Method for monitoring relative gravity of surface subsidence in coal mining process

Abstract

The invention discloses a method for monitoring the relative gravity of surface subsidence in a coal mining process, and the method comprises the steps: arranging a quantum gravity base station at the periphery of a subsidence area, deploying a relative gravimeter in a basin according to a grid, arranging a fiber grating gravity sensing chain in an underground crossheading, and carrying a cold atom gravity gradiometer by adopting an unmanned plane for periodic scanning. A gravitational field-mass migration model is established based on real-time data, coal mining volume, rock stratum density change and earth surface gravity anomaly are associated, geological background interference is eliminated through wavelet multi-scale decomposition, and a gravity change value is optimized through hydrological correction in combination with water level and porosity. And when the corrected gravity change exceeds a threshold value, calculating a rock stratum fracture azimuth angle by using a gravity gradient tensor, inverting an equivalent mining thickness based on the gravity change, dynamically predicting a surface subsidence form in combination with a subsidence basin model, constructing a state vector in combination with gravity, gradient and deformation data, and realizing multi-source fusion prediction through a filtering algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to a method for monitoring ground subsidence, in particular to a method for monitoring relative gravity of ground subsidence in the process of coal mining. BACKGROUND

[0002] In the field of coal mining, ground subsidence monitoring is a key link to ensure safety production and environmental management in mining areas. Traditional ground subsidence monitoring mostly uses single-point measurement methods such as GPS positioning, leveling or synthetic aperture radar (InSAR), etc. Although these methods can obtain deformation data in a certain area, they generally have the defects of single monitoring dimension and discontinuous spatial coverage. For example, GPS and leveling need to rely on fixed measuring points, which are difficult to capture the subtle deformation gradient of the subsidence basin; InSAR is limited by weather and terrain conditions, and cannot realize high-frequency dynamic monitoring, resulting in insufficient real-time and refined monitoring ability of ground subsidence in the process of coal mining.

[0003] In the prior art, there are significant technical bottlenecks in monitoring the changes of ground gravity field caused by coal mining. Traditional gravity monitoring methods can only be based on single-point instruments on the ground, and cannot build a three-dimensional monitoring network integrating surface, underground and well, which is difficult to fully reflect the three-dimensional dynamic process of rock mass migration under the influence of mining. At the same time, the interference of geological background field (such as deep structure movement) and hydrological condition change (such as underground water level fluctuation) will seriously affect the authenticity of gravity monitoring data, and the existing data processing method lacks effective multi-scale interference elimination mechanism, resulting in large error of monitoring results and unable to accurately invert the subsidence evolution law.

[0004] In addition, the existing subsidence prediction model is mostly based on empirical formula or simplified physical model, which does not fully combine the internal relationship between gravity field change and rock mass migration under mining, and is difficult to realize dynamic inversion of equivalent mining thickness and accurate prediction of spatial distribution of subsidence basin. Especially in the face of coal mining under complex geological conditions, the traditional method cannot integrate multi-source monitoring data (such as gravity change value, gravity gradient component and external deformation data), resulting in insufficient timeliness of subsidence early warning and accuracy of fracture azimuth calculation, which cannot meet the demand of high-precision and real-time monitoring for coal mine safety production. SUMMARY

[0005] The present application overcomes the shortcomings of the prior art and provides a method for monitoring relative gravity of ground subsidence in the process of coal mining.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is as follows: a method for monitoring relative gravity of ground subsidence in the process of coal mining, comprising the following steps:

[0007] S1, construct a ground and underground well integrated monitoring network; wherein, quantum gravity reference stations are arranged in the periphery of the subsidence area, relative gravimeters are deployed in the subsidence basin according to the grid density of 100~500m*100~500m, fiber grating gravity sensing chains are arranged in the working face crossheading of the underground well, and cold atom gravity gradiometer is carried by an unmanned aerial vehicle to carry out periodic regional scanning;

[0008] S2, based on the data monitored in real time in step S1, a gravity field-mass migration theoretical model is established, and the formula expression is:

[0009] ;

[0010] In the formula, is the surface gravity change value; G is the gravitational constant; is the coal density; is the extracted volume; d is the average depth; is the rock density change function at the spatial coordinates r is the distance from the mass element to the measuring point;

[0011] S3, the geological background field interference in the surface gravity change value is eliminated by using wavelet multi-scale decomposition, and the formula expression is:

[0012] ;

[0013] In the formula, is the real surface gravity change value; N is the total number of factors of the geological background field; k is a single factor of the geological background field; is the scale coefficient; is the wavelet base function; is the wavelength threshold.

[0014] In a preferred embodiment of the present application, in step S1, the arrangement of the fiber grating gravity sensing chain satisfies the spatial relationship formula:

[0015] ;

[0016] In the formula, is the sensor spacing; H is the average depth.

[0017] In a preferred embodiment of the present application, in step S1, when the unmanned aerial vehicle scans, the scanning path satisfies the spatial sampling theorem:

[0018] ;

[0019] In the formula, is the minimum phase velocity; is the highest deformation frequency.

[0020] In a preferred embodiment of the present application, after step S3, a hydrological disturbance correction is further included.

[0021]

[0022] In the formula, is a corrected surface gravity change value; is a correction coefficient; is a water level change value; is a rock porosity, and the value is 0.15-0.35.

[0023] In a preferred embodiment of the present application, when the corrected surface gravity change value meets a pre-warning threshold, a fracture pre-warning is triggered, and a fracture azimuth angle is calculated by the formula ; wherein, is a rock fracture azimuth angle; is a mixed derivative component of a gravity gradient tensor in the y-z direction; is a mixed derivative component of a gravity gradient tensor in the x-z direction.

[0024] In a preferred embodiment of the present application, the fiber grating gravity sensing chain adopts a temperature compensation model to calculate gravity sensitivity, and the formula expression is:

[0025] ;

[0026] In the formula, g is gravity sensitivity; is a gravity sensitivity coefficient; is a temperature coupling coefficient; T is a downhole temperature; is a gravity sensitive fiber wavelength, is a reference fiber wavelength.

[0027] In a preferred embodiment of the present application, it further includes inversion of equivalent mining thickness based on gravity change for subsidence dynamic prediction; based on the maximum gravity change value obtained by monitoring, combined with the universal gravitational constant, the coal density and the average mining depth data, the equivalent mining thickness of the coal seam is inverted through the equivalent mining thickness calculation formula; then the equivalent mining thickness is substituted into the subsidence basin prediction model, and the maximum subsidence value is taken as the amplitude, and an exponential function with the equivalent mining thickness as the independent variable is used to describe the spatial distribution form of the subsidence basin, wherein the exponential term contains two independent decay terms along the working face strike and tendency, and the decay coefficients are determined by the ratio of the mining depth to the corresponding direction main influence angle tangent value.

[0028] In a preferred embodiment of the present application, a three-dimensional state vector containing subsidence amount, subsidence gradient and time-varying derivative is established, and time series prediction is realized through a state transition matrix; meanwhile, an observation equation is constructed, the gravity change value, the gravity gradient vertical component and the external deformation monitoring data are jointly used as observation input, and a filtering algorithm containing system noise and observation noise is used to realize multi-source data fusion.

[0029] In a preferred embodiment of the present application, when the integrated surface and well monitoring network is constructed in step S1, the relative gravity instrument uses a self-adaptive grid encryption algorithm to dynamically adjust the layout density, and when a local gravity anomaly gradient exceeds a preset threshold, an encryption module is automatically triggered to add a micro gravity sensor in the abnormal area to form a dynamic encryption monitoring network, wherein the encryption grid density is 1.5-3 times the original grid density, and the spacing between the sensors after encryption satisfies the critical spacing formula in the rock stratum movement angle theory.

[0030] In a preferred embodiment of the present application, a dynamic correction coefficient is introduced in the equivalent mining thickness calculation formula, the dynamic correction coefficient is dynamically adjusted based on the real-time monitored rock stratum stress change data through a BP neural network model, and the adaptive improvement of the mining thickness inversion precision in the geological condition mutation area is realized through dynamic correction.

[0031] The correction formula is , wherein, is a reference correction coefficient; is a stress sensitive correction amount; is a real-time stress change value; is a stress reference threshold; is a hyperbolic tangent function, which maps the stress ratio to the interval [-1, 1] to realize smooth transition of stress change.

[0032] The present application solves the defects in the background art, and has the following beneficial effects:

[0033] (1) By arranging quantum gravity reference stations outside the subsidence area, deploying relative gravity instruments in the subsidence basin at a grid density of 100-500 meters, arranging fiber Bragg grating sensing chains in the working face along the slot, and using a UAV to carry a gravity gradiometer for periodic scanning, a full-range, full-range three-dimensional monitoring network is constructed. Among them, the layout spacing of the underground sensing chain is dynamically adjusted according to the average mining depth to ensure that the rock stratum fracture signal can also be captured during deep mining; at the same time, the interference of underground temperature on the monitoring accuracy is offset through double-fiber differential measurement. The UAV scanning path is designed according to the deformation propagation characteristics, avoiding the spatial aliasing problem of monitoring data. Further, the limitation of traditional single-point monitoring is broken, the gravity field monitoring error is controlled within ±1 microgal, the data acquisition efficiency is greatly improved compared with traditional methods, and full-space dynamic monitoring from the ground to the well is realized.

[0034] (2) A gravity field theoretical model considering coal extraction quality loss and overburden density redistribution is established, the dynamic process of mass migration in rock mass is accurately described by three-dimensional space integration, and the calculation error of gravity change under complex geological conditions is reduced compared with traditional simplified models. Wavelet multi-scale decomposition technology is used to eliminate background interference such as deep structure movement, and combined with water level change and rock porosity parameters for hydrological correction, the influence of geological and hydrological factors on monitoring data is effectively eliminated, and the signal-to-noise ratio of real gravity change value is improved, providing high-precision data support for subsidence analysis.

[0035] (3) Based on the inversion of equivalent mining thickness of coal seam from gravity monitoring data, the spatial distribution form of subsidence area can be accurately described by exponential function after substituting into the subsidence basin prediction model, and by constructing a three-dimensional state vector containing subsidence, change gradient and time-varying characteristics, the gravity field data and external deformation monitoring information are fused, the early warning of rock stratum fracture is realized, and the rock stratum fracture azimuth can be calculated through gravity gradient tensor analysis, which significantly improves the real-time early warning ability of coal mine subsidence disaster. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, a brief introduction will be given below to the drawings needed to be used in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings;

[0037] Figure 1 The monitoring method flow chart of the preferred embodiment of the present application. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0039] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, therefore, the scope of protection of the present application is not limited by the specific embodiments disclosed below.

[0040] As Figure 1 shown, a relative gravity monitoring method for surface subsidence in coal mining process comprises the following steps:

[0041] S1, construct a ground and shaft integrated monitoring network; wherein, quantum gravity reference stations are arranged in the periphery of the subsidence area, relative gravimeters are deployed in the subsidence basin according to a grid density of 100-500m*100-500m, fiber grating gravity sensing chains are arranged in the working face crossheading of the shaft, and a cold atom gravity gradiometer is carried by an unmanned aerial vehicle to perform periodic regional scanning.

[0042] In step S1, the arrangement of the fiber grating gravity sensing chain satisfies a spatial relationship:

[0043] ;

[0044] In the formula, is the sensor spacing; H is the average depth. Based on the rock stratum movement angle theory, the sensor spacing is dynamically related to the average depth H: the deeper the mining depth, the wider the rock stratum deformation transmission range, and the monitoring spacing needs to be increased; but when H>250, the monitoring spacing is limited =50m, to ensure that the key layer fracture signal can still be captured when mining at a deep depth.

[0045] The fiber grating gravity sensing chain uses a temperature compensation model to calculate the gravity sensitivity, and the formula expression is:

[0046] ;

[0047] In the formula, g is the gravity sensitivity; is the gravity sensitivity coefficient; is the temperature coupling coefficient; T is the downhole temperature; is the gravity sensitive fiber wavelength, is the reference fiber wavelength. The change of the downhole temperature will cause the fiber wavelength to drift, resulting in distortion of the gravity sensitivity. The model measures the difference between the coaxial double fibers: is the gravity sensitive fiber wavelength, is the reference fiber wavelength; is the temperature coupling coefficient; T is the downhole temperature; and the thermal noise is offset in real time.

[0048] In step S1, when the unmanned aerial vehicle scans, the scanning path satisfies a spatial sampling theorem:

[0049] ;

[0050] In the formula, is the minimum phase velocity; is the maximum deformation frequency. In order to avoid spatial aliasing effects, the flight strip spacing needs to be designed according to the deformation propagation characteristics. The theorem ensures that the sampling interval can distinguish the minimum deformation wavelength.

[0051] Step S1 aims to establish a full-space stereoscopic monitoring system. Quantum gravity reference station provides high-precision absolute reference (±0.5 μGal) to eliminate instrument system error; gridded relative gravimeter realizes continuous monitoring of the surface gravity field with 100-500 m grid density balancing the cost and spatial resolution; downhole optical fiber sensing chain directly senses the mass migration of mining strata; unmanned aerial vehicle-mounted gradient meter obtains gravity gradient tensor through periodic scanning.

[0052] The synergy of space-ground-well covers the whole area affected by mining, solves the problem of blind area of traditional single-point monitoring, and greatly improves the efficiency of gravity data acquisition.

[0053] Further explanation is that the quantum gravity reference station is arranged on the stable bedrock outside the subsidence area, adopts the principle of cold atom interference, provides absolute gravity reference, and the precision is ±0.5 μGal. Its role is not only to eliminate instrument system error, but also to separate the background gravity field changes caused by regional tectonic movement (such as slow uplift of the crust) through long-term observation.

[0054] The gridded relative gravimeter is deployed on the surface of the subsidence basin according to 100-500 m grid density. The grid density is dynamically adjusted according to the mining intensity. In high-intensity mining area, the grid density is 100 m x 100 m, which can capture the details of rapid deformation; in weak deformation area, the grid density is 500 m x 500 m, which can reduce the monitoring cost.

[0055] The instrument selects high-precision spring gravimeter (such as CG-6), which synchronously observes and transmits data to the central processing platform every day.

[0056] S2, based on the data monitored in real time in step S1, a theoretical model of gravity field-mass migration is established, and the formula expression is:

[0057] ;

[0058] In the formula, is the change of surface gravity; G is the gravitational constant; is the density of coal; is the extracted volume; d is the average depth; is the rock density change function at the spatial coordinates ; r is the distance from the mass element to the measuring point. The model starts from the law of conservation of mass, the first term describes the mass loss (negative effect) caused by coal extraction, and the second term represents the density redistribution (positive / negative effect) of overburden rock fracture and compaction. Breakthrough traditional point mass approximation, introduce triple integral to accurately describe the spatial heterogeneity of rock mass.

[0059] S3, wavelet multi-scale decomposition is used to eliminate the interference of geological background field in the change of surface gravity, and the formula expression is:

[0060] ;

[0061] wherein, is the true gravity change value; N is the total number of factors of the geological background field; k is a single factor of the geological background field; is the scale coefficient; is the wavelet base function; is the wavelength threshold. It is used to separate the background interference such as deep tectonic movement (wavelength > 5km).

[0062] After step S3, hydrological interference correction is further included:

[0063]

[0064] wherein, is the corrected gravity change value; is the correction coefficient; is the water level change; is the porosity of the rock stratum, and the value is 0.15-0.35.

[0065] When the corrected gravity change value meets the early warning threshold, fracture early warning is triggered, and the fracture azimuth is calculated by the formula ; wherein, is the rock fracture azimuth; is the mixed derivative component of the gravity gradient tensor in the y-z direction; is the mixed derivative component of the gravity gradient tensor in the x-z direction.

[0066] Further included is inversion of equivalent mining thickness based on gravity change, for subsidence dynamic prediction; based on the maximum gravity change value absolute value obtained by monitoring, combined with the universal gravitation constant, the coal density and the average mining depth data, the equivalent mining thickness of the coal seam is inverted by the equivalent mining thickness calculation formula; then the equivalent mining thickness is substituted into the subsidence basin prediction model, and the maximum subsidence value is taken as the amplitude, and an exponential function with the equivalent mining thickness as the independent variable is used to describe the spatial distribution form of the subsidence basin, wherein the exponential term contains two independent decay terms along the working face strike and tendency, and the decay coefficients are determined by the ratio of the mining depth to the tangent value of the corresponding direction main influence angle.

[0067] A three-dimensional state vector including the subsidence amount, the subsidence gradient and the time-varying derivative is established, and time series prediction is realized through a state transition matrix; at the same time, an observation equation is constructed, the gravity change value, the gravity gradient vertical component and the external deformation monitoring data are used as observation input quantities, and a filtering algorithm containing system noise and observation noise is used to realize multi-source data fusion.

[0068] In another embodiment, when constructing the integrated monitoring network of surface and underground wells in step S1, the adaptive grid encryption algorithm is implemented through the following process: first, initialize the grid density of the surface relative gravimeter to a 200m x 200m reference grid; when a local gravity anomaly gradient exceeding 5μGal / m² threshold is detected in real time, the system automatically triggers the encryption module. The encryption strategy uses a quadtree partitioning algorithm to refine the abnormal area grid to 100m x 100m or 50m x 50m subgrid, and the encrypted grid density reaches 1.5-3 times that of the original grid. During the encryption process, the unmanned aerial vehicle scanning swath spacing is adjusted to 1 / 2 of the encrypted grid to ensure that the spatial sampling rate meets the Nyquist criterion. For example, in the 300m deep area, when a rock layer rupture precursor signal is detected, the fiber Bragg grating sensor chain spacing is dynamically reduced from 60m to 30m, forming a three-dimensional monitoring network and realizing the upgrade of the monitoring dimension from "point-line-plane".

[0069] In the equivalent thickness inversion of step S7, the BP neural network model uses a three-layer structure: the input layer contains 8 characteristic parameters such as real-time stress , reference threshold , and rock porosity; the hidden layer is set to 16 neurons, and the activation function is selected as the tanh function to achieve a smooth mapping from -1 to 1; the output layer outputs a dynamic correction coefficient. The model training uses 2000 groups of historical monitoring data from 2018 to 2024, optimizes the weights through the backpropagation algorithm, and sets the regularization coefficient to 0.01 to prevent overfitting. The correction formula When =1, reaches a maximum value of 0.3m, corresponding to an equivalent thickness correction of 500m in the mining area. This correction coefficient is input into the Kalman filter together with the gravity gradient vertical component and temperature compensation data to improve the prediction accuracy of subsidence to the ±0.1m level after multi-source data fusion.

[0070] The dynamic encryption monitoring network adjusts the sensor layout density in real time to ensure that small deformation signals are captured in areas with sudden changes in geological conditions; the dynamic correction coefficient eliminates the influence of hydrological interference and stress fluctuations on the thickness inversion through stress-gravity coupling mechanism. The synergistic effect of the two makes the system have "adaptive and self-correcting" capabilities, shortening the timeliness of subsidence prediction from the traditional 72 hours to 2 hours in complex geological conditions such as deep mining and fault activation, providing more accurate decision support for coal mine safety production. In specific implementation, the system automatically evaluates the effectiveness of the monitoring network density and correction coefficient every 6 hours, continuously optimizes the monitoring parameters through a closed-loop feedback mechanism, and forms a dynamically evolving technical system.

[0071] ​The above is based on the ideal embodiment of the application, through the above description, relevant personnel can make various changes and modifications without deviating from the technical idea of the application. The technical scope of the application is not limited to the content of the specification, and the technical scope must be determined according to the scope of claims.

Claims

1. A method for monitoring the relative gravity of surface subsidence during coal mining, characterized in that, Includes the following steps: S1. Construct an integrated monitoring network combining ground, air, and wellbore systems; among which... This includes deploying quantum gravity reference stations around the subsidence area, deploying relative gravimeters in the subsidence basin with a grid density of 100~500m*100~500m, arranging fiber optic grating gravity sensing chains in the downhole working face roadway, and using UAVs equipped with cold atom gravity gradiometers to perform periodic regional scanning. S2. Based on the real-time monitoring data from step S1, establish a gravity field-mass transfer theoretical model, with the following formula: ； In the formula, ρ represents the change in Earth's surface gravity; G is the gravitational constant. Density of coal; d represents the extracted volume; d represents the average depth. Spatial coordinates The density variation function of the rock strata at the location; r is the distance from the mass element to the measuring point; S3. Wavelet multi-scale decomposition is used to eliminate the interference of the geological background field in the surface gravity variation values. The formula expression is: ； In the formula, The actual surface gravity variation value is represented by N; the total number of factors in the geological background field is represented by N; and the factor of a single geological background field is represented by k. This is the scaling factor; These are wavelet basis functions; This is the wavelength threshold.

2. The method for monitoring the relative gravity of surface subsidence during coal mining as described in claim 1, characterized in that: In step S1, the arrangement of the fiber grating gravity sensing chain satisfies the spatial relationship: ； In the formula, H represents the sensor spacing; H represents the average depth.

3. The method for monitoring the relative gravity of surface subsidence during coal mining as described in claim 1, characterized in that: In step S1, when the UAV scans, the scanning path satisfies the spatial sampling theorem: ； In the formula, Minimum phase velocity; This is the highest deformation frequency.

4. The method for monitoring the relative gravity of surface subsidence during coal mining as described in claim 1, characterized in that: After performing step S3, hydrological disturbance correction is also included: In the formula, This is the corrected value for the change in surface gravity. For correction factors; This refers to the change in water level. The porosity of the rock stratum is 0.15 to 0.

35.

5. The method for monitoring the relative gravity of surface subsidence during coal mining as described in claim 4, characterized in that: When the corrected surface gravity change value meets the warning threshold, a fault warning is triggered, and the warning is issued via the formula. The rupture azimuth angle was calculated; where, The azimuth of the rock strata fracture; The mixed derivative components of the gravity gradient tensor in the yz direction; The xz-direction mixed derivative components of the gravity gradient tensor.

6. The method for monitoring the relative gravity of surface subsidence during coal mining as described in claim 2, characterized in that: The fiber optic gravity sensing chain uses a temperature-compensated model to calculate gravity sensitivity, and its formula is as follows: ； In the formula, g is the gravity sensitivity; This is the gravity sensitivity coefficient; Here, T is the temperature coupling coefficient; T is the downhole temperature. For gravity-sensitive fiber wavelengths, The reference fiber wavelength.

7. The method for monitoring the relative gravity of surface subsidence during coal mining as described in claim 1, characterized in that: It also includes using gravity variation inversion to calculate equivalent mining thickness for subsidence dynamic prediction; based on the absolute value of the maximum gravity variation obtained from monitoring, combined with the gravitational constant, coal density and average mining depth data, the equivalent mining thickness of the coal seam is inverted through the equivalent mining thickness calculation formula; then the equivalent mining thickness is substituted into the subsidence basin prediction model, and the maximum subsidence value is used as the amplitude, and an exponential function with equivalent mining thickness as the independent variable is used to describe the spatial distribution of the subsidence basin, wherein the exponential term includes two independent attenuation terms along the strike and dip of the working face, and the attenuation coefficient is determined by the ratio of the mining depth to the tangent of the main influence angle of the corresponding direction.

8. The method for monitoring the relative gravity of surface subsidence during coal mining as described in claim 1, characterized in that: A three-dimensional state vector containing subsidence, subsidence gradient, and time-varying derivative is established, and time series prediction is achieved through the state transition matrix. At the same time, an observation equation is constructed, taking gravity change value, vertical component of gravity gradient, and external deformation monitoring data as observation inputs. A filtering algorithm containing system noise and observation noise is used to achieve multi-source data fusion.

9. The method for monitoring the relative gravity of surface subsidence during coal mining as described in claim 1, characterized in that: When constructing the integrated monitoring network of sky, earth, and well in step S1, the relative gravimeter adopts an adaptive grid densification algorithm to dynamically adjust the deployment density. When a local gravity anomaly gradient is detected to exceed a preset threshold, the densification module is automatically triggered to add a micro gravity sensor in the abnormal area to form a dynamic densification monitoring network. The density of the densified grid is 1.5 to 3 times that of the original grid density, and the spacing between the densified sensors satisfies the critical spacing formula in the rock stratum movement angle theory.

10. A method for monitoring the relative gravity of surface subsidence during coal mining as described in claim 7, characterized in that: The equivalent mining thickness calculation formula introduces a dynamic correction coefficient, which is dynamically adjusted based on real-time monitored rock stress change data through a BP neural network model. This dynamic correction enables adaptive improvement of mining thickness inversion accuracy in areas with abrupt geological changes.

Images