Shallow surface goaf detection collection and processing method and system based on multi-well combination

Abstract

The present application relates to the field of geophysical exploration technology, and discloses a shallow surface goaf detection, collection and processing method and system based on multi-well combination; the method comprises the following steps: multiplexing at least two existing geological drill holes, connecting the drill holes in series by using a single optical fiber to form a three-dimensional combined observation network based on multi-well combination; arranging measuring lines and shooting points around the three-dimensional combined observation network, exciting seismic waves at each shooting point by using a mobile frequency-sweeping controllable seismic source, collecting strain rate data in the geological drill holes through the optical fiber in the three-dimensional combined observation network, and synchronously recording the source wavelet signals; pre-processing the collected strain rate data, constructing a three-dimensional velocity model, generating a three-dimensional imaging body, and identifying and delineating the goaf according to the goaf features in the three-dimensional imaging body. The present application can identify hidden goafs in a mining area, provide accurate geological basis for goaf filling and roadway planning, and reduce the risk of mine disasters.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, specifically to a method and system for detecting, acquiring, and processing shallow surface goaf areas based on multi-well collaboration. Background Technology

[0002] During coal mining, unregulated and illegal mining leaves behind numerous small-scale, concealed goaf areas underground, seriously threatening safe production in the mining area. Currently, the detection of shallow surface goaf areas mainly relies on two types of technologies: traditional geophysical methods and geological drilling. However, both have significant drawbacks:

[0003] Traditional geophysical exploration methods include direct current methods and transient electromagnetic methods (TEM) with limited detection depth, susceptibility to electromagnetic interference, and resolution insufficient for detailed characterization of small-scale goaf areas; ground penetrating radar (GPR) is susceptible to near-surface aquifers and topography, and is only suitable for ultra-shallow, dry scenarios without low-resistivity cover; synthetic aperture radar interferometry (InSAR) is susceptible to noise and vegetation interference, resulting in incoherence, and its effectiveness in acquiring long-term, large-scale deformation information is poor; ground seismic exploration (including 3D seismic exploration) is costly, and relies on scattered wave imaging technology for small targets, limiting its application in coal mine goaf detection; while micro-motion exploration has been successfully applied in the detection of some collapse areas and collapse columns, its positioning accuracy for small-scale goaf areas is insufficient.

[0004] Geological borehole exploration requires dense deployment of boreholes, which is not only costly and slow, but also inherently suffers from the incomplete coverage of goaf areas between boreholes, making it impossible to achieve large-scale continuous coverage. While existing vertical seismic profiling (VSP) technology can reduce ground noise interference and acquire more complete wavefield information, traditional single-hole VSP imaging has a conical imaging range, narrow coverage, and significant artifacts in deep and distant areas, making it difficult to completely characterize large-scale goaf areas. Furthermore, the instruments used are expensive and complex, making them difficult to deploy in ordinary geological boreholes. Distributed fiber optic acoustic sensing (DAS) technology has been applied in earthquake monitoring and marine exploration, but has not yet been used for coal mine goaf detection. In addition, traditional seismic sources (explosive sources have high safety risks and cumbersome approval processes; large seismic source vehicles have poor adaptability) cannot meet the needs of efficient three-dimensional detection of shallow surface goaf areas in large open-pit coal mines.

[0005] In summary, existing technologies suffer from problems such as low detection resolution, limited coverage, high cost, low efficiency, and easy omission of mined-out areas between boreholes. There is an urgent need for a method for detecting, collecting, and processing shallow surface mined-out areas that combines high resolution, wide coverage, low cost, and high efficiency. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method and system for the detection, acquisition, and processing of shallow surface goaf areas based on multi-well joint three-dimensional distributed acoustic sensing vertical seismic profiling. This method involves deploying a multi-well series-connected distributed optical fiber in existing geological boreholes and using a mobile frequency-sweeping seismic source to excite high-frequency seismic waves on the ground for high-resolution imaging and detection of goaf areas. This invention integrates multi-well joint observation, three-dimensional distributed optical fiber acoustic sensing-vertical seismic profiling (DAS-VSP) technology, and mobile frequency-sweeping controllable seismic source technology. It is suitable for detecting small-scale, concealed goaf areas formed after illegal mining of coal and other minerals, and can also be extended to high-resolution detection scenarios for various near-surface anomalies.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a method for detecting, acquiring, and processing shallow surface goaf areas based on multi-well collaboration, including:

[0009] Reuse at least two existing geological boreholes and connect them in series using a single optical fiber to form a multi-well joint three-dimensional observation network;

[0010] With a three-dimensional joint observation network as the center, survey lines and excitation points are set up. A mobile frequency-sweeping controllable source is used to excite seismic waves at each excitation point. Strain rate data in geological boreholes are collected through optical fibers in the three-dimensional joint observation network. This observation system that uses the detectors in the boreholes at different depths can be called a vertical seismic profile (VSP). The source wavelet signal is recorded synchronously.

[0011] The acquired strain rate data is preprocessed, including wavelet compression of the source wavelet signal based on the strain rate data and conversion of the strain rate data into velocity data; wave field separation is performed on the wavelet compression results to obtain the up-going reflected wave and the down-going direct wave, and a three-dimensional velocity model is constructed based on the first arrival time of the down-going direct wave obtained by first arrival picking; three-dimensional pre-stack depth migration is performed on the up-going reflected wave based on the three-dimensional velocity model to generate a three-dimensional imaging volume, and the goaf is identified and delineated based on the goaf features in the three-dimensional imaging volume.

[0012] In one embodiment, the method of connecting geological boreholes in series using a single optical fiber to form a multi-well joint three-dimensional observation network specifically includes:

[0013] The downhole optical fiber is lowered to the bottom of each geological borehole in a U-shaped structure to couple the optical fiber with the formation, and the ground optical fiber connecting the downhole optical fiber of each geological borehole is laid by excavating trenches on the ground.

[0014] In one embodiment, the method of deploying survey lines and excitation points centered on a three-dimensional joint observation network, using a mobile frequency-sweeping controllable seismic source to excite seismic waves at each excitation point, and acquiring strain rate data within geological boreholes via optical fibers in the three-dimensional joint observation network while simultaneously recording the source wavelet signal, specifically includes:

[0015] Centered on a three-dimensional joint observation network, parallel survey lines were laid out, with excitation points evenly set on each survey line; a mobile frequency-sweeping controllable source was repeatedly excited at each excitation point, and the strain rate data collected after multiple excitations were superimposed as the final strain rate data for that excitation point; nodal seismographs were used to record the source wavelet signal.

[0016] In one embodiment, the mobile frequency-sweeping controllable source is a controllable source capable of generating continuous broadband repetitive frequency-sweeping signals.

[0017] In one embodiment, the wavelet compression of the source wavelet signal based on strain rate data specifically includes:

[0018] The source wavelet signal is compressed into a Green's function of the subsurface medium response, approximating that of a frequency-sweepable controllable source, using the characteristic deconvolution method. First, the characteristic deconvolution method is used to eliminate wavelet sidelobes. The deconvolution formula is as follows:

[0019] ;

[0020] in, For the Fourier transform of strain rate data, The Fourier transform of the source wavelet signal. for The complex conjugate, To avoid constants with zero denominators; for the deconvolution results Perform an inverse Fourier transform to obtain the Green's function of the underground medium response. As a result of wavelet compression.

[0021] In one embodiment, the conversion of strain rate data into velocity data specifically includes:

[0022] Based on the plane wavefront assumption, the frequency-wavenumber domain is transformed according to the following relationship:

[0023] ;

[0024] In the formula, and These are velocity data and strain rate data in the frequency-wavenumber domain, respectively. and These are angular frequency and wave number, respectively.

[0025] In one embodiment, the construction of a three-dimensional velocity model based on the first arrival time of the downlink direct wave obtained by first arrival picking of the downlink direct wave specifically includes:

[0026] Using the first arrival time of the downlink direct wave obtained from the first arrival picking, a one-dimensional velocity model of each geological borehole is constructed, and a three-dimensional velocity model is generated by smooth interpolation.

[0027] In one embodiment, the step of constructing a one-dimensional velocity model for each geological borehole using the first arrival time of the downlink direct wave obtained by first arrival picking, and generating a three-dimensional velocity model through smooth interpolation, specifically includes: organizing the strain rate data synchronously collected by the three-dimensional joint observation network into shot records corresponding to each excitation point; for each shot record, identifying and picking the first arrival time of the corresponding downlink direct wave, establishing a one-to-one correspondence between the first arrival time and the geological borehole depth corresponding to the shot record, and using the obtained multiple first arrival time-geological borehole depth data pairs, a three-dimensional velocity model for subsequent imaging can be constructed.

[0028] In one embodiment, the step of performing three-dimensional pre-stack depth migration on the up-reflected wave based on a three-dimensional velocity model to generate a three-dimensional imaging volume, and identifying and delineating the goaf area based on the goaf area features in the three-dimensional imaging volume, specifically includes:

[0029] Based on the three-dimensional velocity model, the upward reflected wave is repositioned to its actual underground location to generate a three-dimensional image. The characteristics of the goaf are identified through seismic interpretation: the frequency of the upward reflected wave is reduced, the phase axis is disordered and closed. Combined with the known borehole data of the mining area, the three-dimensional boundary, burial depth and orientation of the goaf are delineated.

[0030] In a second aspect, the present invention provides a computer system including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method of any embodiment of the first aspect.

[0031] Compared with the prior art, the beneficial technical effects of the present invention are:

[0032] With a wide detection coverage and fewer blind spots, it breaks through the limitations of traditional single-hole vertical seismic profile cone imaging. By connecting multiple wells with a single optical fiber to construct a three-dimensional observation network, it achieves the superposition and connection of cone imaging ranges from multiple boreholes, significantly reducing the omission of goaf areas between boreholes.

[0033] With high imaging resolution and precise positioning, it integrates the high-density observation advantages of distributed fiber optic acoustic sensing technology with the anti-ground noise characteristics of vertical seismic profiling technology, and combines the broadband excitation of a mobile frequency sweep source to clearly depict the three-dimensional morphology of small-scale goaf areas.

[0034] High acquisition efficiency and low cost: The mobile sweep frequency source is lightweight and flexible to move; it can reuse existing geological boreholes and further optimize borehole network drilling. Based on the current borehole network density in the mine, the multi-hole combined three-dimensional distributed fiber optic acoustic wave sensing-vertical seismic profile detection method is expected to reduce the number of boreholes per square kilometer from more than 1,000 to less than 400, and reduce the overall project cost by more than 60%.

[0035] With strong adaptability and good scenario expansion, it is not only suitable for detecting shallow surface goaf areas in coal mines, but can also be extended to urban underground subsidence areas, near-surface abnormal structures (such as karst caves and faults) and other scenarios.

[0036] This invention can identify hidden goaf areas in mining areas, providing accurate geological basis for goaf filling and roadway planning, and reducing the risk of mining accidents. At the same time, the technical solution has a high degree of standardization and can be promoted to various coal mines and near-surface detection scenarios, helping mine safety production and geological disaster prevention and control, and has significant social value. Attached Figure Description

[0037] Figure 1 This is a schematic diagram illustrating an embodiment of the present invention.

[0038] Figure 2 This is a flowchart of the method of the present invention.

[0039] Figure 3 This is a record of the actual frequency sweep waveform and the wavelet compression effect based on the precise controllable seismic source excitation of the present invention.

[0040] Figure 4 This is a schematic diagram of a three-dimensional acquisition and observation system according to an embodiment of the present invention.

[0041] Figure 5 This is a diagram showing the results of a three-dimensional distributed fiber optic acoustic wave sensing-vertical seismic profile recording under a precise controllable seismic source used in an embodiment of the present invention.

[0042] Figure 6 This is a schematic diagram of the well drive velocity calculated based on the zero offset record of the three-dimensional distributed fiber optic acoustic wave sensing-vertical seismic profile in an embodiment of the present invention.

[0043] Figure 7 This is a diagram showing the results of three-dimensional distributed fiber optic acoustic wave sensing-vertical seismic profile imaging used in an embodiment of the present invention.

[0044] Figure 8 The images show the three-dimensional distributed fiber optic acoustic wave sensing-vertical seismic profile three-dimensional imaging results and a schematic diagram of the goaf morphology used in the embodiments of the present invention. Detailed Implementation

[0045] A preferred embodiment of the present invention will now be described in detail with reference to the accompanying drawings.

[0046] like Figure 2 As shown, this invention provides a method for detecting, acquiring, and processing shallow surface goaf based on multi-well collaboration, including:

[0047] S1, reuse at least two existing geological boreholes and use a single optical fiber to connect the geological boreholes in series to form a multi-well joint three-dimensional observation network;

[0048] S2 uses a three-dimensional joint observation network as the center to set up survey lines and excitation points. It uses a mobile frequency-sweeping controllable source to excite seismic waves at each excitation point and collects strain rate data in geological boreholes through optical fibers in the three-dimensional joint observation network, while simultaneously recording the source wavelet signal.

[0049] S3 preprocesses the acquired strain rate data, including performing wavelet compression on the source wavelet signal based on the strain rate data and converting the strain rate data into velocity data; performing wavefield separation on the wavelet compression result to obtain the up-going reflected wave and the down-going direct wave, and constructing a three-dimensional velocity model based on the first arrival time of the down-going direct wave obtained by picking the first arrival time of the down-going direct wave; performing three-dimensional pre-stack depth migration on the up-going reflected wave based on the three-dimensional velocity model to generate a three-dimensional imaging volume, and identifying and delineating the goaf area based on the goaf area characteristics in the three-dimensional imaging volume.

[0050] The strain rate data in this invention can also be replaced with strain data.

[0051] The core objective of this invention is to address the challenge of high-precision detection of shallow surface goaf areas by providing a method for the acquisition and processing of shallow surface goaf areas based on multi-well joint three-dimensional distributed fiber optic acoustic sensing-vertical seismic profiling (DAS-VSP). Specifically, this method aims to overcome the limitation of easily overlooked goaf areas between boreholes by constructing a three-dimensional joint observation network through a single fiber optic cable connecting multiple wells, expanding the detection coverage, reducing blind spots between boreholes, and achieving full-area imaging of underground goaf structures. Furthermore, it improves the detection resolution and imaging accuracy of shallow surface goaf areas by utilizing the high spatial resolution and high signal-to-noise ratio advantages of distributed fiber optic acoustic sensing technology, combined with the high-resolution characteristics of in-well observations using vertical seismic profiling technology, to clearly depict the three-dimensional morphology, boundaries, and depth information of small-scale, concealed goaf areas.

[0052] In the detection of goaf areas, mobile frequency-sweeping controllable seismic sources can be used to achieve flexible excitation with wide bandwidth and high output, improving field acquisition efficiency and reducing detection costs. Existing geological boreholes in the mining area (voiding holes, blasting holes, geological exploration holes, etc.) can be reused, eliminating the need for additional borehole construction and further reducing engineering investment. The data processing workflow is optimized to achieve targeted processing of 3D distributed fiber acoustic wave sensing-vertical seismic profiles (DAS-VSP), such as strain-velocity conversion and wavefield separation, and finally imaging is achieved using the 3D pre-stack depth migration method.

[0053] This invention provides a complete solution for detecting goaf areas, standardizing the entire process from field data collection to data processing and imaging interpretation, and providing accurate geological data for goaf area management and safe production in mining areas.

[0054] This invention achieves precise detection of shallow surface goaf areas by constructing a multi-well joint three-dimensional observation network, a mobile frequency-sweeping controllable seismic source, and a full-process data processing technology system. The specific technical solution is divided into three parts: acquisition system construction, data acquisition implementation, and data processing flow.

[0055] 1. Construction of the data acquisition system.

[0056] The core of the acquisition system consists of a multi-well series distributed fiber optic acoustic sensing observation subsystem, a mobile frequency-sweeping controllable seismic source subsystem, and a time synchronization subsystem.

[0057] The multi-well cascaded distributed fiber optic acoustic wave sensing and observation subsystem reuses existing geological boreholes in the mining area (such as sounding holes, blasting holes, and geological exploration holes), and uses a single standard single-mode or multi-mode optical fiber to connect at least two boreholes in series to form a three-dimensional underground observation network. Surface fiber optic deployment: Excavate trenches along the pre-set route, first lay fine soil or fine sand as a protective layer, and then backfill and compact with the excavated soil and sand; reserve fiber optic length at terrain changes or bends to avoid excessive bending and stretching; Downhole fiber optic deployment: After binding the fiber optic cable with a weight, lower it into the borehole in a U-shaped structure. After the fiber optic cable sinks to the bottom, backfill with soil and sand to ensure tight coupling between the fiber optic cable and the formation. The depth of a single fiber optic cable in the borehole should not be less than the burial depth of the target goaf (usually 30m to 100m); The parameters of the distributed fiber optic acoustic wave sensor demodulator are set as follows: monitoring distance 0-50km, spatial resolution 1-10m (adjustable), gauge length 1-10m (adjustable), monitoring frequency 0.01-50kHz, sampling frequency 100kHz, to achieve continuous strain rate signal acquisition throughout the entire well section.

[0058] The mobile frequency-sweeping controllable source subsystem can generate a continuous broadband repeatable frequency sweep signal by automatically controlling the acceleration or deceleration process of the eccentric wheel mass block; at the same time, nodal seismographs are deployed near the source to record the zero-time wavelet of the source for subsequent signal compression.

[0059] The time synchronization subsystem uses GPS time synchronization technology to achieve unified time accuracy for the distributed fiber optic acoustic sensor demodulator, the mobile frequency-sweeping controllable seismic source, and the nodal seismograph, ensuring the time consistency of the acquired data with an error of no more than 1ms.

[0060] Figure 1The ellipse at the top represents a three-dimensional excitation point on the ground, the cylinder represents an existing geological borehole, and the solid black line connecting to the distributed fiber optic acoustic sensor demodulator represents the fiber optic cable, which is deployed in series in a U-shape within each geological borehole. By deploying a single dual-core fiber in the downlink and uplink directions of different boreholes, not only can data with a higher superimposed signal-to-noise ratio be acquired, but it also features high deployment efficiency and cost-effectiveness. This acquisition system can acquire three-dimensional distributed fiber optic acoustic sensor-vertical seismic profile data from multiple boreholes in the work area at once, thereby achieving joint imaging of multi-bore three-dimensional distributed fiber optic acoustic sensor-vertical seismic profiles over a larger coverage area of ​​the work area.

[0061] 2. Data collection implementation.

[0062] Based on the above system, field data acquisition is carried out according to the following steps: Parallel survey lines and excitation points are laid out around the three-dimensional joint observation network, with excitation points evenly distributed along each survey line. If obstacles cause gaps in excitation points, the location of these gaps must be recorded for subsequent data correction. Each excitation point is subjected to 2 to 5 consecutive repeated excitations. By superimposing the acquired signals, random noise is reduced and the signal-to-noise ratio of the data is improved. During the excitation process, the signal reception status of the distributed fiber optic acoustic wave sensor demodulator and the nodal seismograph is monitored in real time to ensure no cable breaks or signal loss. The distributed fiber optic acoustic wave sensor demodulator simultaneously acquires the axial strain rate signal of the optical fiber in each geological borehole, while the nodal seismograph acquires the ground source wavelet signal. The acquired data is stored in real time, and necessary information such as borehole number, excitation point coordinates, and acquisition time are marked.

[0063] 3. Data processing flow.

[0064] The workflow for 3D distributed fiber acoustic wave sensing-vertical seismic profile (DAS-VSP) data processing and imaging consists of three main stages: preprocessing and data processing.

[0065] Preprocessing stage: Based on the source wavelet recorded by nodal seismographs, the characteristic deconvolution method is used to eliminate wavelet sidelobes. The deconvolution formula is:

[0066] (1)

[0067] in, For the Fourier transform of the acquired signal, For the Fourier transform of the source wavelet, for The complex conjugate, It is the minimum value (to avoid the denominator being zero).

[0068] Perform an inverse Fourier transform on the deconvolution result to obtain the Green's function of the subsurface medium response. As a result of wavelet compression.

[0069] Figure 3 (a) in the image represents a nodal array record under precise controllable seismic source excitation. Figure 3 (b) in the figure represents the cross-correlation method used to analyze the cross-correlation method. Figure 3 The result of wavelet compression of the swept frequency signal in (a) Figure 3 (c) in the figure represents the deconvolution method used to process... Figure 3 The result is obtained by wavelet compression of the swept frequency signal in (a). The deconvolution method used in this invention is superior to the cross-correlation method. Figure 3 In the figure, (d) and (e) are the sweep waveforms and corresponding spectra of three consecutive excitations by a precision controllable source, respectively, indicating that the excitation waveforms have good consistency and high repetition rate.

[0070] strain rate along fiber With displacement Velocity in the vertical component The relation is:

[0071] (2)

[0072] Where z is the vertical coordinate. This represents the strain in the vertical component.

[0073] The strain is obtained by integrating the strain rate data in the time domain. However, the strain measured by distributed fiber optic acoustic sensing is the average strain over a finite length L (i.e., gauge length) along the fiber optic axis. , can be represented as:

[0074] (3)

[0075] The gauge length of a distributed fiber optic acoustic wave sensor demodulator has a significant impact on measurement performance. A smaller gauge length results in higher spatial resolution but lower signal-to-noise ratio (SNR); conversely, a larger gauge length results in lower spatial resolution but higher SNR. Generally, a balance needs to be struck between measurement distance, spatial resolution, and SNR.

[0076] Under the assumption of a plane wavefront, the strain is processed and converted into velocity data for seismic imaging:

[0077] (4)

[0078] In the formula, and These are velocity data and strain rate data in the frequency-wavenumber domain, respectively. ω and k are the angular frequency and wave number, respectively.

[0079] Data processing improves data quality by including head editing (supplementing borehole depth, excitation point coordinates, etc.), first arrival picking (extracting the first arrival time of the downlink direct wave), anomalous amplitude suppression (noise elimination), and amplitude compensation (balancing signal energy at different depths / distances); wavefield separation: based on the characteristics of vertical seismic profile data, the uplink reflected wave and the downlink direct wave are separated, and the uplink reflected wave is retained; velocity modeling: using the first arrival time of the downlink direct wave recorded by the zero-bias vertical seismic profile, a one-dimensional velocity model for each borehole is constructed, and then a three-dimensional depth domain velocity model is generated through smooth interpolation.

[0080] The three-dimensional imaging stage employs three-dimensional pre-stack depth migration technology. Based on the aforementioned three-dimensional velocity model, the upward reflected waves are repositioned to their actual underground locations to generate a three-dimensional image. Through seismic interpretation, the characteristics of the goaf are identified: the frequency of reflected waves decreases, the phase axis is disordered and closed. Combined with known borehole data in the mining area, the three-dimensional boundaries, depth, and orientation of the goaf are delineated.

[0081] Example:

[0082] The multi-well combined three-dimensional distributed fiber optic acoustic wave sensing-vertical seismic profiling method proposed in this invention was applied to the detection of goaf areas in an open-pit mine.

[0083] Three-dimensional multi-well distributed fiber optic acoustic wave sensing-vertical seismic profile acquisition was conducted on a platform in an open-pit mine. The acquisition and observation system is shown below. Figure 4 , Figure 4 The red box in the center represents the actual multi-hole 3D distributed fiber optic acoustic wave sensing-vertical seismic profile joint acquisition platform. This platform has completed the stripping of surface loess, providing flat geological conditions for the 3D acquisition work. The orange dots indicate the excitation points (shot points) of the frequency sweep seismic sources used in the 3D acquisition work. The northwest-southeast direction is the main survey line (Inline), and the northeast-southwest direction is the connecting survey line (Xline). The three green triangles represent the geological boreholes (also known as DAS observation boreholes) within the platform used for in-well distributed fiber optic acoustic wave sensing acquisition. The red cross indicates the known locations of boreholes in the mining area where goaf areas have been detected. The depth of the goaf is close to the coal seam floor, approximately 65m below the surface. Using a lightweight, mobile, frequency-sweeping controllable seismic source, active source excitation was completed for six survey lines within three days. The distance between the source survey lines was 8 meters, and the distance between seismic sources within each line was 4 meters. Each source point underwent three consecutive excitations. The third survey line had the longest offset of 183 meters. Due to construction issues, the offsets for the remaining survey lines were 130 meters. Some excitation points were not located due to obstacles. Active source excitation for six survey lines was completed within three days using a lightweight, mobile, frequency-sweeping controllable seismic source (the lines formed by the orange dots in the diagram). Figure 4 The scale of the line segments in the lower left corner, composed of black and white lines, currently represents 100m. Correspondingly, Figure 4The length (nearly east-west) of the red rectangle is 191m, and the width (nearly north-south) of the red rectangle is 68m.

[0084] First, preprocessing is performed, requiring the segmentation of the shot records from the 3D acquisition. Using precise meter markings, the data received by the distributed fiber optic acoustic wave sensor in the well is segmented into different 3D distributed fiber optic acoustic wave sensor-vertical seismic profile shot records. The shot record refers to the sum of all vibration signals recorded by all receivers after a single artificially generated seismic wave. For the 3D distributed fiber optic acoustic wave sensor-vertical seismic profile frequency sweep shot records, the deconvolution method used in this invention is selected for wavelet compression, as shown in the figure. Figure 5 As shown, the original 65-second sweep frequency record is compressed into a record excited by a pulse signal. Figure 5 In the middle (a), the original frequency sweep signal record is recorded. The record is determined by the parameters of the controllable source and the excitation time is 65 seconds. Figure 5 In the middle (b), the wavelet compression is performed by the deconvolution method. Orange represents the downlink direct wave and green represents the uplink reflected wave. Figure 5 (c) represents the record after three excitation superpositions, compared to Figure 5 (b) has a higher signal-to-noise ratio. Further processing of the 3D distributed fiber optic acoustic wave sensing-vertical seismic profile data is then performed, including head editing, first arrival picking, denoising, amplitude compensation, surface consistency deconvolution, and wavefield separation, to improve the quality of the 3D distributed fiber optic acoustic wave sensing-vertical seismic profile data and prepare it for imaging. Figure 5 In the diagram, the red line represents the zero moment of the shot after wavelet compression; below the red line is later than the zero moment, and above the red line is earlier than the zero moment. The blue line represents the starting path of the VSP record (strain rate data). To the left of the blue line are the signals received by the optical cable on the ground, and to the right of the blue line are the signal records received by the optical fiber when it goes down into the borehole, i.e., the vertical section. The further to the right the path is, the deeper the vertical section is.

[0085] This invention requires the use of multi-well three-dimensional distributed fiber optic acoustic sensing-vertical seismic profile zero-offset records to calculate accurate depth-domain P-wave velocities in subsurface media, such as... Figure 6 As shown, Figure 6 (a) in the figure represents the initial arrival time picked up from the zero offset VSP records (strain rate data) of the three boreholes. Figure 6 In (b), the layer velocity is calculated from the first arrival of the zero-bias record picked up from three boreholes, and it is directly related to the depth. By controlling the depth domain velocity through multiple wells, we further perform depth domain imaging on the three-dimensional distributed fiber optic acoustic wave sensing-vertical seismic profile data to track the morphology of underground goaf areas from the imaging results. Figure 7(a), (b), and (c) in the image show the imaging results of the 16th, 24th, and 29th slices of the 3D imaging volume, respectively. The orange double dashed lines in the slices represent the tracking results of the coal seam roof and floor interfaces. The coal seam roof reflection interface is located at a depth of approximately 50m with a positive amplitude polarity, while the coal seam floor reflection interface is located at a depth of approximately 75m with a negative amplitude polarity. Between the two interfaces is a low-velocity coal seam. The coal seam has a thin interlayered structure with a large number of fine scattering and reflections, and the reflection phase axes are mostly discontinuous. Figure 7 In (b), the frequency of the reflected wave within the red box decreases, the phase axis is disordered, and there is a clear trapping characteristic. This characteristic is that of a goaf, and the extent of the goaf can be delineated accordingly. From Figure 8 The imaging results of each slice are shown. Figure 8 The red vertical line in the middle represents Figure 4 The borehole trajectory in the image is indicated by the green vertical line. Figure 4 The DAS observation borehole trajectory shows that the goaf area, circled in red, is rectangular, with a lateral width of approximately 10m and a vertical length of approximately 15m perpendicular to the profile. Multiple sections of the goaf area are delineated as follows: Figure 8 The orange irregular shape in the picture passes through the borehole trajectory and is closer to the coal seam floor vertically in the goaf area, which is consistent with the existing data provided by the mine.

[0086] Actual data results show that the method of this invention only requires a single optical fiber to directly reuse various existing boreholes in the mining area, eliminating the need for additional new boreholes and significantly reducing overall project investment. Combined with a mobile frequency-sweeping controllable seismic source, efficient 3D acquisition can be carried out. Furthermore, combining multiple boreholes in series allows for large-scale joint imaging, and the use of multi-bore joint imaging significantly reduces imaging artifacts, improving the imaging accuracy of goaf areas. We expect that a multi-bore 3D joint observation network, coupled with efficient 3D acquisition using a mobile frequency-sweeping controllable seismic source, will not only solve the problem of narrow detection range of single-bore vertical seismic profiles but also avoid the high cost dilemma of independent multi-bore observation. It can also provide a solution for subsequent borehole network optimization, significantly reducing overall project investment.

[0087] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0088] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple steps or stages, which are not necessarily completed at the same time, but may be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but may be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0089] In one embodiment, the present invention also provides a computer system, which may be a server. The computer system includes a processor, memory, and a network interface connected via a system bus. The processor of the computer system provides computing and control capabilities. The memory of the computer system includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer system stores data used in the above-described methods. The network interface of the computer system is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described methods.

[0090] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0091] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.

[0092] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for detecting, collecting and processing a shallow surface mined-out area based on multi-well combination, characterized in that, The application relates to a method for detecting a goaf in a coal mine. The method comprises the following steps: Multiplexing at least two existing geological boreholes, connecting the geological boreholes in series by using a single optical fiber to form a three-dimensional joint observation network of multiple wells; Laying out survey lines and shooting points around the three-dimensional joint observation network, exciting seismic waves at each shooting point by using a mobile sweepable controlled source, and collecting strain rate data in the geological boreholes by using the optical fiber in the three-dimensional joint observation network, and synchronously recording source wave signals; 2. The method according to claim 1, wherein, Pretreating the collected strain rate data, including wavelet compression of the source wave signals based on the strain rate data, and converting the strain rate data into velocity data; performing wave field separation on the wavelet compression results to obtain uplink reflected waves and downlink direct waves, and constructing a three-dimensional velocity model based on the downlink direct wave first arrival time obtained by performing first arrival picking on the downlink direct waves; performing three-dimensional prestack depth migration on the uplink reflected waves based on the three-dimensional velocity model to generate a three-dimensional imaging volume, and identifying and delineating a goaf according to the goaf characteristics in the three-dimensional imaging volume. The method for connecting the geological boreholes in series by using a single optical fiber to form a three-dimensional joint observation network of multiple wells comprises the following steps:

3. The method according to claim 1, wherein, Lowering downhole optical fibers in a U-shaped structure to the bottom of each geological borehole to couple the optical fibers with the stratum, and laying out a ground optical fiber connecting the downhole optical fibers of the geological boreholes by means of ground excavation trenching. The method for laying out survey lines and shooting points around the three-dimensional joint observation network, exciting seismic waves at each shooting point by using a mobile sweepable controlled source, and collecting strain rate data in the geological boreholes by using the optical fiber in the three-dimensional joint observation network, and synchronously recording source wave signals comprises the following steps:

4. The method according to claim 1, wherein, Laying out parallel survey lines around the three-dimensional joint observation network, and uniformly arranging shooting points on each survey line; repeatedly exciting at each shooting point by using a mobile sweepable controlled source, and superimposing the strain rate data collected after multiple excitations to obtain the final strain rate data of the shooting point; and recording source wave signals by using a node-type seismograph.

5. The method according to claim 1, wherein, The mobile sweepable controlled source is a controlled source capable of generating a continuous wideband repeated sweep signal. The method for performing wavelet compression of the source wave signals based on the strain rate data comprises the following steps: ； in, For the Fourier transform of strain rate data, The Fourier transform of the source wavelet signal. for The complex conjugate, To avoid constants with zero denominators; for the deconvolution results Perform an inverse Fourier transform to obtain the Green's function of the underground medium response. As a result of wavelet compression.

6. The method according to claim 1, wherein, Performing feature deconvolution to compress the source wave signals into a Green function close to the underground medium response of the sweepable controlled source, wherein the feature deconvolution is used to eliminate wavelet sidelobes, and the deconvolution formula is as follows: The method for converting the strain rate data into velocity data comprises the following steps: ； wherein and are velocity data and strain rate data in the frequency-wavenumber domain, respectively, and are angular frequency and wavenumber, respectively.

7. The method according to claim 1, wherein, Converting the strain rate data into velocity data based on a plane wave front assumption according to the following relationship in the frequency-wavenumber domain: The method for constructing a three-dimensional velocity model based on the downlink direct wave first arrival time obtained by performing first arrival picking on the downlink direct waves comprises the following steps: Constructing a one-dimensional velocity model of each geological borehole by using the downlink direct wave first arrival time, and generating a three-dimensional velocity model by using smooth interpolation.

8. The method according to claim 7, wherein, The downgoing direct wave first arrival time obtained by the first arrival picking is used to construct a one-dimensional velocity model of each geological borehole, and a three-dimensional velocity model is generated by smooth interpolation, specifically including: the strain rate data synchronously collected by the three-dimensional joint observation network is sorted into shot records corresponding to each shot point; for each shot record, the first arrival time of the corresponding downgoing direct wave is identified and picked up, and the first arrival time and the corresponding geological borehole depth of the shot record form a one-to-one correspondence relationship, and the obtained multiple first arrival time-geological borehole depth data pairs can be used to construct a three-dimensional velocity model for subsequent imaging.

9. The method according to claim 1, wherein, The three-dimensional pre-stack depth migration of the upgoing reflected wave is performed based on the three-dimensional velocity model to generate a three-dimensional imaging body, and the goaf is identified and delineated according to the goaf feature in the three-dimensional imaging body, specifically including: The upgoing reflected wave is attributed to the real position underground based on the three-dimensional velocity model to generate a three-dimensional imaging body; the goaf feature is identified through seismic interpretation: the frequency of the upgoing reflected wave is reduced, the phase axis is disordered and enclosed, and the three-dimensional boundary, depth and trend of the goaf are delineated in combination with the known goaf borehole data in the mining area.

10. A computer system comprising a memory and a processor, said memory storing a computer program, characterized in that, The processor executes the computer program to realize the steps of the method in any one of claims 1 to 9.

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