length of the ground penetrating radar to obtain intermediate radar data includes: According to the target frequency, the number of sampling points of the initial radar data in the vertical direction and the minimum number of sampling points in a period indicated by the sampling theorem, the initial radar data is encrypted and resampled in the vertical direction to obtain encrypted sampling data; Determine the intercept length of the time window length according to the time window length of the ground penetrating radar, the dielectric constant of the frozen soil, the propagation speeds of the electromagnetic waves of the ground penetrating radar in the air and the frozen soil respectively, and the depth of the frozen soil; The encrypted sampling data matching the truncation length is used as the intermediate radar data.

[0010] In a possible implementation, performing signal gain processing on the intermediate radar data in a vertical direction, and performing multiple signal filtering processes on the processed intermediate radar data in a horizontal direction to obtain target radar data includes: For each scanning track in the vertical direction of the intermediate radar data, using the first gain mode and the depth corresponding to each sampling point in the scanning track, perform gain processing on the signal amplitude of each sampling point in the scanning track to obtain processed intermediate radar data; Performing a first horizontal smoothing process on the processed intermediate radar data to obtain first processed data; Performing horizontal background removal processing on the first processed data to obtain second processed data; The second processed data is subjected to at least one second horizontal smoothing process to obtain the target radar data.

[0011] In a possible implementation, performing a first horizontal smoothing process on the processed intermediate radar data to obtain first processed data includes: The processed intermediate radar data is subjected to a first horizontal smoothing process by using at least one of a sliding window filtering method and a frequency space domain filtering method to obtain first processed data.

[0012] In a possible implementation, performing a first horizontal smoothing process on the processed intermediate radar data by using a sliding window filtering method to obtain first processed data includes: Selecting sampling point values ​​within a sliding window in a horizontal direction under the condition that the local continuous features of the target detection area in the inclined direction are not disconnected and the local disconnected features are not connected; For any time length, determining each first sampling point corresponding to the time length in the horizontal direction from the processed intermediate radar data; A sliding window matching the sampling point value is used to slide on each first sampling point corresponding to the horizontal direction, and a first average value of the signal amplitudes of each first sampling point in each sliding window is used as the signal amplitude of the first sampling point at the target position in the sliding window to obtain the first processed data corresponding to the time length in the horizontal direction.

[0013] In a possible implementation, when the second horizontal smoothing process uses a sliding window filtering method, the sampling point values ​​within the sliding window corresponding to the second horizontal smoothing process are negatively correlated or positively correlated with the sampling point values ​​within the sliding window corresponding to the first horizontal smoothing process.

[0014] In a possible implementation, the processed intermediate radar data is subjected to a first horizontal smoothing process by using a sliding window filtering method and a frequency space domain filtering method to obtain first processed data, including: Using a sliding window filtering method, performing a first horizontal smoothing process on the processed intermediate radar data to obtain first sub-processed data; Performing a first horizontal smoothing process on the processed intermediate radar data by using a frequency space domain filtering method to obtain second sub-processed data; In a case where an error between the first sub-processed data and the second sub-processed data is within a preset range, the first processed data is determined according to mean data of the first sub-processed data and the second sub-processed data.

[0015] In a possible implementation manner, performing horizontal background removal processing on the first processed data to obtain second processed data includes: Determine the sliding window length and the sliding step length according to the signal amplitude of each sampling point in the first processed data; For any time length, determining each second sampling point corresponding to the time length in the horizontal direction from the first processed data; Using a sliding window that matches the sliding window length, sliding on each corresponding second sampling point in the horizontal direction according to the sliding step length to obtain each second sampling point in each sliding window; For any sliding window, determining a second average value of the signal amplitudes of each second sampling point in the sliding window; Subtracting the signal amplitude of each second sampling point in the sliding window from the second average value to obtain a new signal amplitude of each second sampling point in the sliding window; The second processing data is determined according to the new signal amplitude of the second sampling point in each of the sliding windows.

[0016] In a possible implementation manner, performing horizontal background removal processing on the first processed data to obtain second processed data includes: For each measurement section of the ground penetrating radar, construct an initial matrix according to the horizontal coordinates and depth of each sampling point located on the measurement section in the first processed data; Performing feature extraction on the initial matrix to obtain a matrix feature vector; Eliminating the horizontal eigenvectors in the matrix eigenvectors to obtain a target matrix corresponding to the measurement section; The second processed data is determined according to the target matrix corresponding to each of the measurement sections.

[0017] In a possible implementation manner, performing at least one second horizontal smoothing process on the second processed data to obtain the target radar data includes: performing at least one second horizontal smoothing process on the second processed data to obtain third processed data; For each scanning track in the vertical direction in the third processed data, the signal amplitude of each sampling point in the scanning track is gain processed by using the second gain mode and the depth corresponding to each sampling point in the scanning track to obtain the target radar data.

[0018] In a second aspect, the present disclosure also provides an underground vertical structure identification device, including: A determination module is used to determine a target thaw lake to be detected and an underground target detection area corresponding to the target thaw lake from the thaw lakes in the permafrost area of ​​the plateau; A collection module, used to collect data from the target detection area using a ground penetrating radar to obtain initial radar data; the frequency of the ground penetrating radar is less than or equal to the target frequency, and the target frequency is related to the frozen soil depth of the plateau permafrost area; A first processing module is used to perform data encryption and interception processing on the initial radar data according to the target frequency and the time window length of the ground penetrating radar to obtain intermediate radar data; A second processing module is used to perform signal gain processing on the intermediate radar data in the vertical direction, and perform multiple signal filtering processes on the processed intermediate radar data in the horizontal direction to obtain target radar data; The identification module is used to identify the vertical structure in the target detection area according to the target radar data.

[0019] In a third aspect, an optional implementation of the present disclosure further provides a computer device, a processor, and a memory, wherein the memory stores machine-readable instructions executable by the processor, and the processor is used to execute the machine-readable instructions stored in the memory, and when the machine-readable instructions are executed by the processor, the steps of the above-mentioned first aspect, or any possible implementation of the first aspect are performed.

[0020] In a fourth aspect, an optional implementation of the present disclosure further provides a computer program product, including a computer program, which, when executed, implements the above-mentioned first aspect, or the steps in any possible implementation of the first aspect.

[0021] The underground vertical structure identification method, device, computer equipment and program product provided by the embodiments of the present disclosure can accurately screen out areas where underground vertical structures may exist from frozen soil areas by first determining the target thermal melt lake and the target detection area. By encrypting and intercepting the initial radar data collected from the target detection area, and then performing gain processing in the vertical direction, the tendency of the radar signal to attenuate from shallow to deep can be eliminated, so that the amplitude of the radar signal reflected from the deep is basically at the same level as the amplitude of the radar signal reflected from the shallow. Then, multiple signal filtering processes are performed on the radar data in the horizontal direction to eliminate completely horizontal signals in the horizontal direction, retain non-completely horizontal signals, achieve matching with the non-horizontal layered structure of the underground geological strata, and obtain target radar data that can accurately reflect the deep vertical geological structure. Finally, by identifying the target radar data, the vertical structure under the target detection area can be accurately identified.

[0022] For a description of the effects of the above-mentioned underground vertical structure identification device, computer equipment, and computer program product, please refer to the description of the above-mentioned underground vertical structure identification method, which will not be repeated here.

[0023] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following is a brief introduction to the drawings required for use in the embodiments. The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used together with the specification to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can also be obtained based on these drawings without creative work.

[0025] Figure 1 A flow chart of an underground vertical structure identification method provided by an embodiment of the present disclosure is shown; Figure 2 A schematic diagram of an underground vertical structure identification device provided by an embodiment of the present disclosure is shown; Figure 3 A schematic diagram of the structure of a computer device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The components of the embodiments of the present disclosure generally described and shown here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure is not intended to limit the scope of the present disclosure claimed for protection, but merely represents the selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present disclosure.

[0027] In addition, the terms "first", "second", etc. in the description and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein.

[0028] The "multiple or several" mentioned in this article refers to two or more. "And / or" describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0029] Research has found that there is seasonal or permanent frozen soil distribution in the underground space of my country's plateau frozen soil areas. The source rocks at the bottom of the frozen soil in some areas will release and decompose methane, carbon dioxide and other gases and migrate and erupt to the surface. The gas release process will cause the existence of underground vertical structures in the frozen soil area. If the vertical structure formed by gas volatilization under the frozen soil area can be accurately identified, it will be of great help to the detection of coal resources under the frozen soil. However, in the current engineering and environmental exploration work in frozen soil areas, the recognition effect of vertical structures under frozen soil areas is poor. Therefore, how to accurately detect the underground vertical structure under the frozen soil area has become a technical issue worthy of attention.

[0030] Based on the above research, the present disclosure provides an underground vertical structure identification method, device, computer equipment and program product. By first determining the target thermal melt lake and the target detection area, it is possible to accurately screen out the area where the underground vertical structure may exist from the frozen soil area. By encrypting and intercepting the initial radar data collected from the target detection area, and then performing gain processing in the vertical direction, the tendency of the radar signal to attenuate from shallow to deep can be eliminated, so that the amplitude of the radar signal reflected from the deep is basically at the same level as the amplitude of the radar signal reflected from the shallow. Then, multiple signal filtering processes are performed on the radar data in the horizontal direction, which can eliminate the completely horizontal signals in the horizontal direction and retain the non-completely horizontal signals, so as to match the non-horizontal layered structure of the underground geological strata, and obtain the target radar data that can accurately reflect the deep vertical geological structure. Finally, by identifying the target radar data, the vertical structure under the target detection area can be accurately identified.

[0031] The defects existing in the above solutions are the results obtained by the inventor after practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed by the present disclosure for the above problems below should be the contributions made by the inventor to the present disclosure during the disclosure process.

[0032] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0033] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0034] To facilitate understanding of the present embodiment, a method for identifying an underground vertical structure disclosed in the embodiment of the present disclosure is first introduced in detail. The executor of the method for identifying an underground vertical structure provided in the embodiment of the present disclosure is generally a terminal device or other processing device with certain computing capabilities, wherein the terminal device may be a user equipment (UE), a mobile device, a user terminal, a terminal, a personal digital assistant (PDA), a handheld device, a computer device, etc.; in some possible implementation methods, the underground vertical structure identification method may be implemented by a processor calling computer-readable instructions stored in a memory.

[0035] The underground vertical structure identification method provided by the embodiment of the present disclosure is described below by taking a computer device as an example of an execution subject.

[0036] like Figure 1 As shown, it is a flowchart of an underground vertical structure identification method provided by an embodiment of the present disclosure, which may include the following steps: S101: Determine a target thaw lake to be detected and an underground target detection area corresponding to the target thaw lake from thaw lakes in the permafrost region of the plateau.

[0037] Here, for the permafrost area on the plateau, satellite remote sensing images have revealed a series of linear, beaded melt lakes in the area. The two sides of the lake are raised, and the lake area in the middle is the most concave. Affected by a combination of factors such as human engineering and climate warming, the thermal stability of the permafrost area and its underlying areas has been destroyed, and deep gases (such as source rock gas) have erupted upward (detection has found that the concentration of source rock gas above and around the melt lake is very high, indicating the presence of gas upwelling), forming a special underground vertical structure for gas to erupt and migrate upward below the periphery of the lake. Therefore, if the existence of an underground vertical structure under the melt lake can be detected, it can be shown that there is a connection between the formation of the melt lake and underground gas eruption.

[0038] The reason for conducting research on permafrost areas is that the hardness and compactness of permafrost are lower than those of rocks. The vertical structure caused by the volatilization of underground gas (the underground gas is taken as source rock gas in the following text) often has a distinct and strict shape in the permafrost area, so permafrost is the most representative geological structure that guides the vertical structure.

[0039] The target thaw lake is a thaw lake that is determined from multiple thaw lakes and needs to be tested for vertical structure. Among the multiple thaw lakes that appear in the plateau permafrost area, vertical structures may not appear under all the lakes. Therefore, by screening the thaw lakes, the target thaw lakes that may have potential underground vertical structures can be preliminarily screened out, and then the target thaw lakes can be tested, which can effectively reduce the amount of calculation for vertical structure detection. Optionally, each thaw lake can be tested as a target thaw lake.

[0040] The underground target detection area is the area under the target melt lake that needs vertical structure detection. The study found that for any melt lake, its topographic and geological features are similar to the M-shaped geomorphic structure of the surrounding area. Therefore, after determining the target melt lake, the M-shaped underground target detection area can be determined based on the diameter and position of the target melt lake. By subsequently detecting the M-shaped underground target detection area, the complete vertical structure under the melt lake can be detected.

[0041] In specific implementation, for the plateau permafrost area, we can first determine the various melt lakes that appear in the area, then screen out at least one target melt lake from these melt lakes, and determine the M-shaped detection area corresponding to each target melt lake as the corresponding ground target detection area.

[0042] In one embodiment, the above S101 may be implemented according to the following steps: S101-1: Determine the target melt lake based on the shape of the melt lake in the permafrost area of ​​the plateau.

[0043] Here, the melt lakes in the plateau permafrost area often present different shapes due to different degrees of maturity. Specifically, the shapes of melt lakes can include round, oval, and crescent. The underground vertical structure under the circle and oval is very obvious, and the underground vertical structure under the crescent is not yet obvious, which is related to the underground thermal stability. The round shape means that the source rock gas has erupted and the underground vertical structure has taken shape; the oval melt lake is about to mature and the underground vertical structure is forming; the crescent-shaped melt lake is in the early stage, and the frozen soil area below has not loosened yet, and the underground vertical structure has not yet begun to form. Therefore, it can be seen that whether there is an underground vertical structure under the melt lake is related to the maturity of the lake.

[0044] Based on this, in specific implementation, circular and / or elliptical melt lakes can be screened out as target melt lakes according to the shapes of various melt lakes in the permafrost area of ​​the plateau.

[0045] S101-2: Determine the measurement profile length of the ground penetrating radar according to the lake surface diameter of the target thermal melt lake and the third conversion coefficient.

[0046] Here, the embodiment of the present disclosure uses a ground penetrating radar to detect the underground vertical structure, and after measurement, it is found that there is a certain proportional relationship between the measurement section length of the ground penetrating radar and the lake diameter of the target thermal melt lake, and the proportional relationship is the third conversion coefficient. Moreover, the measurement section length determined by the third conversion coefficient can measure the complete vertical structure under the target thermal melt lake.

[0047] Therefore, in a specific implementation, the lake diameter (D_lake) of the target thermal melt lake can be determined according to the longest distance between the opposite banks of the target thermal melt lake, and then the measurement profile length (L_profile) can be determined according to the product of the third conversion coefficient and the lake diameter. For example, the measurement profile length can be determined according to the following formula 1: L_profile=k3×D_lake; (Formula 1) Wherein, k3 is the third conversion coefficient, which can be updated according to each historical underground vertical structure identification result. For example, the third conversion coefficient can be 2.

[0048] Optionally, the initial measurement profile length can be determined according to the product of the third conversion coefficient and the lake diameter, and then the sum of the initial measurement profile length and the preset threshold value is used as the final measurement profile length. For example, for a target thermal melt lake with a diameter of 40 meters, the measurement profile length can be 80 meters. Optionally, the sum of 80 and the preset threshold value can be used as the final measurement profile length, such as 85, to further ensure that the complete vertical structure can be covered, so as to facilitate the subsequent detection of the complete vertical structure.

[0049] S101-3: Determine the target detection area corresponding to the target thermal melt lake according to the measured profile length and the preset detection shape.

[0050] In a specific implementation, the preset detection shape may be an M shape. For a target thermal melt lake, an M-shaped area may be selected as a target detection area according to the area where the target thermal melt lake is located and the length of the measurement section.

[0051] S102: using a ground penetrating radar to collect data on the target detection area to obtain initial radar data; the frequency of the ground penetrating radar is less than or equal to the target frequency, and the target frequency is related to the frozen soil depth in the plateau permafrost area.

[0052] Here, the target frequency is set to ensure that the deepest frozen soil in the plateau permafrost area can be detected, so the target frequency is related to the frozen soil depth. Since the smaller the frequency of the ground penetrating radar, the deeper the detection depth, after determining the target frequency, any frequency less than or equal to the target frequency can be used as the frequency required for the ground penetrating radar to be used. For example, the frozen soil depth is usually about 70 meters, so the target frequency can be 20 MHz, so a low-frequency ground penetrating radar less than or equal to 20 MHz can be used for data collection. The initial radar data is the original radar data collected using the ground penetrating radar in the target detection area.

[0053] In specific implementation, the ground penetrating radar to be used can be selected according to the target frequency. For example, a 20 MHz ground penetrating radar is selected. Then, the ground penetrating radar is used to collect data in the target detection area to obtain initial radar data.

[0054] S103: Encrypt and intercept the initial radar data according to the target frequency and the time window length of the ground penetrating radar to obtain intermediate radar data.

[0055] Here, the time window length can be understood as the detection time length of the ground penetrating radar in the direction vertical to the ground (hereinafter referred to as the vertical direction).

[0056] In specific implementation, the sampling points of the initial radar data in the vertical direction can be encrypted according to the target frequency to obtain the encrypted sampled data, and then the interception length can be determined according to the time window length, and the encrypted sampled data can be intercepted using the interception length to obtain the intermediate radar data. Alternatively, the interception length can be determined according to the time window length, and the initial radar data can be intercepted using the interception length, and then the intercepted radar data can be encrypted in the vertical direction to obtain the intermediate radar data. Among them, when performing data encryption processing, due to the objective continuity of geology, especially in the plateau permafrost area, the frozen soil presents an island-like connection underground (that is, the frozen soil has a certain continuity in the horizontal, vertical and inclined directions). In order to maintain the same effect in the entire lake area and the surrounding area on the entire measurement profile, the initial radar data collected by the ground penetrating radar at each location are resampled using the same sampling strategy to achieve data encryption processing. In this way, using the same encryption method at each location can ensure the continuity of the frozen soil itself, which is conducive to finding non-continuous vertical structures in continuous changes, such as chimney-shaped vertical structures.

[0057] In one embodiment, the above S103 can be implemented according to the following steps: S103-1: According to the target frequency, the number of sampling points of the initial radar data in the vertical direction and the minimum number of sampling points in the period indicated by the sampling theorem, the initial radar data is encrypted and resampled in the vertical direction to obtain encrypted sampling data.

[0058] Here, the number of sampling points of the initial radar data in the vertical direction is determined according to the frequency of the ground penetrating radar. For example, the number of sampling points of a 20 MHz ground penetrating radar is 256. For a 20 MHz ground penetrating radar, the sampling period T=1 / F, F is the frequency, then the period T=1 / F=1 / 20mhz=50ns. Collecting 2 sampling points in one period of the initial radar data can meet the requirement of the minimum number of sampling points in the period indicated by the sampling theorem. In the specific implementation, it can be determined whether the number of sampling points in the period meets the minimum number of sampling points in the period indicated by the sampling theorem according to the target frequency and the number of sampling points of the initial radar data in the vertical direction. If so, the first encrypted value can be used to encrypt and resample the initial radar data in the vertical direction to obtain the encrypted sampled data. If not, the second encrypted value is used to encrypt and resample the initial radar data in the vertical direction to obtain the encrypted sampled data. Among them, the second encrypted value is greater than the first encrypted value.

[0059] For example, the number of sampling points of a 20 MHz ground penetrating radar is 256, the time window length is 6400 nanoseconds (ns), and the sampling rate is 6400ns / 256=25ns. Taking 16x encryption as an example, the number of sampling points of the encrypted sampled data reaches 4096, and the corresponding sampling rate becomes 6400 / 4096=1.5625ns. In this way, by increasing the sampling points and reducing the sampling rate, the changes of the electromagnetic waves of the ground penetrating radar in the vertical direction can be described more precisely.

[0060] S103-2: Determine the intercept length of the time window according to the time window length of the ground penetrating radar, the dielectric constant of the frozen soil, the propagation speeds of the electromagnetic waves of the ground penetrating radar in the air and the frozen soil respectively, and the depth of the frozen soil.

[0061] Here, the relationship between the time window length and the detection depth of the geological radar can be shown as follows: ; (Formula 2) ; (Formula 3) Wherein, v is the propagation speed of the electromagnetic wave of the ground penetrating radar in frozen soil; c is the propagation speed of the electromagnetic wave of the ground penetrating radar in the air, which is 0.3 nanoseconds per meter (0.3m / ns); is the dielectric constant of frozen soil; z is the detection depth of the geological radar; t is the time it takes for the electromagnetic wave of the ground penetrating radar to propagate underground, which is the two-way time. Specifically, t can be the time window length of the ground penetrating radar.

[0062] Specifically, when the dielectric constant of frozen soil is different, the detection depth of geological radar is different. Therefore, according to the above formulas 2 and 3, the window length in which the detection depth calculated at different dielectric constants is greater than or equal to the frozen soil depth can be determined, and this length is used as the interception length. For example, the time window length of the ground penetrating radar is 6400ns, in which the upper half of the radar measurement profile (within 1600 nanoseconds) presents medium and low frequency characteristics as a whole, while the lower half of the profile (outside 1600 nanoseconds) presents low frequency characteristics as a whole. Since the intermediate frequency signal of the ground penetrating radar depicts and reflects the characteristics of the formation more clearly, according to the needs of the detection depth, the data of the upper half of the space can be selected in the depth direction of the geological radar profile for key analysis. Here, the interception length is selected as 1 / 4 of the original length, that is, 1600ns. This time window length is 1 / 4 of the original window length, and the number of sampling points is 1024, which corresponds to 1 / 4 of the number of sampling points after the initial radar data is resampled. Furthermore, when the dielectric constant of frozen soil is 4, the detection depth corresponding to 1600ns is z=120m; when the dielectric constant of frozen soil is 6.25, the detection depth corresponding to 1600ns is z=96m; when the dielectric constant of frozen soil is 9, the detection depth corresponding to 1600ns is z=80m. At this time, the detection depth under the time window length of 1600ns is greater than the frozen soil depth of 70m.

[0063] S103-3: Use the encrypted sampling data that matches the truncation length as the intermediate radar data.

[0064] Exemplarily, based on the time window length of the ground penetrating radar, the encrypted sampled data matching the interception length may be intercepted from the encrypted sampled data as the intermediate radar data.

[0065] S104: performing signal gain processing on the intermediate radar data in the vertical direction, and performing multiple signal filtering processing on the processed intermediate radar data in the horizontal direction to obtain target radar data.

[0066] Here, the signal gain processing may include time-varying gain processing and / or exponential gain processing. The signal filtering processing may include sliding window filtering and / or frequency-space domain filtering (FX filtering for short), where F represents frequency and X represents horizontal space.

[0067] In specific implementation, the intermediate radar data may be subjected to at least one time-varying gain processing and / or exponential gain processing in the vertical direction to obtain the processed intermediate radar data. Then, the processed intermediate radar data may be subjected to multiple consecutive signal filtering processing in the horizontal direction by transforming and using sliding window filtering and frequency space domain filtering to obtain the target radar data.

[0068] S105: Identify the vertical structure in the target detection area according to the target radar data.

[0069] In specific implementation, after obtaining the target radar data, the data image corresponding to the target radar data can be identified and processed to determine whether there is a vertical structure in the target detection area. If it is determined that there is a vertical structure, the specific location and shape of the vertical structure can be determined.

[0070] In this way, by first determining the target thermal melt lake and the target detection area, it is possible to accurately screen out areas where underground vertical structures may exist from the frozen soil area. By encrypting and intercepting the initial radar data collected from the target detection area, and then performing gain processing in the vertical direction, the tendency of the radar signal to attenuate from shallow to deep can be eliminated, so that the amplitude of the radar signal reflected from the deep part is basically at the same level as the amplitude of the radar signal reflected from the shallow part. Then, multiple signal filtering processes are performed on the radar data in the horizontal direction to eliminate the completely horizontal signals in the horizontal direction and retain the non-completely horizontal signals, so as to match the non-horizontal layered structure of the underground geological strata and obtain the target radar data that can accurately reflect the deep vertical geological structure. Finally, by identifying the target radar data, the vertical structure under the target detection area can be accurately identified.

[0071] In one embodiment, the deep underground gas in the frozen soil area is unstable due to human industrial activities, climate warming, frozen soil degradation, etc., and erupts toward the surface, forming a geological structure with vertical characteristics in the frozen soil layer, thereby causing the surface to collapse and form a thaw lake with the vertical structure as the periphery. Therefore, after executing S104, the cause of the formation of the target thaw lake can also be judged according to the position of the detected vertical structure. Specifically, if the vertical structure included in the target detection area is located below the periphery of the target thaw lake, it is determined that the cause of the formation of the target thaw lake is: underground gas eruption.

[0072] For example, the vertical structure directly below the target melt lake may be caused by the eruption of source rock gas, or by other reasons such as the collapse of frozen soil under the target melt lake, while the vertical structure on both sides of the target melt lake is definitely caused by the eruption of source rock gas. Therefore, after detecting the vertical structure, it can be determined whether the vertical structure is located below the periphery of the target melt lake. If so, it can be determined that the target melt lake is formed by underground gas eruption. If not, it is necessary to use other methods in the prior art to further determine the cause of formation.

[0073] In one embodiment, after executing S104, the following steps may also be executed: According to the lake surface diameter of the target thaw lake and the first conversion coefficient, the predicted diameter of the vertical structure corresponding to the bottom of the target thaw lake is determined; according to the permafrost depth, the lake surface diameter and the second conversion coefficient, the predicted depth of the vertical structure corresponding to the bottom of the target thaw lake is determined; according to the target diameter and the predicted diameter of the vertical structure in the target detection area, it is determined whether the first conversion coefficient needs to be adjusted, and, according to the target depth and the predicted depth of the vertical structure in the target detection area, it is determined whether the second conversion coefficient needs to be adjusted.

[0074] Here, the first conversion coefficient k1 between the lake surface diameter of the target thermal melt lake and the predicted diameter of the vertical structure below can be preset based on experience, that is, the relationship between the lake surface diameter and the predicted diameter is as shown in the following formula 4: D_vertical=k1×D_lake; (Formula 4) Where D_vertical represents the predicted diameter of the vertical structure, and D_lake represents the diameter of the lake surface. Usually, the value of k1 is in the interval [1.25,1.5].

[0075] In addition, the deviation △D between the frozen soil depth and the predicted depth of the vertical structure corresponding to the target thermal thaw lake is set based on experience. Among them, △D is an additional depth increment caused by thermal thaw, which may be related to factors such as the size of the lake, topographic and geological characteristics. △D is set to be proportional to the diameter of the lake surface, and the proportional coefficient is the second conversion coefficient k2. Then, the relationship between the predicted depth and the frozen soil depth and the lake surface diameter can be shown as the following formula 5: D_depth=D_frost+k2×D_lake; (Formula 5) Among them, D_depth represents the predicted depth of the vertical structure, and D_frost represents the frozen soil depth.

[0076] Exemplarily, after determining the target thaw lake, the predicted diameter and predicted depth of the vertical structure corresponding to the target thaw lake below can be determined according to the above formulas 4 and 5, as well as the lake surface diameter and frozen soil depth of the target thaw lake. After the vertical structure is determined using the above steps S101 to S105, the actual target diameter and target depth of the vertical structure can be measured. Then, based on the difference between the target diameter and the predicted diameter, determine whether the first conversion coefficient is reasonable. If it is reasonable, there is no need to adjust it; if it is unreasonable, the first conversion coefficient needs to be adjusted according to the target diameter. Similarly, based on the difference between the target depth and the predicted depth, determine whether the second conversion coefficient is reasonable. If it is reasonable, there is no need to adjust it; if it is unreasonable, the second conversion coefficient needs to be adjusted according to the target depth.

[0077] In this way, after continuous fitting of the first conversion coefficient and the second conversion coefficient, an accurate conversion relationship can be finally obtained, which is conducive to predicting the vertical structural characteristics under similar thermal melt lakes that have not been measured based on these conversion relationships.

[0078] In one embodiment, S104 may be implemented according to the following steps: S104-1: For each scanning track in the intermediate radar data in the vertical direction, gain processing is performed on the signal amplitude of each sampling point in the scanning track using the first gain mode and the depth corresponding to each sampling point in the scanning track to obtain processed intermediate radar data.

[0079] Here, the initial radar data has multiple scanning tracks in the vertical direction. Since the intermediate radar data is obtained by intercepting the initial radar data, the scanning track of the intermediate radar data is the same as the scanning track of the initial radar data.

[0080] The first gain mode may be a time-varying gain mode. Optionally, the first gain mode may include a mode of firstly a time-varying gain and then an exponential gain.

[0081] In the specific implementation, for the vertical direction, the signal amplitude fidelity strategy is adopted to eliminate the trend of signal attenuation from shallow to deep, and the goal is to make the signal amplitude of deep reflection and shallow reflection basically at the same level. Specifically, in the vertical direction, a time-varying gain method can be used. According to the depth of each sampling point in each scanning track in the vertical direction, a small gain value (i.e., decibel number) is used for signal gain processing of shallow sampling points, and a larger gain value (i.e., decibel number) is used for signal gain processing of deep sampling points, thereby increasing the amplitude of the deep stratum reflection signal and obtaining the processed intermediate radar data.

[0082] S104-2: Perform a first horizontal smoothing process on the processed intermediate radar data to obtain first processed data.

[0083] Exemplarily, after the gain processing, the signal is amplified, and some noise is also amplified. Therefore, any smoothing processing method in the prior art can be further used to perform a first horizontal smoothing processing on the processed intermediate radar data in the horizontal direction to eliminate the amplified noise and obtain the first processed data.

[0084] Thus, considering that the underground geological strata are non-horizontal layered structures, by using the same gain function and parameters to process the entire two-dimensional profile at the same time depth, that is, using the same gain parameters from beginning to end on the transverse profile for horizontal smoothing, the completely horizontal signals in the horizontal direction can be eliminated, and the non-completely horizontal signals can be retained. In addition, by processing the intermediate radar data vertically first and then horizontally, the fine geological structure boundaries can be delineated in the horizontal direction on the basis of depicting the vertical morphology.

[0085] In one embodiment, the above S104-2 may be implemented according to the following steps: The processed intermediate radar data is subjected to a first horizontal smoothing process by using at least one of a sliding window filtering method and a frequency space domain filtering method to obtain first processed data.

[0086] In specific implementation, a sliding window filtering method may be used to perform a first horizontal smoothing process on the processed intermediate radar data to obtain first processed data; or, a frequency-space domain filtering method (i.e., FX filtering) may be used to perform a first horizontal smoothing process on the processed intermediate radar data to obtain first processed data; or, a sliding window filtering method and a frequency-space domain filtering method may be used to perform a first horizontal smoothing process on the processed intermediate radar data twice in a row to obtain first processed data.

[0087] The sampling point values ​​within the sliding window corresponding to the sliding window filtering method may be preset based on experience or may be selected according to the method described below.

[0088] In one embodiment, the step of performing the first horizontal smoothing process by using a sliding window filtering method may be implemented according to the following steps A1 to A3: A1: Select the sampling point values ​​in the sliding window in the horizontal direction when the local continuous features of the target detection area in the inclined direction are not disconnected and the local disconnected features are not connected.

[0089] During the specific implementation, the local high-frequency noise elimination ideas and methods of fidelity processing are followed, according to the objective geological laws of the frozen soil area, the changes in the local geological structure (horizontal changes, vertical changes, or inclined changes) are focused on, and the local continuous features of the target detection area in the inclined direction are not broken and the local disconnected features are not connected. The sampling point values ​​in the sliding window in the horizontal direction are selected.

[0090] For example, a vertical structure is usually chimney-shaped, and it has certain characteristics in the vertical, horizontal and inclined directions. The features in the vertical direction are not strictly vertical, but inclined at a certain angle. For the horizontal direction, there is an extension in the horizontal direction, and the extension range can be, for example, 20 scanning points or 30 scanning points. When performing the first horizontal smoothing process, if the number of sampling points in the selected sliding window is greater than 20 scanning points or 30 scanning points, the abnormal features will be smoothed out, resulting in the failure to identify these features. If the number of sampling points is too small, the abnormal features will not be detected. Therefore, in the specific implementation, it is necessary to select the sampling point values ​​in the sliding window with the goal of not disconnecting the local continuous features and not connecting the local disconnected features. For example, considering the abnormal or abnormal local subtle changes below the target thermal melt lake, it is found through measurement that these local changes usually occur at 5-10 or 10-20 adjacent scanning points. Therefore, when performing horizontal data smoothing, it is necessary to retain the local reflection characteristic signals of these chimney-shaped vertical structures and cannot smooth them out. Therefore, 5, 10, etc. can be selected as the sampling point values ​​in the sliding window.

[0091] A2: For any time length, determine the first sampling points corresponding to the time length in the horizontal direction from the processed intermediate radar data.

[0092] Exemplarily, for any time length within the vertical time window length, each sampling point corresponding to the time length in the horizontal direction can be determined from the processed intermediate radar data, and these sampling points are used as the first sampling points.

[0093] A3: Use a sliding window that matches the sampling point value to slide on each first sampling point corresponding to the horizontal direction, and use the first average value of the signal amplitudes of each first sampling point in each sliding window as the signal amplitude of the first sampling point at the target position in the sliding window to obtain the first processed data corresponding to the time length in the horizontal direction.

[0094] Here, the target position may be the middle position in the sliding window. For example, when the sliding window includes 5 sampling points, the sampling point at the target position may be the third sampling point among the 5 sampling points, and when the sliding window includes 6 sampling points, the sampling point at the target position may be the third sampling point or the fourth sampling point among the 6 sampling points.

[0095] In a specific implementation, from the horizontal direction, from left to right or from right to left, a sliding window matching the sampling point value is used to slide on each first sampling point corresponding to the horizontal direction, and each first sampling point located in the sliding window during each sliding is obtained. For example, during the first sliding, the first sampling points located in the sliding window are the first 5 sampling points; during the second sliding, the first sampling points located in the sliding window are the 2nd to 6th sampling points; during the third sliding, the first sampling points located in the sliding window are the 3rd to 7th sampling points; and so on, and each first sampling point located in the sliding window during each sliding can be determined. For any sliding, the average value of the signal amplitudes of each first sampling point located in the sliding window during the sliding can be calculated, and the average value is used as the first average value, and the first average value is used as the signal amplitude of the first sampling point located at the target position in the sliding window during the sliding. For example, when the first sampling point in the sliding window is the first five sampling points, the average value of the signal amplitudes of the first five sampling points can be used as the signal amplitude of the third sampling point; when the first sampling point in the sliding window is the second to sixth sampling points, the average value of the signal amplitudes of the second to sixth sampling points can be used as the signal amplitude of the fourth sampling point.

[0096] In this way, through the sliding window filtering method, local random high-frequency noise interference can be effectively eliminated to obtain clear first processed data.

[0097] In one embodiment, when the second horizontal smoothing process uses a sliding window filtering method, the sampling point values ​​within the sliding window corresponding to the second horizontal smoothing process are negatively correlated or positively correlated with the sampling point values ​​within the sliding window corresponding to the first horizontal smoothing process.

[0098] For example, when the second horizontal smoothing process is performed, if the sliding window filtering method is used, if the second horizontal smoothing process also uses the sliding window filtering method, then the sampling point value in the sliding window selected corresponding to the second horizontal smoothing process may be the same as the sampling point value in the sliding window selected corresponding to the first horizontal smoothing process, or may present an increasing or decreasing relationship. For example, the sampling point value selected for the first horizontal smoothing process is 5, and the sampling point value selected for the second horizontal smoothing process is 7.

[0099] Moreover, if the second horizontal smoothing process is performed multiple times, and the sliding window filtering method is used several times among the multiple times, then the sampling point values ​​in the sliding window corresponding to the sliding window filtering methods selected for these times can be the same, or they can be negatively correlated or positively correlated with the sampling point values ​​in the sliding window corresponding to the first horizontal smoothing process.

[0100] In another embodiment, the step of performing the first horizontal smoothing process by using a sliding window filtering method may be implemented according to the following steps B1 to B3: B1: Using a sliding window filtering method, the processed intermediate radar data is subjected to a first horizontal smoothing process to obtain first sub-processed data.

[0101] Exemplarily, a sliding window filtering method may be used to perform a first horizontal smoothing process on the processed intermediate radar data to obtain the first sub-processed data. The specific implementation process of the sliding window filtering method may refer to the above steps A1 to A3, which will not be described in detail here.

[0102] B2: Using the frequency space domain filtering method, the processed intermediate radar data is subjected to the first horizontal smoothing process to obtain the second sub-processed data.

[0103] Exemplarily, the FX filtering method may be used to perform a first horizontal smoothing process on the processed intermediate radar data to obtain second sub-processed data.

[0104] B3: when the error between the first sub-processed data and the second sub-processed data is within a preset range, determining the first processed data according to the mean data of the first sub-processed data and the second sub-processed data.

[0105] Here, the preset range can be set based on experience and is not specifically limited in the embodiments of the present disclosure.

[0106] For example, the maximum difference / mean difference can be determined based on the difference between the signal amplitude of each sampling point in the first sub-processed data and the signal amplitude of the sampling point at the corresponding position in the second sub-processed data, and then it is determined whether the maximum difference / mean difference is within the preset range. If so, the mean amplitude (i.e., mean data) corresponding to each sampling point can be determined based on the signal amplitude of each sampling point in the first sub-processed data and the signal amplitude of each sampling point in the second sub-processed data, and the mean amplitude corresponding to each sampling point is used as the final signal amplitude corresponding to each sampling point. The first processed data is determined based on the final signal amplitude corresponding to each sampling point. If not, any sub-processed data in the first sub-processed data and the second sub-processed data can be used as the first processed data. Or a sub-processed data can be selected as the first processed data based on the distribution of the signal amplitudes corresponding to the first sub-processed data and the second sub-processed data.

[0107] It can be understood that after the first processing data is determined according to B1~B3 above, if the second horizontal smoothing processing uses a sliding window filtering method, the sampling point values ​​within the sliding window corresponding to the second horizontal smoothing processing are negatively correlated or positively correlated with the sampling point values ​​within the sliding window corresponding to the first horizontal smoothing processing.

[0108] S104-3: Perform horizontal background removal processing on the first processed data to obtain second processed data.

[0109] Exemplarily, any horizontal background removal method in the prior art can be used to further remove the background of the first processed data, thereby eliminating local random high-frequency noise interference and horizontal background, and obtaining the reflection signal of the underground bottom layer. The data after the horizontal background removal process is grouped as the second processed data.

[0110] In one embodiment, the above S104-3 can be implemented according to the following steps C1 to C6: C1: Determine the sliding window length and sliding step size according to the signal amplitude of each sampling point in the first processed data.

[0111] Exemplarily, the signal distribution of the sampling points can be determined based on the signal amplitude of each sampling point in the first processed data, and the sliding window length and sliding step length applicable to the entire time length can be determined based on the distribution.

[0112] Optionally, for any time length, the signal distribution of the time length can be determined based on the signal amplitude of each second sampling point corresponding to the time length in the horizontal direction of the first processed data, and the sliding window length and sliding step corresponding to the time length can be determined based on the distribution.

[0113] Optionally, in order to avoid performing multiple background removal on a certain sampling point, the sliding step size may be greater than or equal to the sliding window length.

[0114] For example, the horizontal background value can select 10 meters to 20 meters as a background width, thereby highlighting the local features of 2 meters to 5 meters on the horizontal section.

[0115] C2: For any time length, determine the second sampling points corresponding to the time length in the horizontal direction from the first processed data.

[0116] C3: Using a sliding window that matches the sliding window length, slide on each corresponding second sampling point in the horizontal direction according to the sliding step length to obtain each second sampling point in each sliding window.

[0117] In a specific implementation, when the sliding window length and the sliding step length are applicable to all time lengths, for each second sampling point corresponding to any time length, a sliding window matching the sliding window length can be used to slide on each second sampling point corresponding to the horizontal direction according to the sliding step length, so as to obtain each second sampling point in each sliding window. When there is a corresponding sliding window length and sliding step length for each time length, for each second sampling point corresponding to any time length, a sliding window matching the sliding window length corresponding to the time length can be used to slide on each second sampling point corresponding to the horizontal direction according to the sliding step length corresponding to the time length, so as to obtain each second sampling point in each sliding window. In particular, regarding the process of sliding on the second sampling point using the sliding window, reference can be made to the sliding process in A3 above, which will not be described in detail here.

[0118] C4: For any sliding window, determine a second average value of the signal amplitudes of each second sampling point in the sliding window.

[0119] In a specific implementation, for a sliding window corresponding to any sliding operation, the average value of the signal amplitudes of the second sampling points in the sliding window may be determined, and the average value may be used as the second average value corresponding to the sliding window.

[0120] C5: Subtract the signal amplitude of each second sampling point in the sliding window from the second average value to obtain a new signal amplitude of each second sampling point in the sliding window.

[0121] In specific implementation, for any sliding window corresponding to a sliding operation, the signal amplitude of each second sampling point in the sliding window can be subtracted from the second average value corresponding to the sliding window to obtain a new signal amplitude of each second sampling point in the sliding window. In this way, the abnormal characteristics of each sampling point can be fully obtained to display the discontinuous characteristics of the stratigraphic layer, that is, geological structures such as faults and joints, so as to highlight the channel for gas rise.

[0122] C6: Determine the second processed data according to the new signal amplitude of the second sampling point in each sliding window.

[0123] Exemplarily, the new signal amplitude of the second sampling point in the sliding window corresponding to each sliding at each time length can be used as the second processing data.

[0124] In another embodiment, there is a horizontal signal with a strong amplitude in the two-dimensional measurement profile detected by the detection radar. The horizontal signal affects the judgment of the geological structure. Therefore, in order to improve the accuracy of the judgment of the geological structure, it is necessary to eliminate the horizontal signal, and then obtain the overall geological structure characteristics and local characteristics. For this purpose, the embodiment of the present disclosure provides a method for eliminating horizontal signals using matrix eigenvectors. Specifically, the above S104-3 can be implemented according to the following steps D1 to D4: D1: For each measurement profile of the ground penetrating radar, an initial matrix is ​​constructed according to the horizontal coordinates and depth of each sampling point located on the measurement profile in the first processed data.

[0125] In specific implementation, the ground penetrating radar adopts two-dimensional detection when performing detection, so each sampling point has a horizontal coordinate in the horizontal direction and a depth in the vertical direction. For each measurement section of the ground penetrating radar, each sampling point located on the measurement section can be selected from the first processed data, and then a two-dimensional array is constructed according to the horizontal coordinates and depths of these sampling points to construct an initial matrix.

[0126] D2: Extract features from the initial matrix to obtain the matrix feature vector.

[0127] In specific implementation, the initial matrix can be subjected to feature vector extraction to obtain a matrix feature vector, wherein the matrix feature vector can reflect the horizontal feature, vertical feature, inclined feature and local detail feature of the geological structure.

[0128] D3: Eliminate the horizontal eigenvectors in the matrix eigenvectors to obtain the target matrix corresponding to the measurement profile.

[0129] In specific implementation, the matrix eigenvector can be analyzed according to the parameters in the matrix eigenvector, so as to extract the horizontal eigenvector therein. Then, the extracted horizontal eigenvector can be eliminated to obtain the target matrix corresponding to the measurement section; wherein, the horizontal eigenvector in the target matrix has been basically removed, and only the overall reflection signal and the local reflection signal are retained. Therefore, through the target matrix, the influence of the horizontal signal on the judgment of the geological structure can be effectively reduced, and a more accurate geological structure judgment result can be obtained.

[0130] D4: Determine the second processing data according to the target matrix corresponding to each measurement profile.

[0131] In specific implementation, based on the above D1~D3, the target matrix corresponding to each measurement profile of the ground penetrating radar can be obtained, and then the second processed data can be obtained by inverse transforming each target matrix and then splicing the inverse transformation results.

[0132] S104-4: Perform at least one second horizontal smoothing process on the second processed data to obtain target radar data.

[0133] Here, the smoothing method corresponding to the second horizontal smoothing process may be different from the method corresponding to the first horizontal smoothing process, or may be the same as the smoothing method corresponding to the first horizontal smoothing process.

[0134] Exemplarily, by performing one or more second horizontal smoothing processes on the second processed data, signal noise in the horizontal direction can be further eliminated, thereby obtaining target radar data that can more clearly reflect the underground geological structure.

[0135] In this way, by performing vertical processing - first horizontal smoothing processing - horizontal background removal - at least one second horizontal smoothing processing on the intermediate radar data in sequence, the noise signal in the radar data can be fully filtered out, and the local reflection characteristic signal of the chimney-shaped vertical structure can be retained, which is conducive to the accurate identification of the vertical structure.

[0136] In one embodiment, the above S104-4 may also be implemented according to the following steps: S104-4-1: Perform at least one second horizontal smoothing process on the second processed data to obtain third processed data.

[0137] Exemplarily, the second processed data may be subjected to at least one second horizontal smoothing process, for example, the second horizontal smoothing process may be performed consecutively for multiple times, so as to obtain the third processed data.

[0138] S104-4-2: For each scanning track in the vertical direction in the third processed data, the signal amplitude of each sampling point in the scanning track is gain processed by using the second gain method and the depth corresponding to each sampling point in the scanning track to obtain target radar data.

[0139] Here, the second gain mode may be different from the first gain mode. For example, the first gain mode may be a time-varying gain, and the second gain mode may be an exponential gain mode.

[0140] Exemplarily, an exponential gain method of 2-point gain can be used, the first gain point is set to 0, the second gain point is set to 24, and the exponential gain method is used between the first gain point and the second gain point. Among them, the first gain point and the second gain point can be the sampling points with the shallowest and deepest depths, respectively. For each scanning track in the vertical direction in the third processed data, the signal amplitude of each sampling point in the scanning track can be gain processed by using the exponential gain method and the depth corresponding to each sampling point in the scanning track, thereby obtaining the target radar data.

[0141] In this way, by using the exponential gain method and performing signal processing in the vertical direction, the amplitude of the reflection signal of the entire profile can be continuously amplified, so that the amplitude of the reflection signal of the deep strata and the target area can be further amplified, which is convenient for the recognition and judgment of the abnormal characteristics of the target area and further processing to improve the signal-to-noise ratio, making the abnormality of the target area more obvious.

[0142] In an optional implementation, in order to further enhance the radar data processing effect, after the gain processing is performed using the second gain mode, the gain result can also be subjected to spectrum analysis in the vertical direction and one or more different filterings to obtain the target radar data. The filtering methods used for different filtering times can be the same or different, and the filtering methods here can include high-pass filtering, band-pass filtering, sharpening filtering, wavelet transform filtering, etc. In this way, through spectrum analysis and frequency filtering, low-frequency interference can be eliminated, radar electromagnetic wave signals of medium-frequency components can be obtained, and the reflection signals of underground structures, especially deep reflection signals, can be more finely reflected.

[0143] For example, through spectrum analysis, the frequency distribution characteristics of the entire profile and the frequency distribution of the signal can be obtained. Based on this part, the frequency dividing point of the ultra-low frequency component and the medium frequency component can be determined, and this dividing point can be used as a parameter of high-pass filtering for further filtering processing to obtain the target radar data.

[0144] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0145] Based on the same inventive concept, an underground vertical structure identification device corresponding to the underground vertical structure identification method is also provided in the embodiment of the present disclosure. Since the principle of solving the problem by the device in the embodiment of the present disclosure is similar to the above-mentioned underground vertical structure identification method in the embodiment of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0146] like Figure 2 FIG. 1 is a schematic diagram of an underground vertical structure identification device provided by an embodiment of the present disclosure, comprising: A determination module 201 is used to determine a target thaw lake to be detected and an underground target detection area corresponding to the target thaw lake from the thaw lakes in the plateau permafrost area; The acquisition module 202 is used to acquire data from the target detection area using a ground penetrating radar to obtain initial radar data; the frequency of the ground penetrating radar is less than or equal to the target frequency, and the target frequency is related to the frozen soil depth of the plateau permafrost area; A first processing module 203 is used to perform data encryption and interception processing on the initial radar data according to the target frequency and the time window length of the ground penetrating radar to obtain intermediate radar data; The second processing module 204 is used to perform signal gain processing on the intermediate radar data in the vertical direction, and perform multiple signal filtering processes on the processed intermediate radar data in the horizontal direction to obtain target radar data; The identification module 205 is used to identify the vertical structure in the target detection area according to the target radar data.

[0147] In a possible implementation, the device further includes: The determination module 206, after identifying the vertical structure in the target detection area according to the target radar data, is used to: If the vertical structure included in the target detection area is located below the periphery of the target thaw lake, it is determined that the formation cause of the target thaw lake is: underground gas eruption.

[0148] In a possible implementation, the device further includes: The adjustment module 207, after identifying the vertical structure in the target detection area according to the target radar data, is used to: Determining a predicted diameter of a vertical structure corresponding to below the target thaw lake according to a lake surface diameter of the target thaw lake and a first conversion coefficient; Determining a predicted depth of a vertical structure corresponding to below the target thaw lake according to the frozen soil depth, the lake surface diameter, and a second conversion coefficient; determining whether the first conversion coefficient needs to be adjusted according to the target diameter of the vertical structure in the target detection area and the predicted diameter, and It is determined whether the second conversion coefficient needs to be adjusted according to the target depth of the vertical structure in the target detection area and the predicted depth.

[0149] In a possible implementation manner, the determination module 201, when determining the target thaw lake to be detected and the underground target detection area corresponding to the target thaw lake from the thaw lake in the plateau permafrost region, is used to: Determining the target thaw lake according to the shape of the thaw lake in the plateau permafrost region; Determining the measurement profile length of the ground penetrating radar according to the lake surface diameter of the target thermal melt lake and the third conversion coefficient; A target detection area corresponding to the target thermal melt lake is determined according to the measured section length and the preset detection shape.

[0150] In a possible implementation manner, the first processing module 203, when performing data encryption and interception processing on the initial radar data according to the target frequency and the time window length of the ground penetrating radar to obtain the intermediate radar data, is used to: According to the target frequency, the number of sampling points of the initial radar data in the vertical direction and the minimum number of sampling points in a period indicated by the sampling theorem, the initial radar data is encrypted and resampled in the vertical direction to obtain encrypted sampling data; Determine the intercept length of the time window length according to the time window length of the ground penetrating radar, the dielectric constant of the frozen soil, the propagation speeds of the electromagnetic waves of the ground penetrating radar in the air and the frozen soil respectively, and the depth of the frozen soil; The encrypted sampling data matching the truncation length is used as the intermediate radar data.

[0151] In a possible implementation manner, the second processing module 204, when performing signal gain processing on the intermediate radar data in the vertical direction and performing multiple signal filtering processing on the processed intermediate radar data in the horizontal direction to obtain the target radar data, is configured to: For each scanning track in the vertical direction of the intermediate radar data, using the first gain mode and the depth corresponding to each sampling point in the scanning track, perform gain processing on the signal amplitude of each sampling point in the scanning track to obtain processed intermediate radar data; Performing a first horizontal smoothing process on the processed intermediate radar data to obtain first processed data; Performing horizontal background removal processing on the first processed data to obtain second processed data; The second processed data is subjected to at least one second horizontal smoothing process to obtain the target radar data.

[0152] In a possible implementation manner, the second processing module 204, when performing the first horizontal smoothing process on the processed intermediate radar data to obtain the first processed data, is configured to: The processed intermediate radar data is subjected to a first horizontal smoothing process by using at least one of a sliding window filtering method and a frequency space domain filtering method to obtain first processed data.

[0153] In a possible implementation manner, the second processing module 204, when performing a first horizontal smoothing process on the processed intermediate radar data by using a sliding window filtering method to obtain first processed data, is configured to: Selecting sampling point values ​​within a sliding window in a horizontal direction under the condition that the local continuous features of the target detection area in the inclined direction are not disconnected and the local disconnected features are not connected; For any time length, determining each first sampling point corresponding to the time length in the horizontal direction from the processed intermediate radar data; A sliding window matching the sampling point value is used to slide on each first sampling point corresponding to the horizontal direction, and a first average value of the signal amplitudes of each first sampling point in each sliding window is used as the signal amplitude of the first sampling point at the target position in the sliding window to obtain the first processed data corresponding to the time length in the horizontal direction.

[0154] In a possible implementation, when the second horizontal smoothing process uses a sliding window filtering method, the sampling point values ​​within the sliding window corresponding to the second horizontal smoothing process are negatively correlated or positively correlated with the sampling point values ​​within the sliding window corresponding to the first horizontal smoothing process.

[0155] In a possible implementation manner, the second processing module 204, when performing a first horizontal smoothing process on the processed intermediate radar data by using a sliding window filtering method and a frequency space domain filtering method to obtain the first processed data, is configured to: Using a sliding window filtering method, performing a first horizontal smoothing process on the processed intermediate radar data to obtain first sub-processed data; Performing a first horizontal smoothing process on the processed intermediate radar data by using a frequency space domain filtering method to obtain second sub-processed data; In a case where an error between the first sub-processed data and the second sub-processed data is within a preset range, the first processed data is determined according to mean data of the first sub-processed data and the second sub-processed data.

[0156] In a possible implementation manner, the second processing module 204, when performing horizontal background removal processing on the first processed data to obtain second processed data, is used to: Determine the sliding window length and the sliding step length according to the signal amplitude of each sampling point in the first processed data; For any time length, determining each second sampling point corresponding to the time length in the horizontal direction from the first processed data; Using a sliding window that matches the sliding window length, sliding on each corresponding second sampling point in the horizontal direction according to the sliding step length to obtain each second sampling point in each sliding window; For any sliding window, determining a second average value of the signal amplitudes of each second sampling point in the sliding window; Subtracting the signal amplitude of each second sampling point in the sliding window from the second average value to obtain a new signal amplitude of each second sampling point in the sliding window; The second processing data is determined according to the new signal amplitude of the second sampling point in each of the sliding windows.

[0157] In a possible implementation manner, the second processing module 204, when performing horizontal background removal processing on the first processed data to obtain second processed data, is used to: For each measurement section of the ground penetrating radar, construct an initial matrix according to the horizontal coordinates and depth of each sampling point located on the measurement section in the first processed data; Performing feature extraction on the initial matrix to obtain a matrix feature vector; Eliminating the horizontal eigenvectors in the matrix eigenvectors to obtain a target matrix corresponding to the measurement section; The second processed data is determined according to the target matrix corresponding to each of the measurement sections.

[0158] In a possible implementation manner, the second processing module 204, when performing the second horizontal smoothing process on the second processed data at least once to obtain the target radar data, is used to: performing at least one second horizontal smoothing process on the second processed data to obtain third processed data; For each scanning track in the vertical direction in the third processed data, the signal amplitude of each sampling point in the scanning track is gain processed by using the second gain mode and the depth corresponding to each sampling point in the scanning track to obtain the target radar data.

[0159] For descriptions of the processing flow of each module in the device and the interaction flow between each module, reference may be made to the relevant descriptions in the above method embodiment, which will not be described in detail here.

[0160] Based on the same technical concept, the embodiment of the present application also provides a computer device. Figure 3 FIG. 1 is a schematic diagram of a computer device provided in an embodiment of the present application, including: Processor 301, memory 302 and bus 303. The memory 302 stores machine-readable instructions executable by the processor 301, and the processor 301 is used to execute the machine-readable instructions stored in the memory 302. When the machine-readable instructions are executed by the processor 301, the processor 301 executes the following steps: S101: Determine the target thaw lake to be detected and the underground target detection area corresponding to the target thaw lake from the thaw lake in the plateau permafrost area; S102: Use ground penetrating radar to collect data in the target detection area to obtain initial radar data; the frequency of the ground penetrating radar is less than or equal to the target frequency, and the target frequency is related to the frozen soil depth in the plateau permafrost area; S103: According to the target frequency and the time window length of the ground penetrating radar, the initial radar data is encrypted and intercepted to obtain intermediate radar data; S104: Perform signal gain processing on the intermediate radar data in the vertical direction, and perform multiple signal filtering processing on the processed intermediate radar data in the horizontal direction to obtain target radar data; and S105: According to the target radar data, identify the vertical structure in the target detection area.

[0161] The above-mentioned memory 302 includes internal memory 3021 and external memory 3022; the memory 3021 here is also called internal memory, which is used to temporarily store the calculation data in the processor 301, as well as the data exchanged with the external memory 3022 such as a hard disk. The processor 301 exchanges data with the external memory 3022 through the internal memory 3021. When the computer device is running, the processor 301 communicates with the memory 302 through the bus 303, so that the processor 301 executes the execution instructions mentioned in the above-mentioned method embodiment.

[0162] The present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the underground vertical structure identification method described in the above method embodiment are executed. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0163] The embodiments of the present disclosure also provide a computer program product, which carries a program code. The instructions included in the program code can be used to execute the steps of the software update method described in the above method embodiment. For details, please refer to the above method embodiment, which will not be repeated here.

[0164] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK).

[0165] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present disclosure, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0166] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0167] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0168] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0169] If the technical solution of this application involves personal information, the product using the technical solution of this application has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using the technical solution of this application has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, the personal information processing rules are notified by obvious signs / information, and the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

[0170] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the aforementioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the aforementioned embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be based on the protection scope of the claims.

Claims

1. A method for identifying underground vertical structures, characterized in that: include: Determine a target thaw lake to be detected and an underground target detection area corresponding to the target thaw lake from the thaw lakes in the permafrost region of the plateau; Using ground penetrating radar to collect data on the target detection area to obtain initial radar data; The frequency of the ground penetrating radar is less than or equal to the target frequency, and the target frequency is related to the frozen soil depth in the permafrost region of the plateau; According to the target frequency and the time window length of the ground penetrating radar, the initial radar data is encrypted and intercepted to obtain intermediate radar data; Performing signal gain processing on the intermediate radar data in the vertical direction, and performing multiple signal filtering processes on the processed intermediate radar data in the horizontal direction to obtain target radar data; Based on the target radar data, vertical structures in the target detection area are identified.

2. The method according to claim 1, characterized in that After identifying the vertical structure in the target detection area according to the target radar data, the method further includes: If the vertical structure included in the target detection area is located below the periphery of the target thaw lake, it is determined that the formation cause of the target thaw lake is: underground gas eruption.

3. The method according to claim 1, characterized in that: After identifying the vertical structure in the target detection area according to the target radar data, the method further includes: Determining a predicted diameter of a vertical structure corresponding to below the target thaw lake according to a lake surface diameter of the target thaw lake and a first conversion coefficient; Determining a predicted depth of a vertical structure corresponding to below the target thaw lake according to the frozen soil depth, the lake surface diameter, and a second conversion coefficient; determining whether the first conversion coefficient needs to be adjusted according to the target diameter of the vertical structure in the target detection area and the predicted diameter, and It is determined whether the second conversion coefficient needs to be adjusted according to the target depth of the vertical structure in the target detection area and the predicted depth.

4. The method according to claim 1, characterized in that: The step of determining a target thaw lake to be detected and an underground target detection area corresponding to the target thaw lake from the thaw lake in the plateau permafrost region includes: Determining the target thaw lake according to the shape of the thaw lake in the plateau permafrost region; Determining the measurement profile length of the ground penetrating radar according to the lake surface diameter of the target thermal melt lake and the third conversion coefficient; A target detection area corresponding to the target thermal melt lake is determined according to the measured section length and the preset detection shape.

5. The method according to claim 1, characterized in that The step of encrypting and intercepting the initial radar data according to the target frequency and the time window length of the ground penetrating radar to obtain intermediate radar data includes: According to the target frequency, the number of sampling points of the initial radar data in the vertical direction and the minimum number of sampling points in a period indicated by the sampling theorem, the initial radar data is encrypted and resampled in the vertical direction to obtain encrypted sampling data; Determine the intercept length of the time window length according to the time window length of the ground penetrating radar, the dielectric constant of the frozen soil, the propagation speeds of the electromagnetic waves of the ground penetrating radar in the air and the frozen soil respectively, and the depth of the frozen soil; The encrypted sampling data matching the truncation length is used as the intermediate radar data.

6. The method according to claim 1, characterized in that The performing signal gain processing on the intermediate radar data in the vertical direction, and performing multiple signal filtering processing on the processed intermediate radar data in the horizontal direction to obtain target radar data, includes: For each scanning track in the vertical direction of the intermediate radar data, using the first gain mode and the depth corresponding to each sampling point in the scanning track, perform gain processing on the signal amplitude of each sampling point in the scanning track to obtain processed intermediate radar data; Performing a first horizontal smoothing process on the processed intermediate radar data to obtain first processed data; Performing horizontal background removal processing on the first processed data to obtain second processed data; The second processed data is subjected to at least one second horizontal smoothing process to obtain the target radar data.

7. The method according to claim 6, characterized in that Performing a first horizontal smoothing process on the processed intermediate radar data to obtain first processed data includes: The processed intermediate radar data is subjected to a first horizontal smoothing process by using at least one of a sliding window filtering method and a frequency space domain filtering method to obtain first processed data.

8. The method according to claim 7, characterized in that The processed intermediate radar data is subjected to a first horizontal smoothing process by using a sliding window filtering method to obtain first processed data, including: Selecting sampling point values ​​within a sliding window in a horizontal direction under the condition that the local continuous features of the target detection area in the inclined direction are not disconnected and the local disconnected features are not connected; For any time length, determining each first sampling point corresponding to the time length in the horizontal direction from the processed intermediate radar data; A sliding window matching the sampling point value is used to slide on each first sampling point corresponding to the horizontal direction, and a first average value of the signal amplitudes of each first sampling point in each sliding window is used as the signal amplitude of the first sampling point at the target position in the sliding window to obtain the first processed data corresponding to the time length in the horizontal direction.

9. The method according to claim 8, characterized in that When the second horizontal smoothing process uses a sliding window filtering method, the sampling point values ​​within the sliding window corresponding to the second horizontal smoothing process are negatively correlated or positively correlated with the sampling point values ​​within the sliding window corresponding to the first horizontal smoothing process.

10. The method according to claim 7, characterized in that The processed intermediate radar data is subjected to a first horizontal smoothing process by using a sliding window filtering method and a frequency space domain filtering method to obtain first processed data, including: Using a sliding window filtering method, performing a first horizontal smoothing process on the processed intermediate radar data to obtain first sub-processed data; Performing a first horizontal smoothing process on the processed intermediate radar data by using a frequency space domain filtering method to obtain second sub-processed data; In a case where an error between the first sub-processed data and the second sub-processed data is within a preset range, the first processed data is determined according to mean data of the first sub-processed data and the second sub-processed data.

11. The method according to claim 6, characterized in that The step of performing horizontal background removal processing on the first processed data to obtain second processed data includes: Determine the sliding window length and the sliding step length according to the signal amplitude of each sampling point in the first processed data; For any time length, determining each second sampling point corresponding to the time length in the horizontal direction from the first processed data; Using a sliding window that matches the sliding window length, sliding on each corresponding second sampling point in the horizontal direction according to the sliding step length to obtain each second sampling point in each sliding window; For any sliding window, determining a second average value of the signal amplitudes of each second sampling point in the sliding window; Subtracting the signal amplitude of each second sampling point in the sliding window from the second average value to obtain a new signal amplitude of each second sampling point in the sliding window; The second processing data is determined according to the new signal amplitude of the second sampling point in each of the sliding windows.

12. The method according to claim 6, characterized in that The performing horizontal background removal processing on the first processed data to obtain second processed data includes: For each measurement section of the ground penetrating radar, construct an initial matrix according to the horizontal coordinates and depth of each sampling point located on the measurement section in the first processed data; Performing feature extraction on the initial matrix to obtain a matrix feature vector; Eliminating the horizontal eigenvectors in the matrix eigenvectors to obtain a target matrix corresponding to the measurement section; The second processed data is determined according to the target matrix corresponding to each of the measurement sections.

13. The method according to claim 6, characterized in that The step of performing at least one second horizontal smoothing process on the second processed data to obtain the target radar data includes: performing at least one second horizontal smoothing process on the second processed data to obtain third processed data; For each scanning track in the vertical direction in the third processed data, the signal amplitude of each sampling point in the scanning track is gain processed by using the second gain mode and the depth corresponding to each sampling point in the scanning track to obtain the target radar data.

14. An underground vertical structure identification device, characterized in that: include: A determination module is used to determine a target thaw lake to be detected and an underground target detection area corresponding to the target thaw lake from the thaw lakes in the permafrost area of ​​the plateau; A collection module, used to collect data from the target detection area using a ground penetrating radar to obtain initial radar data; The frequency of the ground penetrating radar is less than or equal to the target frequency, and the target frequency is related to the frozen soil depth in the permafrost region of the plateau; A first processing module is used to perform data encryption and interception processing on the initial radar data according to the target frequency and the time window length of the ground penetrating radar to obtain intermediate radar data; A second processing module is used to perform signal gain processing on the intermediate radar data in the vertical direction, and perform multiple signal filtering processes on the processed intermediate radar data in the horizontal direction to obtain target radar data; The identification module is used to identify the vertical structure in the target detection area according to the target radar data.

15. A computer device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and the processor is used to execute the machine-readable instructions stored in the memory. When the machine-readable instructions are executed by the processor, the processor executes the steps of the underground vertical structure identification method according to any one of claims 1 to 13.

16. A computer program product comprising a computer program, characterized in that When the computer program is executed by a computer device, the computer device executes the steps of the underground vertical structure identification method according to any one of claims 1 to 13.

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