Three-dimensional micro-motion detection method for grouting quality of coal mine goaf

Abstract

The application relates to the technical field of grouting quality detection, and provides a coal mine goaf grouting quality three-dimensional micro-motion detection method.The detection method comprises the following steps: for a to-be-detected goaf, a three-dimensional micro-motion dense array is laid out, and a micro-motion detection device is configured; the micro-motion detection device is arranged at each measuring point position of the three-dimensional micro-motion dense array, and micro-motion time sequence data of the to-be-detected goaf is collected; a Rayleigh wave dispersion curve is extracted through an extended spatial autocorrelation (ESPAC) method, and a three-dimensional structure model of underground apparent S-wave velocity corresponding to different stages of a whole grouting cycle is obtained through inversion; apparent S-wave velocity slices corresponding to the elevations of different stages of the whole grouting cycle are extracted; each level of apparent S-wave velocity threshold standard is obtained, and the apparent S-wave velocity in the apparent S-wave velocity slices is compared with the apparent S-wave velocity threshold standard; the apparent S-wave velocity slices are subjected to time sequence difference dynamic analysis; and the grouting quality analysis result of the to-be-detected goaf is output in combination with the comparison result and the time sequence difference dynamic analysis result.

Description

Technical Field

[0001] This application relates to the field of grouting quality testing technology, specifically to a three-dimensional micro-motion testing method for grouting quality in coal mine goaf areas. Background Technology

[0002] Currently, the quality inspection of grouting in coal mine goaf areas mainly relies on core drilling and conventional geophysical methods, which have many technical shortcomings. Core drilling is a single-point inspection, with poor representativeness, high cost, and low efficiency, and cannot comprehensively reflect the grouting effect of the area. Conventional two-dimensional geophysical methods have insufficient spatial resolution, making it difficult to achieve three-dimensional fine imaging of the underground medium and accurately delineate the spatial distribution of grouting blind areas. Existing micro-motion detection methods mostly adopt a single static detection mode, which can only obtain the final state after grouting and cannot achieve dynamic monitoring of the entire grouting cycle, nor can it track the diffusion trajectory and filling process of the grout in real time. At the same time, existing methods mostly use a single apparent shear wave velocity threshold comparison for quality evaluation, which cannot distinguish between natural low-velocity geological bodies and unfilled goaf areas, resulting in serious ambiguity problems, low detection accuracy, and difficulty in meeting the requirements of detection accuracy and reliability for grouting treatment projects in coal mine goaf areas. Summary of the Invention

[0003] This application provides a three-dimensional micro-motion detection method for the grouting quality of coal mine goaf areas, which solves the problems of low detection accuracy and difficulty in meeting the requirements of detection precision and reliability in the existing technology.

[0004] A three-dimensional micro-motion detection method for grouting quality in coal mine goaf areas according to an embodiment of the first aspect of this application, the detection method includes: For the goaf to be detected, a three-dimensional micro-motion dense array of stations adapted to the depth of the goaf is deployed, and micro-motion detection equipment that has undergone consistency testing is configured. The micro-motion detection device is arranged at each measuring point of the three-dimensional micro-motion dense array. The micro-motion time series data of the goaf to be detected at different stages of the grouting cycle are collected by the micro-motion detection device, and the collected raw micro-motion time series data are preprocessed. Based on the preprocessed micro-motion time series data, the Rayleigh wave dispersion curve was extracted by the extended spatial autocorrelation ESPAC method, and the three-dimensional structural model of the underground apparent shear wave velocity corresponding to different stages of the grouting cycle was obtained by inversion. Based on the depth of the goaf to be detected, the apparent shear wave velocity slices corresponding to the elevations of different stages of the grouting cycle are extracted from the three-dimensional structural model of the underground apparent shear wave velocity corresponding to different stages of the grouting cycle. Obtain the apparent shear wave velocity threshold standards at each level established by combining borehole core sampling and grouting construction records, and compare the apparent shear wave velocity in the apparent shear wave velocity slice with the apparent shear wave velocity threshold standards; Time-difference dynamic analysis of the apparent shear wave velocity slices; By combining the apparent shear wave velocity comparison results with the time-series differential dynamic analysis results, the grouting quality analysis results of the goaf to be detected are output.

[0005] According to one embodiment of this application, the deployment of a three-dimensional micro-motion dense array of stations adapted to the burial depth of the goaf to be detected includes: When the depth of the goaf to be detected is within 150m, the observation radius of the array is determined to be 35m-45m. The three-dimensional micro-motion dense array is deployed by arranging the main array at a point spacing of 2m×2m, and then densifying the array to a point spacing of 1m×1m within a 20m radius around the grouting well.

[0006] According to one embodiment of this application, the configuration of the micro-motion detection device that has undergone conformity testing includes: Nodal seismographs were used as data acquisition devices, along with broadband detectors. After connecting all data acquisition units and detectors, conduct an instrument consistency test with 10-20 minutes of synchronous acquisition to verify the consistency of waveforms in each channel; The internal clock of each data acquisition unit is automatically and in real time calibrated using the standard time signal of the satellite navigation system, so that the synchronization error between multiple data acquisition units is less than 10μs.

[0007] According to one embodiment of this application, the step of arranging the micro-motion detection device at each measuring point position of the three-dimensional micro-motion dense array, and collecting micro-motion time-series data of the goaf to be detected at different stages of the grouting cycle through the micro-motion detection device, includes: Continuous micro-motion time-series data for a duration of not less than 40 minutes were collected during the baseline period before grouting, every 2 hours during grouting, and the stabilization period after grouting.

[0008] According to one embodiment of this application, the step of extracting Rayleigh wave dispersion curves based on preprocessed micro-motion time-series data using the Extended Spatial Autocorrelation (ESPAC) method, and inverting to obtain a three-dimensional structural model of the underground apparent shear wave velocity corresponding to different stages of the entire grouting cycle, includes: Using each measuring point of the three-dimensional micro-motion dense array as the central measuring point, all measuring points within a set array radius around the central measuring point are combined to form an irregular dense array combination with the central measuring point as the core. Using the spatial autocorrelation characteristics of the preprocessed micro-motion time series data of all measuring points within the irregular dense array combination, the Rayleigh wave dispersion curve of the central measuring point is calculated by the ESPAC method. By traversing all the measuring points of the three-dimensional micro-motion dense array, the Rayleigh wave dispersion curve of each measuring point is calculated point by point. The Rayleigh wave dispersion curve was inverted using the empirical formula of the half-wave method to obtain the apparent shear wave velocity data corresponding to different depths at each measuring point. The apparent shear wave velocity data from all measuring points are interpolated, smoothed, and fitted in three dimensions to generate a three-dimensional structural model of the underground apparent shear wave velocity corresponding to different stages of the grouting cycle.

[0009] According to one embodiment of this application, the step of extracting apparent shear wave velocity slices corresponding to different elevations of different stages of the grouting cycle from the three-dimensional structural model of the apparent shear wave velocity at different stages of the grouting cycle, based on the burial depth of the goaf to be detected, includes: The interface with a sharp change in wave velocity in the three-dimensional structural model of apparent shear wave velocity in the underground is identified as the bedrock interface within the elevation range of 1150m~1160m. The slice corresponding to the 1100m elevation that best matches the actual burial depth of the goaf to be detected is taken as the main analysis slice. The slices corresponding to the 1090m and 1110m elevations were extracted as auxiliary analysis slices.

[0010] According to one embodiment of this application, obtaining the various levels of apparent shear wave velocity threshold standards established by combining borehole coring and grouting construction records includes: Based on the physical characteristics of the ungrouted goaf and collapse fracture zone revealed by the borehole, a first low-velocity threshold range is set to characterize the unfilled state. Based on the physical characteristics of local grouting voids or uneven grout diffusion, a second medium-speed threshold range is set to characterize the partial filling state. Based on the physical characteristics of intact and dense rock mass after grouting, a third high-speed threshold range is set to characterize the fully filled state.

[0011] According to one embodiment of this application, the temporal differential dynamic analysis of the apparent shear wave velocity slice includes: The dynamic change of the apparent shear wave velocity is calculated by subtracting the apparent shear wave velocity slice during the grouting process from the apparent shear wave velocity slice during the reference period pixel by pixel. Based on the dynamic change of the apparent shear wave velocity, a spatiotemporal evolution cloud map of slurry diffusion is generated to dynamically track the diffusion trajectory and filling status of the slurry.

[0012] According to one embodiment of this application, after generating the spatiotemporal evolution cloud map of slurry diffusion, the method further includes: In the spatiotemporal evolution cloud map of the slurry diffusion, a closed low-velocity anomaly region is identified where the apparent shear wave velocity increase is lower than a set change threshold and the current absolute apparent shear wave velocity falls into the unfilled threshold range. Extract the boundary of the closed low-velocity anomaly region, automatically delineate the locally unfilled residual cavities and slurry diffusion blind zones, and obtain the spatial coordinates of the slurry diffusion blind zones.

[0013] According to one embodiment of this application, the step of combining the apparent shear wave velocity comparison results with the time-series differential dynamic analysis results to output the grouting quality analysis results of the goaf to be detected includes: Based on the apparent shear wave velocity comparison results, the area of ​​the region in the apparent shear wave velocity slice that falls into the third high-speed threshold interval corresponding to the fully filled state is statistically analyzed, as well as the total coverage area of ​​the apparent shear wave velocity slice, and the overall filling rate of the region and the medium density improvement rate of each partition are calculated. Based on the overall filling rate of the area, the density improvement rate of the medium in each zone, and the spatial coordinates of the grout diffusion blind zone, a grouting quality analysis result is generated, which includes the overall filling rate of the area, the density improvement rate of the medium in each zone, the distribution map of supplementary grouting points, and the optimization scheme of grouting pressure and grout mix ratio parameters.

[0014] The above-described one or more technical solutions in the embodiments of this application have at least one of the following technical effects: This application employs a three-dimensional micro-motion dense array adapted to the burial depth of the goaf, achieving three-dimensional fine imaging of the underground medium. This solves the problem of insufficient spatial resolution in traditional two-dimensional geophysical exploration methods and can accurately delineate the spatial distribution of grouting blind zones. By collecting micro-motion time-series data at different stages of the entire grouting cycle, dynamic monitoring of the grouting process is achieved, allowing real-time tracking of the grout diffusion trajectory and filling status changes, thus overcoming the shortcomings of single static detection in reflecting the grouting process. A dual detection system combining apparent shear wave velocity threshold comparison and time-series differential dynamic analysis is constructed, effectively distinguishing between natural low-velocity geological bodies and unfilled goaf areas, solving the problem of multiple solutions in single evaluation methods, and significantly improving the accuracy of grouting quality detection. This method does not require artificial seismic sources, is safe and efficient in construction, and has low cost. It can provide comprehensive scientific technical support for the design, construction, and acceptance of grouting treatment projects in coal mine goaf areas, ensuring safe production in coal mines.

[0015] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the three-dimensional micro-motion detection method for grouting quality in coal mine goaf areas provided in this application.

[0018] Figure 2 This is a schematic diagram of the three-dimensional micro-motion dense array layout provided in this application.

[0019] Figure 3 This is a schematic diagram of the micro-motion detection principle provided in this application.

[0020] Figure 4 This is a schematic diagram of the micro-motion single-point detection data processing provided in this application. Detailed Implementation

[0021] The embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but should not be used to limit the scope of this application.

[0022] In the description of the embodiments of this application, it should be noted that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application. In addition, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0023] In the description of the embodiments of this application, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of this application based on the specific circumstances.

[0024] In the embodiments of this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0025] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0026] A three-dimensional micro-motion detection method for grouting quality in coal mine goaf areas according to an embodiment of the first aspect of this application, such as... Figure 1 The procedure includes the following steps: For the goaf to be detected, a three-dimensional micro-motion dense array adapted to the depth of the goaf is deployed, and micro-motion detection equipment that has undergone consistency testing is configured; the micro-motion detection equipment is placed at each measuring point of the three-dimensional micro-motion dense array, and micro-motion time-series data of the goaf to be detected at different stages of the grouting cycle are collected through the micro-motion detection equipment, and the collected raw micro-motion time-series data are preprocessed; based on the preprocessed micro-motion time-series data, Rayleigh wave dispersion curves are extracted by the extended spatial autocorrelation (ESPAC) method, and the underground apparent shear waves corresponding to different stages of the grouting cycle are obtained by inversion. A three-dimensional structural model of velocity is generated. Based on the depth of the goaf to be investigated, apparent shear wave velocity slices corresponding to different elevations at different stages of the grouting cycle are extracted from the three-dimensional structural model of the underground apparent shear wave velocity. Threshold standards for apparent shear wave velocity at each level are obtained by combining borehole core sampling and grouting construction records, and the apparent shear wave velocity in the apparent shear wave velocity slices is compared with the threshold standards. Temporal differential dynamic analysis is performed on the apparent shear wave velocity slices. Combining the apparent shear wave velocity comparison results and the temporal differential dynamic analysis results, the grouting quality analysis results of the goaf to be investigated are output.

[0027] This method, based on the surface wave dispersion characteristics of natural micro-vibration signals, enables full-cycle, high-precision, and non-destructive detection of grouting quality in coal mine goaf areas. Natural, weak vibrations, or micro-vibration signals, generated by nature and human activities, exist on the Earth's surface at all times and in all places. These signals consist of volume waves (P-waves, S-waves) and surface waves (Rayleigh waves, Lafleigh waves), with vertical surface wave energy accounting for over 70% of the total signal energy. Although the amplitude and morphology of micro-vibration signals vary with time and space, they possess statistical stability within a certain spatiotemporal range. The transverse wave velocity structure of the underground medium can be inverted by extracting the Rayleigh wave dispersion curve. Since transverse wave velocity is positively correlated with the density and integrity of the soil and rock mass, it can accurately reflect the changes in the physical characteristics of the medium before and after grouting in the goaf area.

[0028] The three-dimensional micro-motion detection method in this application adopts a simultaneous acquisition and point-by-point inversion working mode, which has significant advantages over the traditional two-dimensional detection method: First, it has a wide deployment range and high construction efficiency, and does not require artificial seismic sources, enabling rapid detection of large areas; second, the irregular dense array can receive seismic source signals from all directions, with strong anti-interference ability and better data quality; third, the synchronous acquisition at different stages of the entire cycle effectively avoids data deviations caused by different times and changes in the field source, and can dynamically track the diffusion process of slurry and changes in filling status.

[0029] This method constructs a dual detection system of "static grading + dynamic verification": static grading evaluation of grouting quality is achieved based on absolute wave velocity values ​​through apparent shear wave velocity threshold comparison, while dynamic verification of the grouting process is achieved based on wave velocity changes through time-series differential dynamic analysis. The combination of these two methods can accurately delineate unfilled, partially filled, and fully filled areas, and effectively distinguish between natural low-velocity geological bodies and grout-unfilled areas. This solves the ambiguity problem inherent in single detection methods, significantly improving the accuracy and reliability of grouting quality detection, and providing scientific and technical support for the design, construction, and acceptance of grouting treatment projects in coal mine goaf areas.

[0030] According to one embodiment of this application, for the goaf to be detected, a three-dimensional micro-motion dense array of stations adapted to the depth of the goaf is deployed, including: when the depth of the goaf to be detected is within 150m, the observation radius of the array is determined to be 35m-45m; the three-dimensional micro-motion dense array is deployed by setting up the main array with a point spacing of 2m×2m, and densifying it to a point spacing of 1m×1m within a 20m range around the grouting well.

[0031] In practical engineering applications, the effective exploration depth of micro-motion detection is approximately 3 to 5 times the observation radius of the array. For the detection needs of coal mine goaf areas shallower than 150m, an array observation radius of 35m-45m can meet the exploration depth requirements, with 40m being the preferred standard array observation radius, which can achieve effective detection of strata within a depth range of 120m-200m.

[0032] The main array employs a uniform 2m×2m point spacing, ensuring uniform spatial resolution across the entire detection area and comprehensively covering the goaf distribution. Within a 20m radius of the grouting well, the point spacing is increased to 1m×1m, significantly improving the detection accuracy of the core grouting area and accurately capturing the fine characteristics of grout diffusion and local filling effects. This "uniform layout + localized densification" array configuration balances the comprehensiveness of the detection range with the precision of key areas, effectively improving the spatial resolution of grouting quality detection while maintaining overall detection efficiency.

[0033] According to one embodiment of this application, a micro-motion detection device that has undergone consistency testing is configured, including: using a nodal seismograph as a data acquisition unit and a broadband geophone; after connecting all data acquisition units and geophones, conducting an instrument consistency test with synchronous acquisition for 10-20 minutes to verify the consistency of waveforms in each channel; and automatically and in real-time calibrating the internal clock of each data acquisition unit using the standard time signal of the satellite navigation system to ensure that the synchronization error between multiple data acquisition units is less than 10μs.

[0034] The nodal seismograph features an integrated wireless intelligent design, eliminating the need for cable connections and facilitating rapid deployment and retrieval in the field. Its built-in high-capacity battery supports continuous field operations for over 35 days, meeting the continuous monitoring needs of long-cycle grouting processes. The accompanying broadband geophone is preferably a 5Hz natural frequency model, effectively acquiring high-frequency micro-motion signals above 1Hz, suitable for precise detection in shallow surface goaf areas.

[0035] Instrument consistency testing is a crucial step in ensuring the quality of micro-motion detection data. The test duration is set at 10-20 minutes, with a preferred synchronous acquisition duration of 15 minutes. This ensures thorough verification of waveform and phase consistency across channels, eliminates unstable instruments, and guarantees consistent response characteristics across all acquisition channels. Real-time clock calibration is performed using a satellite navigation system (such as GPS or BeiDou) to ensure that the synchronization error between multiple data acquisition units is less than 10 μs. This guarantees precise time synchronization of micro-motion signals from different measurement points, providing a reliable data foundation for subsequent spatial autocorrelation analysis and dispersion curve extraction.

[0036] According to one embodiment of this application, a micro-motion detection device is arranged at each measuring point of a three-dimensional micro-motion dense array. The micro-motion time-series data of the goaf to be detected at different stages of the grouting cycle are collected by the micro-motion detection device, including: continuous micro-motion time-series data with a duration of not less than 40 minutes are collected during the baseline period before grouting, every 2 hours during grouting, and the stabilization period after grouting.

[0037] Time-series data acquisition at different stages of the entire grouting cycle is the foundation for dynamic evaluation of grouting quality. Baseline data acquisition before grouting obtains the original apparent shear wave velocity characteristics of the goaf in its ungrouted state, serving as the benchmark for all subsequent comparative analyses. Data acquisition every 2 hours during grouting allows real-time tracking of the grout diffusion process and dynamic changes in the filling state, enabling timely detection of any abnormalities. Post-grouting stabilization data acquisition obtains the final stable state of the medium after grouting, used to evaluate the final effectiveness of the grouting treatment.

[0038] A single acquisition session should be set to at least 40 minutes to ensure sufficient acquisition of effective micro-motion signals, meet statistical stability requirements, and improve the accuracy and reliability of dispersion curve extraction. During data acquisition, good coupling between the detector and the ground should be ensured to avoid the impact of human interference and environmental noise on data quality.

[0039] According to one embodiment of this application, based on preprocessed micro-motion time-series data, Rayleigh wave dispersion curves are extracted using the Extended Spatial Autocorrelation (ESPAC) method, and a three-dimensional structural model of the apparent shear wave velocity corresponding to different stages of the grouting cycle is obtained by inversion. This includes: using each measuring point of a three-dimensional micro-motion dense array as a central measuring point, combining all measuring points within a set array radius around the central measuring point to form an irregular dense array combination with the central measuring point as its core; utilizing the spatial autocorrelation characteristics of the preprocessed micro-motion time-series data of all measuring points within the irregular dense array combination, calculating the Rayleigh wave dispersion curve of the central measuring point using the ESPAC method; traversing all measuring points of the three-dimensional micro-motion dense array, calculating the Rayleigh wave dispersion curve for each measuring point; inverting the Rayleigh wave dispersion curve using the half-wave method empirical formula to obtain the apparent shear wave velocity data corresponding to different depths at each measuring point; and interpolating, smoothing, and three-dimensionally fitting the apparent shear wave velocity data of all measuring points to generate a three-dimensional structural model of the apparent shear wave velocity corresponding to different stages of the grouting cycle.

[0040] The Extended Spatial Autocorrelation (ESPAC) method is specifically designed for dispersion curve extraction in irregular dense arrays. It can fully utilize the high-density measurement point data of a three-dimensional micro-motion array to improve the extraction accuracy and resolution of the dispersion curve. An irregular dense array is formed with each measurement point as the center. The dispersion curve is calculated using the spatial autocorrelation characteristics of the micro-motion time-series data of all measurement points within the array. This effectively suppresses random noise and improves the signal-to-noise ratio.

[0041] The half-wave empirical formula inversion is a fast and stable method for inverting apparent shear wave velocity. Its basic principle is that the penetration depth of a Rayleigh wave is approximately half its wavelength; by using the phase velocities of Rayleigh waves at different frequencies, the apparent shear wave velocity at the corresponding depth can be calculated. This method requires no complex initial model setup, has high computational efficiency, and is suitable for rapid inversion of large-scale three-dimensional micro-motion data. By interpolating, smoothing, and 3D fitting the apparent shear wave velocity data from all measuring points, a continuous 3D structural model of the subsurface apparent shear wave velocity is generated. This model can intuitively and three-dimensionally display the velocity distribution characteristics of the subsurface medium, providing fundamental data for subsequent elevation slice extraction and grouting quality analysis.

[0042] According to one embodiment of this application, based on the depth of the goaf to be detected, slices of apparent shear wave velocity at different elevations corresponding to different stages of the grouting cycle are extracted from the three-dimensional structural model of the apparent shear wave velocity at different stages of the grouting cycle. This includes: identifying the wave velocity abrupt change interface in the three-dimensional structural model of the apparent shear wave velocity at elevations of 1150m to 1160m and determining it as the bedrock interface; selecting the slice corresponding to the 1100m elevation, which best matches the actual depth of the goaf to be detected, as the main analysis slice; and extracting slices corresponding to the 1090m and 1110m elevations as auxiliary analysis slices.

[0043] For example, in the Loess Plateau region of northern Shaanxi, there is usually a significant difference in wave velocity between the Quaternary loess layer and the underlying Jurassic bedrock, which manifests as a steep wave velocity transition interface in the elevation range of 1150m to 1160m. This interface can serve as an important marker for stratigraphic division and can be used as a depth benchmark for calibrating the apparent shear wave velocity model.

[0044] For coal seam goaf areas with a burial depth of approximately 100m, the slice corresponding to the 1100m elevation best matches the actual burial depth of the goaf, clearly demonstrating its planar distribution characteristics and grouting filling effect; therefore, it is used as the primary analysis slice. Slices corresponding to the 1090m and 1110m elevations are extracted as auxiliary analysis slices. These allow for a comprehensive analysis of the spatial distribution characteristics of the goaf and the vertical diffusion range of the grout from different depth levels, avoiding analytical biases caused by single-elevation slices and improving the comprehensiveness and accuracy of grouting quality evaluation.

[0045] According to one embodiment of this application, the apparent shear wave velocity threshold standards at various levels are obtained by combining borehole core sampling and grouting construction records, including: setting a first low-velocity threshold range representing the unfilled state based on the physical characteristics of the ungrouted goaf and collapsed fracture zone revealed by the borehole; setting a second medium-velocity threshold range representing the partially filled state based on the physical characteristics of the local grouting void or uneven grout diffusion phenomenon; and setting a third high-velocity threshold range representing the fully filled state based on the physical characteristics of the intact and dense rock mass after grouting.

[0046] Establishing threshold standards for apparent shear wave velocity at each level is the core basis for grading and evaluating grouting quality. These standards must be calibrated in conjunction with core drilling data and grouting construction records to ensure they match actual geological conditions. In ungrouted goafs and collapsed fractured zones, due to rock fragmentation and well-developed pores, the apparent shear wave velocity is significantly reduced, corresponding to the first low-velocity threshold range. In locally grouted goafs or areas with uneven grout diffusion, where the rock mass is partially filled with grout, the apparent shear wave velocity increases but remains lower than that of intact rock, corresponding to the second medium-velocity threshold range. In intact, dense rock masses after grouting, where pores are completely filled with grout, the apparent shear wave velocity recovers to near the original rock level, corresponding to the third high-velocity threshold range. Through this three-level threshold standard classification, grouting quality can be divided into three levels: unfilled, partially filled, and fully filled, achieving a quantitative grading and evaluation of grouting quality.

[0047] According to one embodiment of this application, a time-series differential dynamic analysis of the apparent shear wave velocity slice includes: performing pixel-by-pixel subtraction between the apparent shear wave velocity slice during the grouting process and the apparent shear wave velocity slice during the reference period to calculate the dynamic change in apparent shear wave velocity; and generating a grout diffusion spatiotemporal evolution cloud map based on the dynamic change in apparent shear wave velocity to dynamically track the grout diffusion trajectory and filling status.

[0048] Temporal differential dynamic analysis is a key technology for dynamically monitoring the grouting process by comparing the changes in apparent shear wave velocity at different stages before and after grouting. By subtracting the apparent shear wave velocity slices during the grouting process from those at the baseline period before grouting pixel by pixel, the increase in apparent shear wave velocity at each location can be quantitatively calculated, intuitively reflecting the change in medium density caused by grouting. Based on the dynamic changes in apparent shear wave velocity, a spatiotemporal evolution cloud map of grout diffusion is generated, which can visually display the diffusion range and direction of grout at different times and spaces, dynamically tracking the grout filling process and promptly identifying grout diffusion blind zones and unfilled areas. This dynamic analysis method can effectively distinguish the velocity changes caused by grouting from the velocity differences of natural geological bodies, solving the problem of multiple solutions in static threshold comparison and improving the accuracy of grouting quality detection.

[0049] According to one embodiment of this application, after generating the spatiotemporal evolution cloud map of slurry diffusion, the method further includes: identifying, in the spatiotemporal evolution cloud map of slurry diffusion, a closed-loop low-velocity anomaly region where the apparent shear wave velocity increase is lower than a set change threshold and the current absolute apparent shear wave velocity falls within the unfilled threshold range; extracting the boundary of the closed-loop low-velocity anomaly region, automatically delineating locally unfilled residual cavities and slurry diffusion blind zones, and obtaining the spatial coordinates of the slurry diffusion blind zones.

[0050] By employing a dual criterion of "velocity increase + absolute velocity value," residual unfilled areas after grouting can be accurately identified. If the apparent shear wave velocity increase is below a set threshold, it indicates that the area has not been effectively affected by grouting; if the current absolute apparent shear wave velocity falls within the unfilled threshold range, it indicates that the rock mass in that area remains fractured and loose. A closed-loop low-velocity anomaly area that simultaneously meets both conditions is a locally unfilled residual cavity or a grout diffusion blind zone. Automatically extracting the boundary of the anomaly area and obtaining its spatial coordinates provides precise target locations for subsequent supplementary grouting projects, enabling targeted treatment of residual unfilled areas and avoiding material waste and project delays caused by indiscriminate grouting.

[0051] According to one embodiment of this application, combining the apparent shear wave velocity comparison results and the time-series differential dynamic analysis results, the grouting quality analysis results of the goaf to be detected are output, including: based on the apparent shear wave velocity comparison results, calculating the area of ​​the region where the apparent shear wave velocity falls into the third high-speed threshold interval corresponding to the fully filled state in the apparent shear wave velocity slice, and the total coverage area of ​​the apparent shear wave velocity slice, and calculating the overall filling rate of the region and the medium density improvement rate of each zone; based on the overall filling rate of the region, the medium density improvement rate of each zone, and the spatial coordinates of the grout diffusion blind zone, generating grouting quality analysis results including the overall filling rate of the region, the medium density improvement rate of each zone, the distribution map of supplementary grouting points, and the optimization scheme of grouting pressure and grout ratio parameters.

[0052] The overall filling rate of the area is a core quantitative indicator for evaluating the effectiveness of grouting treatment. By statistically analyzing the ratio of the area of ​​the fully filled area to the total detected area, the degree of grouting filling in the entire detected area can be quantitatively reflected. The improvement rate of medium density in each zone can reflect the grouting reinforcement effect in different areas, providing a basis for zoned evaluation and targeted treatment. Based on the quantitative evaluation results and the spatial coordinates of the grout diffusion blind zone, a distribution map of supplementary grouting points is generated, clarifying the specific locations where supplementary grouting is needed. Combining grouting construction records and actual site conditions, optimizing grouting pressure and grout mix ratio parameters can improve the effectiveness and efficiency of supplementary grouting. The final output of the grouting quality analysis results includes quantitative indicators, spatial distribution maps, and parameter optimization schemes, providing comprehensive and scientific technical guidance for the acceptance and subsequent construction of grouting treatment projects in coal mine goaf areas.

[0053] The three-dimensional micro-motion dense array layout adopted in this application is as follows: Figure 2 As shown. Figure 2Eight common micro-motion array layouts are shown: (a) nested triangle, (b) single circle, (c) concentric multiple circles, (d) cross, (e) dense array, (f) linear array, (g) L-shaped array, and (h) rhomboid array. This application adopts the (e) dense array, whose high-density measurement point distribution can effectively improve detection accuracy and anti-interference ability, and can realize full-area fine detection of grouting quality in coal mine goaf areas.

[0054] The micro-motion detection principle of this application is as follows: Figure 3 As shown. Figure 3 This is a schematic diagram of the micro-motion detection principle, which fully demonstrates the physical process of the generation, propagation and inversion of the apparent shear wave velocity structure of natural micro-motion signals, and is the core scientific basis of the method in this application. Figure 3 The lower three-dimensional diagram illustrates the generation and propagation characteristics of micro-motion signals: human activities such as vehicles and engineering operations on the ground are the main sources of high-frequency micro-motion signals at shallow surfaces. These signals propagate underground in the form of volume waves and surface waves, with surface waves accounting for over 70% of the total signal energy and being the primary target for micro-motion detection. Surface waves of different frequencies have different penetration depths; low-frequency surface waves have greater penetration depths, while high-frequency surface waves have smaller penetration depths. This characteristic enables this application to detect the grouting effect of goaf areas at different underground depths using micro-motion signals collected from the ground. Figure 3 The enlarged image in the upper left corner illustrates the dispersion characteristics of surface waves: surface waves of different frequencies (wavelengths) have different phase velocities. The red long waves (low frequencies) have high phase velocities and large penetration depths, while the purple short waves (high frequencies) have low phase velocities and small penetration depths. This application utilizes this characteristic to extract Rayleigh wave dispersion curves from micro-motion time series data using the Extended Spatial Autocorrelation (ESPAC) method. Figure 3 The coordinate graph in the upper right corner shows the correspondence between the dispersion curve and the subsurface S-wave velocity structure: the horizontal axis represents frequency, the vertical axis represents phase velocity, the black curve is the extracted Rayleigh wave dispersion curve, the colored dotted line is the inverted subsurface S-wave velocity structure, and the bar chart on the right shows the corresponding actual stratum S-wave velocity distribution. This application uses the half-wave method empirical formula to invert the dispersion curve, obtain the apparent shear wave velocity data corresponding to different depths at each measuring point, and then generate a three-dimensional structural model of the subsurface apparent shear wave velocity.

[0055] The data processing flow for micro-motion single-point detection in this application is as follows: Figure 4 As shown. Figure 4 The core processing steps, from combining array data to inverting and obtaining the subsurface apparent shear wave velocity layer structure, are fully demonstrated and correspond completely to the dispersion curve extraction and inversion steps described in claim 5. Figure 4The left side shows a schematic diagram of a nested triangular array with the central measuring point 0 as the core, illustrating the basic array combination principle of micro-motion detection. In this application, each measuring point of the three-dimensional micro-motion dense array is used as the central measuring point, and all measuring points within a predetermined array radius around it are combined to form an irregular dense array combination. Its data processing principle is the same as that shown in the schematic diagram. Figure 1 In this study, the spatial autocorrelation characteristics of the micro-motion signals at each measuring point within the array were used to extract dispersion information. Figure 4 The middle section shows the extracted Rayleigh wave dispersion curve, with frequency on the horizontal axis and phase velocity on the vertical axis. The curve exhibits typical dispersion characteristics of decreasing phase velocity with increasing frequency, reflecting the stratigraphic information corresponding to different depths for Rayleigh waves of different frequencies. This application calculates the dispersion curve of each central measuring point point by point using the Extended Spatial Autocorrelation (ESPAC) method, effectively suppressing random noise and ensuring high accuracy and high resolution of the data. Figure 4 The right side shows the inverted subsurface apparent shear wave velocity layer structure. Vs1, Vs2, and Vs3 correspond to the apparent shear wave velocity values ​​at different depths, with the velocity increasing with depth, consistent with the velocity distribution pattern of normal strata. This application uses the half-wave method empirical formula to invert the dispersion curve, obtaining apparent shear wave velocity data at different depths for each measuring point. Then, through interpolation smoothing and three-dimensional fitting, a three-dimensional structural model of the subsurface apparent shear wave velocity in the entire detection area is generated, providing basic data for subsequent grouting quality analysis.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate this application and are not intended to limit this application. Although this application has been described in detail with reference to the embodiments, those skilled in the art should understand that various combinations, modifications, or equivalent substitutions of the technical solutions of this application do not depart from the spirit and scope of the technical solutions of this application and should be covered within the scope of the claims of this application.

Claims

1. A three-dimensional micro-motion detection method for grouting quality in coal mine goaf areas, characterized in that, include: For the goaf to be detected, a three-dimensional micro-motion dense array of stations adapted to the depth of the goaf is deployed, and micro-motion detection equipment that has undergone consistency testing is configured. The micro-motion detection device is arranged at each measuring point of the three-dimensional micro-motion dense array. The micro-motion time series data of the goaf to be detected at different stages of the grouting cycle are collected by the micro-motion detection device, and the collected raw micro-motion time series data are preprocessed. Based on the preprocessed micro-motion time series data, the Rayleigh wave dispersion curve was extracted by the extended spatial autocorrelation ESPAC method, and the three-dimensional structural model of the underground apparent shear wave velocity corresponding to different stages of the grouting cycle was obtained by inversion. Based on the depth of the goaf to be detected, the apparent shear wave velocity slices corresponding to the elevations of different stages of the grouting cycle are extracted from the three-dimensional structural model of the underground apparent shear wave velocity corresponding to different stages of the grouting cycle. Obtain the apparent shear wave velocity threshold standards at each level established by combining borehole core sampling and grouting construction records, and compare the apparent shear wave velocity in the apparent shear wave velocity slice with the apparent shear wave velocity threshold standards; Perform time-difference dynamic analysis on the apparent shear wave velocity slices; By combining the apparent shear wave velocity comparison results with the time-series differential dynamic analysis results, the grouting quality analysis results of the goaf to be detected are output.

2. The three-dimensional micro-motion detection method for grouting quality in coal mine goaf areas according to claim 1, characterized in that, The aforementioned method involves deploying a three-dimensional micro-motion dense array of stations adapted to the depth of the goaf, targeting the goaf to be detected. When the depth of the goaf to be detected is within 150m, the observation radius of the array is determined to be 35m-45m. The three-dimensional micro-motion dense array is deployed by arranging the main array at a point spacing of 2m×2m, and then densifying the array to a point spacing of 1m×1m within a 20m radius around the grouting well.

3. The three-dimensional micro-motion detection method for grouting quality in coal mine goaf areas according to claim 1, characterized in that, The micro-motion detection device, whose configuration has undergone consistency testing, includes: Nodal seismographs were used as data acquisition devices, along with broadband detectors. After connecting all data acquisition units and detectors, conduct an instrument consistency test with 10-20 minutes of synchronous acquisition to verify the consistency of waveforms in each channel; The internal clock of each data acquisition unit is automatically and in real time calibrated using the standard time signal of the satellite navigation system, so that the synchronization error between multiple data acquisition units is less than 10μs.

4. The three-dimensional micro-motion detection method for grouting quality in coal mine goaf areas according to claim 1, characterized in that, The process involves arranging the micro-motion detection devices at various measuring points within the three-dimensional micro-motion dense array, and collecting micro-motion time-series data of the goaf under investigation at different stages of the grouting cycle using the micro-motion detection devices, including: Continuous micro-motion time-series data for a duration of not less than 40 minutes were collected during the baseline period before grouting, every 2 hours during grouting, and the stabilization period after grouting.

5. The three-dimensional micro-motion detection method for grouting quality in coal mine goaf areas according to claim 1, characterized in that, Based on the preprocessed micro-motion time-series data, Rayleigh wave dispersion curves are extracted using the Extended Spatial Autocorrelation (ESPAC) method, and a three-dimensional structural model of the apparent shear wave velocity at different stages of the grouting cycle is obtained through inversion, including: Using each measuring point of the three-dimensional micro-motion dense array as the central measuring point, all measuring points within a set array radius around the central measuring point are combined to form an irregular dense array combination with the central measuring point as the core. Using the spatial autocorrelation characteristics of preprocessed micro-motion time series data of all measuring points within the irregular dense array combination, the Rayleigh wave dispersion curve of the central measuring point is calculated by the ESPAC method. By traversing all the measuring points of the three-dimensional micro-motion dense array, the Rayleigh wave dispersion curve of each measuring point is calculated point by point. The Rayleigh wave dispersion curve was inverted using the empirical formula of the half-wave method to obtain the apparent shear wave velocity data corresponding to different depths at each measuring point. The apparent shear wave velocity data from all measuring points are interpolated, smoothed, and fitted in three dimensions to generate a three-dimensional structural model of the underground apparent shear wave velocity corresponding to different stages of the grouting cycle.

6. The three-dimensional micro-motion detection method for grouting quality in coal mine goaf areas according to claim 1, characterized in that, Based on the depth of the goaf to be detected, the apparent shear wave velocity slices corresponding to different elevations at different stages of the grouting cycle are extracted from the three-dimensional structural model of the apparent shear wave velocity at different stages of the grouting cycle, including: The interface with a sharp change in wave velocity in the three-dimensional structural model of apparent shear wave velocity in the underground 1150m~1160m elevation range was identified and determined to be the bedrock interface. The slice corresponding to the 1100m elevation that best matches the actual burial depth of the goaf to be detected is taken as the main analysis slice. The slices corresponding to the 1090m and 1110m elevations were extracted as auxiliary analysis slices.

7. The three-dimensional micro-motion detection method for grouting quality in coal mine goaf areas according to claim 1, characterized in that, The acquisition of the various levels of apparent shear wave velocity threshold standards established by combining borehole core sampling and grouting construction records includes: Based on the physical characteristics of the ungrouted goaf and collapse fracture zone revealed by the borehole, a first low-velocity threshold range is set to characterize the unfilled state. Based on the physical characteristics of local grouting voids or uneven grout diffusion, a second medium-speed threshold range is set to characterize the partial filling state. Based on the physical characteristics of intact and dense rock mass after grouting, a third high-speed threshold range is set to characterize the fully filled state.

8. The three-dimensional micro-motion detection method for grouting quality in coal mine goaf areas according to claim 7, characterized in that, The time-difference dynamic analysis of the apparent shear wave velocity slices includes: The dynamic change of the apparent shear wave velocity is calculated by subtracting the apparent shear wave velocity slice during the grouting process from the apparent shear wave velocity slice during the reference period pixel by pixel. Based on the dynamic change of the apparent shear wave velocity, a spatiotemporal evolution cloud map of slurry diffusion is generated to dynamically track the diffusion trajectory and filling status of the slurry.

9. The three-dimensional micro-motion detection method for grouting quality in coal mine goaf areas according to claim 8, characterized in that, Following the generation of the slurry diffusion spatiotemporal evolution cloud map, the following is also included: In the spatiotemporal evolution cloud map of the slurry diffusion, a closed low-velocity anomaly region is identified where the apparent shear wave velocity increase is lower than a set change threshold and the current absolute apparent shear wave velocity falls into the unfilled threshold range. Extract the boundary of the closed low-velocity anomaly region, automatically delineate the locally unfilled residual cavities and slurry diffusion blind zones, and obtain the spatial coordinates of the slurry diffusion blind zones.

10. The three-dimensional micro-motion detection method for grouting quality in coal mine goaf areas according to claim 9, characterized in that, The method combines the apparent shear wave velocity comparison results with the time-series differential dynamic analysis results to output the grouting quality analysis results for the goaf to be detected, including: Based on the apparent shear wave velocity comparison results, the area of ​​the region in the apparent shear wave velocity slice that falls into the third high-speed threshold interval corresponding to the fully filled state is statistically analyzed, as well as the total coverage area of ​​the apparent shear wave velocity slice, and the overall filling rate of the region and the medium density improvement rate of each partition are calculated. Based on the overall filling rate of the area, the density improvement rate of the medium in each zone, and the spatial coordinates of the grout diffusion blind zone, a grouting quality analysis result is generated, which includes the overall filling rate of the area, the density improvement rate of the medium in each zone, the distribution map of supplementary grouting points, and the optimization scheme of grouting pressure and grout mix ratio parameters.

Images