A method and system for monitoring the migration of a slurry for layer separation grouting

Abstract

The present application belongs to the technical field of geological engineering monitoring and multi-physical field intelligent sensing, and discloses a method and system for monitoring the migration of off-layer grouting slurry, which constructs an acoustics-optics-electrics-electromagnetics multi-physical field coupling monitoring system; arranges grouting holes and monitoring borehole arrays in the target stratum, installs distributed optical fiber sensors, borehole ultrasonic imagers, resistivity measuring devices and transient electromagnetic detection devices to form a three-dimensional monitoring network; synchronously collects sound field, light field, electric field and magnetic field data; fuses the four-field monitoring data with the overburden multi-parameter observation data, constructs a three-dimensional overburden separation model of the coal seam mining, generates a slurry diffusion three-dimensional visualization model through numerical simulation; based on the four-field monitoring response differences of the off-layer space before and after grouting, combined with the monitoring frequency domain correction model of the slurry diffusion multi-index, evaluates the diffusion range, density and filling effect of the grouting slurry, and triggers an abnormal early warning.

Description

Technical Field

[0001] This invention belongs to the field of geological engineering monitoring and multi-physics intelligent sensing technology, and particularly relates to a method and system for monitoring the migration of grout in delamination grouting. Background Technology

[0002] During the grouting process for overburden separation, the migration and diffusion range of the grout are often difficult to predict accurately, making it difficult to evaluate the grouting effect and threatening the overall project progress and construction safety. Therefore, real-time monitoring of the grouting process is crucial. Without a scientific understanding of the grout flow distribution in the separation space, it is difficult to adjust grouting parameters in a timely manner and ensure effective filling of the separation.

[0003] Currently, commonly used monitoring methods include drilling, flow monitoring, and pressure monitoring. With increasing demands for monitoring accuracy and timeliness, geophysical exploration methods are gradually becoming an important supplement to or even replacement of traditional monitoring methods. Geophysical exploration utilizes changes in physical fields such as gravity, magnetic fields, electric fields, electromagnetic wave fields, or seismic wave fields to infer underground structures and rock strata distribution, offering advantages such as large detection range, high efficiency, and high automation. However, single geophysical methods often exhibit multiple solutions or fail to meet real-time monitoring requirements in complex geological environments; therefore, the integrated application and joint inversion of multiple geophysical methods will provide greater reliability.

[0004] 1. In terms of monitoring dimensions, traditional methods rely on a single physical field (such as resistivity or sound waves) to monitor slurry movement, resulting in one-sided parameter acquisition. For example, the ultrasonic method can only reflect the reflected signal at the slurry interface and cannot simultaneously acquire rheological properties (such as viscosity) or filling density. The resistivity method is easily affected by the formation water content and has difficulty distinguishing the resistance difference between slurry diffusion and natural fractures.

[0005] 2. In terms of spatiotemporal resolution, traditional methods such as drilling and point sensors have a vertical resolution >1m, but the data acquisition cycle is long and intermittent sampling leads to missed detection of rapid slurry diffusion processes (such as the formation time of water inrush channels <30 seconds).

[0006] 3. Regarding inversion error and reliability, single geophysical methods (such as electromagnetic imaging) are significantly affected by stratigraphic anisotropy. Single-field data inversion lacks cross-validation, has a high error rate, and cannot distinguish between slurry fronts and geological noise.

[0007] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0008] (1) At present, the monitoring of grout migration in overburden separation grouting is limited to a single physical field (such as resistivity or sound waves), and cannot simultaneously obtain multi-dimensional parameters such as grout rheological properties, diffusion path and filling density.

[0009] (2) Traditional monitoring methods for grout migration in overburden separation grouting have insufficient spatiotemporal resolution, with vertical resolution generally >1m, making it difficult to identify thin separation spaces (<0.5m); the data acquisition cycle is long, making it impossible to capture the rapid diffusion process of grout; at the same time, the horizontal positioning error is large, resulting in inaccurate positioning of the grouting target area; and relying on manual sampling or point sensors can only obtain intermittent data, making it impossible to capture the transient process of grout diffusion.

[0010] (3) The data obtained by traditional overburden separation grouting grout migration monitoring methods have large inversion errors, single field data inversion has multiple solutions, the error rate is generally greater than 25%, there is a lack of multi-physics field data cross-validation mechanism, and it is significantly affected by the anisotropy of the strata. Summary of the Invention

[0011] To overcome the problems existing in related technologies, the present invention discloses a method and system for monitoring the migration of grout in delamination grouting, and particularly relates to a method and system for monitoring the migration of grout in delamination grouting based on the integration of acoustic, optical, electrical, and magnetic fields. The technical solution is as follows:

[0012] This invention is implemented as follows: a method for monitoring the migration of grout during delamination, comprising the following steps:

[0013] S1. Construct an acoustic-optical-electrical-electromagnetic multi-physics field coupling monitoring system and verify it through indoor model experiments;

[0014] S2, grouting holes and monitoring borehole arrays are laid out in the target stratum, and distributed fiber optic sensors, borehole ultrasonic imagers, resistivity measuring devices and transient electromagnetic detection devices are installed to form a three-dimensional monitoring network.

[0015] S3, simultaneously acquires sound field, light field, electric field and magnetic field data;

[0016] S4 integrates the monitoring data from four sites with the multi-parameter observation data of the overburden to construct a three-dimensional overburden separation model for coal seam mining, and generates a three-dimensional visualization model of slurry diffusion through numerical simulation.

[0017] S5, based on the four-field monitoring response differences of the delamination space before and after grouting, combined with the monitoring frequency domain correction model of grout diffusion multiple indicators, evaluates the diffusion range, density and filling effect of the grout, and triggers anomaly warning.

[0018] In step S1, the indoor model test includes:

[0019] Distributed fiber grating networks were used to simulate the distribution of rock fractures;

[0020] By cross-validating ultrasonic imaging and electromagnetic wave imaging, a threshold for identifying slurry diffusion patterns was established.

[0021] Based on multi-condition test data, a mapping relationship between resistivity change rate and slurry concentration was constructed.

[0022] In step S2, the layout of the monitoring borehole array satisfies:

[0023] The spacing between grouting holes should be 3-5 times the working face height.

[0024] The fiber optic sensors are arranged in segments along the entire depth of the borehole, with a segment interval of ≤0.5m;

[0025] The radial distance between the transient electromagnetic detection device and the grouting hole is 1.2-1.8 times the thickness of the target layer being monitored;

[0026] The distributed fiber optic sensor uses a Brillouin optical time-domain reflectometer with a spatial resolution ≤0.1m and a sampling frequency ≥10Hz; the transient electromagnetic detection device has a transmission frequency of 25-750Hz and a turn-off time <2μs.

[0027] In step S3, the synchronous acquisition of sound field, light field, electric field, and magnetic field data includes:

[0028] The sound field module acquires data on ultrasonic propagation delay and amplitude attenuation.

[0029] The optical field module collects fiber strain distribution and temperature gradient changes;

[0030] The electric field module measures the dynamic changes in formation resistivity using high-density electrical resistivity.

[0031] The magnetic field module inverts the fracture development characteristics through electromagnetic wave imaging.

[0032] In step S4, the three-dimensional overburden separation model of coal seam mining is a dynamic geological model constructed by fusing four fields of data: acoustic attenuation, fiber strain, resistivity change and electromagnetic imaging, and combining them with the COMSOL-FLAC3D coupling algorithm.

[0033] The construction of dynamic geological models using the COMSOL-FLAC3D coupling algorithm includes:

[0034] The monitoring data of four fields—sound, light, electric, and magnetic fields—were fused with multi-parameter observation data of the overburden. Wavelet packet decomposition was performed on the acoustic signal to extract the energy proportion in the 2-10kHz frequency band, and a linear relationship was established with the slurry saturation: R²=0.93. Temperature compensation was applied to the resistivity data to eliminate environmental interference. The four-field data were fused using DS evidence theory to generate a slurry diffusion confidence cloud map with a confidence level >85%.

[0035] Using the fused data and the COMSOL-FLAC3D coupling algorithm, a three-dimensional overburden delamination model for coal seam mining was constructed. By quantitatively characterizing the opening, azimuth, and development height of the delamination space during mining, the spatial evolution characteristics of the arch delamination during grouting were predicted.

[0036] The core parameters of the three-dimensional overburden separation model for coal seam mining include the rock layer fracture angle, the rate of change of separation aperture, and the connectivity of the fracture network. Among them, the rock layer fracture angle is obtained by ultrasonic amplitude inversion, the rate of change of separation aperture is calculated by fiber optic strain data, and the connectivity of the fracture network is analyzed by electromagnetic imaging.

[0037] COMSOL Multiphysics and FLAC3D are coupled to model and realize multi-physics collaborative inversion and cross-scale analysis. The acoustic module of COMSOL is used to process ultrasonic attenuation data, and the geotechnical module of FLAC3D is used to analyze the fiber strain and delamination opening changes, so as to realize the coupling of acoustic-optical-mechanical multi-field data.

[0038] Numerical simulation is used to generate a three-dimensional visualization model of slurry diffusion, which intuitively shows the diffusion of slurry in the delamination space;

[0039] The input parameters for coupled modeling using COMSOL Multiphysics and FLAC3D include:

[0040] The correlation function between the acoustic attenuation coefficient and the slurry viscosity is:

[0041] ;

[0042] Where α(f, η) is the correlation function between the acoustic attenuation coefficient and the slurry viscosity, f is the ultrasonic frequency, η is the slurry viscosity, β is the viscous coupling coefficient, which is determined by the slurry type and calibrated experimentally; α0 is the background attenuation coefficient.

[0043] The rate of change of delamination aperture retrieved from fiber strain data is:

[0044] ;

[0045] Where k is the strain-aperture conversion coefficient, and Δb / Δt is the rate of change of delamination aperture. The rate of change of fiber strain over time;

[0046] The overlap weighting coefficient between the resistivity anomaly region and the electromagnetic imaging results is:

[0047] ;

[0048] Where ω is the overlap weighting coefficient; e is the natural constant, which has no meaning, and y=e xIt is an exponential function with the natural constant e as its base; ρ e For resistivity anomalies, ρ m For electromagnetic imaging anomalies, σ is the normalization parameter, and A is the normalization parameter. e ∩A m A represents the area of ​​overlap in the anomalous space. e This represents the total area of ​​resistivity anomalies.

[0049] In step S5, the monitoring frequency domain correction model is based on a dynamic error compensation system established using multi-physics field data, which includes:

[0050] Resistivity-concentration mapping function;

[0051] Acoustic attenuation-viscosity correlation matrix, shear rate 50-200s -1 Applicable;

[0052] Electromagnetic wave-fracture aperture conversion coefficient;

[0053] Time base alignment of data with different sampling rates is achieved through frequency domain Fourier transform. The specific process is as follows:

[0054] ① Acquire sound field, light field, electric field and magnetic field data simultaneously through different sensors;

[0055] ② By using Fourier transform, time series data is decomposed into a superposition of sine and cosine waves of different frequencies, and data with different sampling rates is transformed from the time domain to the frequency domain;

[0056] Perform Fourier transforms on the high and low sampling rate signals respectively to convert the time-domain signals into frequency-domain representations;

[0057] The formula for continuous Fourier transform is:

[0058] ;

[0059] After frequency domain transformation, the signal is represented as a complex number with different frequency components; in the continuous Fourier transform formula, x(t) represents the time domain signal, t represents the time variable, f represents the frequency variable, X(f) represents the frequency domain signal, and j represents the imaginary unit;

[0060] ③ In the frequency domain, analyze the spectral characteristics of each dataset, including frequency components, amplitude and phase information, determine a common sampling rate based on spectral analysis, and then use digital signal processing technology to convert each dataset to a common sampling rate;

[0061] The phase spectrum is adjusted based on the sampling time offset; if the time domain offset is Δt, then the frequency domain phase correction factor is... ;

[0062] ④ After resampling, the data is converted from the frequency domain back to the time domain by inverse Fourier transform;

[0063] The formula for the inverse Fourier transform is:

[0064] ;

[0065] The establishment of the monitoring frequency domain correction model includes:

[0066] Wavelet packet decomposition was performed on the acoustic signal, and the energy ratio of the 2-10kHz frequency band was extracted as an indicator of slurry saturation.

[0067] Temperature-pressure joint compensation is applied to resistivity data to generate dynamic correction curves;

[0068] Noise interference was eliminated by analyzing the spatial correlation between electromagnetic wave trend tomography and fiber optic temperature gradient.

[0069] In step S5, the triggering conditions for the abnormal warning include:

[0070] The slurry diffusion rate exceeded 1.2 times the coal seam advance rate;

[0071] The resistivity difference before and after grouting is <15% and the fiber strain mutation is >200με;

[0072] The minimum distance between the fracture network displayed by electromagnetic imaging and the preset seepage-proof curtain is <2m.

[0073] This method also includes a grouting effect evaluation system, which quantifies the grouting effectiveness through the following indicators:

[0074] Grout filling rate: The proportion of filling volume in the three-dimensional model based on the fusion and inversion of four-field data;

[0075] Density index: a weighted average of ultrasonic amplitude attenuation rate and resistivity change rate;

[0076] Stability coefficient: The standard deviation threshold of fiber strain fluctuation within 72 hours after grouting is ≤50με;

[0077] The method also includes an adaptive data fusion module that performs the following operations:

[0078] (1) Time-frequency domain synchronous calibration of sound field and light field data:

[0079] Fourier transforms were performed on the acoustic emission signal and the distributed fiber strain data respectively. After unifying the transformation to the same sampling rate, an inverse transform was performed to achieve time synchronization, with the error controlled within 0.1s. The sound field spectrum was calibrated using the frequency response curve of the acoustic sensor, and the optical field data was matched with the frequency domain characteristics of the fiber strain to eliminate the inherent frequency response differences of the sensor. Based on the time delay Δ... t Adjust the frequency domain phase factor to ensure phase consistency of the acoustic and optical signals;

[0080] (2) Perform gridded kriging interpolation on the electric and magnetic field data:

[0081] The spatial correlation between resistivity and electromagnetic anomalies was analyzed. An exponential semivariogram was selected to fit the spatial structure. Electric field data was used as the main variable and magnetic field as the covariate. The Kriging equations were solved to obtain the grid node weight coefficients and generate an interpolated surface with a resolution of 0.5m×0.5m.

[0082] (3) The confidence probability of multi-field data is calculated using the Dempster-Shafer evidence theory to generate a slurry diffusion confidence cloud map;

[0083] Confidence probability is a quantitative indicator characterizing the reliability of data, and its calculation formula is:

[0084] ;

[0085] Where Pr (c1≤μ≤c2) represents the probability that parameter μ falls within this interval, c1 and c2 are the upper and lower limits of the confidence interval, μ is the true value of the population parameter, and α is the significance level;

[0086] The grout diffusion confidence cloud map is a three-dimensional probability distribution field generated by Kriging interpolation. It displays the probability of grout presence in different regions through color gradients. The red region has a probability >90%, corresponding to a high confidence area with consistency >80% across the four data fields; the blue region has a probability <30%, reflecting a region to be verified due to anomalies in single-field data. This cloud map can be dynamically updated with a cycle of ≤1 minute, providing an intuitive basis for adjusting the grouting process.

[0087] Another object of the present invention is to provide a grout migration monitoring system for delamination grouting, which is used to regulate the grout migration monitoring method for delamination grouting, and the system includes:

[0088] The three-dimensional monitoring network establishment module is used to construct an acoustic-optical-electrical-electromagnetic multi-physics field coupled monitoring system. It deploys grouting holes and monitoring borehole arrays in the target stratum, and installs distributed fiber optic sensors, borehole ultrasonic imagers, resistivity measuring devices and transient electromagnetic detection devices to form a three-dimensional monitoring network.

[0089] The data fusion module is used to simultaneously collect sound field, light field, electric field and magnetic field data, integrate the four field monitoring data with the multi-parameter observation data of the overburden, construct a three-dimensional overburden separation model of coal seam mining, and generate a three-dimensional visualization model of slurry diffusion through numerical simulation.

[0090] The grout migration monitoring module is used to evaluate the diffusion range, density and filling effect of the grout based on the four-field monitoring response differences in the delamination space before and after grouting, combined with the monitoring frequency domain correction model of grout diffusion multiple indicators, and to trigger abnormal early warning.

[0091] Furthermore, when applied to coal mine overburden separation grouting projects, the spatiotemporal resolution of this monitoring system meets the following requirements:

[0092] Vertical resolution ≤ 0.3m, horizontal resolution ≤ 0.8m;

[0093] Dynamic data update cycle ≤ 1 minute, abnormal event response delay ≤ 5 seconds

[0094] Combining all the above technical solutions, the beneficial effects of this invention are as follows:

[0095] First, this invention achieves comprehensive sensing of multiple parameters, including rheological properties, spatial distribution, and fracture development, during slurry diffusion through simultaneous acquisition of four fields: acoustic field (ultrasonic attenuation), optical field (fiber optic strain), electric field (resistivity), and magnetic field (electromagnetic imaging). Compared to traditional single-field monitoring, the data dimensionality is significantly improved, accurately identifying the morphology of the slurry front and the filling state of the delamination space, thus solving the problem of one-sided monitoring data under complex geological conditions.

[0096] This invention employs BOTDR fiber optic sensing technology (0.1m spatial resolution) combined with a minute-level data update system, achieving a vertical monitoring accuracy of 0.3m and a horizontal accuracy of 0.8m. Compared to traditional methods with positioning errors exceeding 2m, it can accurately capture the dynamic migration process of slurry within thin delamination layers (<0.5m), making it particularly suitable for the rapid identification of water inrush channels (formation time <30s), improving spatiotemporal resolution by up to 15 times.

[0097] This invention, based on COMSOL-FLAC3D coupled modeling and multiphysics data fusion algorithms, controls the inversion error to within 8%. By setting triple early warning conditions of velocity / resistivity / strain (response delay ≤ 5s), intelligent detection of abnormal slurry diffusion is achieved.

[0098] Secondly, this invention establishes a four-dimensional sensing system consisting of sound field (ultrasound), light field (fiber optic strain), electric field (resistivity), and magnetic field (electromagnetic imaging), and develops a multi-physics data fusion algorithm; constructs a high-density monitoring network to achieve minute-level data updates; establishes a frequency domain correction model, develops a COMSOL-FLAC3D coupled inversion algorithm, and sets a triple verification mechanism (ultrasound-electromagnetic-resistivity cross-verification); constructs a real-time early warning system, sets multi-level triggering conditions, and develops a three-dimensional visualization early warning interface to achieve monitoring of grout migration during delamination grouting.

[0099] Third, this invention, through an acoustic-optical-electrical-electromagnetic multi-physics coupled monitoring system, is expected to reduce the slurry diffusion positioning error from 25% to less than 10% using traditional methods, reducing ineffective grouting by approximately 30%, and saving a single coal mine approximately 5 million yuan in grouting material costs annually. Minute-level early warning response can significantly reduce the missed detection rate of water inrush channels, avoiding direct losses from single water inrush accidents. This invention drives the industrialization demand for high-end monitoring equipment such as distributed fiber optic sensors and transient electromagnetic devices. The slurry diffusion reliability cloud map generated by this invention based on the COMSOL-FLAC3D coupled model can provide value-added data services such as grouting effect evaluation reports for mining areas.

[0100] Fourth, this invention is the first in the world to achieve the simultaneous fusion of four data fields—acoustic wave attenuation, fiber optic strain, resistivity, and electromagnetic imaging—breaking through the limitations of single geophysical exploration methods in terms of multiple solutions and filling the gap in multi-physics field collaborative monitoring. With a vertical resolution ≤0.3m and a horizontal resolution ≤0.8m, it fills the technical gap in real-time monitoring of thin-layer delamination slurry migration, and is particularly suitable for complex coal seam conditions in China. Furthermore, by generating slurry diffusion probability cloud maps using Dempster-Shafer evidence theory, it solves the subjectivity problem of traditional manual experience-based interpretation and achieves data-driven decision-making. Attached Figure Description

[0101] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure;

[0102] Figure 1 This is a flowchart of the grout migration monitoring method provided in the embodiments of the present invention;

[0103] Figure 2 This is a schematic diagram of the grout migration monitoring method provided in this embodiment of the invention;

[0104] Figure 3 This is a diagram of the four-field coupling simulation physical model provided in the embodiments of the present invention;

[0105] In the figure: 1. Sandstone physical model; 2. Distributed fiber grating (FBG) sensor; 3. Piezoelectric ceramic transducer; 4. Transient electromagnetic device. Detailed Implementation

[0106] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0107] The innovation of the grout migration monitoring method and system provided in this invention is as follows:

[0108] (1) Establish a four-dimensional sensing system consisting of sound field (ultrasound) + light field (fiber strain) + electric field (resistivity) + magnetic field (electromagnetic imaging);

[0109] (2) Develop multiphysics data fusion algorithms;

[0110] (3) Construct a high-density monitoring network to achieve minute-level data updates;

[0111] (4) Establish a frequency domain correction model, develop a COMSOL-FLAC3D coupled inversion algorithm, and set up a triple verification mechanism (ultrasound-electromagnetism-resistivity cross verification).

[0112] (5) Construct a real-time early warning system, set multi-level triggering conditions, and develop a three-dimensional visualization early warning interface.

[0113] Based on the principle of this monitoring method, it is also applicable to the following scenarios:

[0114] (1) Quality control of tunnel lining grouting: The location of voids after lining is detected by transient electromagnetic method, and the grout diffusion range is quantified by resistivity CT method to locate the weak grouting area; the lining deformation can be fed back in real time by fiber optic strain monitoring to correct the grouting pressure parameters, prevent structural cracking, and the grout solidification effect can be evaluated by acoustic time delay analysis to ensure continuous closure of the anti-seepage curtain.

[0115] (2) Dam reinforcement and seepage repair: The seepage channels are inverted by combining high-density electrical resistivity tomography and distributed optical fiber thermometry to locate the piping risk area, and the development of internal cracks in the dam is assessed by ultrasonic detection to guide the construction path of crack grouting.

[0116] (3) Urban underground space seepage prevention project: When grouting the joints of subway tunnels, distributed optical fiber and acoustic vibration are used to monitor the flow state of the grout. The timing of grouting is optimized by combining resistivity data. Underground cavities are identified by relying on ground radar (magnetic field data) and resistivity method (electric field data) to prevent leakage after lining.

[0117] Example 1, as Figure 1 and Figure 2 As shown, the method for monitoring the migration of grouting fluid in delamination provided in this embodiment of the invention includes the following steps:

[0118] S1. Construct an acoustic-optical-electrical-electromagnetic multi-physics field coupling monitoring system and verify it through indoor model experiments;

[0119] The feasibility and accuracy of the system for monitoring the diffusion of grout in delamination were verified through indoor model tests, and the flow and diffusion mechanism of gangue grout in the rock stratum was analyzed; the physical model diagram of the four-field coupling simulation is shown below. Figure 3 .

[0120] 1. Experimental model setup:

[0121] like Figure 3 As shown, a sandstone physical model 1 with dimensions of 5m×5m×3m is used to simulate the delamination structure of coal mine overburden. An artificial fracture network is pre-installed inside the model, with fracture widths ranging from 2-8mm.

[0122] Distributed fiber grating (FBG) sensors 2 were deployed on the surface and inside the model, arranged at intervals of 0.5m along the crack direction, for a total of 120 measuring points;

[0123] Install piezoelectric ceramic transducers 3 (operating frequency 20kHz-2MHz) at the four corners of the model for transmitting / receiving ultrasonic signals.

[0124] 2. Configuration of monitoring equipment in four fields:

[0125] Sound field module: Employs an ultrasonic transmitter and receiver array with a sampling frequency of 1MHz;

[0126] Optical field module: Brillouin optical time domain reflectometer (BOTDR) is used, with a spatial resolution of 0.1m and a sampling frequency of 10Hz;

[0127] Electric field module: Deploy a quadrupole high-density resistivity transilluminator to measure changes in formation resistivity;

[0128] Magnetic field module: Deploy transient electromagnetic device 4 (transmission frequency 25-750Hz, off time 1.8μs) to collect electromagnetic induction signals.

[0129] 3. Grout injection and data acquisition:

[0130] Inject gangue slurry (water-cement ratio 1:1) at a grouting rate of 0.2 m³ / min;

[0131] Simultaneous data collection from four fields:

[0132] Sound field: Record the change in ultrasonic wave propagation delay; when the travel time increases by ≥12μs / m, the slurry front is determined to have arrived.

[0133] Optical field: Monitors the sudden change in fiber strain, with a threshold set at 200με, triggering a local alarm;

[0134] Electric field: Real-time plotting of resistivity contour lines; when the resistivity decrease rate is >30%, it is marked as the slurry diffusion zone;

[0135] Magnetic field: The fracture propagation morphology was inverted using electromagnetic wave tomography with a resolution of 0.3m × 0.3m.

[0136] S2, grouting holes and monitoring borehole arrays are laid out in the target stratum, and distributed fiber optic sensors, borehole ultrasonic imagers, resistivity measuring devices and transient electromagnetic detection devices are installed to form a three-dimensional monitoring network.

[0137] S3, simultaneously acquires sound field, light field, electric field and magnetic field data;

[0138] The sound field module acquires data on ultrasonic propagation delay and amplitude attenuation.

[0139] The optical field module collects fiber strain distribution and temperature gradient changes;

[0140] The electric field module measures the dynamic changes in formation resistivity using high-density electrical resistivity.

[0141] The magnetic field module inverts fracture development characteristics through electromagnetic wave imaging;

[0142] S4 integrates the monitoring data from four sites with the multi-parameter observation data of the overburden to construct a three-dimensional overburden separation model for coal seam mining, and generates a three-dimensional visualization model of slurry diffusion through numerical simulation.

[0143] The three-dimensional overburden delamination model for coal seam mining is a dynamic geological model constructed by integrating data from four fields: acoustic attenuation, fiber optic strain, resistivity change, and electromagnetic imaging, combined with the COMSOL-FLAC3D coupling algorithm. This model can quantitatively characterize the opening (accuracy ±0.3m), azimuth (±5°), and development height of the delamination space during mining. Its core parameters include the rock fracture angle (derived through ultrasonic amplitude inversion), the rate of change of delamination opening (calculated from fiber optic strain data), and the connectivity of the fracture network (analyzed by electromagnetic imaging). Compared to traditional two-dimensional models, it can more accurately predict the spatial evolution characteristics of "arch-shaped delamination" during grouting.

[0144] The process of constructing a dynamic geological model using the COMSOL-FLAC3D coupling algorithm includes:

[0145] ① First, the monitoring data of the four fields of sound field, light field, electric field and magnetic field are fused with the multi-parameter observation data of the overlying rock;

[0146] ② Using the fused data and the COMSOL-FLAC3D coupling algorithm, a three-dimensional overburden separation model for coal seam mining is constructed. By quantitatively characterizing the opening (accuracy ±0.3m), azimuth (±5°), and development height of the separation space during mining, the spatial evolution characteristics of "arch separation" during grouting are accurately predicted.

[0147] ③ The core parameters of the three-dimensional overburden separation model for coal seam mining include the rock fracture angle (inverted by ultrasonic amplitude), the rate of change of separation opening (calculated by fiber optic strain data), and the fracture network connectivity (analyzed by electromagnetic imaging).

[0148] ④ COMSOL Multiphysics and FLAC3D are coupled to model and realize multi-physics field collaborative inversion and cross-scale analysis. The ultrasonic attenuation data is processed by the acoustic module of COMSOL and analyzed by the geotechnical module of FLAC3D to analyze the fiber strain and delamination opening change, thus realizing the coupling of acoustic-optical-mechanical multi-field data.

[0149] ⑤ Through numerical simulation, a three-dimensional visualization model of slurry diffusion is generated to intuitively show the diffusion of slurry in the delamination space.

[0150] Note: In the method described in this invention, the rock stratum fracture angle, the rate of change of delamination aperture, and the fracture network connectivity are obtained or calculated through specific means, rather than being directly calculated using formulas. The following are the methods for obtaining or calculating these parameters:

[0151] Method for obtaining the fracture angle of rock strata: The fracture angle of rock strata is obtained through ultrasonic amplitude inversion. This means that the fracture angle of rock strata can be indirectly inferred by utilizing the propagation characteristics of ultrasonic waves in rock strata, especially the attenuation of the amplitude.

[0152] Method for estimating the rate of change of delamination opening: The rate of change of delamination opening is estimated using fiber optic strain data. Fiber optic sensors can detect changes in rock strain, and by analyzing this data, the rate of change of the delamination opening can be calculated.

[0153] Fracture Network Connectivity Analysis Method: Fracture network connectivity is analyzed using electromagnetic imaging. Electromagnetic imaging technology can detect the development of fractures in rock strata, including their location, morphology, and connectivity, thereby enabling the analysis of fracture network connectivity.

[0154] S5, based on the four-field monitoring response differences of the delamination space before and after grouting, combined with the monitoring frequency domain correction model of grout diffusion multiple indicators, evaluates the diffusion range, density and filling effect of the grout, and triggers anomaly warning.

[0155] The monitoring frequency domain correction model is a dynamic error compensation system based on multi-physics data, which mainly includes:

[0156] The quantitative evaluation index for the monitoring frequency domain correction model is:

[0157] Fill rate: The four-field data fusion shows that the filling volume accounts for 82% (design requirement ≥75%).

[0158] Density Index: Ultrasonic attenuation rate 32% + resistivity change rate 25%, overall score 68 (pass threshold 60);

[0159] Stability coefficient: The standard deviation of fiber strain fluctuation within 72 hours after grouting is 45με (threshold ≤50με).

[0160] Resistivity-concentration mapping function (water-cement ratio 0.6-1.2 range error <3%).

[0161] Acoustic attenuation-viscosity correlation matrix (shear rate 50-200s) -1 (Applicable)

[0162] Electromagnetic wave-fracture aperture conversion coefficient (fracture identification rate of 92% for 0.1-5mm fractures);

[0163] Time base alignment of data with different sampling rates is achieved by using frequency domain Fourier transform, so that the time synchronization error of the four fields is controlled within 0.1s.

[0164] The process is as follows:

[0165] ① Acquire sound field, light field, electric field and magnetic field data simultaneously through different sensors. These sensors may have different sampling rates, resulting in the acquired time series data not being completely aligned on the time axis.

[0166] ② In order to align these data with different sampling rates, it is necessary to decompose the time series data into a superposition of sine and cosine waves of different frequencies through Fourier transform, and transform the data with different sampling rates from the time domain to the frequency domain.

[0167] Perform Fourier transforms on the high and low sampling rate signals respectively to convert the time-domain signals into frequency-domain representations.

[0168] The formula for continuous Fourier transform is:

[0169] ;

[0170] After frequency domain transformation, the signal is represented as a complex number with different frequency components; in the continuous Fourier transform formula, x(t) represents the time domain signal, t represents the time variable, f represents the frequency variable, X(f) represents the frequency domain signal, and j represents the imaginary unit;

[0171] After frequency domain transformation, the signal is represented in complex form (amplitude spectrum and phase spectrum) of different frequency components, which facilitates subsequent alignment processing.

[0172] ③ In the frequency domain, the spectral characteristics of each dataset can be analyzed, including frequency components, amplitude, and phase information. Based on the spectral analysis, a common sampling rate can be determined, which should be high enough to capture the important frequency components in all datasets. Then, digital signal processing techniques (such as interpolation or resampling algorithms) are used to transform each dataset to this common sampling rate.

[0173] The phase spectrum is adjusted based on the sampling time offset. If the time domain offset is Δt, the frequency domain phase correction factor is... This ensures phase continuity after time base alignment.

[0174] ④ After resampling, the data is converted from the frequency domain back to the time domain by inverse Fourier transform.

[0175] The formula for the inverse Fourier transform is:

[0176] .

[0177] The indoor model test in step S1 provided in this embodiment of the invention includes:

[0178] Distributed fiber grating networks were used to simulate the distribution of rock fractures;

[0179] By cross-validating ultrasonic imaging and electromagnetic wave imaging, a threshold for identifying slurry diffusion patterns was established.

[0180] Based on multi-condition test data, a mapping relationship between resistivity change rate and slurry concentration was constructed.

[0181] The deployment of the monitoring borehole array in step S2 of the embodiment of the present invention satisfies the following:

[0182] The spacing between grouting holes should be 3-5 times the working face height.

[0183] The fiber optic sensors are arranged in segments along the entire depth of the borehole, with a segment interval of ≤0.5m;

[0184] The radial distance between the transient electromagnetic detection device and the grouting hole is 1.2-1.8 times the thickness of the target layer being monitored.

[0185] The numerical simulation software in step S4 of this embodiment of the invention uses COMSOL Multiphysics and FLAC3D coupled modeling, and the input parameters include:

[0186] The correlation function between the acoustic attenuation coefficient and the slurry viscosity is:

[0187] ;

[0188] Where α(f, η) is the correlation function between the acoustic attenuation coefficient and the slurry viscosity, f is the ultrasonic frequency, η is the slurry viscosity, β is the viscous coupling coefficient, which is determined by the slurry type and calibrated experimentally; α0 is the background attenuation coefficient.

[0189] The rate of change of delamination aperture retrieved from fiber strain data is:

[0190] ;

[0191] Where k is the strain-aperture conversion coefficient, and Δb / Δt is the rate of change of delamination aperture. The rate of change of fiber strain over time;

[0192] The overlap weighting coefficient between the resistivity anomaly region and the electromagnetic imaging results is:

[0193] ;

[0194] Where ω is the overlap weighting coefficient; e is the natural constant, which has no meaning, and y=e x It is an exponential function with the natural constant e as its base; ρ e For resistivity anomalies, ρ m For electromagnetic imaging anomalies, σ is the normalization parameter, and A is the normalization parameter. e ∩A m A represents the area of ​​overlap in the anomalous space. e This represents the total area of ​​resistivity anomalies.

[0195] The core purpose of using COMSOL Multiphysics and FLAC3D coupled modeling is to achieve multiphysics co-inversion and cross-scale analysis, which is specifically reflected in:

[0196] By processing ultrasonic attenuation data through COMSOL's acoustic module (elastic wave interface) and combining it with FLAC3D's geotechnical mechanics module to analyze the changes in fiber strain and delamination, multi-field data coupling of acoustic-optical-mechanical fields is achieved, solving the problem of incomplete physical field coverage by a single software.

[0197] COMSOL's high-frequency acoustic simulation (μs level) and FLAC3D's long-term rock deformation analysis (annual scale) are coupled through dynamic mesh technology, which can capture the instantaneous diffusion characteristics of slurry and predict the long-term stability of overburden delamination.

[0198] The resistivity-concentration mapping relationship was calibrated using COMSOL's optimization module, and the position of the slurry front was corrected using FLAC3D's solid-fluid coupling algorithm, so that the inversion error of the three-dimensional model was controlled within 8%.

[0199] The method for establishing the monitoring frequency domain correction model in step S5 of the present invention includes:

[0200] Wavelet packet decomposition was performed on the acoustic signal, and the energy ratio of the 2-10kHz frequency band was extracted as an indicator of slurry saturation.

[0201] Temperature-pressure joint compensation is applied to resistivity data to generate dynamic correction curves;

[0202] Noise interference was eliminated by analyzing the spatial correlation between electromagnetic wave trend tomography and fiber optic temperature gradient.

[0203] The triggering conditions for the abnormal warning in step S5 provided in this embodiment of the invention include:

[0204] The slurry diffusion rate exceeded 1.2 times the coal seam advance rate;

[0205] The resistivity difference before and after grouting is <15% and the fiber strain mutation is >200με;

[0206] The minimum distance between the fracture network displayed by electromagnetic imaging and the preset seepage-proof curtain is <2m.

[0207] The grout migration monitoring method for delamination grouting provided in this embodiment of the invention also includes a grouting effect evaluation system, which quantifies the grouting effectiveness through the following indicators:

[0208] Grout filling rate: The proportion of filling volume in the three-dimensional model based on the fusion and inversion of four-field data;

[0209] Density index: a weighted average of ultrasonic amplitude attenuation rate and resistivity change rate;

[0210] Stability coefficient: The standard deviation threshold of fiber strain fluctuation within 72 hours after grouting is ≤50με.

[0211] The distributed optical fiber sensor provided in this embodiment of the invention uses a Brillouin optical time domain reflectometer (BOTDR) with a spatial resolution ≤0.1m and a sampling frequency ≥10Hz; the transient electromagnetic detection device has a transmission frequency of 25-750Hz and a turn-off time <2μs.

[0212] The grout migration monitoring method for delamination grouting provided in this embodiment of the invention also includes an adaptive data fusion module, which performs the following operations:

[0213] (1) Time-frequency domain synchronous calibration of sound field and light field data:

[0214] Fourier transforms were performed on the acoustic emission signal and the distributed fiber strain data respectively. After unifying them to the same sampling rate, an inverse transform was performed to achieve time synchronization, with the error controlled within 0.1s. Then, the sound field spectrum was calibrated using the frequency response curve of the acoustic sensor. The optical field data was matched with the frequency domain characteristics of the fiber strain to eliminate the inherent frequency response differences of the sensor. Based on the time delay Δ... tAdjust the frequency domain phase factor to ensure phase consistency of the acoustic and optical signals;

[0215] (2) Perform gridded kriging interpolation on the electric and magnetic field data:

[0216] The spatial correlation between resistivity and electromagnetic anomalies was analyzed. An exponential semivariogram was selected to fit the spatial structure. Electric field data was used as the main variable and magnetic field as the covariate. The Kriging equations were solved to obtain the grid node weight coefficients and generate an interpolated surface with a resolution of 0.5m×0.5m.

[0217] (3) The confidence probability of multi-field data is calculated using Dempster-Shafer evidence theory to generate a slurry diffusion confidence cloud map.

[0218] Confidence probability is a quantitative indicator characterizing the reliability of data, and its calculation formula is:

[0219] ;

[0220] Where Pr (c1≤μ≤c2) represents the probability that parameter μ falls within this interval, c1 and c2 are the upper and lower limits of the confidence interval, μ is the true value of the population parameter, and α is the significance level;

[0221] The grout diffusion confidence cloud map is a three-dimensional probability distribution field generated using Kriging interpolation. It displays the probability of grout presence in different regions through a color gradient (red-yellow-blue). The red region (probability > 90%) corresponds to a high confidence area with data consistency > 80% across four data points, while the blue region (probability < 30%) reflects areas requiring verification due to anomalies in single-data points. This cloud map can be dynamically updated (cycle ≤ 1 minute), providing a direct basis for adjusting grouting processes.

[0222] Example 2, the grout migration monitoring system provided in this embodiment of the invention specifically includes:

[0223] The three-dimensional monitoring network establishment module is used to construct an acoustic-optical-electrical-electromagnetic multi-physics field coupled monitoring system. It deploys grouting holes and monitoring borehole arrays in the target stratum, and installs distributed fiber optic sensors, borehole ultrasonic imagers, resistivity measuring devices and transient electromagnetic detection devices to form a three-dimensional monitoring network.

[0224] The specific components of an acoustic-optical-electrical-electromagnetic multiphysics coupled monitoring system are as follows:

[0225] Distributed optical fiber: Brillouin optical time domain reflectometers (BOTDR) are deployed along the entire depth of the borehole to monitor overburden strain and temperature gradient;

[0226] Ultrasonic imager: Installed in adjacent boreholes, with a transmission frequency of 500kHz and a scanning interval of 10 minutes;

[0227] High-density resistivity resistivity meter: Electrode spacing is 1m, measuring resistivity distribution before and after grouting;

[0228] Transient electromagnetic device: emission frequency 500Hz, turn-off time 1.5μs, inversion fracture development depth.

[0229] The equipment functions as long as they meet the above requirements.

[0230] Verification standard: The method for monitoring the migration of grouting fluid in delamination grouting according to claim 1, characterized in that, in step S5, the triggering conditions for abnormal early warning include:

[0231] The slurry diffusion rate exceeded 1.2 times the coal seam advance rate;

[0232] The resistivity difference before and after grouting is <15% and the fiber strain mutation is >200με;

[0233] The minimum distance between the fracture network displayed by electromagnetic imaging and the preset seepage-proof curtain is <2m.

[0234] The data fusion module is used to simultaneously collect sound field, light field, electric field and magnetic field data, integrate the four field monitoring data with the multi-parameter observation data of the overburden, construct a three-dimensional overburden separation model of coal seam mining, and generate a three-dimensional visualization model of slurry diffusion through numerical simulation.

[0235] The grout migration monitoring module is used to evaluate the diffusion range, density and filling effect of the grout based on the four-field monitoring response differences in the delamination space before and after grouting, combined with the monitoring frequency domain correction model of grout diffusion multiple indicators, and to trigger abnormal early warning.

[0236] Example 3, when applied to coal mine overburden separation grouting projects, the spatiotemporal resolution of the separation grouting slurry migration monitoring system provided in this embodiment of the invention satisfies:

[0237] Vertical resolution ≤ 0.3m, horizontal resolution ≤ 0.8m;

[0238] Dynamic data update cycle ≤ 1 minute, abnormal event response delay ≤ 5 seconds.

[0239] Taking a coal mine as an example, the specific steps of using the method of this invention are as follows:

[0240] Step 1: Deployment of the monitoring system

[0241] 1. Drilling layout parameters:

[0242] The spacing between grouting holes (grouting 1, grouting 2, grouting 3) is 15m (3.75 times the mining height of 4m).

[0243] The monitoring borehole array is arranged along the working face, with a total of 6 boreholes, extending to a depth of 8m above and below the separation layer;

[0244] The fiber optic sensor segments are spaced 0.5m apart, and the transient electromagnetic device is 6m radially away from the grouting hole (1.2 times the target layer thickness of 5m).

[0245] 2. Equipment Deployment:

[0246] Distributed optical fiber: BOTDR is deployed along the entire depth of the borehole to monitor overburden strain and temperature gradient;

[0247] Ultrasonic imager: Installed in adjacent boreholes, with a transmission frequency of 500kHz and a scanning interval of 10 minutes;

[0248] High-density resistivity resistivity meter: Electrode spacing is 1m, measuring resistivity distribution before and after grouting;

[0249] Transient electromagnetic device: emission frequency 500Hz, turn-off time 1.5μs, inversion fracture development depth.

[0250] Step 2: Real-time monitoring and early warning

[0251] 1. Data Acquisition and Processing:

[0252] During the grouting process, the data update cycle for the four fields is 30 seconds (normal mode) and 5 seconds (emergency mode).

[0253] Light field data showed that the delamination aperture increased from 2.1 mm to 4.8 mm, triggering a level one warning.

[0254] The electric field data inversion revealed a slurry diffusion radius of 6.3 m and a resistivity difference rate of 18% (not triggered threshold).

[0255] 2. Abnormal event response:

[0256] When the slurry diffusion velocity reaches 0.35 m / s (1.4 times the coal seam advance velocity of 0.25 m / s), the system triggers a level two alarm;

[0257] The grouting pressure is automatically adjusted to 8MPa, and risk areas are marked on the 3D visualization interface.

[0258] Electromagnetic imaging showed that the fracture network was 1.8m away from the seepage prevention curtain, triggering a level-three emergency response, stopping grouting and initiating curtain reinforcement.

[0259] Step 3: Evaluation of Grouting Effect

[0260] 1. Quantitative evaluation indicators:

[0261] Fill rate: The four-field data fusion shows that the filling volume accounts for 82% (design requirement ≥75%).

[0262] Density Index: Ultrasonic attenuation rate 32% + resistivity change rate 25%, overall score 68 (pass threshold 60);

[0263] Stability coefficient: The standard deviation of fiber strain fluctuation within 72 hours after grouting is 45με (threshold ≤50με).

[0264] 2. Project benefits:

[0265] Grouting effectiveness increased to 89%, reducing drilling verification times by 40%;

[0266] The slurry waste rate was reduced from 15% to 7%, saving approximately 1.2 million yuan per working face.

[0267] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention and within the spirit and principles of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for monitoring the migration of grout during delamination, characterized in that, The method includes the following steps: S1. Construct an acoustic-optical-electrical-electromagnetic multi-physics field coupling monitoring system and verify it through indoor model experiments; S2, grouting holes and monitoring borehole arrays are laid out in the target stratum, and distributed fiber optic sensors, borehole ultrasonic imagers, resistivity measuring devices and transient electromagnetic detection devices are installed to form a three-dimensional monitoring network. S3, simultaneously acquires sound field, light field, electric field and magnetic field data; S4 integrates the monitoring data from four sites with the multi-parameter observation data of the overburden to construct a three-dimensional overburden separation model for coal seam mining, and generates a three-dimensional visualization model of slurry diffusion through numerical simulation. The three-dimensional overburden separation model for coal seam mining is a dynamic geological model constructed by integrating four fields of data: acoustic attenuation, fiber strain, resistivity change, and electromagnetic imaging, combined with the COMSOL-FLAC3D coupling algorithm. The construction of dynamic geological models using the COMSOL-FLAC3D coupling algorithm includes: The monitoring data of four fields—sound, light, electric, and magnetic fields—were fused with multi-parameter observation data of the overlying rock. Wavelet packet decomposition was performed on the acoustic signal to extract the energy proportion in the 2-10kHz frequency band, and a linear relationship was established with the slurry saturation. R2=0.93 Temperature compensation was applied to the resistivity data to eliminate environmental interference; the four-field data were fused using DS evidence theory to generate a slurry diffusion confidence cloud map with a confidence level >85%; Using the fused data and the COMSOL-FLAC3D coupling algorithm, a three-dimensional overburden delamination model for coal seam mining was constructed. By quantitatively characterizing the opening, azimuth, and development height of the delamination space during mining, the spatial evolution characteristics of the arch delamination during grouting were predicted. The core parameters of the three-dimensional overburden separation model for coal seam mining include the rock layer fracture angle, the rate of change of separation aperture, and the connectivity of the fracture network. Among them, the rock layer fracture angle is obtained by ultrasonic amplitude inversion, the rate of change of separation aperture is calculated by fiber optic strain data, and the connectivity of the fracture network is analyzed by electromagnetic imaging. COMSOL Multiphysics and FLAC3D are coupled to model and realize multi-physics collaborative inversion and cross-scale analysis. The acoustic module of COMSOL is used to process ultrasonic attenuation data, and the geotechnical module of FLAC3D is used to analyze the fiber strain and delamination opening changes, so as to realize the coupling of acoustic-optical-mechanical multi-field data. Numerical simulation is used to generate a three-dimensional visualization model of slurry diffusion, which intuitively shows the diffusion of slurry in the delamination space; The input parameters for coupled modeling using COMSOL Multiphysics and FLAC3D include: The correlation function between the acoustic attenuation coefficient and the slurry viscosity is: ； Where α(f, η) is the correlation function between the acoustic attenuation coefficient and the slurry viscosity, f is the ultrasonic frequency, η is the slurry viscosity, β is the viscous coupling coefficient, which is determined by the slurry type and calibrated experimentally; α0 is the background attenuation coefficient. The rate of change of delamination aperture retrieved from fiber strain data is: ； Where k is the strain-aperture conversion coefficient, and Δb / Δt is the rate of change of delamination aperture. The rate of change of fiber strain over time; The overlap weighting coefficient between the resistivity anomaly region and the electromagnetic imaging results is: ； Where ω is the overlap weighting coefficient; e is the natural constant, which has no meaning, and y=e x It is an exponential function with the natural constant e as its base; ρ e For resistivity anomalies, ρ m For electromagnetic imaging anomalies, σ is the normalization parameter, and A is the normalization parameter. e ∩A m A represents the area of ​​overlap in the anomalous space. e This represents the total area of ​​the resistivity anomaly. S5, based on the four-field monitoring response differences of the delamination space before and after grouting, combined with the monitoring frequency domain correction model of grout diffusion multiple indicators, evaluates the diffusion range, density and filling effect of the grout, and triggers anomaly warning.

2. The method for monitoring the migration of grouting fluid in delamination grouting according to claim 1, characterized in that, In step S1, the indoor model test includes: Distributed fiber grating networks were used to simulate the distribution of rock fractures; By cross-validating ultrasonic imaging and electromagnetic wave imaging, a threshold for identifying slurry diffusion patterns was established. Based on multi-condition test data, a mapping relationship between resistivity change rate and slurry concentration was constructed.

3. The method for monitoring the migration of grouting fluid in delamination grouting according to claim 1, characterized in that, In step S2, the layout of the monitoring borehole array satisfies: The spacing between grouting holes should be 3-5 times the working face height. The fiber optic sensors are arranged in segments along the entire depth of the borehole, with a segment interval of ≤0.5m; The radial distance between the transient electromagnetic detection device and the grouting hole is 1.2-1.8 times the thickness of the target layer being monitored; The distributed fiber optic sensor uses a Brillouin optical time-domain reflectometer with a spatial resolution ≤0.1m and a sampling frequency ≥10Hz; the transient electromagnetic detection device has a transmission frequency of 25-750Hz and a turn-off time <2μs.

4. The method for monitoring the migration of grouting fluid in delamination grouting according to claim 1, characterized in that, In step S3, the synchronous acquisition of sound field, light field, electric field, and magnetic field data includes: The sound field module acquires data on ultrasonic propagation delay and amplitude attenuation. The optical field module collects fiber strain distribution and temperature gradient changes; The electric field module measures the dynamic changes in formation resistivity using high-density electrical resistivity. The magnetic field module inverts the fracture development characteristics through electromagnetic wave imaging.

5. The method for monitoring the migration of grouting fluid in delamination grouting according to claim 1, characterized in that, In step S5, the monitoring frequency domain correction model is based on a dynamic error compensation system established using multi-physics field data, which includes: Resistivity-concentration mapping function; Acoustic attenuation-viscosity correlation matrix, shear rate 50-200s -1 Applicable; Electromagnetic wave-fracture aperture conversion coefficient; Time base alignment of data with different sampling rates is achieved through frequency domain Fourier transform. The specific process is as follows: ① Acquire sound field, light field, electric field and magnetic field data simultaneously through different sensors; ② By using Fourier transform, time series data is decomposed into a superposition of sine and cosine waves of different frequencies, and data with different sampling rates is transformed from the time domain to the frequency domain; Perform Fourier transforms on the high and low sampling rate signals respectively to convert the time-domain signals into frequency-domain representations; The formula for continuous Fourier transform is: ； After frequency domain transformation, the signal is represented in complex form as different frequency components; In the formula for continuous Fourier transform, x(t) represents the time domain signal, t represents the time variable, f represents the frequency variable, X(f) represents the frequency domain signal, and j represents the imaginary unit; ③ In the frequency domain, analyze the spectral characteristics of each dataset, including frequency components, amplitude and phase information, determine a common sampling rate based on spectral analysis, and then use digital signal processing technology to convert each dataset to a common sampling rate; The phase spectrum is adjusted based on the sampling time offset; if the time domain offset is Δt, then the frequency domain phase correction factor is... ; ④ After resampling, the data is converted from the frequency domain back to the time domain by inverse Fourier transform; The formula for the inverse Fourier transform is: ； The establishment of the monitoring frequency domain correction model includes: Wavelet packet decomposition was performed on the acoustic signal, and the energy ratio of the 2-10kHz frequency band was extracted as an indicator of slurry saturation. Temperature-pressure joint compensation is applied to resistivity data to generate dynamic correction curves; Noise interference was eliminated by analyzing the spatial correlation between electromagnetic wave trend tomography and fiber optic temperature gradient.

6. The method for monitoring the migration of grouting fluid in delamination grouting according to claim 1, characterized in that, In step S5, the triggering conditions for the abnormal warning include: The slurry diffusion rate exceeded 1.2 times the coal seam advance rate; The resistivity difference before and after grouting is <15% and the fiber strain mutation is >200με; The minimum distance between the fracture network displayed by electromagnetic imaging and the preset seepage-proof curtain is <2m.

7. The method for monitoring the migration of grouting fluid in delamination grouting according to claim 1, characterized in that, This method also includes a grouting effect evaluation system, which quantifies the grouting effectiveness through the following indicators: Grout filling rate: The proportion of filling volume in the three-dimensional model based on the fusion and inversion of four-field data; Density index: a weighted average of ultrasonic amplitude attenuation rate and resistivity change rate; Stability coefficient: The standard deviation threshold of fiber strain fluctuation within 72 hours after grouting is ≤50με; The method also includes an adaptive data fusion module that performs the following operations: (1) Time-frequency domain synchronous calibration of sound field and light field data: Fourier transforms were performed on the acoustic emission signal and the distributed fiber strain data respectively. After unifying the transformation to the same sampling rate, an inverse transform was performed to achieve time synchronization, with the error controlled within 0.1s. The sound field spectrum was calibrated using the frequency response curve of the acoustic sensor, and the optical field data was matched with the frequency domain characteristics of the fiber strain to eliminate the inherent frequency response differences of the sensor. Based on the time delay Δ... t Adjust the frequency domain phase factor to ensure phase consistency of the acoustic and optical signals; (2) Perform gridded kriging interpolation on the electric and magnetic field data: The spatial correlation between resistivity and electromagnetic anomalies was analyzed. An exponential semivariogram was selected to fit the spatial structure. Electric field data was used as the main variable and magnetic field as the covariate. The Kriging equations were solved to obtain the grid node weight coefficients and generate an interpolated surface with a resolution of 0.5m×0.5m. (3) The confidence probability of multi-field data is calculated using the Dempster-Shafer evidence theory to generate a slurry diffusion confidence cloud map; Confidence probability is a quantitative indicator characterizing the reliability of data, and its calculation formula is: ； Where Pr (c1≤μ≤c2) represents the probability that parameter μ falls within this interval, c1 and c2 are the upper and lower limits of the confidence interval, respectively, μ is the true value of the population parameter, and α is the significance level.

8. A grout migration monitoring system for delamination grouting, characterized in that, This system is used to regulate the grout migration monitoring method according to any one of claims 1-7, and the system includes: The three-dimensional monitoring network establishment module is used to construct an acoustic-optical-electrical-electromagnetic multi-physics field coupled monitoring system. It deploys grouting holes and monitoring borehole arrays in the target stratum, and installs distributed fiber optic sensors, borehole ultrasonic imagers, resistivity measuring devices and transient electromagnetic detection devices to form a three-dimensional monitoring network. The data fusion module is used to simultaneously collect sound field, light field, electric field and magnetic field data, integrate the four field monitoring data with the multi-parameter observation data of the overburden, construct a three-dimensional overburden separation model of coal seam mining, and generate a three-dimensional visualization model of slurry diffusion through numerical simulation. The grout migration monitoring module is used to evaluate the diffusion range, density and filling effect of the grout based on the four-field monitoring response differences in the delamination space before and after grouting, combined with the monitoring frequency domain correction model of grout diffusion multiple indicators, and to trigger abnormal early warning.

9. The grout migration monitoring system according to claim 8, characterized in that, When applied to coal mine overburden separation grouting projects, the spatiotemporal resolution of this monitoring system meets the following requirements: Vertical resolution ≤ 0.3m, horizontal resolution ≤ 0.8m; Dynamic data update cycle ≤ 1 minute, abnormal event response delay ≤ 5 seconds.

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