A method for real-time monitoring of thickness of burned and altered rock layer based on geophysical prospecting and thermodynamic model

Abstract

This invention discloses a real-time monitoring method for the thickness of scorched rock strata based on geophysical and thermodynamic models, belonging to the fields of data processing and engineering monitoring technology. It includes three steps: multi-source geophysical data acquisition and processing, scorched rock strata structure inversion, and real-time thickness monitoring. In the scorched rock strata structure inversion, a thermodynamic rock physics forward model is constructed, and physical property constraints are established by combining geophysical characteristics such as temperature gradient, resistivity change rate, and acoustic energy density. The three-dimensional underground physical property structure and thickness distribution are obtained through multi-physics joint inversion. In the real-time thickness monitoring, the thickness distribution of scorched rock strata is updated in real time and risk warnings are provided through differential updates, thickness change rate calculations, and risk threshold judgments. This method can accurately and continuously reconstruct the three-dimensional structure of the scorched zone, achieving high-precision real-time monitoring and early warning of scorched rock strata thickness. It is applicable to scenarios such as underground energy engineering, mine safety monitoring, and management of special rock strata combustion processes.

Description

Technical Field

[0001] This invention relates to the field of data processing and engineering monitoring technology, specifically to a method for real-time monitoring of the thickness of scorched rock layers based on geophysical and thermodynamic models. Background Technology

[0002] The real-time monitoring method for the thickness of scorched rock layers based on geophysical exploration and thermodynamic models is a comprehensive monitoring technology that relies on geophysical exploration data and the thermodynamic response law of rock mass. Its basic idea is to obtain physical field information reflecting the evolution process of scorched rock layers through multi-source geophysical exploration, and combine it with thermodynamic models describing heat conduction, heat diffusion and changes in rock mass properties to establish a dynamic monitoring system that can reflect the renewal of scorched boundaries and changes in thickness.

[0003] The main functions of this type of method include: enabling continuous tracking of the burning process, simultaneously sensing the characteristics of changes in core physical properties such as temperature, conductivity, and rock mass integrity over time; accurately reconstructing the spatial structure and thickness distribution of burned rock strata, avoiding the ambiguity and judgment bias caused by relying on a single geophysical method; supporting real-time thickness updates and risk identification, enabling early identification of rapid burning, transition zone expansion, and potentially structurally weak areas; and providing reliable monitoring data for underground energy engineering, coal seam spontaneous combustion control, and rock strata combustion research, thereby improving engineering safety and process control capabilities.

[0004] As a comprehensive monitoring tool that integrates geophysical exploration technology and thermodynamic modeling, this method features cross-physical field, multi-scale and real-time operation, which can significantly improve the accuracy, reliability and engineering applicability of determining the thickness of igneous rock layers.

[0005] However, existing methods for real-time monitoring of the thickness of igneous rock layers have technical problems such as poor coupling of multi-source geophysical data, significant monitoring blind spots, and difficulty in unified modeling of physical field spatiotemporal data.

[0006] Existing methods for inverting the structure of pyromorphic rock layers have technical problems such as lack of thermodynamic constraints, multiple solutions to inversion results, and fuzzy physical property boundaries leading to unstable structural reconstruction.

[0007] Existing real-time thickness monitoring methods suffer from technical problems such as delayed thickness change updates, inability to provide early warnings of rapid localized burning, and highly subjective risk assessment mechanisms. Summary of the Invention

[0008] To address the above issues and overcome the shortcomings of existing technologies, this invention provides a method for real-time monitoring of the thickness of scorched rock strata based on geophysical and thermodynamic models. The technical solution adopted by this invention is as follows: This invention provides a method for real-time monitoring of the thickness of scorched rock strata based on geophysical and thermodynamic models, which includes the following steps:

[0009] Step S1: Acquisition and processing of multi-source geophysical data;

[0010] Step S2: Inversion of the structure of the pyrochemically altered rock strata;

[0011] Step S3: Real-time monitoring of the thickness of the igneous rock layer.

[0012] Further, in step S1, the multi-source geophysical data acquisition and processing is used to obtain multi-dimensional physical field data reflecting the combustion state of the scorched rock. Specifically, it involves setting up a monitoring area and deploying a distributed temperature sensing system, a high-density resistivity electrode array, and a microseismic monitoring network to perform synchronous data acquisition and standardized preprocessing to obtain a spatiotemporal dataset of the scorched rock strata, including the following steps:

[0013] Step S11: Monitoring area deployment, used to construct a geophysical observation system for the monitoring space. Specifically, based on the geological structure information and deployment requirements of the monitoring area, temperature fiber optic cable laying, electrode array deployment, and microseismic station positioning and calibration are carried out using sensor equipment deployment methods. Monitoring equipment installation, spatial coordinate calibration, and deployment integrity verification are also performed.

[0014] Step S12: Spatiotemporal data acquisition, used to synchronously acquire temperature field, resistivity field and acoustic wave field information. Specifically, based on the deployed temperature sensing fiber, resistivity electrode array and microseismic monitoring array, a synchronous acquisition method triggered by a unified time reference is used to acquire continuous time series of temperature, resistivity and microseismic waveforms to obtain raw acquisition data. The raw acquisition data specifically includes raw temperature, resistivity and acoustic wave data.

[0015] Step S13: Monitoring data gridding, used to perform spatial mapping and grid discretization on the original acquired data. Specifically, based on the original acquired data and the spatial coordinates of the monitoring equipment, a three-dimensional spatial gridding discretization method is used to perform unified three-dimensional grid cell spatial mapping of multi-physics data to obtain spatially consistent three-dimensional gridded monitoring data.

[0016] Step S14: Multi-source data standardization is used to unify the dimensions and align the scales of different physical quantities. Specifically, based on the spatially consistent three-dimensional gridded monitoring data, standardization processing methods such as data denoising, normalization, and anomaly removal are used to perform physical quantity scale unification, noise suppression, and numerical stability enhancement processing to obtain standardized multi-source data.

[0017] Step S15: Physical field feature information extraction, used to extract the dominant features of burn-in. Specifically, based on standardized multi-source data, a multi-physical field feature extraction method is used to extract the features of temperature gradient, relative resistivity change rate and acoustic density, so as to obtain multi-source physical field feature data of the burn-in process.

[0018] The multiphysics feature extraction method specifically involves sequentially extracting temperature gradient, resistivity change rate, and acoustic energy density, and constructing a burn-off coupling indicator index to comprehensively evaluate the burn-off intensity.

[0019] Specifically, the temperature gradient extraction operation involves using a three-dimensional central difference operator to calculate the three-dimensional gradient of the temperature field in order to identify regions of rapid temperature change.

[0020] The resistivity change rate extraction operation specifically involves calculating the relative change rate based on the resistivity difference between adjacent time slices to identify the low-resistivity band and its dynamic evolution.

[0021] The acoustic energy density extraction operation specifically involves obtaining the energy integral value by performing envelope calculation or short-time Fourier transform on the micro-vibration waveform to identify the micro-vibration energy anomalous enhancement area, thereby obtaining multi-source physical field characteristic data reflecting the burn-through process.

[0022] Step S16: Construction of the spatiotemporal dataset of the scorched rock strata. Specifically, the temperature gradient, resistivity change rate and acoustic energy density are spatially reprojected in the same three-dimensional grid coordinates, and the monitoring data in the form of a four-dimensional tensor is constructed according to the time series. The data are then fused to form a spatiotemporal dataset of the scorched rock strata that meets the requirements of the subsequent inversion model.

[0023] The spatiotemporal dataset of the scorched rock strata specifically includes temperature field, resistivity field, and acoustic field data.

[0024] Further, in step S2, the inversion of the scorched rock strata structure is used to reconstruct the spatial structure and thickness distribution of the scorched rock. Specifically, based on the spatiotemporal dataset of the scorched rock strata, a thermodynamic rock physics model is constructed and physical property constraints derived from geophysical exploration are embedded. Through multi-physics joint inversion calculation, the scorched rock strata structure is inverted to obtain a scorched rock strata thickness grid model, including the following steps:

[0025] Step S21: Parametric modeling of scorched rock strata, used to parametrically express the physical property parameters (temperature gradient, effective resistivity, rock mass integrity) of scorched zone, transition zone and unscorched zone. Specifically, based on the spatiotemporal dataset of scorched rock strata, the parametric modeling method is used to parametrically model temperature, resistivity, acoustic parameters and phase index to obtain the parametric model of physical properties.

[0026] Step S22: Thermodynamic rock physics forward model construction, which is used to establish a numerical forward model of temperature property response relationship based on thermal conductivity and rock physical properties. Specifically, based on the property parameterization model, a forward simulation method based on thermal conduction mechanism and rock property response law is used to perform forward prediction of temperature, resistivity and acoustic properties of grid cells to obtain a thermophysical coupled three-dimensional forward response field.

[0027] Step S23: Construction of geophysical property constraints, which is used to transform temperature, resistivity and acoustic wave characteristics into constraint conditions to improve inversion stability. Specifically, based on the thermal property coupled three-dimensional forward response field, feature matching, boundary constraints and gradient constraints are constructed in sequence to express the constraints of the front physical boundary, property gradient and observation bias, and obtain multi-physics field constraint conditions.

[0028] Step S24: Construction of joint inversion objective function, used to construct a multi-field coupled inversion objective function that simultaneously minimizes temperature residual, resistivity residual and acoustic propagation error. Specifically, based on the thermophysical property coupled three-dimensional forward response field and multi-physics field constraints, the multi-field coupling error construction method is used to construct a multi-physics field joint inversion objective function including residual term, constraint term and regularization term, thus obtaining the multi-field coupled inversion objective function;

[0029] Step S25: Multiphysics joint inversion solution. The underground physical property distribution that satisfies the consistency of the three types of geophysical data is obtained through iterative optimization. Specifically, based on the multi-field coupled inversion objective function and the physical property parameterization model, the gradient iterative optimization solution method is used to update the multiphysics parameters and solve the inversion convergence to obtain the three-dimensional underground physical property inversion data.

[0030] Step S26: Construction of the scorched rock layer thickness grid model. The three-dimensional underground physical property inversion data obtained by inversion is transformed into a scorched zone thickness distribution model. Specifically, based on the three-dimensional underground physical property inversion data and combined with the phase classification standard, the thickness extraction and partition reconstruction methods are used to calculate the spatial structure and thickness of the burning zone, scorched zone and unburned zone, and obtain the scorched rock layer thickness grid model.

[0031] The thickness mesh model includes the three-dimensional physical properties and thickness distribution of the combustion zone, the burn-through zone, and the unburned zone.

[0032] Further, in step S3, the real-time monitoring of the thickness of the igneous rock layer is used to achieve dynamic perception and risk warning of the igneous rock thickness. Specifically, based on the igneous rock layer thickness grid model and continuously updated geophysical observation data, the thickness change rate is calculated and the risk threshold is judged, and the visualization monitoring platform is driven to output the thickness distribution and warning results, including the following steps:

[0033] Step S31: Dynamic thickness update calculation, used to iteratively update the thickness grid model of the scorched rock layer in real time by updating the collected geophysical data. Specifically, based on the latest collected temperature, resistivity, and acoustic monitoring data and the thickness grid model of the scorched rock layer, a dynamic thickness update method is used to perform differential update and local inversion correction to obtain an updated thickness distribution model.

[0034] Step S32: Thickness change rate calculation, used to calculate the thickness change rate and trend within a continuous monitoring period. Specifically, based on the updated thickness distribution model of adjacent monitoring periods, the time series change analysis method is used to obtain the thickness difference, change rate calculation and change trend extraction to obtain the thickness change rate and change trend data.

[0035] Step S33: Monitoring risk threshold judgment, which is used to determine the risk level based on the preset thickness decay rate threshold, burn-in depth threshold and temperature anomaly threshold. Specifically, based on the thickness change rate and change trend data, a multi-physics field monitoring anomaly index is constructed, and the risk threshold judgment method is used to perform threshold matching, multi-factor weighted judgment and weak zone identification to obtain the risk level judgment result.

[0036] Step S34: Real-time thickness monitoring generates a three-dimensional real-time thickness distribution map, a time series change curve, and graded risk warning information. Specifically, based on the updated thickness distribution model and risk level determination results, a visualization rendering and monitoring information output method is used to render the thickness layer, generate dynamic curves, and release real-time warning information to obtain the real-time thickness distribution map, change curves, and risk warning information.

[0037] The beneficial effects achieved by the present invention using the above solution are as follows:

[0038] (1) In view of the technical problems in the existing real-time monitoring methods for the thickness of scorched rock layers, such as poor coupling of multi-source geophysical data, significant monitoring blind spots, and difficulty in unified modeling of physical field spatiotemporal data, this solution creatively adopts an intelligent path that combines structural inversion and real-time thickness monitoring with multi-source geophysical data. This allows for continuous tracking of the advancement of scorched boundaries, thickness evolution, and local anomaly changes, solving the problems of discontinuous monitoring, fragmented physical fields, and coarse thickness determination in existing methods.

[0039] (2) In view of the technical problems in the existing inversion methods of scorched rock strata structure, such as lack of thermodynamic constraints, easy multiple solutions in the inversion results, and fuzzy physical property boundaries leading to unstable structural reconstruction, this scheme creatively adopts the construction of a thermodynamic rock physics model and embeds physical property characteristic constraints derived from geophysical exploration. Through multi-physics field joint inversion calculation, the scorched rock strata structure is inverted. The constructed thickness grid model has stronger physical consistency, stability and interpretability, effectively avoiding the problems of "skipping layers", "virtual layers" or boundary offset in the structural reconstruction of traditional methods.

[0040] (3) In view of the technical problems in the existing real-time thickness monitoring methods, such as the lag in thickness change updates, the inability to provide early warning of local rapid burning, and the strong subjectivity of the risk judgment mechanism, this solution creatively adopts an optimized real-time thickness monitoring method that combines thickness change rate calculation and risk threshold judgment. This realizes online and second-level updates of thickness changes, early identification of weak zones and rapid burning areas, and objectification, quantification and automation of risk classification. This solves the engineering pain points of traditional methods, such as monitoring lag, alarm delay and inability to identify high-risk areas in advance, and greatly improves the safety and real-time response capability of actual engineering. Attached Figure Description

[0041] Figure 1 A flowchart illustrating a real-time monitoring method for the thickness of scorched rock layers based on geophysical and thermodynamic models provided by this invention.

[0042] Figure 2 This is a flowchart illustrating the multi-source geophysical data acquisition and processing process in step S1.

[0043] Figure 3 This is a schematic diagram of the process for inverting the structure of the burned rock strata in step S2;

[0044] Figure 4 This is a flowchart illustrating the real-time monitoring of the thickness of the scorched rock layer in step S3.

[0045] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0046] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0047] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0048] Example 1, see Figure 1This invention provides a method for real-time monitoring of the thickness of scorched rock strata based on geophysical and thermodynamic models. The method includes the following steps:

[0049] Step S1: Acquisition and processing of multi-source geophysical data;

[0050] Step S2: Inversion of the structure of the pyrochemically altered rock strata;

[0051] Step S3: Real-time monitoring of the thickness of the igneous rock layer.

[0052] By performing the above operations, this solution addresses the technical problems in existing real-time monitoring methods for burnt rock layer thickness, such as poor coupling of multi-source geophysical data, significant monitoring blind spots, and difficulty in unified modeling of spatiotemporal physical field data. Specifically, in existing technologies, most thickness monitoring methods rely on a single geophysical means (such as using only resistivity, electromagnetic waves, or temperature fields), resulting in the inability to simultaneously capture changes in physical properties in the burning zone, burnt zone, and unburnt zone. For example, resistivity data can reflect changes in conductivity but lags behind temperature changes; microseismic data can reflect the integrity of the rock mass but is not sensitive to the absolute burnt depth; single-fiber temperature often cannot cover the entire area, resulting in monitoring blind spots and misidentification of local anomalies; in addition, the different sampling frequencies, dimensions, and spatial densities of different physical fields lead to difficulties in spatiotemporal alignment and the inability to update thickness changes in real time. This solution creatively adopts an intelligent path that combines structural inversion with multi-source geophysical data and real-time thickness monitoring, thereby continuously tracking the advance of burnt boundaries, thickness evolution, and local anomaly changes, solving the problems of discontinuous monitoring, fragmented physical fields, and coarse thickness determination in existing methods.

[0053] Example 2, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S1, the multi-source geophysical data acquisition and processing is used to obtain multi-dimensional physical field data reflecting the combustion state of the scorched rock. Specifically, it involves setting up a monitoring area and deploying a distributed temperature sensing system, a high-density resistivity electrode array, and a microseismic monitoring network to perform synchronous data acquisition and standardized preprocessing to obtain a spatiotemporal dataset of the scorched rock strata. The steps include:

[0054] Step S11: Monitoring area deployment, used to construct a geophysical observation system for the monitoring space. Specifically, based on the geological structure information and deployment requirements of the monitoring area, temperature fiber optic cable laying, electrode array deployment, and microseismic station positioning and calibration are carried out using sensor equipment deployment methods. Monitoring equipment installation, spatial coordinate calibration, and deployment integrity verification are also performed.

[0055] To quantitatively evaluate the rationality of the spatial distribution of monitoring equipment, this embodiment introduces a monitoring deployment balance index, calculated using the following formula:

[0056] ;

[0057] In the formula, This is a monitoring deployment balance index, where N is the number of monitoring points, i is the monitoring point index, and di is the spatial distance from the monitoring point to its nearest neighbor. It is the average nearest neighbor distance; the monitoring deployment balance index The closer to 1, the more evenly the distribution, which can reduce monitoring blind spots and improve the stability and sensitivity of the overall monitoring network;

[0058] Preferably, the sensor deployment method specifically comprises three operations: laying temperature optical fibers, deploying electrode arrays, and calibrating the location of microseismic stations. Specifically, the temperature optical fiber laying operation involves laying the temperature optical fibers at fixed intervals along a predetermined trajectory according to the deployment path and density requirements, recording their three-dimensional coordinates, and verifying the spatial sequence of the fiber nodes using linear interpolation. The electrode array deployment operation involves deploying electrodes within the monitoring area in a regular grid format or with boundary densification, based on the detection depth and inversion resolution requirements, recording the position of each electrode, and verifying contact resistance. The microseismic station location calibration operation involves obtaining the initial station coordinates using a geometric trilateration method and performing least-squares correction of the arrival error using standard source signals to achieve precise spatial positioning and complete deployment of the microseismic stations.

[0059] Step S12: Spatiotemporal data acquisition, used to synchronously acquire temperature field, resistivity field and acoustic wave field information. Specifically, based on the deployed temperature sensing fiber, resistivity electrode array and microseismic monitoring array, a synchronous acquisition method triggered by a unified time reference is used to acquire continuous time series of temperature, resistivity and microseismic waveforms to obtain raw acquisition data. The raw acquisition data specifically includes raw temperature, resistivity and acoustic wave data.

[0060] Preferably, the unified time reference triggered synchronous acquisition method specifically involves sequentially performing time reference unification, temperature data acquisition, resistivity data acquisition, and acoustic waveform acquisition operations. Specifically, the time reference unification operation involves providing a unified time source based on GNSS / NTP, performing timestamp calibration and jitter compensation on all acquisition devices to ensure alignment consistency across device sampling. The temperature data acquisition operation involves acquiring a temperature sequence distributed along a temperature fiber at a fixed sampling period and recording its spatial index. The resistivity data acquisition operation involves performing multi-pole or quad-pole resistivity measurement according to a preset excitation current sequence and recording apparent resistivity data. The acoustic waveform acquisition operation involves continuously recording micro-vibration waveforms at high frequency and attaching corresponding station timestamps and three-dimensional coordinate information to obtain raw acquisition data including temperature, resistivity, and acoustic waveform data.

[0061] Step S13: Monitoring data gridding, used to perform spatial mapping and grid discretization on the original acquired data. Specifically, based on the original acquired data and the spatial coordinates of the monitoring equipment, a three-dimensional spatial gridding discretization method is used to perform unified three-dimensional grid cell spatial mapping of multi-physics data to obtain spatially consistent three-dimensional gridded monitoring data.

[0062] Preferably, the three-dimensional spatial gridding discretization method specifically involves sequentially performing three-dimensional grid construction, spatial mapping, and time alignment operations. Specifically, the three-dimensional grid construction operation involves establishing a regular or adaptively densified three-dimensional regular grid based on the spatial range and resolution requirements of the monitoring area. The spatial mapping operation involves using a combination of nearest neighbor mapping and inverse distance weighted interpolation to map the temperature, resistivity, and acoustic energy of the acquisition points to corresponding grid cells to obtain spatially continuous data. The time alignment operation involves resampling various physical property data according to a uniform time step to ensure temporal synchronization consistency of different physical fields, thereby forming a spatially consistent three-dimensional gridded dataset.

[0063] Step S14: Multi-source data standardization is used to unify the dimensions and align the scales of different physical quantities. Specifically, based on the spatially consistent three-dimensional gridded monitoring data, standardization processing methods such as data denoising, normalization, and anomaly removal are used to perform physical quantity scale unification, noise suppression, and numerical stability enhancement processing to obtain standardized multi-source data.

[0064] Preferably, the standardization processing method specifically involves sequentially performing data denoising, normalization, and outlier removal operations. Specifically, the data denoising operation involves smoothing the temperature, resistivity, and acoustic energy data using sliding window smoothing and wavelet threshold denoising. The normalization operation specifically uses z-score normalization based on the mean and standard deviation to represent different physical quantities within the same scale. The outlier removal operation specifically uses a local density anomaly detection algorithm to identify and remove abnormal points and sudden noise points in the equipment, thereby forming stable and reliable standardized multi-source data.

[0065] Step S15: Physical field feature information extraction, used to extract the dominant features of burn-in. Specifically, based on standardized multi-source data, a multi-physical field feature extraction method is used to extract the features of temperature gradient, relative resistivity change rate and acoustic density, so as to obtain multi-source physical field feature data of the burn-in process.

[0066] Preferably, the multiphysics feature extraction method specifically involves sequentially performing temperature gradient extraction, resistivity change rate extraction, and acoustic energy density extraction operations, and constructing a burn-off coupling indicator index to comprehensively evaluate the burn-off intensity.

[0067] Specifically, the temperature gradient extraction operation involves using a three-dimensional central difference operator to calculate the three-dimensional gradient of the temperature field in order to identify regions of rapid temperature change.

[0068] The resistivity change rate extraction operation specifically involves calculating the relative change rate based on the resistivity difference between adjacent time slices to identify the low-resistivity band and its dynamic evolution.

[0069] The acoustic energy density extraction operation specifically involves obtaining the energy integral value by performing envelope calculation or short-time Fourier transform on the micro-vibration waveform to identify the micro-vibration energy anomalous enhancement area, thereby obtaining multi-source physical field characteristic data reflecting the burn-through process.

[0070] As a further optimization of this embodiment, a burn-in coupling indicator index is constructed to comprehensively evaluate the burn-in intensity. The calculation formula is as follows:

[0071] ;

[0072] In the formula, This is the burn-in coupling indicator index, where x is the horizontal plane coordinate, y is the vertical plane coordinate, z is the depth coordinate, and t is the time index. It is temperature weighting. It is the three-dimensional gradient of the temperature field. It is a three-dimensional temperature field gradient reference value, used to characterize the typical temperature gradient in the unburnt region. It is resistivity weight. It is the relative change in resistivity. It is a reference resistivity, used to characterize the background resistivity of the unburned region. E is the sound wave energy weight, and E is the sound wave energy density. ref It is the reference acoustic energy density, used to characterize the background microseismic energy of the unburned area;

[0073] Step S16: Construction of the spatiotemporal dataset of the scorched rock strata. Specifically, the temperature gradient, resistivity change rate and acoustic energy density are spatially reprojected in the same three-dimensional grid coordinates, and the monitoring data in the form of a four-dimensional tensor is constructed according to the time series. The data are then fused to form a spatiotemporal dataset of the scorched rock strata that meets the requirements of the subsequent inversion model.

[0074] The spatiotemporal dataset of the scorched rock strata specifically includes temperature field, resistivity field, and acoustic field data.

[0075] Example 3, see Figure 1 , Figure 3This embodiment is based on the above embodiment. In step S2, the inversion of the scorched rock strata structure is used to reconstruct the spatial structure and thickness distribution of the scorched rock. Specifically, based on the spatiotemporal dataset of the scorched rock strata, a thermodynamic rock physics model is constructed and physical property constraints derived from geophysical exploration are embedded. Through multi-physics joint inversion calculation, the scorched rock strata structure is inverted to obtain a scorched rock strata thickness grid model, including the following steps:

[0076] Step S21: Parametric modeling of scorched rock strata, used to parametrically express the physical property parameters (temperature gradient, effective resistivity, rock mass integrity) of scorched zone, transition zone and unscorched zone. Specifically, based on the spatiotemporal dataset of scorched rock strata, the parametric modeling method is used to parametrically model temperature, resistivity, acoustic parameters and phase index to obtain the parametric model of physical properties.

[0077] Preferably, a multi-property fusion parameterization method based on fuzzy membership is adopted, and a comprehensive index of the burn-transformation phase is introduced. The formula used to quantify the burning degree of the mesh cells is as follows:

[0078] ;

[0079] In the formula, It is the comprehensive index of the phase transformation state after burning, with a value range of [0,1]. It is a temperature weighting coefficient. It is a normalized value of the temperature gradient characteristic. It is the resistivity weighting coefficient. It is the normalized value of the relative resistivity change rate characteristic. It is the sound wave weighting coefficient. It is the normalized value of the acoustic density characteristic;

[0080] More preferably, based on the comprehensive index of the burn-through phase state The distribution range divides the underground medium into: the combustion core zone ( >0.8), burn-in affected area ( ) and unburned background area ( <0.4), thus constructing a non-uniform initial model;

[0081] Step S22: Thermodynamic rock physics forward model construction, which is used to establish a numerical forward model of temperature property response relationship based on thermal conductivity and rock physical properties. Specifically, based on the property parameterization model, a forward simulation method based on thermal conduction mechanism and rock property response law is used to perform forward prediction of temperature, resistivity and acoustic properties of grid cells to obtain a thermophysical coupled three-dimensional forward response field.

[0082] Preferably, the forward simulation method is based on the unsteady heat conduction equation and coupled with the thermosensitive properties of rock resistivity for calculation. The calculation formula is as follows:

[0083] ;

[0084] In the formula, It is the density of the rock. Specific heat capacity is T, temperature field distribution is T, and time parameter is t. It is a heat conduction term. It is about finding the gradient operator. It is thermal conductivity. It is the three-dimensional gradient of the temperature field. The intensity of the endogenous heat source generated by spontaneous combustion of coal seam is obtained by back-calculating the heat flux density increment per unit volume based on the temperature gradient distribution calculated in step S15 and the rock thermal conductivity coefficient.

[0085] More preferably, a correlation equation between resistivity and temperature can be introduced to correct the forward prediction results. The calculation formula is as follows:

[0086] ;

[0087] In the formula, This is a resistivity prediction result corrected by introducing the correlation equation between resistivity and temperature. It is the background resistivity at the reference temperature. It is the temperature sensitivity coefficient of rock resistivity, and T0 is the reference temperature;

[0088] The formula for calculating the thermophysical property coupled three-dimensional forward response field is as follows:

[0089] ;

[0090] In the formula, It is a three-dimensional forward response field coupled with thermal properties, where m is the sub-vector index in the property parameterization model. It is a temperature value predicted based on thermodynamic evolution. It is a resistivity value predicted based on thermodynamic evolution. It is a sound wave energy value based on thermodynamic evolution prediction, specifically calculated based on the mapping function between rock mass integrity and temperature distribution;

[0091] More preferably, the calculation formula for the mapping function based on rock mass integrity and temperature distribution is as follows:

[0092] ;

[0093] In the formula, It is a mapping function based on the integrity of the rock mass and the temperature distribution. This is the reference value for the background sound wave energy in the unburned area, i.e., the reference sound wave energy density. It is a temperature-induced thermal damage factor used to characterize the decrease in rock integrity caused by microcracks resulting from differences in mineral expansion at high temperatures. This is the structural attenuation coefficient, used to reflect the intensity of sound wave scattering attenuation due to increased rock porosity during the burning process; wherein, the calculation formula for the temperature-induced thermal damage factor is:

[0094] ;

[0095] In the formula, It is the maximum damage limit value, characterizing the residual integrity of rock after it has been completely pulverized or cavitated. It is the thermal damage rate constant, determined by the thermodynamic experimental parameters of the rock. It is the critical temperature for burn-through damage, and the preferred value range is 400℃ to 600℃;

[0096] Step S23: Construction of geophysical property constraints, which is used to transform temperature, resistivity and acoustic wave characteristics into constraint conditions to improve inversion stability. Specifically, based on the thermal property coupled three-dimensional forward response field, feature matching, boundary constraints and gradient constraints are constructed in sequence to express the constraints of the front physical boundary, property gradient and observation bias, and obtain multi-physics field constraint conditions.

[0097] Preferably, the calculation formula for the multiphysics constraint condition is as follows:

[0098] ;

[0099] In the formula, It is a multiphysics constraint. It is a sub-vector of temperature in the inversion model. It is a sub-vector of resistivity in the inversion model. It is a sub-vector of the sound wave in the inversion model;

[0100] Step S24: Construction of joint inversion objective function, used to construct a multi-field coupled inversion objective function that simultaneously minimizes temperature residual, resistivity residual and acoustic propagation error. Specifically, based on the thermophysical property coupled three-dimensional forward response field and multi-physics field constraints, the multi-field coupling error construction method is used to construct a multi-physics field joint inversion objective function including residual term, constraint term and regularization term, thus obtaining the multi-field coupled inversion objective function;

[0101] The formula for calculating the multi-field coupled inversion objective function is as follows:

[0102] ;

[0103] In the formula, It is the objective function for multi-field coupling inversion, where j is the index of the physical field category, and its values ​​include temperature, resistivity, and sound waves. It is a data reliability weight. It is standardized multi-source data. These are the predicted data for the three-dimensional forward response field coupled with the thermal properties of the j-th type of physical field. It is the cross-gradient constraint factor, used to adjust the enforcement strength of multi-field boundary consistency. R is the smoothing regularization parameter, and R is the second-order difference operator used to suppress inversion noise and ensure spatial continuity.

[0104] Step S25: Multiphysics joint inversion solution. The underground physical property distribution that satisfies the consistency of the three types of geophysical data is obtained through iterative optimization. Specifically, based on the multi-field coupled inversion objective function and the physical property parameterization model, the gradient iterative optimization solution method is used to update the multiphysics parameters and solve the inversion convergence to obtain the three-dimensional underground physical property inversion data.

[0105] Step S26: Construction of the scorched rock layer thickness grid model. The three-dimensional underground physical property inversion data obtained by inversion is transformed into a scorched zone thickness distribution model. Specifically, based on the three-dimensional underground physical property inversion data and combined with the phase classification standard, the thickness extraction and partition reconstruction methods are used to calculate the spatial structure and thickness of the burning zone, scorched zone and unburned zone, and obtain the scorched rock layer thickness grid model.

[0106] Preferably, the sintered rock layer thickness grid model specifically uses the phase state integral extraction method to calculate the sintered rock layer thickness at each plane coordinate, and the calculation formula is as follows:

[0107] ;

[0108] In the formula, It represents the thickness of the calcined rock layer at each planar coordinate. It is the upper bound of the phase integral. It is the lower bound of the phase integral. It is an indicator function. It is a comprehensive index of the burn-through phase state calculated based on the inversion results. This is the threshold for determining burn-in, with a preferred value of 0.4;

[0109] The thickness mesh model includes the three-dimensional physical properties and thickness distribution of the combustion zone, the burn-through zone, and the unburned zone.

[0110] By performing the above operations, this method addresses the technical problems in existing methods for inverting the structure of scorched rock strata, such as the lack of thermodynamic constraints, the susceptibility of multiple interpretations in the inversion results, and the instability of structural reconstruction due to fuzzy physical property boundaries. Specifically, traditional structural inversion is mainly based on single-field data (such as resistivity inversion). Since resistivity changes are affected by factors such as temperature, lithology, and water content, the inversion is highly ambiguous. In actual scorched rock strata, the same decrease in resistivity may originate from either a temperature increase or a loose rock mass structure. The changes in acoustic wave propagation velocity are complex in the transition zone, making the velocity-thickness relationship-based inversion prone to errors in transition zone positioning. Without constraints from physical mechanisms such as heat conduction and heat diffusion, false high-temperature zones or false low-resistivity zones may appear at the inversion boundaries. This scheme creatively adopts the construction of a thermodynamic rock physics model and embeds physical property constraints derived from geophysical exploration. Through multi-physics field joint inversion calculations, the structure of scorched rock strata is inverted. The constructed thickness grid model has stronger physical consistency, stability, and interpretability, effectively avoiding the problems of "skipped layers," "virtual layers," or boundary offsets in structural reconstruction in traditional methods.

[0111] Example 4, see Figure 1 , Figure 4 This embodiment is based on the above embodiment. In step S3, the real-time monitoring of the thickness of the igneous rock layer is used to realize dynamic perception and risk warning of the thickness of the igneous rock layer. Specifically, based on the igneous rock layer thickness grid model and continuously updated geophysical observation data, the thickness change rate is calculated and the risk threshold is judged, and the visualization monitoring platform is driven to output the thickness distribution and warning results, including the following steps:

[0112] Step S31: Dynamic thickness update calculation, used to iteratively update the thickness grid model of the scorched rock layer in real time by updating the collected geophysical data. Specifically, based on the latest collected temperature, resistivity, and acoustic monitoring data and the scorched rock layer thickness grid model, a dynamic thickness update method is used for differential update and local inversion correction to obtain an updated thickness distribution model. The calculation formula is as follows:

[0113] ;

[0114] In the formula, H(x,y,t) is the thickness of the scorched rock layer at the spatial coordinate (x,y) at time t. It is the time interval between adjacent monitoring cycles. It is standardized multi-source data collected at time t. These are the observed and predicted values ​​calculated based on the physical property parameter model from the previous monitoring period. It is the generalized inverse of the Jacobian matrix of the thickness response to the observation, used to map the observation residuals into thickness corrections;

[0115] Step S32: Thickness change rate calculation, used to calculate the thickness change rate and trend within a continuous monitoring period. Specifically, based on the updated thickness distribution model of adjacent monitoring periods, the time series change analysis method is used to obtain the thickness difference, change rate calculation and change trend extraction to obtain the thickness change rate and change trend data.

[0116] The formula for calculating the thickness change rate is:

[0117] ;

[0118] In the formula, It is the thickness change rate. It is a moment Below, the thickness of the igneous rock layer at spatial coordinates (x,y);

[0119] The formula for calculating the trend data is:

[0120] ;

[0121] In the formula, It is data on changing trends. It is a moment The thickness change rate below;

[0122] Step S33: Monitoring risk threshold judgment, which is used to determine the risk level based on the preset thickness decay rate threshold, burn-in depth threshold and temperature anomaly threshold. Specifically, based on the thickness change rate and change trend data, a multi-physics field monitoring anomaly index is constructed, and the risk threshold judgment method is used to perform threshold matching, multi-factor weighted judgment and weak zone identification to obtain the risk level judgment result.

[0123] Preferably, the calculation formula for the multiphysics monitoring anomaly index is as follows:

[0124] ;

[0125] In the formula, It is a comprehensive risk indicator based on multiple physics fields. It is the weight of the abnormal thickness change rate. This is the safety threshold for the rate of thickness change, preferably ranging from 0.01 m / d to 0.30 m / d, and more preferably from 0.03 m / d to 0.15 m / d. It is the weight of thickness scale abnormality. The thickness of the calcined rock strata is a safe threshold, preferably 0.5m to 8m, more preferably 1m to 5m; when the overlying strata are thin or in near-surface areas, it is preferably 0.5m to 3m, and when in deep coal seams or high-temperature, oxygen-rich areas, it is preferably 3m to 8m. It is the weight of temperature anomalies. This is the maximum temperature value within the current monitoring period. It is the temperature abnormality safety threshold, preferably 80℃~200℃, more preferably 120℃~180℃. When monitoring is carried out near the coal body or fire source, it can be preferably 150℃~250℃. When far-field monitoring is carried out or the environmental temperature drift is large, it can be preferably 80℃~150℃.

[0126] More preferably, the calculation formula for the risk level determination result based on the multiphysics monitoring anomaly indicators is as follows:

[0127] ;

[0128] In the formula, This is the result of the risk level assessment;

[0129] Step S34: Real-time thickness monitoring generates a three-dimensional real-time thickness distribution map, a time series change curve, and graded risk warning information. Specifically, based on the updated thickness distribution model and risk level determination results, a visualization rendering and monitoring information output method is used to render the thickness layer, generate dynamic curves, and release real-time warning information to obtain a real-time thickness distribution map, change curves, and risk warning information.

[0130] Preferably, the risk level determination result can be superimposed onto the thickness mesh model using a three-dimensional spatial interpolation function, and the calculation formula is as follows:

[0131] ;

[0132] In the formula, Output is the visualization rendering output, and Render is the visualization rendering function, which is used to map the thickness distribution, rate of change and risk level information into the output results of the three-dimensional layer and dynamic monitoring interface.

[0133] By performing the above operations, this solution addresses the technical problems of existing real-time thickness monitoring methods, such as delayed thickness change updates, inability to provide early warnings for rapid localized burning, and highly subjective risk assessment mechanisms. Specifically, traditional thickness monitoring is usually based on batch processing inversion, with long update cycles (from hours to days). When the burning rate is fast or the local rock mass temperature rises suddenly, the system cannot capture sudden thickness reductions in time, leading to delays in identifying potential hazard areas, overestimation or underestimation of the burning rate, and risk levels relying on subjective experience thresholds without physical basis. This solution creatively adopts an optimized real-time thickness monitoring method that combines thickness change rate calculation with risk threshold judgment. This achieves online, second-level updates of thickness changes, early identification of weak zones and rapidly burning areas, and objectification, quantification, and automation of risk classification. This solves the engineering pain points of traditional methods, such as delayed monitoring, delayed alarms, and inability to identify high-risk areas in advance, significantly improving the safety and real-time response capabilities of actual engineering projects.

[0134] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0135] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

[0136] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for real-time monitoring of the thickness of scorched rock strata based on geophysical and thermodynamic models, characterized in that: The method includes the following steps: Step S1: Multi-source geophysical data acquisition and processing, including synchronous data acquisition and standardized preprocessing, to obtain a spatiotemporal dataset of the calcined rock strata; Step S2: Inversion of the structure of the scorched rock strata. Based on the spatiotemporal dataset of the scorched rock strata, a thermodynamic rock physics model is constructed and the physical property constraints derived from geophysical exploration are embedded. Through multi-physics joint inversion calculation, the structure of the scorched rock strata is inverted to obtain the thickness grid model of the scorched rock strata. The inversion of the structure of the ignited rock strata includes the following steps: Parametric modeling of scorched rock strata involves parametrically expressing the physical properties of the scorched zone, transition zone, and unscorched zone. Based on the spatiotemporal dataset of scorched rock strata, a comprehensive index of scorched phase state is introduced to quantify the scorching degree of grid cells. Parametric modeling of temperature, resistivity, acoustic parameters, and phase state indices is then performed to obtain a parametric model of physical properties. The physical properties specifically include temperature gradient, effective resistivity, and rock mass integrity parameters; The thermodynamic rock physics forward model is constructed based on the physical property parameterization model. The temperature, resistivity and acoustic properties of the grid cells are predicted forward according to the thermal conductivity and rock physical properties to obtain the thermophysical coupled three-dimensional forward response field. Geophysical property constraints are constructed by sequentially performing feature matching, boundary constraints, and gradient constraints based on the thermal property coupled three-dimensional forward response field. Constraints are expressed for the physical boundary of the front, property gradient, and observation bias, resulting in multiphysics constraint conditions. The joint inversion objective function is constructed by constructing a multi-physics joint inversion objective function that includes residual terms, constraint terms, and regularization terms, based on the thermophysical property coupled three-dimensional forward response field and multi-physics constraints. Multiphysics joint inversion solution: Based on the multi-field coupled inversion objective function and the physical property parameterization model, multiphysics parameter update and inversion convergence solution are performed to obtain three-dimensional subsurface physical property inversion data; The thickness grid model of the scorched rock strata was constructed by calculating the spatial structure and thickness of the burning zone, scorched zone and unburned zone based on the three-dimensional underground physical property inversion data and the phase classification standard. Step S3: Real-time monitoring of the thickness of the igneous rock layer. Based on the igneous rock layer thickness grid model and continuously updated geophysical observation data, the thickness change rate is calculated and the risk threshold is judged, and the thickness distribution and early warning results are output. 2.The real-time monitoring method for the thickness of the burnt and altered rock layer based on geophysical prospecting and thermodynamic model according to claim 1, characterized in that: In step S1, the multi-source geophysical data acquisition and processing is used to obtain multi-dimensional physical field data reflecting the combustion state of the scorched rock. Specifically, a monitoring area is set up, and a distributed temperature sensing system, a high-density resistivity electrode array, and a microseismic monitoring network are deployed to perform synchronous data acquisition and standardized preprocessing to obtain a spatiotemporal dataset of the scorched rock layer.

3. The method for real-time monitoring of the thickness of scorched rock strata based on geophysical and thermodynamic models according to claim 2, characterized in that: In step S1, the multi-source geophysical data acquisition and processing includes the following steps: The monitoring area setup involves three sequential operations: laying temperature fiber optic cables, setting up electrode arrays, and calibrating the positioning of microseismic stations. Monitoring equipment installation, spatial coordinate calibration, and setup integrity verification are also performed. Spatiotemporal data acquisition involves sequentially performing time base unification, temperature data acquisition, resistivity data acquisition, and acoustic waveform acquisition to obtain raw acquisition data, which specifically includes raw temperature, resistivity, and acoustic waveform data. The monitoring data is gridded, and three-dimensional grid construction, spatial mapping and time alignment operations are performed sequentially to obtain spatially consistent three-dimensional gridded monitoring data. Multi-source data standardization involves sequentially performing data denoising, normalization, and outlier removal operations to obtain standardized multi-source data. Physical field feature information extraction: Extract the dominant features of burning; Based on standardized multi-source data, perform temperature gradient extraction, resistivity change rate extraction and acoustic energy density extraction operations in sequence; Construct a burning coupling indicator index; Comprehensively evaluate the burning intensity; and obtain multi-source physical field feature data of the burning process. The construction of the spatiotemporal dataset of the scorched rock strata involves spatially reprojecting the temperature gradient, resistivity change rate, and acoustic energy density within the same three-dimensional grid coordinates, constructing monitoring data in the form of a four-dimensional tensor according to the time series, and fusing them to form the spatiotemporal dataset of the scorched rock strata. The spatiotemporal dataset of the scorched rock strata includes temperature field, resistivity field, and acoustic field data.

4. The method for real-time monitoring of the thickness of scorched rock strata based on geophysical and thermodynamic models according to claim 3, characterized in that: In step S1, the temperature gradient extraction operation specifically involves using a three-dimensional central difference operator to calculate the three-dimensional gradient of the temperature field in order to identify regions of rapid temperature change. The resistivity change rate extraction operation specifically involves calculating the relative change rate based on the resistivity difference between adjacent time slices to identify the low-resistivity band and its dynamic evolution. The acoustic energy density extraction operation specifically involves obtaining the energy integral value by performing envelope calculation or short-time Fourier transform on the micro-vibration waveform to identify the micro-vibration energy anomalous enhancement area, thereby obtaining multi-source physical field characteristic data reflecting the burning process.

5. The method for real-time monitoring of the thickness of scorched rock strata based on geophysical and thermodynamic models according to claim 1, characterized in that: In step S2, the thickness mesh model includes the three-dimensional physical property structure and thickness distribution of the combustion zone, the burnt zone, and the unburned zone.

6. The method for real-time monitoring of the thickness of scorched rock strata based on geophysical and thermodynamic models according to claim 5, characterized in that: In step S3, the real-time monitoring of the thickness of the burnt rock layer includes the following steps: dynamic thickness update calculation, thickness change rate calculation, monitoring risk threshold judgment, and real-time thickness monitoring.

7. The method for real-time monitoring of the thickness of scorched rock strata based on geophysical and thermodynamic models according to claim 6, characterized in that: The thickness dynamic update calculation is based on the latest collected temperature, resistivity, acoustic monitoring data and the scorched rock layer thickness grid model, and performs differential update and local inversion correction to obtain the updated thickness distribution model; The thickness change rate is calculated by taking the thickness difference, calculating the change rate, and extracting the change trend based on the updated thickness distribution model of adjacent monitoring periods, so as to obtain the thickness change rate and change trend data. The risk threshold judgment is based on the thickness change rate and change trend data, constructing multi-physics field monitoring anomaly indicators, performing threshold matching, multi-factor weighted judgment and weak zone identification, and obtaining the risk level judgment result. The real-time thickness monitoring, based on the updated thickness distribution model and risk level determination results, performs thickness layer rendering, dynamic curve generation, and real-time early warning information release to obtain a real-time thickness distribution map, change curve, and risk early warning information.

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