Underground vertical structure recognition method, device, computer equipment and program product

Abstract

The present disclosure provides a method, apparatus, computer equipment, and program product for identifying underground vertical structures, wherein the method comprises: 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; acquiring data from the target detection area using a ground-penetrating radar to obtain initial radar data; the frequency of the ground-penetrating radar being less than or equal to the target frequency, and the target frequency being related to the permafrost depth in the plateau permafrost region; encrypting and intercepting the initial radar data based on 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 vertical structures in the target detection area based on the target radar data.

Description

Technical Field

[0001] The present disclosure relates to the field of geological engineering technology, and in particular to a method, apparatus, computer equipment, and program product for identifying underground vertical structures. Background Art

[0002] Permafrost areas on the plateau may contain large amounts of underground gases (e.g., source rock gases), such as methane and ethane. Detecting these gases plays an important role in determining whether coal resources exist in permafrost areas. Because these gases evaporate underground, they form unique vertical structures within the permafrost. Accurately identifying these structures allows for effective detection of the corresponding gases.

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

[0004] The embodiments of the present disclosure at least provide a method, apparatus, computer equipment, 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:

[0006] 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;

[0007] Using a ground-penetrating radar to collect data from the target detection area to obtain initial radar data; the frequency of the ground-penetrating radar is less than or equal to a target frequency, and the target frequency is related to the frozen soil depth of the plateau permafrost area;

[0008] 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;

[0009] 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;

[0010] A vertical structure in the target detection area is identified based on the target radar data.

[0011] In a possible implementation, after identifying the vertical structure in the target detection area according to the target radar data, the method further includes:

[0012] If the vertical structure included in the target detection area is located below the periphery of the target melt lake, it is determined that the formation cause of the target melt lake is: underground gas eruption.

[0013] In a possible implementation, after identifying the vertical structure in the target detection area according to the target radar data, the method further includes:

[0014] Determining a predicted diameter of a vertical structure corresponding to below the target thaw lake based on the lake surface diameter of the target thaw lake and a first conversion coefficient;

[0015] 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;

[0016] determining whether the first conversion coefficient needs to be adjusted based on the target diameter and the predicted diameter of the vertical structure in the target detection area, and

[0017] Whether the second conversion coefficient needs to be adjusted is determined according to the target depth of the vertical structure in the target detection area and the predicted depth.

[0018] 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:

[0019] determining the target thaw lake according to the shape of the thaw lake in the plateau permafrost region;

[0020] determining a measurement profile length of a ground penetrating radar according to a lake surface diameter of the target thermal melt lake and a third conversion coefficient;

[0021] A target detection area corresponding to the target thermal melt lake is determined according to the measurement section length and the preset detection shape.

[0022] 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:

[0023] performing encrypted resampling on the initial radar data in the vertical direction 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 within a period indicated by a sampling theorem to obtain encrypted sampled data;

[0024] Determining a cutoff 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 ground penetrating radar electromagnetic waves in the air and the frozen soil, respectively, and the depth of the frozen soil;

[0025] The encrypted sampling data matching the truncation length is used as the intermediate radar data.

[0026] In one 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:

[0027] For each vertical scanning track in the intermediate radar data, performing gain processing on the signal amplitude of each sampling point in the scanning track using a first gain mode and a depth corresponding to each sampling point in the scanning track to obtain processed intermediate radar data;

[0028] performing a first horizontal smoothing process on the processed intermediate radar data to obtain first processed data;

[0029] performing horizontal background removal processing on the first processed data to obtain second processed data;

[0030] Perform at least one second horizontal smoothing process on the second processed data to obtain the target radar data.

[0031] In a possible implementation, performing a first horizontal smoothing process on the processed intermediate radar data to obtain first processed data includes:

[0032] 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.

[0033] In a possible implementation, performing a first horizontal smoothing process on the processed intermediate radar data using a sliding window filtering method to obtain first processed data includes:

[0034] Selecting sampling point values within a sliding window in a horizontal direction, provided that the local continuous features of the target detection area in the oblique direction are not disconnected and the local disconnected features are not connected;

[0035] For any time length, determining each first sampling point corresponding to the time length in the horizontal direction from the processed intermediate radar data;

[0036] A sliding window that matches the sampling point value is used to slide on each corresponding first sampling point in 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.

[0037] 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.

[0038] In one possible implementation, performing a first horizontal smoothing process on the processed intermediate radar data using a sliding window filtering method and a frequency-space domain filtering method to obtain first processed data includes:

[0039] Performing a first horizontal smoothing process on the processed intermediate radar data using a sliding window filtering method to obtain first sub-processed data;

[0040] performing a first horizontal smoothing process on the processed intermediate radar data using a frequency-space domain filtering method to obtain second sub-processed data;

[0041] When 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.

[0042] In a possible implementation, performing horizontal background removal processing on the first processed data to obtain second processed data includes:

[0043] determining a sliding window length and a sliding step size according to the signal amplitude of each sampling point in the first processed data;

[0044] For any time length, determining each second sampling point corresponding to the time length in the horizontal direction from the first processed data;

[0045] 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 size to obtain each second sampling point in each sliding window;

[0046] For any sliding window, determining a second average value of signal amplitudes of each second sampling point within the sliding window;

[0047] 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;

[0048] Second processed data is determined according to a new signal amplitude of the second sampling point in each of the sliding windows.

[0049] In a possible implementation, performing horizontal background removal processing on the first processed data to obtain second processed data includes:

[0050] 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;

[0051] Performing feature extraction on the initial matrix to obtain a matrix feature vector;

[0052] Eliminating the horizontal eigenvectors in the matrix eigenvectors to obtain a target matrix corresponding to the measurement section;

[0053] The second processed data is determined according to the target matrix corresponding to each of the measurement sections.

[0054] In a possible implementation, performing at least one second horizontal smoothing process on the second processed data to obtain the target radar data includes:

[0055] performing at least one second horizontal smoothing process on the second processed data to obtain third processed data;

[0056] For each scanning track in the vertical direction in the third processed data, gain processing is performed on the signal amplitude of each sampling point in the scanning track using the second gain method and the depth corresponding to each sampling point in the scanning track to obtain the target radar data.

[0057] In a second aspect, the present disclosure further provides an underground vertical structure identification device, comprising:

[0058] a determination module, configured to determine a target thaw lake to be detected and an underground target detection area corresponding to the target thaw lake from among the thaw lakes in the permafrost region of the plateau;

[0059] an acquisition module, configured 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 being less than or equal to a target frequency, and the target frequency being related to the frozen soil depth in the plateau permafrost area;

[0060] a first processing module, configured 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;

[0061] a second processing module, configured to perform signal gain processing on the intermediate radar data in a vertical direction, and perform multiple signal filtering processes on the processed intermediate radar data in a horizontal direction to obtain target radar data;

[0062] An identification module is used to identify vertical structures in the target detection area based on the target radar data.

[0063] 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.

[0064] 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.

[0065] 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. The radar data is then subjected to multiple signal filtering processes in the horizontal direction to eliminate completely horizontal signals in the horizontal direction and retain non-completely horizontal signals, thereby matching the non-horizontal layered structure of the underground geological strata and obtaining 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.

[0066] 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.

[0067] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments. The drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used 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 those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without inventive effort.

[0069] Figure 1 A flowchart of an underground vertical structure identification method provided by an embodiment of the present disclosure is shown;

[0070] Figure 2 A schematic diagram of an underground vertical structure identification device provided by an embodiment of the present disclosure is shown;

[0071] Figure 3 A schematic structural diagram of a computer device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions 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 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 are within the scope of protection of the present disclosure.

[0073] In addition, the terms "first," "second," and the like in the description and claims of the embodiments of the present disclosure and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, such that the embodiments described herein can be practiced in an order other than that shown or described herein.

[0074] In this document, "multiple or several" refers to two or more. "And / or" describes the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0075] Research has found that seasonal or permafrost is distributed underground in permafrost areas of my country's plateau. In some areas, source rocks at the bottom of the permafrost release decomposed gases such as methane and carbon dioxide, which migrate and erupt toward the surface. This gas release process can lead to the existence of underground vertical structures in the permafrost area. Accurately identifying the vertical structures formed by gas volatilization beneath permafrost areas would be of great help in detecting coal resources beneath the permafrost. However, current permafrost engineering and environmental exploration work is not effective in identifying vertical structures beneath permafrost areas. Therefore, how to accurately detect underground vertical structures beneath permafrost areas has become a technical issue worthy of attention.

[0076] Based on the above research, the present disclosure provides a method, device, computer equipment and program product for identifying underground vertical structures. 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 permafrost 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. The radar data is then subjected to multiple signal filtering processes in the horizontal direction to eliminate completely horizontal signals in the horizontal direction and retain non-completely horizontal signals, thereby matching the non-horizontal layered structure of the underground geological strata and obtaining 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.

[0077] The defects in the above solutions are the results obtained by the inventors after practice and careful research. Therefore, the process of discovering the above problems and the solutions proposed by this disclosure for the above problems below should be the contributions made by the inventors to this disclosure during the disclosure process.

[0078] 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, it does not need to be further defined or explained in subsequent drawings.

[0079] It is understandable that before using the technical solutions disclosed in the various embodiments of this disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved in this 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.

[0080] To facilitate understanding of this embodiment, a method for identifying underground vertical structures disclosed in an embodiment of the present disclosure is first introduced in detail. The executor of the method for identifying underground vertical structures provided in an 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.

[0081] 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.

[0082] like Figure 1 FIG. 1 is a flowchart of a method for identifying an underground vertical structure provided by an embodiment of the present disclosure, which may include the following steps:

[0083] 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.

[0084] Satellite remote sensing images of the plateau's permafrost have revealed a series of linear, beaded thaw lakes, with raised sides and a central, concave area. This is due to a combination of factors, including human engineering and climate change, which have disrupted the thermal stability of the permafrost and its underlying layers. This has led to the upward eruption of gases from deep within the permafrost (such as source rock gas). Testing has revealed high concentrations of source rock gas above and around the thaw lakes, indicating gas upwelling. This has resulted in the formation of unique underground vertical structures beneath the lakes, indicating upward gas migration. Therefore, the detection of these structures beneath the thaw lakes suggests a link between the formation of the thaw lakes and underground gas eruptions.

[0085] 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 in the following text is taken as the source rock gas) often has a clear and strict shape in the permafrost area, so permafrost is the most representative geological structure that guides the vertical structure.

[0086] Target thaw lakes are identified from among multiple thaw lakes as requiring vertical structure testing. Not all thaw lakes found in permafrost areas of the plateau may harbor vertical structures. Therefore, screening these thaw lakes allows for the initial identification of target thaw lakes with potential underground vertical structures. Testing these target thaw lakes can effectively reduce the computational complexity of vertical structure testing. Alternatively, each thaw lake can be tested as a target thaw lake.

[0087] The underground target detection area is the area beneath the target melt lake that requires vertical structural inspection. Research has found that any melt lake resembles an M-shaped geomorphic structure with the surrounding terrain and geology. Therefore, after identifying the target melt lake, an M-shaped underground target detection area can be determined based on its diameter and location. Subsequent inspection of this M-shaped underground target detection area allows the complete vertical structure beneath the melt lake to be detected.

[0088] In specific implementation, for the permafrost area on the plateau, we can first determine the various thaw lakes that appear in the area, then screen out at least one target thaw lake from these thaw lakes, and determine the M-shaped detection area corresponding to each target thaw lake as the corresponding ground target detection area.

[0089] In one embodiment, the above S101 may be implemented as follows:

[0090] S101-1: Determine the target thaw lake based on the shape of the thaw lake in the permafrost area of the plateau.

[0091] Here, melt lakes in permafrost areas of the plateau often exhibit different shapes due to varying degrees of maturity. Specifically, melt lakes can be circular, elliptical, or crescent-shaped. The underground vertical structure beneath circular and elliptical shapes is very distinct, while beneath crescent-shaped ones, the structure is less pronounced. This is related to underground thermal stability. A circular melt lake indicates that source rock gas has erupted and the underground vertical structure has taken shape; an elliptical melt lake is nearing maturity and the underground vertical structure is forming; a crescent-shaped melt lake is in its early stages, with the underlying permafrost still intact and the underground vertical structure yet to form. Therefore, the presence of underground vertical structure beneath a melt lake is related to the maturity of the lake.

[0092] Based on this, in specific implementation, circular and / or elliptical thaw lakes can be screened out as target thaw lakes according to the shapes of various thaw lakes in the plateau permafrost area.

[0093] S101-2: Determine the measurement profile length of the ground penetrating radar based on the lake surface diameter of the target thermal melt lake and the third conversion coefficient.

[0094] In this disclosure, ground-penetrating radar (GPR) is used to detect underground vertical structures. Measurements show a proportional relationship between the GPR's measurement profile length and the diameter of the target thaw lake. This proportional relationship serves as the third conversion coefficient. Furthermore, the measurement profile length determined using the third conversion coefficient can be used to measure the complete vertical structure beneath the target thaw lake.

[0095] Therefore, in specific implementations, the lake diameter (D_lake) of the target melt lake can be determined based on the longest distance between the opposite shores of the target melt lake. The measurement profile length (L_profile) can then be determined based on the product of the third conversion coefficient and the lake diameter. For example, the measurement profile length can be determined using the following formula:

[0096] L_profile = k3 × D_lake; (Formula 1)

[0097] Here, k3 is a 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.

[0098] Optionally, the initial measurement profile length can be determined based on the product of the third conversion coefficient and the lake diameter. The final measurement profile length can then be calculated by adding the initial measurement profile length and a preset threshold. For example, for a target melt lake with a diameter of 40 meters, the measurement profile length can be 80 meters. Alternatively, the final measurement profile length can be calculated by adding 80 and a preset threshold, such as 85, to further ensure coverage of the entire vertical structure and facilitate subsequent detection of the entire vertical structure.

[0099] S101-3: Determine the target detection area corresponding to the target thermal melt lake based on the measurement profile length and the preset detection shape.

[0100] In specific implementation, the preset detection shape can be an M shape. For the target thermal melt lake, the M-shaped area can be selected as the target detection area according to the area where the target thermal melt lake is located and the measurement section length.

[0101] S102: Using a ground penetrating radar to collect data from 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 permafrost depth in the plateau permafrost area.

[0102] The target frequency setting here must ensure detection of the deepest permafrost depths in permafrost areas of the plateau. Therefore, the target frequency is related to the permafrost depth. Since lower GPR frequencies increase detection depths, after determining the target frequency, any frequency less than or equal to the target frequency can be used as the desired GPR frequency. For example, since the permafrost depth is typically around 70 meters, the target frequency could be 20 MHz, allowing data collection using a low-frequency GPR less than or equal to 20 MHz. Initial radar data is the raw radar data collected by the GPR within the target detection area.

[0103] In specific implementations, the desired ground-penetrating radar can be selected based on the target frequency. For example, a 20 MHz ground-penetrating radar can be selected. The ground-penetrating radar is then used to collect data in the target detection area to obtain initial radar data.

[0104] 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.

[0105] Here, the time window length can be understood as the detection time length of the ground penetrating radar in the direction perpendicular to the ground (hereinafter referred to as the vertical direction).

[0106] In specific implementations, the initial radar data can be encrypted at the vertical sampling points based on the target frequency to obtain encrypted sampled data. A truncation length is then determined based on the time window length, and the encrypted sampled data is truncation processed using the truncation length to obtain intermediate radar data. Alternatively, the truncation length can be determined based on the time window length, and the initial radar data can be truncation processed using the truncation length. The truncation data can then be encrypted vertically to obtain intermediate radar data. During data encryption, due to the objective continuity of geology, particularly in plateau permafrost regions, where the permafrost exhibits island-like connectivity underground (i.e., the permafrost exhibits a certain degree of continuity horizontally, vertically, and diagonally), to achieve consistent results across the entire measurement profile for the entire lake and pond area and surrounding areas, the initial radar data collected at each location by the ground-penetrating radar is resampled using the same sampling strategy to achieve data encryption. Using the same encryption method at each location ensures the continuity of the permafrost itself, facilitating the identification of discontinuous vertical structures, such as chimney-shaped vertical structures, within continuous variations.

[0107] In one embodiment, the above S103 can be implemented according to the following steps:

[0108] S103-1: Based on the target frequency, the number of sampling points of the initial radar data in the vertical direction, and the minimum number of sampling points within a period indicated by the sampling theorem, the initial radar data is encrypted and resampled in the vertical direction to obtain encrypted sampled data.

[0109] Here, the number of vertical sampling points in the initial radar data is determined by the frequency of the ground-penetrating radar. For example, a 20 MHz ground-penetrating radar has 256 sampling points. For a 20 MHz ground-penetrating radar, the sampling period T = 1 / F, where F is the frequency. Thus, period T = 1 / F = 1 / 20 MHz = 50 ns. Collecting two sampling points within one period of the initial radar data satisfies the minimum number of sampling points per period as required by the sampling theorem. In specific implementations, the target frequency and the number of vertical sampling points in the initial radar data can be used to determine whether the number of sampling points within a period meets the minimum number of sampling points per period as required by the sampling theorem. If so, the initial radar data can be vertically resampled using the first encryption value to obtain encrypted sampled data. If not, the initial radar data can be vertically resampled using the second encryption value to obtain encrypted sampled data. The second encryption value is greater than the first encryption value.

[0110] For example, a 20 MHz ground-penetrating radar has 256 sampling points, a time window length of 6400 nanoseconds (ns), and a sampling rate of 6400ns / 256 = 25ns. Taking 16x encryption as an example, the number of sampling points in the encrypted data reaches 4096, and the corresponding sampling rate becomes 6400 / 4096 = 1.5625ns. By increasing the number of sampling points and reducing the sampling rate, the vertical variations of the ground-penetrating radar electromagnetic waves can be more precisely described.

[0111] 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 ground penetrating radar electromagnetic waves in the air and frozen soil respectively, and the frozen soil depth.

[0112] Here, the relationship between the time window length and the detection depth of the geological radar can be expressed as follows: ; (Formula 2)

[0113] ; (Formula 3)

[0114] Where 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 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.

[0115] Specifically, the detection depth of ground penetrating radar varies depending on the dielectric constant of frozen soil. Therefore, using Formulas 2 and 3 above, we can determine a window length where the detection depth calculated for different dielectric constants is greater than or equal to the frozen soil depth. This length is used as the cutoff length. For example, the time window length of a ground penetrating radar is 6400 nanoseconds. The upper half of the radar measurement profile (within 1600 nanoseconds) exhibits generally low- to medium-frequency characteristics, while the lower half (outside 1600 nanoseconds) exhibits generally low-frequency characteristics. Because the intermediate-frequency signal of a ground penetrating radar more clearly depicts and reflects stratigraphic characteristics, data from the upper half of the depth-direction of the ground penetrating radar profile can be selected for focused analysis based on the required detection depth. Here, the cutoff length is chosen to be 1 / 4 of the original length, i.e., 1600 nanoseconds. This time window length is 1 / 4 of the original window length, resulting in a sampling point count of 1024, which corresponds to 1 / 4 of the number of sampling points after resampling the initial radar data. Furthermore, when the dielectric constant of permafrost is 4, the detection depth corresponding to 1600 ns is z = 120 m; when the dielectric constant of permafrost is 6.25, the detection depth corresponding to 1600 ns is z = 96 m; and when the dielectric constant of permafrost is 9, the detection depth corresponding to 1600 ns is z = 80 m. In these cases, the detection depth in the 1600 ns time window is greater than the permafrost depth of 70 m.

[0116] S103-3: Use the encrypted sampled data that matches the truncation length as the intermediate radar data.

[0117] For example, based on the time window length of the ground penetrating radar, encrypted sampled data that matches the interception length can be intercepted from the encrypted sampled data as the intermediate radar data.

[0118] S104: 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.

[0119] 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 (abbreviated as FX filtering), where F represents frequency and X represents horizontal space.

[0120] In a specific implementation, the intermediate radar data can be subjected to at least one time-varying gain processing and / or exponential gain processing in the vertical direction to obtain processed intermediate radar data. Then, the processed intermediate radar data is subjected to multiple consecutive signal filtering processes in the horizontal direction using sliding window filtering and frequency-space domain filtering to obtain target radar data.

[0121] S105: Identify vertical structures in the target detection area based on the target radar data.

[0122] In a 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 a vertical structure is determined to exist, the specific location and shape of the vertical structure can be determined.

[0123] In this way, by first determining the target thaw lake and target detection area, it is possible to accurately screen out areas in the permafrost area where underground vertical structures may exist. 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 radar signals to attenuate from shallow to deep can be eliminated, so that the amplitude of the radar signal reflected from deep areas is roughly the same as that of the radar signal reflected from shallow areas. Multiple signal filtering processes are then performed on the radar data in the horizontal direction to eliminate completely horizontal signals while retaining non-completely horizontal signals, achieving a match with the non-horizontal layered structure of the underground geological strata, and obtaining target radar data that accurately reflects the deep vertical geological structure. Finally, by identifying the target radar data, the vertical structure beneath the target detection area can be accurately identified.

[0124] In one embodiment, deep underground gas in a permafrost region becomes unstable due to human industrial activities, climate warming, permafrost degradation, and other factors, erupting toward the surface and forming a geological structure with vertical characteristics within the permafrost layer. This, in turn, causes surface collapse and forms a thaw lake with the vertical structure as its perimeter. Therefore, after executing S104, the cause of the target thaw lake formation can also be determined based on the location of the detected vertical structure. Specifically, if the vertical structure included in the target detection area is located below the perimeter of the target thaw lake, the cause of the target thaw lake formation is determined to be underground gas eruption.

[0125] For example, a vertical structure directly below a target melt lake could be caused by source rock gas eruption or other factors, such as permafrost collapse beneath the target melt lake. However, vertical structures on either side of the target melt lake are definitely caused by source rock gas eruption. Therefore, after detecting a vertical structure, it can be determined whether it lies below the perimeter of the target melt lake. If so, the target melt lake can be confirmed to have been formed by underground gas eruption. If not, other methods available in the prior art are needed to further determine the cause of formation.

[0126] In one embodiment, after executing S104, the following steps may also be executed:

[0127] Based on 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; based on 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; based on 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 based on 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.

[0128] Here, a first conversion coefficient k1 between the surface diameter of the target melt lake and the predicted diameter of the vertical structure below can be pre-set based on experience. That is, the relationship between the lake surface diameter and the predicted diameter is shown in the following formula 4:

[0129] D_vertical = k1 × D_lake; (Formula 4)

[0130] Where D_vertical represents the predicted diameter of the vertical structure, and D_lake represents the lake surface diameter. Typically, the value of k1 is in the range [1.25, 1.5].

[0131] Furthermore, based on experience, a deviation ΔD is set between the permafrost depth and the predicted depth of the corresponding vertical structure below the target thaw lake. ΔD is an additional depth increment due to thaw, which may be related to factors such as the size of the lake, topography, and geology. ΔD is assumed to be proportional to the lake diameter, with the proportionality coefficient being the second conversion coefficient k2. The relationship between the predicted depth, permafrost depth, and lake diameter can be expressed as follows:

[0132] D_depth = D_frost + k2 × D_lake; (Formula 5)

[0133] Where D_depth represents the predicted depth of the vertical structure, and D_frost represents the frozen soil depth.

[0134] For example, 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 based on the above-mentioned formulas 4 and 5, as well as the lake surface diameter and permafrost depth of the target thaw lake. After determining the vertical structure using the above-mentioned 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, it is determined whether the first conversion coefficient is reasonable. If it is reasonable, it does not need to be adjusted; 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, it can be determined whether the second conversion coefficient is reasonable. If it is reasonable, it does not need to be adjusted; if it is unreasonable, the second conversion coefficient needs to be adjusted according to the target depth.

[0135] 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.

[0136] In one embodiment, S104 may be implemented as follows:

[0137] S104-1: For each vertical scanning track in the intermediate radar data, gain processing is performed on the signal amplitude of each sampling point in the scanning track using a first gain mode and the depth corresponding to each sampling point in the scanning track to obtain processed intermediate radar data.

[0138] Here, the initial radar data has multiple scanning tracks in a vertical direction. Since the intermediate radar data is obtained by intercepting the initial radar data, the scanning tracks of the intermediate radar data are the same as those of the initial radar data.

[0139] The first gain mode may be a time-varying gain mode. Optionally, the first gain mode may include a mode of firstly performing a time-varying gain and then performing an exponential gain.

[0140] In practice, a signal amplitude fidelity strategy is employed in the vertical direction to eliminate the tendency of signals to attenuate from shallow to deep depths. The goal is to maintain the amplitude of signals reflected from deep and shallow layers at roughly the same level. Specifically, a time-varying gain method can be employed in the vertical direction. Based on the depth of each sampling point within each vertical scan track, a small gain value (i.e., decibel) is applied to shallow sampling points, while a larger gain value (i.e., decibel) is applied to deep sampling points. This increases the amplitude of signals reflected from deep strata, resulting in processed intermediate radar data.

[0141] S104-2: Perform a first horizontal smoothing process on the processed intermediate radar data to obtain first processed data.

[0142] For example, after the gain processing, the signal is amplified, and some noise is also amplified. Therefore, any smoothing processing method in the existing technology 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.

[0143] Considering the non-horizontal structure of underground geological strata, by processing the entire 2D profile at the same time depth using the same gain function and parameters—that is, applying the same gain parameters throughout the horizontal profile for horizontal smoothing—we can eliminate completely horizontal signals while retaining non-perfectly horizontal signals. Furthermore, by first processing the intermediate radar data vertically and then horizontally, we can delineate fine geological structural boundaries horizontally, while still delineating the vertical morphology.

[0144] In one embodiment, the above S104-2 may be implemented according to the following steps:

[0145] 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.

[0146] In a 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 the first processed data. Alternatively, 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 the first processed data. Alternatively, 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 the first processed data.

[0147] 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 later.

[0148] In one embodiment, the step of performing the first horizontal smoothing process using a sliding window filtering method may be implemented according to the following steps A1 to A3:

[0149] A1: Select the sampling point values within the horizontal sliding window when the local continuous features of the target detection area in the oblique direction are not broken and the local disconnected features are not connected.

[0150] During the specific implementation, the local high-frequency noise elimination ideas and methods of fidelity processing are followed, and according to the objective geological laws of the permafrost area, the changes in local geological structures (changes in the horizontal direction, vertical direction, or in the inclined direction) are focused on. The local continuous features of the target detection area in the inclined direction are not broken and the local disconnected features are not connected, and the sampling point values within the horizontal sliding window are selected.

[0151] 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 range of this extension 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 inability 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 local continuous features and not connecting 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 smoothing of the data, it is necessary to specially retain the local reflection characteristic signals of these chimney-shaped vertical structures and cannot smooth and filter them out. Therefore, 5, 10, etc. can be selected as the sampling point values in the sliding window.

[0152] 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.

[0153] Illustratively, for any time length within the vertical time window length, various sampling points corresponding to the time length in the horizontal direction may be determined from the processed intermediate radar data, and these sampling points may be used as first sampling points.

[0154] A3: Use a sliding window that matches the sampling point value to slide across each corresponding first sampling point in the horizontal direction, and use the first average 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 first processed data corresponding to the time length in the horizontal direction.

[0155] Here, the target position may be the middle position within the sliding window. For example, if the sliding window includes five sampling points, the sampling point at the target position may be the third sampling point among the five sampling points. If the sliding window includes six sampling points, the sampling point at the target position may be the third sampling point or the fourth sampling point among the six sampling points.

[0156] In a specific implementation, a sliding window matching the sampling point values is used to slide horizontally from left to right or from right to left on the corresponding first sampling points in the horizontal direction, thereby obtaining the first sampling points within the sliding window during each slide. For example, during the first slide, the first sampling points within the sliding window are the first five sampling points; during the second slide, the first sampling points within the sliding window are the second to sixth sampling points; during the third slide, the first sampling points within the sliding window are the third to seventh sampling points; and so on, thereby determining the first sampling points within the sliding window during each slide. For any slide, the average signal amplitude of the first sampling points within the sliding window during that slide can be calculated, and this average value can be used as the first average value, which is then used as the signal amplitude of the first sampling point at the target position within the sliding window during that slide. For example, when the first sampling point in the sliding window is the first five sampling points, the average 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 of the signal amplitudes of the second to sixth sampling points can be used as the signal amplitude of the fourth sampling point.

[0157] 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.

[0158] 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.

[0159] For example, if a sliding window filtering method is used during the second horizontal smoothing process, then the sample point values within the sliding window selected for the second horizontal smoothing process may be the same as the sample point values within the sliding window selected for the first horizontal smoothing process, or may be in an increasing or decreasing relationship. For example, the sample point value selected for the first horizontal smoothing process is 5, and the sample point value selected for the second horizontal smoothing process is 7.

[0160] 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.

[0161] In another embodiment, the step of performing the first horizontal smoothing process using a sliding window filtering method may be implemented according to the following steps B1 to B3:

[0162] B1: Using a sliding window filtering method, perform a first horizontal smoothing process on the processed intermediate radar data to obtain first sub-processed data.

[0163] For example, a sliding window filtering method can 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 can refer to steps A1 to A3 above and will not be repeated here.

[0164] B2: Using the frequency-space domain filtering method, the processed intermediate radar data is smoothed at the first level to obtain the second sub-processed data.

[0165] Exemplarily, the processed intermediate radar data may be smoothed at the first level using an FX filtering method to obtain second sub-processed data.

[0166] B3: When the error between the first sub-processed data and the second sub-processed data is within a preset range, determine the first processed data according to the average data of the first sub-processed data and the second sub-processed data.

[0167] Here, the preset range can be set based on experience and is not specifically limited in the embodiment of the present disclosure.

[0168] For example, a maximum / 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. Then, a determination can be made as to whether the maximum / mean difference is within a 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. 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, either the first or second sub-processed data can be used as the first processed data. Alternatively, a sub-processed data can be selected as the first processed data based on the distribution of the signal amplitudes corresponding to each of the first and second sub-processed data.

[0169] It can be understood that after the first processing data is determined according to B1 to B3 above, if 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.

[0170] S104-3: Perform horizontal background removal processing on the first processed data to obtain second processed data.

[0171] For example, any horizontal background removal method known in the art can be used to further remove background from 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 horizontal background removal is combined into the second processed data.

[0172] In one embodiment, the above S104-3 can be implemented according to the following steps C1 to C6:

[0173] C1: Determine the sliding window length and sliding step size according to the signal amplitude of each sampling point in the first processed data.

[0174] 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.

[0175] 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.

[0176] Optionally, in order to avoid performing multiple background removal operations on a certain sampling point, the sliding step size may be greater than or equal to the sliding window length.

[0177] For example, the horizontal background value can be selected as a background width of 10 meters to 20 meters, thereby highlighting the local features of 2 meters to 5 meters on the horizontal section.

[0178] C2: For any time length, determine the second sampling points corresponding to the time length in the horizontal direction from the first processed data.

[0179] 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 size to obtain each second sampling point in each sliding window.

[0180] In a specific implementation, if the sliding window length and sliding step size 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 horizontally across the corresponding second sampling points according to the sliding step size, thereby obtaining each second sampling point within each sliding window. If a sliding window length and sliding step size exist 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 horizontally across the corresponding second sampling points according to the sliding step size corresponding to the time length, thereby obtaining each second sampling point within each sliding window. The process of sliding the sliding window across the second sampling points can refer to the sliding process described in A3 above and will not be further described here.

[0181] C4: For any sliding window, determine a second average value of the signal amplitudes of each second sampling point in the sliding window.

[0182] 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.

[0183] 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.

[0184] In specific implementations, for any sliding window corresponding to a slide, the signal amplitude at each second sampling point within the sliding window can be subtracted from the second average value corresponding to the sliding window to obtain the new signal amplitude for each second sampling point within the sliding window. This allows the full capture of the abnormal characteristics of each sampling point, thereby revealing the discontinuities of the stratigraphic layer, namely, geological structures such as faults and joints, and thus highlighting the channels through which gas rises.

[0185] C6: Determine second processed data according to the new signal amplitude of the second sampling point in each sliding window.

[0186] Exemplarily, the new signal amplitude of the second sampling point in the sliding window corresponding to each sliding in each time length can be used as the second processing data.

[0187] In another embodiment, a strong horizontal signal exists in the two-dimensional measurement profile detected by the detection radar. This horizontal signal affects the judgment of the geological structure. Therefore, in order to improve the accuracy of the geological structure judgment, it is necessary to eliminate the horizontal signal and then obtain the overall geological structure characteristics and local characteristics. To this end, 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:

[0188] 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.

[0189] In practice, ground-penetrating radar uses two-dimensional detection, so each sampling point has a horizontal coordinate and a vertical depth. For each measurement profile of the ground-penetrating radar, each sampling point located on the measurement profile can be selected from the first processed data. Then, an initial matrix is constructed using a two-dimensional array formed from the horizontal coordinates and depths of these sampling points.

[0190] D2: Perform feature extraction on the initial matrix to obtain the matrix eigenvector.

[0191] In a specific implementation, the initial matrix can be subjected to eigenvector extraction to obtain matrix eigenvectors, wherein the matrix eigenvectors can reflect the horizontal characteristics, vertical characteristics, tilt characteristics and local detail characteristics of the geological structure.

[0192] D3: Eliminate the horizontal eigenvectors in the matrix eigenvectors to obtain the target matrix corresponding to the measurement profile.

[0193] In specific implementations, the matrix eigenvectors can be analyzed based on their parameters to extract the horizontal eigenvectors. These extracted horizontal eigenvectors can then be eliminated to obtain the target matrix corresponding to the measurement profile. The horizontal eigenvectors in the target matrix have been largely eliminated, leaving only the overall and local reflection signals. Therefore, the target matrix can effectively reduce the impact of horizontal signals on geological structure determination, resulting in more accurate geological structure determination results.

[0194] D4: Determine the second processing data according to the target matrix corresponding to each measurement section.

[0195] In specific implementation, based on the above D1 to 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.

[0196] S104-4: Perform at least one second horizontal smoothing process on the second processed data to obtain target radar data.

[0197] 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.

[0198] Illustratively, 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.

[0199] 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.

[0200] In one embodiment, the above S104-4 may also be implemented according to the following steps:

[0201] S104-4-1: Perform at least one second horizontal smoothing process on the second processed data to obtain third processed data.

[0202] 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 multiple times in succession, thereby obtaining the third processed data.

[0203] S104-4-2: For each scanning track in the vertical direction in the third processed data, use the second gain method and the depth corresponding to each sampling point in the scanning track to perform gain processing on the signal amplitude of each sampling point in the scanning track to obtain target radar data.

[0204] 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.

[0205] For example, a two-point exponential gain method can be used, with the first gain point set to 0 and the second gain point set to 24, with an exponential gain method applied between the first and second gain points. The first and second gain points can be the shallowest and deepest sampling points, respectively. For each vertical scan track in the third processed data, the signal amplitude of each sampling point within the scan track can be gain-processed using the exponential gain method and the depth corresponding to each sampling point within the scan track, thereby generating target radar data.

[0206] 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 stratum 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.

[0207] In an optional embodiment, to further enhance radar data processing, after gain processing using the second gain method, the gain results can be subjected to vertical spectral analysis and one or more different filtering techniques to obtain target radar data. The filtering methods used in these different filtering techniques can be the same or different, and can include high-pass filtering, band-pass filtering, sharpening filtering, wavelet transform filtering, and the like. In this way, through spectral analysis and frequency filtering, low-frequency interference can be eliminated, and radar electromagnetic wave signals with medium-frequency components can be obtained, more precisely reflecting the reflection signals of underground structures, especially those deep within.

[0208] 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 the parameter of high-pass filtering for further filtering processing to obtain the target radar data.

[0209] Those skilled in the art will understand that in the above-mentioned method of the specific implementation method, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0210] 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.

[0211] 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:

[0212] A determination module 201 is configured to determine a target thaw lake to be detected and an underground target detection area corresponding to the target thaw lake from among the thaw lakes in the permafrost region of the plateau;

[0213] An acquisition module 202 is configured 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 a target frequency, and the target frequency is related to the permafrost depth of the plateau permafrost area;

[0214] A first processing module 203 is configured 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;

[0215] a second processing module 204 configured to perform signal gain processing on the intermediate radar data in a vertical direction and perform multiple signal filtering processes on the processed intermediate radar data in a horizontal direction to obtain target radar data;

[0216] The identification module 205 is configured to identify vertical structures in the target detection area based on the target radar data.

[0217] In a possible implementation, the device further includes:

[0218] The determination module 206 is configured to, after identifying the vertical structure in the target detection area based on the target radar data,:

[0219] If the vertical structure included in the target detection area is located below the periphery of the target melt lake, it is determined that the formation cause of the target melt lake is: underground gas eruption.

[0220] In a possible implementation, the device further includes:

[0221] The adjustment module 207 is configured to, after identifying the vertical structure in the target detection area based on the target radar data,:

[0222] Determining a predicted diameter of a vertical structure corresponding to below the target thaw lake based on the lake surface diameter of the target thaw lake and a first conversion coefficient;

[0223] 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;

[0224] determining whether the first conversion coefficient needs to be adjusted based on the target diameter and the predicted diameter of the vertical structure in the target detection area, and

[0225] Whether the second conversion coefficient needs to be adjusted is determined according to the target depth of the vertical structure in the target detection area and the predicted depth.

[0226] In a possible implementation, 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 configured to:

[0227] determining the target thaw lake according to the shape of the thaw lake in the plateau permafrost region;

[0228] determining a measurement profile length of a ground penetrating radar according to a lake surface diameter of the target thermal melt lake and a third conversion coefficient;

[0229] A target detection area corresponding to the target thermal melt lake is determined according to the measurement section length and the preset detection shape.

[0230] In a possible implementation, 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 intermediate radar data, is configured to:

[0231] performing encrypted resampling on the initial radar data in the vertical direction 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 within a period indicated by a sampling theorem to obtain encrypted sampled data;

[0232] Determining a cutoff 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 ground penetrating radar electromagnetic waves in the air and the frozen soil, respectively, and the depth of the frozen soil;

[0233] The encrypted sampling data matching the truncation length is used as the intermediate radar data.

[0234] In a possible implementation, the second processing module 204, when 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, is configured to:

[0235] For each vertical scanning track in the intermediate radar data, performing gain processing on the signal amplitude of each sampling point in the scanning track using a first gain mode and a depth corresponding to each sampling point in the scanning track to obtain processed intermediate radar data;

[0236] performing a first horizontal smoothing process on the processed intermediate radar data to obtain first processed data;

[0237] performing horizontal background removal processing on the first processed data to obtain second processed data;

[0238] Perform at least one second horizontal smoothing process on the second processed data to obtain the target radar data.

[0239] In a possible implementation, 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:

[0240] 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.

[0241] In a possible implementation, the second processing module 204, when performing a first horizontal smoothing process on the processed intermediate radar data using a sliding window filtering method to obtain first processed data, is configured to:

[0242] Selecting sampling point values within a sliding window in a horizontal direction, provided that the local continuous features of the target detection area in the oblique direction are not disconnected and the local disconnected features are not connected;

[0243] For any time length, determining each first sampling point corresponding to the time length in the horizontal direction from the processed intermediate radar data;

[0244] A sliding window that matches the sampling point value is used to slide on each corresponding first sampling point in 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.

[0245] 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.

[0246] In a possible implementation, the second processing module 204, when performing a first horizontal smoothing process on the processed intermediate radar data using a sliding window filtering method and a frequency-space domain filtering method to obtain first processed data, is configured to:

[0247] Performing a first horizontal smoothing process on the processed intermediate radar data using a sliding window filtering method to obtain first sub-processed data;

[0248] performing a first horizontal smoothing process on the processed intermediate radar data using a frequency-space domain filtering method to obtain second sub-processed data;

[0249] When 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.

[0250] In a possible implementation, the second processing module 204, when performing horizontal background removal processing on the first processed data to obtain second processed data, is configured to:

[0251] determining a sliding window length and a sliding step size according to the signal amplitude of each sampling point in the first processed data;

[0252] For any time length, determining each second sampling point corresponding to the time length in the horizontal direction from the first processed data;

[0253] 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 size to obtain each second sampling point in each sliding window;

[0254] For any sliding window, determining a second average value of signal amplitudes of each second sampling point within the sliding window;

[0255] 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;

[0256] Second processed data is determined according to a new signal amplitude of the second sampling point in each of the sliding windows.

[0257] In a possible implementation, the second processing module 204, when performing horizontal background removal processing on the first processed data to obtain second processed data, is configured to:

[0258] 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;

[0259] Performing feature extraction on the initial matrix to obtain a matrix feature vector;

[0260] Eliminating the horizontal eigenvectors in the matrix eigenvectors to obtain a target matrix corresponding to the measurement section;

[0261] The second processed data is determined according to the target matrix corresponding to each of the measurement sections.

[0262] In a possible implementation, 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 configured to:

[0263] performing at least one second horizontal smoothing process on the second processed data to obtain third processed data;

[0264] For each scanning track in the vertical direction in the third processed data, gain processing is performed on the signal amplitude of each sampling point in the scanning track using the second gain method and the depth corresponding to each sampling point in the scanning track to obtain the target radar data.

[0265] For descriptions of the processing flow of each module in the device and the interaction flow between each module, reference can be made to the relevant descriptions in the above method embodiment, which will not be described in detail here.

[0266] 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 according to an embodiment of the present invention, comprising:

[0267] Processor 301, memory 302, and bus 303. Memory 302 stores machine-readable instructions executable by processor 301. Processor 301 is configured to execute the machine-readable instructions stored in memory 302. When the machine-readable instructions are executed by processor 301, processor 301 performs the following steps: S101: determining a target thaw lake to be detected and an underground target detection area corresponding to the target thaw lake from thaw lakes in a permafrost region of the plateau; S102: acquiring data from the target detection area using a ground-penetrating radar (GPR) to obtain initial radar data; the GPR frequency being less than or equal to the target frequency, and the target frequency being related to the permafrost depth in the permafrost region of the plateau; S103: encrypting and intercepting the initial radar data based on the target frequency and the time window length of the GPR to obtain intermediate radar data; S104: performing vertical signal gain processing on the intermediate radar data and performing multiple horizontal signal filtering processes on the processed intermediate radar data to obtain target radar data; and S105: identifying vertical structures in the target detection area based on the target radar data.

[0268] 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 the hard disk. The processor 301 exchanges data with the external memory 3022 through the memory 3021. When the computer device is running, the processor 301 and the memory 302 communicate through the bus 303, so that the processor 301 executes the execution instructions mentioned in the above method embodiment.

[0269] The present disclosure also provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program executes the steps of the method for identifying underground vertical structures described in the above method embodiment. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0270] The embodiments of the present disclosure also provide a computer program product, which carries 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 and will not be repeated here.

[0271] The computer program product may be implemented in hardware, software, or a combination thereof. In one embodiment, the computer program product is implemented as a computer storage medium. In another embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0272] Those skilled in the art can clearly understand that, for the convenience and brevity 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.

[0273] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0274] 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.

[0275] 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. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute 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 code, 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.

[0276] If the technical solution of this application involves personal information, the product that applies the technical solution of this application has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing personal information. If the technical solution of this application involves sensitive personal information, the product that applies the technical solution of this application has obtained the individual's separate consent before processing sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, a clear and prominent sign is 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 they agree to the collection of their personal information; or on the personal information processing device, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload their personal information; among which, 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.

[0277] 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 scope of protection of the present disclosure is not limited thereto. Although the present disclosure has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-mentioned embodiments within the technical scope disclosed in the present disclosure, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection 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 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 a target frequency, and the target frequency is related to the frozen soil depth in the permafrost region of the plateau; 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 a vertical direction, and performing multiple signal filtering processes on the processed intermediate radar data in a horizontal direction to obtain target radar data; identifying vertical structures in the target detection area based on the target radar data; 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 based on the 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 based on the target diameter and the predicted diameter of the vertical structure in the target detection area, and Whether the second conversion coefficient needs to be adjusted is determined according to the target depth of the vertical structure in the target detection area and the predicted depth.

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 melt lake, it is determined that the formation cause of the target melt lake is: underground gas eruption.

3. 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 permafrost region of the plateau includes: determining the target thaw lake according to the shape of the thaw lake in the plateau permafrost region; determining a measurement profile length of a ground penetrating radar according to a lake surface diameter of the target thermal melt lake and a third conversion coefficient; A target detection area corresponding to the target thermal melt lake is determined according to the measurement section length and the preset detection shape.

4. The method according to claim 1, wherein 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: performing encrypted resampling on the initial radar data in the vertical direction 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 within a period indicated by a sampling theorem to obtain encrypted sampled data; Determining a cutoff 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 ground penetrating radar electromagnetic waves 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.

5. The method according to claim 1, wherein The 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 vertical scanning track in the intermediate radar data, performing gain processing on the signal amplitude of each sampling point in the scanning track using a first gain mode and a depth corresponding to 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; Perform at least one second horizontal smoothing process on the second processed data to obtain the target radar data.

6. The method according to claim 5, 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.

7. The method according to claim 6, characterized in that Performing a first horizontal smoothing process on the processed intermediate radar data using a sliding window filtering method to obtain first processed data, including: Selecting sampling point values within a sliding window in a horizontal direction, provided that the local continuous features of the target detection area in the oblique 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 that matches the sampling point value is used to slide on each corresponding first sampling point in 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.

8. The method according to claim 7, 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.

9. The method according to claim 6, characterized in that Performing a first horizontal smoothing process on the processed intermediate radar data using a sliding window filtering method and a frequency-space domain filtering method to obtain first processed data, including: Performing a first horizontal smoothing process on the processed intermediate radar data using a sliding window filtering method to obtain first sub-processed data; performing a first horizontal smoothing process on the processed intermediate radar data using a frequency-space domain filtering method to obtain second sub-processed data; When 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.

10. The method according to claim 5, characterized in that The step of performing horizontal background removal processing on the first processed data to obtain second processed data includes: determining a sliding window length and a sliding step size according to a 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 size to obtain each second sampling point in each sliding window; For any sliding window, determining a second average value of signal amplitudes of each second sampling point within 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; Second processing data is determined according to the new signal amplitude of the second sampling point in each of the sliding windows.

11. The method according to claim 5, 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.

12. The method according to claim 5, characterized in that The 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, gain processing is performed on the signal amplitude of each sampling point in the scanning track using the second gain method and the depth corresponding to each sampling point in the scanning track to obtain the target radar data.

13. An underground vertical structure identification device, characterized in that: include: a determination module, configured to determine a target thaw lake to be detected and an underground target detection area corresponding to the target thaw lake from among the thaw lakes in the permafrost region of the plateau; An acquisition module 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 a target frequency, and the target frequency is related to the frozen soil depth in the permafrost region of the plateau; a first processing module, configured 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, configured to perform signal gain processing on the intermediate radar data in a vertical direction, and perform multiple signal filtering processes on the processed intermediate radar data in a horizontal direction to obtain target radar data; an identification module, configured to identify vertical structures in the target detection area based on the target radar data; The adjustment module is configured to, after identifying the vertical structure in the target detection area based on the target radar data, determine the predicted diameter of the vertical structure corresponding to below the target thaw lake based on the lake surface diameter of the target thaw lake and the first conversion coefficient; determine the predicted depth of the vertical structure corresponding to below the target thaw lake based on the permafrost depth, the lake surface diameter, and the second conversion coefficient; determine whether the first conversion coefficient needs to be adjusted based on the target diameter and the predicted diameter of the vertical structure in the target detection area; and determine whether the second conversion coefficient needs to be adjusted based on the target depth and the predicted depth of the vertical structure in the target detection area.

14. 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 configured to execute the machine-readable instructions stored in the memory. When the machine-readable instructions are executed by the processor, the processor performs the steps of the underground vertical structure identification method according to any one of claims 1 to 12.

15. 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 12.

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