Hydraulic ring geological monitoring system and monitoring method thereof

Abstract

The invention relates to the technical field of hydraulic ring geology monitoring, in particular to a hydraulic ring geology monitoring system and a monitoring method thereof.The hydraulic ring geology monitoring method comprises the steps that a microseismic sensing array and a distributed optical fiber temperature sensing network are arranged, and three-dimensional vibration waveform data and axial temperature gradient data of a target area are collected; and feature parameters such as micro-seismic event space positioning, vibration energy release and temperature anomaly gradient are extracted through space-time registration processing. And calculating a seepage mutation correlation index based on the characteristic parameters, inputting the seepage mutation correlation index into a pre-constructed three-dimensional seepage-stress coupling model, generating a formation pore pressure distribution cloud picture and a rock mass stability coefficient K, and improving the seepage mutation monitoring precision by dynamically adjusting a weight coefficient of the seepage mutation correlation index and updating boundary conditions of the coupling model. According to the invention, the detection capability of seepage mutation is improved, a real-time and accurate prediction result can be provided for a complex geological environment, and effective support is provided for safety monitoring in the fields of mines, underground engineering and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogeological, engineering geological and environmental geological monitoring, and particularly relates to a hydrogeological, engineering geological and environmental geological monitoring system and a monitoring method thereof. Background Technique

[0002] With the acceleration of the processes of industrialization and urbanization, the exploitation of groundwater resources, geological engineering construction and the development of mineral resources increasingly involve complex hydrogeological, engineering geological and environmental geological environments. In these environments, problems such as sudden changes in seepage and changes in rock mass stability pose threats to the safety and sustainability of projects. Especially in the fields of hydraulic fracturing, groundwater seepage and geological disaster monitoring, timely and accurate monitoring and assessment of sudden changes in seepage and changes in rock mass stability have become the key to ensuring project safety.

[0003] Most current hydrogeological, engineering geological and environmental geological monitoring technologies rely on single types of sensors or monitoring means, such as microseismic monitoring, temperature monitoring or stress monitoring, etc. These traditional methods have certain limitations in the assessment of sudden changes in seepage and rock mass stability. In addition, most existing seepage-stress coupling models are based on simplified assumptions or static parameters, lacking a dynamic adjustment mechanism considering changes in the geological environment. Especially in the selection of input parameters and model updating, traditional methods often rely on fixed parameters and preset boundary conditions and cannot dynamically optimize and adjust the model according to real-time monitoring data. Existing technologies often have inaccurate or lagging problems when dealing with sudden changes in seepage and stress coupling problems in complex geological environments, and cannot timely reflect the dynamic changes of rock masses and water flow systems, resulting in limitations in the timeliness and accuracy of monitoring results. Summary of the Invention

[0004] The present invention provides a hydrogeological, engineering geological and environmental geological monitoring system and a monitoring method thereof.

[0005] A hydrogeological, engineering geological and environmental geological monitoring method includes the following steps:

[0006] S1, arranging a microseismic sensor array and a distributed optical fiber temperature sensing network, collecting three-dimensional vibration waveform data and axial temperature gradient data of a target area, and forming a multi-source monitoring data set;

[0007] S2, performing spatio-temporal registration processing on the multi-source monitoring data set, extracting microseismic event spatial positioning parameters, vibration energy release parameters and temperature anomaly gradient parameters, and generating a characteristic parameter set;

[0008] S3, calculating seepage mutation correlation degree indexes based on the characteristic parameter set, including microseismic event spatial aggregation degree, temperature gradient change rate and energy release cumulative amount;

[0009] S4, inputting the seepage mutation correlation degree indexes into a pre-constructed three-dimensional seepage-stress coupling model to generate a formation pore pressure distribution nephogram and a rock mass stability coefficient K;

[0010] S5. Trigger hierarchical early warnings according to the time series change rate dK / dt of the rock mass stability coefficient K.

[0011] S6. Based on historical early warning data and on-site review results, use the particle swarm optimization algorithm to dynamically adjust the weight coefficient of the seepage mutation correlation index, and update the boundary conditions of the three-dimensional seepage-stress coupling model.

[0012] Optionally, S1 includes:

[0013] S11. Deploy a microseismic sensor array: Deploy microseismic sensors at multiple key positions in the target area for real-time acquisition of three-dimensional vibration waveform data of the target area.

[0014] S12. Deploy a distributed optical fiber temperature sensing network: Deploy a distributed optical fiber temperature sensor network in the target area for acquisition of axial temperature gradient data of the target area.

[0015] S13. Through the combined action of the microseismic sensor array and the optical fiber temperature sensing network, real-time acquire three-dimensional vibration waveform data and axial temperature gradient data of the target area, including the time series waveform of vibration, spectrum analysis information, temperature distribution, and temperature change trend, to form a multi-source monitoring data set.

[0016] S13. Data synchronization and time series calibration: Perform time synchronization processing on the vibration waveform data collected by the microseismic sensor array and the temperature gradient data collected by the optical fiber temperature sensing network.

[0017] Optionally, S2 includes:

[0018] S21. Perform spatio-temporal registration processing: Perform spatio-temporal registration processing on the three-dimensional vibration waveform data and axial temperature gradient data in the multi-source monitoring data set.

[0019] S22. Extract microseismic event spatial positioning parameters: Based on the vibration waveform data after spatio-temporal registration, use the time delay estimation method to extract the spatial positioning parameters of microseismic events.

[0020] S23. Extract vibration energy release parameters: Based on the vibration waveform data of microseismic events, use the spectrum analysis method to calculate the vibration energy release parameters of microseismic events.

[0021] S24. Extract temperature anomaly gradient parameters: Based on the temperature gradient data after spatio-temporal registration, calculate the change rate of temperature and the temperature anomaly gradient.

[0022] S25. Generate a characteristic parameter set: According to the extracted microseismic event spatial positioning parameters, vibration energy release parameters, and temperature anomaly gradient parameters, generate a characteristic parameter set containing multi-dimensional information.

[0023] Optionally, S3 includes:

[0024] S31, calculating the spatial aggregation degree of microseismic events: calculating the spatial aggregation degree of microseismic events based on the extracted spatial location parameters of microseismic events;

[0025] S32, calculating the temperature gradient change rate: calculating the change rate of the temperature gradient based on the extracted temperature anomaly gradient data;

[0026] S33, calculating the cumulative energy release: calculating the cumulative energy release of microseismic events based on the extracted vibration energy release parameters;

[0027] S34, calculating the seepage mutation correlation index: calculating the seepage mutation correlation index by combining the spatial aggregation degree of microseismic events, the temperature gradient change rate and the cumulative energy release.

[0028] Optionally, S4 includes:

[0029] S41, establishing a three-dimensional seepage-stress coupling model: constructing a three-dimensional seepage-stress coupling model based on the calculated seepage mutation correlation index;

[0030] S42, inputting the seepage mutation correlation index into the model: inputting the calculated seepage mutation correlation index as an input parameter into the pre-constructed three-dimensional seepage-stress coupling model.

[0031] S43, generating a formation pore pressure distribution nephogram: generating a formation pore pressure distribution nephogram of the target area based on the input seepage mutation correlation index and the calculation results of the three-dimensional seepage-stress coupling model;

[0032] S44, calculating the rock mass stability coefficient K: calculating the rock mass stability coefficient K of the target area based on the formation pore pressure distribution nephogram obtained in S43;

[0033] S45, generating a rock mass stability distribution map: generating a rock mass stability distribution map according to the calculated rock mass stability coefficient K.

[0034] Optionally, S5 includes:

[0035] S51, calculating the time series change rate of the rock mass stability coefficient K: calculating its time series change rate dK / dt according to the numerical results of the rock mass stability coefficient K generated by the three-dimensional seepage-stress coupling model, and this change rate reflects the dynamic change of the rock mass stability;

[0036] S52, setting warning thresholds: setting two thresholds to trigger different warning levels;

[0037] S53, Early warning signal output and response: According to the triggered early warning level, corresponding alarm information is output through the early warning management platform or communication system.

[0038] Optionally, two thresholds are set in S52 to trigger different early warning levels, including:

[0039] Level 1 early warning: When dK / dt ≥ 0.15, a level 1 early warning signal is triggered, indicating that the rock mass stability has dropped sharply, there is a high risk, the system sends a warning notice to relevant personnel, and starts emergency response measures;

[0040] Level 2 early warning: When 0.08 ≤ dK / dt < 0.15, a level 2 early warning signal is triggered, indicating that there are certain changes in the rock mass stability, monitoring and assessment need to be strengthened, but it is not yet urgent. The system sends a warning notice to relevant personnel, requiring further attention and assessment of the rock mass stability.

[0041] Optionally, S6 includes:

[0042] S61, Collect historical early warning data and on-site review results: Collect and store historical early warning data, which includes the rock mass stability coefficient K value, time series change rate, early warning level and their corresponding on-site review results at the time of each early warning trigger;

[0043] S62, Application of particle swarm optimization algorithm: Design a particle swarm optimization algorithm to dynamically adjust the weight coefficient of the seepage mutation correlation index;

[0044] S63, Update the weight coefficient of the seepage mutation correlation index: The weight coefficient combination corresponding to the global best particle position obtained by the particle swarm optimization algorithm is the optimized weight coefficient of the seepage mutation correlation index. Use the optimized weight coefficient to replace the original weight coefficient and recalculate the seepage mutation correlation index;

[0045] S64, Update the boundary conditions of the three-dimensional seepage-stress coupling model: Update the boundary conditions of the three-dimensional seepage-stress coupling model according to the optimized seepage mutation correlation index, including the permeability of the pore medium, stress distribution and fluid flow characteristics, and recalculate and update the calculation results of the seepage and stress coupling model by the finite difference method to obtain a more accurate rock mass stability assessment.

[0046] A hydrogeological and engineering geological monitoring system for implementing the above-mentioned hydrogeological and engineering geological monitoring method, including the following modules:

[0047] Data acquisition module: Arrange a microseismic sensor array and a distributed optical fiber temperature sensing network to collect three-dimensional vibration waveform data and axial temperature gradient data of the target area, and form a multi-source monitoring data set;

[0048] Data processing module: Perform spatio-temporal registration processing on multi-source monitoring data sets, extract microseismic event spatial positioning parameters, vibration energy release parameters, and temperature anomaly gradient parameters, and generate a set of characteristic parameters;

[0049] Seepage mutation index calculation module: Calculate seepage mutation correlation indexes based on the set of characteristic parameters, including microseismic event spatial aggregation degree, temperature gradient change rate, and energy release cumulative amount;

[0050] Coupled model calculation module: Used to input the seepage mutation correlation indexes into a pre-constructed three-dimensional seepage-stress coupled model to generate a formation pore pressure distribution nephogram and a rock mass stability coefficient K;

[0051] Early warning module: Used to trigger hierarchical early warnings according to the time series change rate dK / dt of the rock mass stability coefficient K;

[0052] Optimization and adjustment module: Based on historical early warning data and on-site review results, use the particle swarm optimization algorithm to dynamically adjust the weight coefficients of the seepage mutation correlation indexes and update the boundary conditions of the three-dimensional seepage-stress coupled model.

[0053] Advantages of the present invention:

[0054] In the present invention, by deploying a microseismic sensor array and a distributed optical fiber temperature sensing network, three-dimensional vibration waveform data and axial temperature gradient data are collected in real time to achieve multi-source monitoring of the target area. Based on the spatio-temporal registration processing of the multi-source data sets, microseismic event spatial positioning parameters, vibration energy release parameters, and temperature anomaly gradient parameters can be extracted to generate a set of characteristic parameters. By calculating the seepage mutation correlation indexes, seepage mutation changes can be effectively detected and identified. Combining with the time series change rate of the rock mass stability coefficient, hierarchical early warning signals can be triggered in a timely manner. This data-driven early warning method can significantly improve the real-time response ability of seepage mutations and provide accurate technical support for safety monitoring in fields such as mines, water conservancy, and geology.

[0055] In the present invention, the particle swarm optimization algorithm is used to dynamically adjust the weight coefficients of the seepage mutation correlation indexes, enabling the model to be optimized in real time according to historical early warning data and on-site review results. Under the action of the particle swarm optimization algorithm, the error between the seepage mutation correlation indexes and the actual rock mass stability can be effectively reduced, and the prediction accuracy can be improved. The optimized weight coefficients further update the boundary conditions of the three-dimensional seepage-stress coupled model to ensure that the model has higher adaptability and accuracy under different geological conditions. This innovation can continuously improve the prediction accuracy and reliability of the monitoring system in complex environments and effectively prevent the occurrence of geological disasters such as rock mass rupture and landslides. Description of the drawings

[0056] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0057] Figure 1 Schematic diagram of the method flow of the embodiment of the present invention;

[0058] Figure 2 Schematic diagram of the system flow of the embodiment of the present invention. Detailed implementation manners

[0059] The present invention will be described in detail below in conjunction with the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0060] It should be noted that in the specification, when referring to "an embodiment", "embodiment", "exemplary embodiment", "some embodiments", etc., it indicates that the described embodiment may include specific features, structures or characteristics, but not necessarily every embodiment includes such specific features, structures or characteristics. In addition, when combining embodiments to describe specific features, structures or characteristics, implementing such features, structures or characteristics in combination with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0061] Generally, terms can be understood at least in part from their use in the context. For example, at least in part depending on the context, the term "one or more" used herein can be used to describe any feature, structure or characteristic in a singular sense, or can be used to describe a combination of features, structures or characteristics in a plural sense. In addition, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but rather, at least in part depending on the context, allowing for the existence of other factors that may not be explicitly described.

[0062] As Figure 1 shown, a hydrogeological and environmental geological monitoring method includes the following steps:

[0063] S1, arranging a microseismic sensor array and a distributed fiber optic temperature sensing network, collecting three-dimensional vibration waveform data and axial temperature gradient data of the target area, and forming a multi-source monitoring data set;

[0064] S2. Perform spatio-temporal registration processing on the multi-source monitoring data set, extract the spatial location parameters, vibration energy release parameters, and temperature anomaly gradient parameters of microseismic events, and generate a set of characteristic parameters;

[0065] S3. Calculate the seepage mutation correlation index based on the set of characteristic parameters, including the spatial aggregation degree of microseismic events, the temperature gradient change rate, and the cumulative energy release;

[0066] S4. Input the seepage mutation correlation index into the pre-constructed three-dimensional seepage-stress coupling model to generate the formation pore pressure distribution cloud map and the rock mass stability coefficient K;

[0067] S5. Trigger hierarchical early warnings according to the time series change rate dK / dt of the rock mass stability coefficient K;

[0068] S6. Based on the historical early warning data and the on-site review results, use the particle swarm optimization algorithm to dynamically adjust the weight coefficients of the seepage mutation correlation index and update the boundary conditions of the three-dimensional seepage-stress coupling model.

[0069] S1 includes:

[0070] S11. Install a microseismic sensor array: Install microseismic sensors at multiple key positions in the target area to collect the three-dimensional vibration waveform data of the target area in real time. The microseismic sensors can collect seismic or vibration signals in all directions, including vibration frequency, vibration amplitude, and vibration waveform. The microseismic sensor array can achieve spatial distributed monitoring in the target area and has high spatial resolution;

[0071] S12. Install a distributed optical fiber temperature sensing network: Install a distributed optical fiber temperature sensor network in the target area to collect the axial temperature gradient data of the target area. The optical fiber temperature sensor network can continuously monitor the temperature change through optical fiber sensing technology, collect the temperature data at different depth positions in real time, and form temperature gradient distribution data. The temperature data includes the temperature value and temperature change rate of each sensing point;

[0072] S13. Through the combined action of the microseismic sensor array and the optical fiber temperature sensing network, collect the three-dimensional vibration waveform data and axial temperature gradient data of the target area in real time, including the time series waveform of vibration, spectrum analysis information, temperature distribution, and temperature change trend, and form a multi-source monitoring data set;

[0073] S13. Data synchronization and time series calibration: Perform time synchronization processing on the vibration waveform data collected by the microseismic sensor array and the temperature gradient data collected by the optical fiber temperature sensing network to ensure the consistency of the two data on the time axis, and then form a set of multi-source synchronized data for subsequent analysis and processing.

[0074] S2 includes:

[0075] S21. Perform spatio-temporal registration processing: Perform spatio-temporal registration processing on the three-dimensional vibration waveform data and axial temperature gradient data in the multi-source monitoring dataset. The spatio-temporal registration processing includes:

[0076] According to the clock synchronization calibration information of the acquisition device, align the vibration waveform data and temperature gradient data collected by each sensor to a unified time axis;

[0077] According to the spatial positions of the sensors, combined with the measurement accuracies of the sensors, map the collected data into the three-dimensional coordinate system of the target area to ensure the consistency of the data spatial positions;

[0078] Perform interpolation processing on the data to fill in the data missing due to sensor failures or signal interruptions to ensure the integrity and continuity of the data;

[0079] S22. Extract the spatial positioning parameters of microseismic events: Based on the vibration waveform data after spatio-temporal registration, use the time delay estimation method to extract the spatial positioning parameters of microseismic events. The specific method is as follows:

[0080] First, based on the vibration signals received by the multi-channel sensors, use the time delay estimation method to calculate the time delays between different sensors;

[0081] Then, according to the time delays and the spatial positions of the sensors, use the triangulation method to determine the source positions of microseismic events (including the longitude, latitude, and depth of the source);

[0082] Furthermore, extract the occurrence time and epicenter position of microseismic events, establish a spatial relationship model between the source and the sensors, and complete the spatial positioning of microseismic events.

[0083] S23. Extract the vibration energy release parameters: Based on the vibration waveform data of microseismic events, use the spectrum analysis method to calculate the vibration energy release parameters of microseismic events. The specific method is as follows:

[0084] First, perform Fourier transform on the vibration waveform signal to obtain the spectrum information of the vibration signal;

[0085] Then, based on the spectrum information, calculate the total energy release amount of microseismic events, and the calculation is as follows:

[0086]

[0087] where E is the energy release amount, X(f) is the amplitude of the vibration signal at frequency f, Δf is the frequency resolution, and f1 and f2 are the frequency ranges of spectrum analysis;

[0088] Furthermore, calculate the release rate of vibration energy (the energy release amount per unit time);

[0089] Finally, extract the frequency characteristics of the vibration, including the main frequency and the bandwidth, and analyze the vibration intensity and duration to evaluate the energy release characteristics of the microseismic events;

[0090] S24. Extract the temperature anomaly gradient parameter: Based on the temperature gradient data after spatio-temporal registration, calculate the rate of change of temperature and the temperature anomaly gradient;

[0091] The rate of change of temperature (ΔT / Δt) represents the rate of temperature change per unit time and is calculated as:

[0092]

[0093] where, T t is the temperature at time t, T t+1 is the temperature at time t + 1, and t and t + 1 are adjacent time nodes;

[0094] Analyze the temperature anomaly gradient region, identify the abnormal regions of temperature change within the target area, and determine whether there is a correlation with the spatial location of the microseismic events;

[0095] Based on the amplitude and distribution characteristics of the abnormal temperature gradient and the spatial location, calibrate the severity of the temperature anomaly;

[0096] S25. Generate a set of characteristic parameters: According to the extracted spatial location parameters, vibration energy release parameters, and temperature anomaly gradient parameters of the microseismic events, generate a set of characteristic parameters containing multi-dimensional information. The set of characteristic parameters includes microseismic events, vibration energy, and temperature anomaly data within each monitoring period, providing a basis for the subsequent calculation and analysis of the correlation degree of seepage mutation.

[0097] S3 includes:

[0098] S31. Calculate the spatial aggregation degree of microseismic events: Based on the extracted spatial location parameters of microseismic events, calculate the spatial aggregation degree of microseismic events. The specific method is as follows:

[0099] First, divide the target area into several spatial grids according to the source locations of the microseismic events;

[0100] Count the microseismic events within each grid and calculate the number of events within each grid;

[0101] Then, based on the number of events and the spatial distribution of the grids, calculate the spatial aggregation degree Q using the following formula:

[0102]

[0103] where, N cell is the number of microseismic events within a certain grid, N total is the total number of microseismic events within the entire area, and ri is the distance - weighted factor of microseismic events within the grid, representing the spatial distribution of events. n is the number of grids. The spatial aggregation degree Q reflects the degree of concentration of microseismic events in space. The higher the aggregation degree, the more concentrated the area where microseismic events occur;

[0104] S32. Calculate the temperature gradient change rate: Based on the extracted temperature anomaly gradient data, calculate the change rate of the temperature gradient ΔT / Δt. The specific method is as follows:

[0105] Select several monitoring points within the target area and extract the temperature data of these monitoring points at different time nodes;

[0106] Calculate the temperature change amount ΔT between adjacent time points for each monitoring point and divide it by the time interval Δt to obtain the temperature gradient change rate;

[0107] Based on the temperature change rate data of all monitoring points, calculate the overall temperature change rate within the area. The formula is as follows:

[0108]

[0109] where, is the temperature of monitoring point i at time t, is the temperature of monitoring point i at time t + 1. n is the total number of monitoring points, and Δt is the time interval;

[0110] The temperature gradient change rate reflects the change rate of temperature in different time periods and is used to evaluate the impact of temperature anomalies on seepage mutation;

[0111] S33. Calculate the cumulative energy release amount: Based on the extracted vibration energy release parameters, calculate the cumulative energy release amount ΣE of microseismic events. The specific method is as follows:

[0112] First, extract the energy release amount E of all microseismic events and accumulate the energy release amount of each microseismic event;

[0113] Then, calculate the cumulative energy release amount ΣE, which is calculated as:

[0114]

[0115] where, E i is the energy release amount of the i - th microseismic event, and n is the total number of all microseismic events;

[0116] The cumulative energy release amount ΣE reflects the total energy release situation of all microseismic events within the target area and is used to evaluate the possibility of seepage mutation;

[0117] S34. Calculate the seepage mutation correlation index: Combine the spatial aggregation degree of microseismic events, the temperature gradient change rate, and the cumulative energy release to calculate the seepage mutation correlation index. The specific method is as follows:

[0118] Through the weighted average method or the multi-factor analysis method, synthesize the spatial aggregation degree Q, the temperature gradient change rate ΔT / Δt, and the cumulative energy release ΣE into a comprehensive correlation index D, which is expressed as:

[0119]

[0120] where w1, w2, and w3 are the weight coefficients of the spatial aggregation degree, the temperature gradient change rate, and the cumulative energy release, respectively, and satisfy w1 + w2 + w3 = 1;

[0121] This seepage mutation correlation index D is used to quantify the relationship between microseismic events, temperature changes, and energy release, so as to evaluate the possibility of seepage mutation occurrence.

[0122] S4 includes:

[0123] S41. Establish a three-dimensional seepage-stress coupling model: Based on the calculated seepage mutation correlation index, construct a three-dimensional seepage-stress coupling model as follows:

[0124] (1) Model framework: Combine the geological conditions and hydraulic conditions of the target area to establish the coupling relationship between seepage and stress. This model includes two main parts:

[0125] The seepage equation, which describes the flow of fluid in porous media;

[0126] The stress equation, which describes the mechanical response of the formation under stress.

[0127] (2) Seepage equation (extended form of Darcy's law): First, based on Darcy's law (or its extended form), define the seepage equation, which is expressed as:

[0128]

[0129] where, is the permeability at the regional point, considering the influence of the spatial aggregation degree of microseismic events, the temperature gradient change rate, and the cumulative energy release, p is the pore pressure, ρ is the fluid density, g is the acceleration due to gravity, and q is the source term, representing the water flow per unit volume or the external source term;

[0130] (3) Stress equation: The stress equation is based on the equilibrium equation of solid mechanics and describes the distribution of the stress field in the formation, which is expressed as:

[0131]

[0132] Among them, is the stress tensor, is the body force source term, representing the external force inside the rock mass;

[0133] (4) Coupling relationship: The coupling relationship between seepage and stress is established through the interaction between fluid and solid. The change of pore pressure will affect the stress distribution of the rock mass, and the strain of the rock mass will in turn affect the seepage path. In the model, the seepage equation and the stress equation interact with each other to form a coupled equation set, and these two types of equations need to be solved simultaneously;

[0134] (5) Input parameters: Based on the seepage mutation correlation index, the input parameters of the model include:

[0135] Spatial aggregation degree of microseismic events: Reflects the degree of aggregation of microseismic events in space;

[0136] Temperature gradient change rate: Reflects the rate of temperature change, which has an impact on seepage characteristics and stress field changes;

[0137] Cumulative energy release: Represents the energy released by microseismic events, which affects the stress field and seepage path;

[0138] (6) Numerical solution method: The finite difference method is used to discretize and solve the coupled equations. The specific steps are as follows:

[0139] Discretize the target area into a three-dimensional grid, and calculate the pore pressure and stress tensor at each grid node;

[0140] Perform difference discretization on the seepage equation and the stress equation respectively to obtain the corresponding difference equation sets;

[0141] Within each time step, solve the seepage equation and the stress equation simultaneously through an iterative method to obtain the pore pressure and stress field distributions at each time point.

[0142] At each grid node, constrain the equations through boundary conditions and initial conditions to ensure the accuracy of the calculation results.

[0143] (7) Initial conditions and boundary conditions:

[0144] Initial conditions: At time t = 0, assume that the initial pore pressure, stress field, and temperature distribution are known;

[0145] Boundary conditions: Set the boundary conditions of pore pressure and stress according to the geological characteristics of the target area;

[0146] (8) Output results: Through numerical solution, the model outputs the following results:

[0147] Formation pore pressure distribution: Shows the change of pore pressure at different positions;

[0148] Rock mass stress field distribution: showing the stress distribution inside the formation;

[0149] Stability analysis: combining the stress field and seepage conditions of the rock mass, further conducting the stability analysis of the rock mass, and providing a decision-making basis for subsequent early warning and disaster prevention;

[0150] S42, inputting the seepage mutation correlation index into the model: taking the calculated seepage mutation correlation index as an input parameter and inputting it into the pre-constructed three-dimensional seepage-stress coupling model. The specific method is as follows:

[0151] According to the value of the seepage mutation correlation index D, adjust the parameters related to seepage mutation in the model, such as permeability change, pore pressure change, etc.;

[0152] Combining the characteristics of microseismic events, temperature gradient changes, and energy release, set the non-linear coupling term in the model to simulate the influence of seepage mutation on the stress field;

[0153] During the model calculation process, continuously monitor the change of the seepage mutation correlation index D, and update the boundary conditions in the coupling model in a timely manner to reflect the influence of actual monitoring data on the model.

[0154] S43, generating the stratum pore pressure distribution nephogram: based on the input seepage mutation correlation index and the calculation results of the three-dimensional seepage-stress coupling model, generate the stratum pore pressure distribution nephogram of the target area. The specific method is as follows:

[0155] According to the pore pressure data obtained from the model calculation, use the interpolation algorithm (such as Kriging interpolation or spline interpolation) to extend the calculation results to the entire target area to form the spatial distribution of pore pressure;

[0156] The generated pore pressure distribution diagram is presented in a three-dimensional form, which can clearly show the pore pressure changes at different positions, especially the change trend in the seepage mutation area;

[0157] The pore pressure distribution nephogram can be presented through visualization software for monitoring personnel to analyze the pressure states at different positions.

[0158] S44, calculating the rock mass stability coefficient K: based on the stratum pore pressure distribution nephogram obtained in S43, calculate the rock mass stability coefficient K of the target area. The specific method is as follows:

[0159] According to the rock mass characteristics of the target area, establish a calculation model for rock mass stability, which considers the mutual influence of pore pressure, stress field, and geological structure;

[0160] Using common rock mass stability analysis methods (such as the Mohr-Coulomb criterion or the Venturi criterion), calculate the stability coefficient K of the rock mass according to the pore pressure, rock mass strength, and stress field. The formula is as follows:

[0161]

[0162] Where, σ max is the maximum principal stress, σ min is the minimum principal stress, and τ max is the maximum shear stress;

[0163] The rock mass stability coefficient K represents the safety factor of the rock mass. The larger the K value, the higher the stability of the rock mass. Conversely, there may be a risk of instability;

[0164] S45, generate a rock mass stability distribution map: According to the calculated rock mass stability coefficient K, generate a rock mass stability distribution map. The specific method is as follows:

[0165] Correspond the rock mass stability coefficients K at different positions with the spatial coordinates of the target area, and use an interpolation algorithm to obtain the stability distribution;

[0166] The generated stability distribution map shows the rock mass stability conditions at various locations in the target area, highlighting potential unstable areas for subsequent decision-making reference.

[0167] S5 includes:

[0168] S51, calculate the time series change rate of the rock mass stability coefficient K: According to the numerical results of the rock mass stability coefficient K generated by the three-dimensional seepage-stress coupling model, calculate its time series change rate dK / dt, which reflects the dynamic change of the rock mass stability;

[0169] The time series change rate dK / dt of the rock mass stability coefficient K is calculated by the numerical difference method and is expressed as:

[0170]

[0171] Where, K(t) is the rock mass stability coefficient at time t, Δt is the time step, and K(t + Δt) is the rock mass stability coefficient at time t + Δt;

[0172] S52, set the warning threshold: Set two thresholds to trigger different warning levels;

[0173] S53, warning signal output and response: According to the triggered warning level, output the corresponding alarm information through the warning management platform or communication system. The alarm content includes:

[0174] The numerical value of the current rock mass stability coefficient K;

[0175] Calculation results of the time series change rate dK / dt;

[0176] Activated warning level (Level 1 warning or Level 2 warning);

[0177] Warning activation time and possible affected areas;

[0178] Warning response: For a Level 1 warning, initiate an emergency response procedure, arrange for an expert group to conduct on-site inspections and further evaluations of the area, and implement necessary reinforcement or evacuation measures.

[0179] For a Level 2 warning, increase the monitoring frequency, arrange for further analysis and evaluation, and ensure that the rock mass stability is within a controllable range.

[0180] In S52, two thresholds are set to trigger different warning levels, including:

[0181] Level 1 warning: When dK / dt ≥ 0.15, trigger a Level 1 warning signal, indicating that the rock mass stability has decreased sharply, there is a high risk, the system sends a warning notice to relevant personnel, and initiates emergency response measures;

[0182] Level 2 warning: When 0.08 ≤ dK / dt < 0.15, trigger a Level 2 warning signal, indicating that there are certain changes in the rock mass stability, which requires enhanced monitoring and evaluation, but it is not yet urgent. The system sends a warning notice to relevant personnel, requesting further attention and evaluation of the rock mass stability.

[0183] S6 includes:

[0184] S61, collecting historical warning data and on-site review results: Collect and store historical warning data, which includes the rock mass stability coefficient K value, time series change rate, warning level, and their corresponding on-site review results at the time of each warning trigger;

[0185] The on-site review results include on-site observations of the rock mass stability changes, borehole test data, seepage characteristics analysis, and structural reinforcement treatment, etc.;

[0186] S62, application of the particle swarm optimization algorithm: Design a particle swarm optimization algorithm to dynamically adjust the weight coefficient of the seepage mutation correlation index, including:

[0187] S621, optimizing the weight coefficient through the particle swarm algorithm to minimize the warning error and make the seepage mutation correlation index more consistent with the actual rock mass stability;

[0188] Each particle represents a combination of weight coefficients, and the fitness function of the particle is an error metric based on historical data, defined as follows:

[0189]

[0190] where the error i is the difference between the seepage mutation correlation index calculated based on the current weight coefficient and the actual stability when the i-th early warning is triggered, and n is the number of historical early warning data;

[0191] S622, Particle swarm initialization and update: Initialize each particle of the particle swarm. The position of the particle is represented as a combination of weight coefficients of the seepage mutation correlation index, expressed as:

[0192] W = (W1, W2, W3);

[0193] where W1, W2, and W3 represent the weight coefficients of the spatial aggregation degree of microseismic events, the change rate of temperature gradient, and the cumulative amount of energy release, respectively;

[0194] The position and velocity of each particle are updated by the following formula:

[0195]

[0196] where is the updated velocity of the i-th particle, is the position (i.e., weight coefficient) of the i-th particle, is the historical best position of the i-th particle, and G best is the global best position of all particles. c1 and c2 are acceleration constants that control the weights of individual and global searches. r1 and r2 are random numbers in the range [0, 1], and ω is the inertia weight used to balance the search speed of the particles;

[0197] S623, Calculate fitness and update the particle swarm:

[0198] Calculate the new fitness function value using the combination of weight coefficients after each particle position update;

[0199] If the updated fitness value is less than the historical best fitness value, update the historical best position and global best position of the particle;

[0200] According to the update rule of the particle swarm optimization algorithm, repeat particle update and fitness calculation until the stop condition is met (such as reaching the maximum number of iterations or the fitness change is less than the preset threshold);

[0201] S63, Update the weight coefficients of the seepage mutation correlation index: The combination of weight coefficients corresponding to the global best particle position obtained by the particle swarm optimization algorithm is the optimized weight coefficient of the seepage mutation correlation index. Use the optimized weight coefficient W * to replace the original weight coefficient and recalculate the seepage mutation correlation index D * , and its calculation formula is as follows:

[0202]

[0203] Among them, D * is the optimized seepage mutation correlation index, and Q, ∑E are respectively the spatial aggregation degree of microseismic events, the temperature gradient change rate, and the cumulative energy release;

[0204] S64, update the boundary conditions of the three-dimensional seepage-stress coupling model: According to the optimized seepage mutation correlation index D * Update the boundary conditions of the three-dimensional seepage-stress coupling model, including the permeability of the pore medium, the stress distribution, and the fluid flow characteristics, and recalculate and update the calculation results of the seepage and stress coupling model by the finite difference method to obtain a more accurate evaluation of the rock mass stability.

[0205] As Figure 2 shown, a hydrogeological monitoring system for implementing the above-mentioned hydrogeological monitoring method includes the following modules:

[0206] Data acquisition module: Arrange a microseismic sensor array and a distributed fiber optic temperature sensing network to collect three-dimensional vibration waveform data and axial temperature gradient data of the target area to form a multi-source monitoring data set;

[0207] Data processing module: Perform spatio-temporal registration processing on the multi-source monitoring data set, extract the spatial location parameters of microseismic events, the vibration energy release parameters, and the temperature anomaly gradient parameters to generate a set of characteristic parameters;

[0208] Seepage mutation index calculation module: Calculate the seepage mutation correlation index based on the set of characteristic parameters, including the spatial aggregation degree of microseismic events, the temperature gradient change rate, and the cumulative energy release;

[0209] Coupled model calculation module: Used to input the seepage mutation correlation index into a pre-constructed three-dimensional seepage-stress coupling model to generate a cloud map of the formation pore pressure distribution and the rock mass stability coefficient K;

[0210] Early warning module: Used to trigger a hierarchical early warning according to the time series change rate dK / dt of the rock mass stability coefficient K;

[0211] Optimization and adjustment module: Based on historical early warning data and on-site review results, use the particle swarm optimization algorithm to dynamically adjust the weight coefficient of the seepage mutation correlation index and update the boundary conditions of the three-dimensional seepage-stress coupling model.

[0212] The present invention encompasses any alternatives, modifications, equivalent methods, and solutions that are within the spirit and scope of the present invention. To enable the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention. However, those skilled in the art can fully understand the present invention even without the description of these details. Additionally, well-known methods, processes, procedures, components, and circuits, etc., are not described in detail to avoid unnecessary confusion with the essence of the present invention.

[0213] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as within the protection scope of the present invention.

Claims

1. A hydrogeological and environmental geological monitoring method, characterized in that It includes the following steps: S1. Deploy a microseismic sensor array and a distributed fiber optic temperature sensing network, collect three-dimensional vibration waveform data and axial temperature gradient data of the target area, and form a multi-source monitoring data set; S2. Perform spatio-temporal registration processing on the multi-source monitoring data set, extract spatial location parameters, vibration energy release parameters and temperature anomaly gradient parameters of microseismic events, and generate a set of characteristic parameters; S3. Calculate the seepage mutation correlation index based on the set of characteristic parameters, including the spatial aggregation degree of microseismic events, the temperature gradient change rate and the cumulative energy release; S4. Input the seepage mutation correlation index into a pre-constructed three-dimensional seepage-stress coupling model to generate a formation pore pressure distribution cloud map and a rock mass stability coefficient K; S5. Trigger hierarchical early warning according to the time series change rate dK / dt of the rock mass stability coefficient K; S6. Based on historical early warning data and on-site review results, use the particle swarm optimization algorithm to dynamically adjust the weight coefficients of the seepage mutation correlation index, and update the boundary conditions of the three-dimensional seepage-stress coupling model.

2. The hydrogeological, engineering geological and environmental geological monitoring method according to claim 1, characterized in that, The S1 includes: S11. Deploy a microseismic sensor array: Deploy microseismic sensors at multiple key positions in the target area to collect three-dimensional vibration waveform data of the target area in real time; S12. Deploy a distributed fiber optic temperature sensing network: Deploy a distributed fiber optic temperature sensor network in the target area to collect axial temperature gradient data of the target area; S13. Through the combined action of the microseismic sensor array and the fiber optic temperature sensing network, collect three-dimensional vibration waveform data and axial temperature gradient data of the target area in real time, and form a multi-source monitoring data set; S13. Data synchronization and time series calibration: Perform time synchronization processing on the vibration waveform data collected by the microseismic sensor array and the temperature gradient data collected by the fiber optic temperature sensing network.

3. A hydrogeological and environmental geological monitoring method according to claim 2, characterized in that, The S2 includes: S21. Perform spatio-temporal registration processing: Perform spatio-temporal registration processing on the three-dimensional vibration waveform data and axial temperature gradient data in the multi-source monitoring data set; S22. Extract spatial location parameters of microseismic events: Based on the vibration waveform data after spatio-temporal registration, use the time delay estimation method to extract the spatial location parameters of microseismic events; S23. Extract vibration energy release parameters: Based on the vibration waveform data of microseismic events, use the spectrum analysis method to calculate the vibration energy release parameters of microseismic events; S24. Extract temperature anomaly gradient parameters: Based on the temperature gradient data after spatio-temporal registration, calculate the change rate of temperature and the temperature anomaly gradient; S25. Generate a set of characteristic parameters: According to the extracted spatial location parameters, vibration energy release parameters and temperature anomaly gradient parameters of microseismic events, generate a set of characteristic parameters containing multi-dimensional information.

4. A hydrogeological and environmental geological monitoring method according to claim 3, characterized in that The S3 includes: S31. Calculate the spatial aggregation degree of microseismic events: Based on the extracted spatial location parameters of microseismic events, calculate the spatial aggregation degree of microseismic events; S32. Calculate the temperature gradient change rate: Based on the extracted temperature anomaly gradient data, calculate the change rate of the temperature gradient; S33. Calculate the cumulative energy release: Based on the extracted vibration energy release parameters, calculate the cumulative energy release of microseismic events; S34. Calculate the seepage mutation correlation index: Combine the spatial aggregation degree of microseismic events, the temperature gradient change rate, and the cumulative energy release to calculate the seepage mutation correlation index.

5. A hydrogeological and environmental geological monitoring method according to claim 4, characterized in that The said S4 includes: S41. Establish a three-dimensional seepage-stress coupling model: Based on the calculated seepage mutation correlation index, construct a three-dimensional seepage-stress coupling model; S42. Input the seepage mutation correlation index into the model: Take the calculated seepage mutation correlation index as an input parameter and input it into the pre-constructed three-dimensional seepage-stress coupling model; S43. Generate the stratum pore pressure distribution cloud map: Based on the input seepage mutation correlation index and the calculation results of the three-dimensional seepage-stress coupling model, generate the stratum pore pressure distribution cloud map of the target area; S44. Calculate the rock mass stability coefficient K: Based on the stratum pore pressure distribution cloud map obtained in S43, calculate the rock mass stability coefficient K of the target area; S45. Generate the rock mass stability distribution map: Generate the rock mass stability distribution map according to the calculated rock mass stability coefficient K.

6. The hydrogeological, engineering geological and environmental geological monitoring method according to claim 5, wherein, The said S5 includes: S51. Calculate the time series change rate of the rock mass stability coefficient K: According to the numerical results of the rock mass stability coefficient K generated by the three-dimensional seepage-stress coupling model, calculate its time series change rate dK / dt; S52. Set the warning thresholds: Set two thresholds to trigger different warning levels; S53. Output and respond to the warning signal: According to the triggered warning level, output the corresponding alarm information through the warning management platform or communication system.

7. A hydrogeological and environmental geological monitoring method according to claim 6, characterized in that, The two thresholds set in the said S52 to trigger different warning levels include: Level-I warning: When dK / dt ≥ 0.15, trigger the Level-I warning signal, and the system sends a warning notice to relevant personnel and initiates emergency response measures; Level-II warning: When 0.08 ≤ dK / dt < 0.15, trigger the Level-II warning signal, and the system sends a warning notice to relevant personnel.

8. A hydrogeological and environmental geological monitoring method according to claim 6, characterized in that, The said S6 includes: S61. Collect historical warning data and on-site review results: Collect and store historical warning data, which includes the rock mass stability coefficient K value, time series change rate, warning level, and their corresponding on-site review results at the time of each warning trigger; S62. Apply the particle swarm optimization algorithm: Design the particle swarm optimization algorithm to dynamically adjust the weight coefficients of the seepage mutation correlation index; S63. Update the weight coefficients of the seepage mutation correlation index: The weight coefficient combination corresponding to the global best particle position obtained by the particle swarm optimization algorithm is the optimized weight coefficient of the seepage mutation correlation index. Use the optimized weight coefficient to replace the original weight coefficient and recalculate the seepage mutation correlation index; S64. Update the boundary conditions of the three-dimensional seepage-stress coupling model: Update the boundary conditions of the three-dimensional seepage-stress coupling model according to the optimized seepage mutation correlation index, including the permeability of the pore medium, stress distribution, and fluid flow characteristics, and recalculate and update the calculation results of the seepage and stress coupling model by the finite difference method.

9. A hydrogeological and environmental geological monitoring system for implementing a hydrogeological and environmental geological monitoring method according to any one of claims 1-8, characterized in that, It includes the following modules: Data acquisition module: Deploy a microseismic sensor array and a distributed optical fiber temperature sensing network to collect three-dimensional vibration waveform data and axial temperature gradient data of the target area, and form a multi-source monitoring data set; Data processing module: Perform spatio-temporal registration processing on the multi-source monitoring data set, extract the spatial location parameters, vibration energy release parameters, and temperature anomaly gradient parameters of microseismic events, and generate a set of characteristic parameters; Seepage mutation index calculation module: Calculate the seepage mutation correlation index based on the set of characteristic parameters, including the spatial aggregation degree of microseismic events, the temperature gradient change rate, and the cumulative energy release; Coupled model calculation module: Used to input the seepage mutation correlation index into a pre-constructed three-dimensional seepage-stress coupled model to generate a formation pore pressure distribution cloud map and the rock mass stability coefficient K; Early warning module: Used to trigger hierarchical early warnings according to the time series change rate dK / dt of the rock mass stability coefficient K; Optimization and adjustment module: Based on historical early warning data and on-site review results, use the particle swarm optimization algorithm to dynamically adjust the weight coefficients of the seepage mutation correlation index and update the boundary conditions of the three-dimensional seepage-stress coupled model.

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