Collapse column identification method and device and storage medium

Abstract

The invention relates to the technical field of coal mining, and discloses a collapse column identification method and device and a storage medium, and the method comprises the following steps: carrying out the time-frequency analysis of seismic data, and converting the seismic data from a time domain to a frequency domain; performing frequency division on the seismic data converted into the frequency domain to obtain a plurality of single-frequency data volumes with fixed frequencies; performing characteristic value coherent processing on each single-frequency data body to obtain a coherent attribute body of each frequency; and extracting attribute values of the coherence attribute body of each frequency along the coal seam to obtain a coal seam coherence attribute graph of each frequency. According to the method, the mutual interference of seismic responses of different frequencies can be eliminated, the abnormal response of the collapse column hidden in the time domain can be highlighted in the frequency domain, and the depiction of the edge of the abnormal response is enhanced, so that the interpretation precision of the collapse column is improved, and the collapse column with small size or close distance can be interpreted and identified.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mining, and particularly relates to a method, device and storage medium for identifying collapse columns. Background Art

[0002] Collapse columns are common disaster-causing factors in coal mining, and three-dimensional seismic exploration is the most commonly used means for exploring collapse columns. Traditional three-dimensional seismic exploration is often based on time-domain data volumes, with low interpretation accuracy. The boundaries of the collapse columns in the interpretation results are often larger than the actual exposed ones, making it difficult to identify the boundaries of collapse columns and small-sized (major axis diameter less than 50m) collapse columns, and also unable to distinguish collapse columns that are relatively close. Moreover, seismic responses of different frequencies interfere with each other, resulting in a low coincidence degree between the identified positions of collapse columns and the actual exposed situations, and the interpretation accuracy of collapse columns is not high. Therefore, a new method for interpreting and identifying collapse columns needs to be designed to improve the interpretation accuracy of collapse columns. Summary of the Invention

[0003] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a method for identifying collapse columns, which can improve the interpretation accuracy of collapse columns.

[0004] To solve the above problems, the technical solution adopted by the present invention is as follows: A method for identifying collapse columns includes the following steps:

[0005] Perform time-frequency analysis on seismic data to transform the seismic data from the time domain to the frequency domain;

[0006] Perform frequency division on the seismic data transformed into the frequency domain to obtain multiple single-frequency data volumes with fixed frequencies;

[0007] Perform eigenvalue coherence processing on each single-frequency data volume to obtain coherence attribute volumes for each frequency;

[0008] Extract attribute values along the coal seam from the coherence attribute volumes for each frequency to obtain coal seam coherence attribute maps for each frequency.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: This method for identifying collapse columns can eliminate the mutual interference of seismic responses of different frequencies by transforming the seismic data from the time domain to the frequency domain and performing frequency division processing, so that the abnormal responses of collapse columns hidden in the time domain can be highlighted in the frequency domain. At the same time, this method for identifying collapse columns also uses the coherence enhancement technology to depict the edges of abnormal responses, improve the interpretation accuracy of collapse columns, and thus can interpret and identify small-sized or relatively close collapse columns.

[0010] After the step of performing frequency division on the seismic data transformed into the frequency domain to obtain multiple single-frequency data volumes with fixed frequencies in the above-mentioned method for identifying collapse columns, the following steps are further included:

[0011] Perform multi-window dip scanning on each single-frequency data volume to obtain the dip data volume of the formation;

[0012] Under the constraint of the dip data volume, perform structure-oriented filtering on each single-frequency data volume.

[0013] In the above-mentioned collapse column identification method, in the step of performing time-frequency analysis on seismic data and converting the seismic data from the time domain to the frequency domain, the seismic data is converted from the time domain to the frequency domain through the generalized S transform.

[0014] In the above-mentioned collapse column identification method, the window function of the generalized S transform uses a Gaussian window, and the function of the Gaussian window is as follows:

[0015]

[0016] where λ is the adjustment factor, P is the attenuation factor, and f is the frequency function of the seismic data.

[0017] In the above-mentioned collapse column identification method, in the step of performing eigenvalue coherence processing on each single-frequency data volume to obtain the coherence attribute volume of each frequency, the range of the reciprocal of the adjacent eigenvalues in the eigenvalue coherence processing is between 3 and 9 traces, and the length of the sliding time window is 1.5 - 2 times the apparent period of the seismic wave.

[0018] A computer-readable storage medium stores a computer program, and when the computer program is called and executed by a processor, the above-mentioned collapse column identification method is implemented.

[0019] A collapse column identification device includes a processor and a memory, the processor is electrically connected to the memory, and the processor can implement the above-mentioned collapse column identification method by calling and executing the computer program in the memory.

[0020] A collapse column identification system includes: a time-frequency analysis unit for converting seismic data from the time domain to the frequency domain; a frequency division unit for performing frequency division on the seismic data converted to the frequency domain to obtain multiple single-frequency data volumes of fixed frequencies; an eigenvalue coherence processing unit for performing eigenvalue coherence processing on each single-frequency data volume to obtain the coherence attribute volume of each frequency; a collapse column analysis unit for extracting attribute values from the coherence attribute volume of each frequency to obtain the coal seam coherence attribute map of each frequency.

[0021] The above-mentioned collapse column identification system further includes: a multi-window dip scanning unit for performing multi-window dip scanning on each single-frequency data volume to obtain the dip data volume of the formation; a structure-oriented filtering unit for performing structure-oriented filtering on each single-frequency data volume under the constraint of the dip data volume.

[0022] The above-mentioned collapse column identification system, the time-frequency analysis unit converts seismic data from the time domain to the frequency domain through the generalized S transform.

[0023] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flowchart of the collapse column identification method according to the first embodiment of the present invention;

[0025] Figure 2 It is a flowchart of the collapse column identification method according to the second embodiment of the present invention;

[0026] Figure 3 It is a principle block diagram of the collapse column identification system according to the embodiment of the present invention;

[0027] Figure 4 It is a schematic diagram of the search process of multi-window dip scanning;

[0028] Figure 5 It is a schematic diagram of the multi-window dip scanning process according to the embodiment of the present invention;

[0029] Figure 6 It is a schematic diagram of the time-frequency response during time-frequency analysis according to the embodiment of the present invention;

[0030] Figure 7 It is a spectrum analysis diagram of the target layer according to the embodiment of the present invention;

[0031] Figure 8 It is the Inline Dip data volume obtained after multi-window dip scanning according to the embodiment of the present invention;

[0032] Figure 9 It is the Crossline Dip data volume obtained after multi-window dip scanning according to the embodiment of the present invention;

[0033] Figure 10 It is the original single-frequency data volume of 40 Hz according to the embodiment of the present invention;

[0034] Figure 11 It is the single-frequency data volume of 40 Hz after structural-oriented filtering according to the embodiment of the present invention;

[0035] Figure 12 It is the in-seam coherence attribute map in the time domain of a certain mining area in Shanxi;

[0036] Figure 13 It is the in-seam coherence attribute map of 20 Hz obtained by using the method according to the embodiment of the present invention in a certain mining area in Shanxi;

[0037] Figure 14It is a 30Hz in-seam coherence attribute map obtained by using the method of the embodiment of the present invention in a certain mining area in Shanxi;

[0038] Figure 15 It is a 40Hz in-seam coherence attribute map obtained by using the method of the embodiment of the present invention in a certain mining area in Shanxi;

[0039] Figure 16 It is a 50Hz in-seam coherence attribute map obtained by using the method of the embodiment of the present invention in a certain mining area in Shanxi;

[0040] Figure 17 It is a 60Hz in-seam coherence attribute map obtained by using the method of the embodiment of the present invention in a certain mining area in Shanxi;

[0041] Figure 18 It is a comparison map of the superposition display of the in-seam coherence attribute map in the time domain and the actually revealed collapse column in a certain mining area in Shanxi;

[0042] Figure 19 It is a comparison map of the superposition display of the 50Hz in-seam coherence attribute map and the actually revealed collapse column in a certain mining area in Shanxi. Detailed implementation manners

[0043] The following details the embodiments of the present invention. Referring to Figure 1 , the embodiments of the present invention provide a method for identifying collapse columns, including the following steps:

[0044] Perform time-frequency analysis on seismic data to transform the seismic data from the time domain to the frequency domain;

[0045] Perform frequency division on the seismic data transformed into the frequency domain to obtain multiple single-frequency data volumes with fixed frequencies;

[0046] Perform eigenvalue coherence processing on each single-frequency data volume to obtain coherence attribute volumes for each frequency;

[0047] Extract attribute values along the coal seam for the coherence attribute volumes of each frequency to obtain in-seam coherence attribute maps for each frequency. This method for identifying collapse columns, by transforming seismic data from the time domain to the frequency domain and then through frequency division processing, eliminates the mutual interference of seismic responses at different frequencies, making the abnormal responses of collapse columns submerged in the time domain prominent in the frequency domain. At the same time, this method also performs eigenvalue coherence attribute processing on each single-frequency data volume after frequency division, using the coherence enhancement technology to depict the edges of abnormal responses, further improving the accuracy of collapse column interpretation. This method for identifying collapse columns has a higher accuracy of collapse column interpretation compared to traditional identification methods in the time domain, can interpret and identify small-sized or closely spaced collapse columns, and shortens the interpretation cycle.

[0048] It is understandable that seismic data can be transformed from the time domain to the frequency domain through time-frequency analysis methods such as the Short-Time Fourier Transform (STFT), Wavelet Transform (WT), Generalized S-Transform (GST), and Matching Pursuit. In this embodiment, the Generalized S-Transform is used to transform seismic data from the time domain to the frequency domain. The Generalized S-Transform inherits the advantages of the Short-Time Fourier Transform and the Wavelet Transform, has stronger self-adaptability, can be adjusted through parameters, achieves a higher time-frequency resolution, and has better global optimality than Matching Pursuit, with higher computational efficiency. In this embodiment, the window function uses a Gaussian window. To have frequency self-adaptability and improve flexibility, the Gaussian window function is defined as shown in the following formula:

[0049]

[0050] In the formula, λ is the adjustment factor, P is the attenuation factor, and f is the frequency function of the original signal. At this time, the expression of the Generalized S-Transform is as shown in the following formula:

[0051]

[0052] In the formula, X(t) is the original signal, and w(t) is the window function. The Generalized S-Transform not only retains the time-varying characteristics of the original data but also has frequency self-adaptability. The size of the window function can be controlled by adjusting λ and P, thereby controlling the time-frequency resolution. In some embodiments, in order to obtain the optimal adjustment factor and attenuation factor, swarm intelligence algorithms such as genetic algorithms can be used to dynamically obtain the optimal adjustment factor and attenuation factor. The product of the frequency resolution and the time resolution is used as the objective function to calculate the fitness of each individual in the population. After a finite number of iterations, the optimal individual is obtained to control the size of the window function to ensure the optimal time-frequency resolution and further improve the accuracy of the interpretation of the karst collapse column.

[0053] In this embodiment, when λ = 1.3 and P = 1.1, it can ensure that both the time resolution (longitudinal) and the frequency resolution (lateral) of the frequency-domain data volume are relatively high. As Figure 6 shown, taking seismic data as the original data, transforming it from the time domain to the frequency domain, a four-dimensional data volume composed of lines, traces, time, and frequency is obtained. This data volume is a full-frequency band data volume. After frequency division processing, several single-frequency data volumes with fixed frequencies are obtained. In practical applications, the dominant frequency band of the frequency-domain data can be determined through spectral analysis, and only the single-frequency data volume of the dominant frequency band needs to be subjected to eigenvalue coherence processing to save calculation time and improve work efficiency. Although theoretically, data volumes with different frequencies can reflect karst collapse columns of different scales, when it is in the non-dominant frequency band, the signal-to-noise ratio is low and the multi-solution problem is serious, which is not conducive to the fine interpretation of the karst collapse column. Figure 4 This is the spectral analysis diagram of the target layer in this embodiment. It can be seen from the figure that the dominant frequency band is approximately 20Hz - 45Hz.

[0054] In some embodiments, in order to enhance the continuity and discontinuity characteristics of seismic data, better preserve the effective frequency components and vertical resolution of the original seismic data volume, and further improve the recognition accuracy of small-scale geological bodies, referring to Figure 2 as shown, before the eigenvalue coherence attribute processing, it is necessary to filter each single-frequency data volume along the tectonic direction of the formation. Specifically, it is necessary to first perform multi-window dip scanning on the single-frequency data volume to obtain the dips of the formation of each single-frequency data volume in the Inline direction (the direction parallel to the seismic acquisition line) and the Crossline direction (the direction perpendicular to the seismic acquisition line), and obtain two dip data volumes, Inline Dip and Crossline Dip, such as Figure 6 and Figure 7 shown, and then input these two dip data volumes into the structure-oriented filtering model to obtain a single-frequency data volume with enhanced seismic event coherence, thereby making the structural edges more prominent.

[0055] In this embodiment, referring to Figure 4 , the multi-window dip scanning adopts the multi-window analysis method proposed by Kuwahara et al. Nine identical windows are determined around the analysis point, the coherence value is calculated inside the window, and the window with the largest coherence value is selected as the optimal coherence window. The instantaneous dip of the largest coherence value is the formation dip. The main parameters of the multi-window dip scanning are: time window length, maximum scanning dip, reference velocity, etc. The specific values of these parameters can be obtained through experiments. In this embodiment, the time window length is 10 ms, and the geological features are depicted most clearly. Since the formation in the area is relatively flat, the maximum scanning dip can be 30°, and the reference velocity is 3000 m / s. Referring to Figure 5 shown, in this embodiment, when the dip is +5°, the coherence value is the largest, so the formation dip is +5°.

[0056] The structure-oriented filtering technology is a tensor-based anisotropic diffusion filtering technology, and its model expression is shown as follows:

[0057]

[0058] where u is the seismic data, t is the diffusion time, D is the diffusion tensor, ε is the continuity factor, and the value range of ε is [0,1]. When ε = 0, it means that the formation is discontinuous and close to the lithology boundary; when ε = 1, it means that the formation is continuous. Taking the 40 Hz single-frequency data volume as an example, Figure 9 the profile of the single-frequency data volume after structure-oriented filtering is as shown in Figure 10 shown.

[0059] In this embodiment, the third-generation eigenvalue coherence is used to process the single-frequency data volume after structure-oriented filtering, and the coherence of any seismic trace u is calculated t0The coherence value at a certain point is estimated using multiple adjacent seismic traces. First, dip scanning is performed, and the covariance matrix is constructed using the seismic data within the coherence window after scanning:

[0060]

[0061] The eigenvalues γ of the covariance matrix are calculated to estimate the coherence value, and the coherence value is equal to the ratio of the maximum eigenvalue to the sum of all eigenvalues, as shown in the following formula:

[0062]

[0063] where M is the number of adjacent traces, and T is the sliding time window. Generally, the more adjacent traces involved in the coherence calculation, the greater the averaging effect, and the lower the resolution of the collapse columns. At this time, the prominent ones are mainly the larger collapse columns. On the contrary, when the number of adjacent traces is small, the averaging effect is small, and the lateral resolution will be improved. Therefore, when performing seismic coherence processing, the number of adjacent traces involved in the calculation should be selected according to different geological research purposes. In addition, the main parameters of eigenvalue coherence processing also include the maximum dip angle, etc. In practice, the number of adjacent traces generally uses 3, 5, or 9. In this embodiment, 5 traces are used. The appropriate sliding time window should be 1.5 - 2 times the apparent period of the seismic wave. In this embodiment, a 50ms sliding time window is used. The maximum dip angle is the apparent dip angle, which is the time difference between two adjacent traces towards the axis. In this embodiment, it is determined that 3 in this area can meet the requirements. After obtaining the coherence attribute volume, synthetic seismic records are made using the acoustic wave, density, and other logging curves of the boreholes to determine the target horizons, layer calibration is performed for each main target layer, then the reflection wave horizons of the coal seams are carefully traced, and then layer interpolation is carried out to finally obtain the coal seam horizon plan of the whole area. Using the coal seam horizon plan, the instantaneous attributes of the coherence attribute volume are extracted along the coal seam, and the coherence attribute map along the coal seam of each single-frequency data volume can be obtained.

[0064] Refer to Figures 12 to 17 , which are the coherence attribute maps along the coal seam in the time domain, 20Hz, 30Hz, 40Hz, 50Hz, and 60Hz respectively for a certain mining area in Shanxi, located on the northeast edge of the Qinshui Basin, the west side of the middle section of the third uplift zone of the Neocathaysian system, and east of the east wing of the front arc of the Qilüheshan epsilon-shaped structure. The collapse columns in this mining area are very developed, distributed in sheets and strips, and have circular or elliptical closure characteristics on the plane. It can be seen from the figure that compared with the time domain, the coherence attributes along the coal seam in the frequency domain have obvious advantages. The collapse columns with close distances that cannot be distinguished in the time domain can be clearly distinguished in the frequency domain, and the edges of the collapse columns that are not obvious in the time domain can be clearly outlined in the frequency domain, as shown in the boxed part of the figure. After coherence enhancement processing, the coherence attributes along the coal seam at frequencies of 30Hz, 40Hz, and 50Hz have similar responses to the collapse columns, with high resolution and signal-to-noise ratio, and the edges of the collapse columns are clear. It is slightly worse at 20Hz, and there is obvious noise at 60Hz, resulting in false structures. Figure 18It is a schematic diagram of the mined-out working face and the driven roadway, and the circled part is the actually exposed collapse column. Refer to Figure 19 , by superimposing and displaying the in-seam coherence attribute map in the 50 Hz frequency domain with Figure 18 , it can be seen that compared with the time domain, the actually exposed collapse columns are clearly reflected in the frequency domain coherence attribute, with clear edges and higher coincidence with the actual exposure.

[0065] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which can implement the above-mentioned collapse column identification method when executed by a processor.

[0066] In some possible implementation manners, each aspect of the collapse column identification method provided by the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a device, the program code is used to cause the control device to execute the steps in the collapse column identification method according to various exemplary implementation manners of the present application described above in this specification.

[0067] Based on the same inventive concept, an embodiment of the present invention further provides an identification device for implementing the above-mentioned collapse column identification method, including a processor and a memory. The memory is electrically connected to the processor. When the processor executes the computer program stored on the memory, the above-mentioned collapse column identification method is implemented.

[0068] In a possible design, the processor may include one or more processing units. The processor and the memory can be implemented on the same chip or separately on independent chips. The processor can be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the collapse column identification method disclosed in combination with the embodiments of the present application can be directly implemented by the execution of the hardware processor or by the combination of the hardware and software modules in the processor.

[0069] As a non-volatile computer-readable storage medium, the memory can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The memory may include at least one type of storage medium, for example, it may include flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memory, magnetic disk, optical disc, and so on. The memory is any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in the embodiments of the present application may also be a circuit or any other device capable of implementing a storage function, for storing program instructions and / or data.

[0070] By designing and programming the processor, the code corresponding to the collapse column identification method introduced in the foregoing embodiments can be solidified into the chip, so that the chip can execute the steps of the collapse column identification method of the embodiments shown in the present invention when running. How to design and program the processor is a well-known technology to those skilled in the art and will not be elaborated here.

[0071] Based on the same inventive concept, the embodiments of the present invention also provide a collapse column identification system for implementing the above-mentioned collapse column identification method. Referring to Figure 3 as shown, it includes a time-frequency analysis unit, a frequency division unit, a multi-window dip scanning unit, a structure-oriented filtering unit, an eigenvalue coherence processing unit, and a collapse column analysis unit. Among them, the time-frequency analysis unit is used to transform seismic data from the time domain to the frequency domain, and the frequency division unit is used to perform frequency division on the seismic data transformed to the frequency domain to obtain single-frequency data volumes of multiple fixed frequencies. The multi-window dip scanning unit is used to perform multi-window dip scanning on each single-frequency data volume to obtain the dip data of the formation. The structure-oriented filtering unit is used to perform structure-oriented filtering on each single-frequency data volume under the constraint of the dip data volume. The eigenvalue coherence processing unit is used to perform eigenvalue coherence processing on each single-frequency data volume that has undergone structure-oriented filtering to obtain the coherence attribute volume of each frequency. The collapse column analysis unit is used to extract attribute values from the coherence attribute volumes of each frequency to obtain the coal seam coherence attribute maps of each frequency, and finally, the accurate interpretation of the collapse column is realized through the coal seam coherence attribute maps.

[0072] In some embodiments, the time-frequency analysis unit converts seismic data from the time domain to the frequency domain through the generalized S transform. The window function uses a Gaussian window, and the function of the Gaussian window is as shown in the above formula (1). It has high self-adaptability to frequency and can improve the flexibility of conversion. The eigenvalue coherence processing unit performs eigenvalue coherence processing on the single-frequency data volume after structure-oriented filtering using the third-generation eigenvalue coherence.

[0073] In some embodiments, the time-frequency analysis unit further includes a time window optimization unit. The time window optimization unit uses swarm intelligence algorithms such as genetic algorithms or ant colony algorithms to find the optimal solutions of the adjustment factor and attenuation factor of the Gaussian window function with the goal of maximizing the product of time resolution and frequency resolution, so as to obtain the optimal time-frequency resolution and further improve the accuracy of goaf interpretation.

[0074] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of this application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0075] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0077] In the description of the present invention, unless otherwise clearly defined, terms such as "set", "installed", "connected", etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific content of the technical solution.

[0078] The above-mentioned embodiments are only the preferred embodiments of the present invention, and the scope of protection of the present invention cannot be limited thereby. Any non-substantial changes and substitutions made by those skilled in the art based on the present invention fall within the scope of protection required by the present invention.

Claims

1. A method for identifying a collapse column, characterized in that, It includes the following steps: Perform time-frequency analysis on seismic data to transform the seismic data from the time domain to the frequency domain; Perform frequency division on the seismic data transformed into the frequency domain to obtain multiple single-frequency data volumes with fixed frequencies; Perform eigenvalue coherence processing on each single-frequency data volume to obtain the coherence attribute volume for each frequency; Extract attribute values from the coherence attribute volume for each frequency along the coal seam to obtain the coal seam coherence attribute map for each frequency.

2. The method for identifying a collapse column according to claim 1, wherein After the step of performing frequency division on the seismic data transformed into the frequency domain to obtain multiple single-frequency data volumes with fixed frequencies, it further includes the step of: Perform multi-window dip scanning on each single-frequency data volume to obtain the dip data volume of the formation; Perform structure-oriented filtering on each single-frequency data volume under the constraint of the dip data volume.

3. The method for identifying a collapse column according to claim 1, characterized in that, In the step of performing time-frequency analysis on the seismic data to transform the seismic data from the time domain to the frequency domain, the seismic data is transformed from the time domain to the frequency domain through the generalized S transform.

4. The method for identifying a collapse column according to claim 3, wherein The window function of the generalized S transform adopts a Gaussian window, and the function of the Gaussian window is as follows: where λ is the adjustment factor, P is the attenuation factor, and f is the frequency function of the seismic data.

5. The method for identifying a subsidence column according to claim 1, characterized in that In the step of performing eigenvalue coherence processing on each single-frequency data volume to obtain the coherence attribute volume for each frequency, the range of the reciprocal of the adjacent eigenvalues is between 3 and 9 traces, and the length of the sliding time window is 1.5 - 2 times the apparent period of the seismic wave.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is called and executed by the processor, it implements the collapse column identification method according to any one of claims 1 to 5.

7. A device for identifying a subsidence column, characterized in that, It includes a processor and a memory, the processor is electrically connected to the memory, and the processor can implement the collapse column identification method according to any one of claims 1 to 5 by calling and executing the computer program in the memory.

8. A subsidence column identification system, characterized in that, It includes: A time-frequency analysis unit for transforming seismic data from the time domain to the frequency domain; A frequency division unit for performing frequency division on the seismic data transformed into the frequency domain to obtain multiple single-frequency data volumes with fixed frequencies; An eigenvalue coherence processing unit for performing eigenvalue coherence processing on each single-frequency data volume to obtain the coherence attribute volume for each frequency; A collapse column analysis unit for extracting attribute values from the coherence attribute volume for each frequency to obtain the coal seam coherence attribute map for each frequency.

9. The subsidence column identification system according to claim 8, wherein It further includes: A multi-window dip scanning unit for performing multi-window dip scanning on each single-frequency data volume to obtain the dip data volume of the formation; A structure-oriented filtering unit for performing structure-oriented filtering on each single-frequency data volume under the constraint of the dip data volume.

10. The subsidence column identification system according to claim 8, characterized in that, The time-frequency analysis unit transforms the seismic data from the time domain to the frequency domain through the generalized S transform.

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