455 results about "Microseism" patented technology

The invention relates to the technical field of geophysical exploration, and particularly discloses a tunnel surrounding rock stability automatic monitoring and early warning method and system. The method comprises the steps that surrounding rock microseismic signals, convergence displacement and osmotic pressure data are collected through a space-time synchronization array to generate a multi-source fusion data set; based on a dynamic constitutive model separation frequency dispersion effect, extracting correlation characteristics of a microseismic event rate and a displacement rate; generating an instability energy density index; dynamically switching impact or creep damage criteria according to the deformation behavior of the surrounding rock; inputting the indexes into corresponding criteria to generate a damage state result; and if the result exceeds the threshold value, verifying the spatial focusing property of the micro-seismic event cluster, and outputting an early warning instruction when the focusing degree exceeds the critical value. Through multi-source fusion and a dynamic criterion mechanism, the accuracy of tunnel surrounding rock instability early warning is remarkably improved.

The invention discloses a self-adaptive grouting control plugging method based on mining-induced fracture real-time monitoring, and relates to the technical field of mine safety engineering and hydrogeology. Through cooperative work of the grouting mechanism, the data monitoring system, the data collecting and processing module and the self-adaptive control module, automation and intellectualization of grouting protection can be achieved, and through introduction of the fracture roughness coefficient and the time-dependent viscosity, rough fracture flow resistance and grout rheology and time-varying characteristics are quantified, and the grout diffusion radius calculation error is reduced. The data weight is dynamically adjusted through a micro-seismic travel time residual error and sound wave velocity field joint inversion algorithm in combination with a weighted robust LM algorithm, and accurate fracture positioning is achieved. And a PID gain compensation and saturation function mechanism is adopted, so that self-adaptive safe and efficient grouting is realized. The fracture dynamic state is monitored in real time through the multi-source data fusion technology, grouting parameters are dynamically optimized in combination with an intelligent algorithm, complex geological interference is effectively restrained, slurry waste is reduced, and efficient and accurate plugging of mining-induced fractures is achieved.

An asymmetric high stress tunnel rock burst micro-seismic sensor arrangement method, relating to the technical field of tunnel engineering. On the basis of tunnel surrounding rock geological data, three-dimensional numerical simulation is carried out for asymmetric high stress tunnel stability on the basis of asymmetric in-situ rock geo-stress distribution and tunnel section geometric characteristics, and asymmetric high stress tunnel energy is calculated; the location of a rock burst risk area is determined; micro-seismic sensors are arranged in front of and behind a stress concentration area; the area at a high risk and nearby areas thereof are always incorporated in two micro-seismic sensor arrays arranged in front of and behind a tunnel face. The micro-seismic sensors work cooperatively and are staggered in space, the mounting and arrangement of the micro-seismic sensors changes forwards and backwards along with the stress concentration area, and the micro-seismic sensors always keep a certain distance range with the stress area. The tunnel stress concentration area and more nearby effective micro-crack source signals can be captured by the micro-seismic sensors, thereby improving the precision of locating a micro-seismic source and laying a foundation for accurate prediction and forecast of deep-buried hard rock tunnel disasters.

The invention relates to the technical field of coal mine water disaster prevention and control, and particularly discloses a hidden structure detection system for mine water disaster intelligent monitoring, early warning and prevention, which is used for solving the problems of low mine hidden structure detection precision, mine water disaster early warning lag and large seismic wave propagation path simulation error. Comprising a multi-source data comprehensive detection module, a real-time imaging module, a micro-seismic multi-scale intelligent analysis module, a seismic wave signal identification module, a regional difference analysis module and an intelligent data processing module. According to the invention, through fusion of multi-source data, real-time imaging, micro-seismic analysis, seismic wave identification and intelligent data processing technologies, precise detection of a mine hidden structure, high-precision prediction of mine earthquakes and water disasters and real-time risk early warning are realized, and timeliness and accuracy of mine water disaster prevention and control are improved.

The invention discloses a rock burst global monitoring and dynamic and static far and near field cooperative early warning method and system, and the early warning method comprises the steps: determining a stope space key monitoring region and a stress wave type based on a dynamic load stress wave far field generation and propagation mechanism, delineating a working face surrounding rock high-stress region, and analyzing the structural characteristics of a rock stratum. Arranging a micro-seismic sensor in a far-field key area, and obtaining parameters such as seismic source fracture size, energy and magnitude through waveform inversion; an acoustoelectric system is arranged in a near-field high-stress area, coal rock fracture characteristics are dynamically monitored, and the static load energy storage state of surrounding rock is quantitatively analyzed. And in combination with a stress wave propagation attenuation coefficient, a far and near field fusion early warning criterion under the action of dynamic and static energy superposition is established, and advanced accurate early warning of the rock burst is realized through collaborative analysis of multi-source monitoring data. The disaster-pregnant space global monitoring technology fusing earthquake, sound and electricity is provided, the chain evolution disaster-causing process of dynamic load generation, cross-space propagation and working face surrounding rock damage is considered, and the accuracy of rock burst early warning can be remarkably improved.

The invention discloses a coal and rock mass fracture seismic source and stress field joint inversion method and system, and the method comprises the following steps: arranging a multi-channel sonic sensor in a coal and rock mass, and collecting original acoustic emission / microseismic signal data; the head wave arrival time / amplitude of the filtered effective waveform data is automatically picked up through an improved AIC (Akaike Information Code), quantitative evaluation is carried out on the quality of a picked-up result, and high-precision positioning is carried out on waveform signals with a high signal-to-noise ratio and a low signal-to-noise ratio; constructing a tension fracture seismic source model based on a displacement discontinuous tensor, obtaining a moment tensor component of a fracture event, and calculating to obtain a spatial orientation, a tension-shear attribute, a deviation angle alpha, energy release and a moment magnitude of the fracture; establishing a stress field inversion model of the tension-shear fracture seismic source, and realizing the inversion of the stress direction and size of the tension-shear fracture seismic source; and a slip direction included angle minimization objective function is established for iteration until convergence conditions are met, and joint iteration inversion of the fracture and the stress field is realized.

The invention relates to the technical field of karst tunnel collapse early warning, in particular to a karst tunnel collapse intelligent early warning method, system and equipment based on micro-seismic multi-precursor characteristics. According to the technical scheme, the method comprises the steps of multi-scale micro-seismic monitoring network construction, multi-modal micro-seismic feature processing, quantum neural network early warning model construction and self-adaptive early warning and risk assessment. According to the invention, through omnibearing data acquisition of the multi-scale micro-seismic monitoring network, different depth sensors and an intelligent transmission mode, monitoring is accurate and stable, through combination of multi-modal feature extraction fusion, time domain, frequency domain and waveform multi-dimensional information mining, surrounding rock conditions are accurately judged, early warning analysis is enhanced by using quantum characteristics through the quantum neural network, and the early warning accuracy is improved. And self-adaptive threshold adjustment and multi-factor risk assessment are matched, accurate early warning is achieved, collapse is efficiently prevented, and tunnel construction safety is powerfully guaranteed.

The invention relates to the technical field of rockburst early warning, and provides a tunnel rockburst early warning method based on micro-seismic activity space aggregation degree, which comprises the following steps: 1) arranging micro-seismic monitoring sensors, and building a micro-seismic monitoring system; 2) counting spatial distribution characteristics between a position where rockburst occurs and a tunnel face in a tunnel excavation process, and delimiting a calculation area of a micro-seismic activity spatial aggregation degree; 3) exporting the occurrence position, the occurrence time and the released energy of the micro-seismic monitoring signal in the calculation area to calculate the time-space gathering degree of the micro-seismic activity; 4) counting a micro-seismic activity space-time gathering degree evolution law of different levels of rockburst, establishing a mapping relation between the excavation tunnel rockburst occurrence risk level and the micro-seismic activity space-time gathering degree, and constructing a rockburst risk early warning model; and 5) calculating the gathering degree value of the microseismic activity in the microseismic activity space-time gathering degree calculation area in real time, and calculating and determining the potential rockburst occurrence probability and grade in the early warning area. According to the invention, tunnel rockburst early warning can be well carried out.

A vibration P wave arrival time pickup method of a lightweight transfer learning model belongs to the technical field of micro-seismic monitoring, and comprises the following steps: 1, importing earthquake and micro-seismic event data; 2, preprocessing the data in the step 1; and 3, making a training set and a test set by using the data in the step 2. And 4, constructing a seismic data arrival time pickup model. And 5, inputting data of the seismic data training set in the step 3 to the pickup model in the step 4 for training. And 6, inputting data of the seismic data test set in the step 3 to the pickup model in the step 4 for P-wave arrival time pickup, and evaluating the pickup effect of the model. And 7, inputting the micro-seismic data to the qualified pickup model in the step 6 for transfer learning, and finely adjusting the model to realize the arrival time pickup of the micro-seismic data. According to the method, the P-wave arrival time picking method with high accuracy and reliability is provided.

The invention discloses a rock burst advanced early warning method based on cumulative damage spatial-temporal distribution characteristics, and the method comprises the steps: obtaining all micro-seismic data of a to-be-predicted region, the micro-seismic data being composed of the three-dimensional coordinates and corresponding energy of each point in the predicted region; dividing the to-be-predicted area into a plurality of grids, and obtaining all microseismic data in each grid; based on all the micro-seismic data in each grid, calculating the fractal dimension of the roadway surrounding rock damage of each grid; based on all microseismic data in each grid, the number of crack planes, the crack area and the crack shape in each grid are obtained; and on the basis of the parting dimension, the number of the crack planes, the crack area and the crack shape in each grid, the rock burst danger degree of each grid is obtained, and early warning is performed according to the danger degree. According to the method, the parting dimension is obtained based on the microseismic data, meanwhile, various indexes of cracks are introduced, the danger degree of rock burst is obtained, early warning is conducted, and the timeliness and accuracy of early warning are improved.

The invention discloses a micro-seismic multichannel data processing method and system based on a DBSCAN algorithm, and belongs to the technical field of micro-seismic monitoring. According to the invention, through feature vector construction and optimization distance calculation, the clustering reliability is enhanced; a multi-dimensional feature vector Xi containing P-wave refinement arrival time ti, an amplitude Ai and space coordinates (xi, yi, zi) is constructed, distance calculation is performed by combining a normalized cosine distance with wave velocity constraint, a micro-seismic wave propagation rule is fully utilized, the discrimination capability of DBSCAN clustering on physical consistency is enhanced, and the robustness of a screening result is improved, so that noise interference can be effectively processed in a complex environment, and the accuracy and the robustness of the screening result are improved. And the stability and the reliability of a micro-seismic positioning result under different geological conditions are ensured.

The invention belongs to the field of surrounding rock rockburst early warning, and provides a rockburst early warning method and system based on micro-seismic energy and support stress coupling monitoring, which are used for carrying out deep gold mine roadway surrounding rock micro-seismic monitoring and anchor rod support stress monitoring. Establishing a micro-seismic energy data set, and carrying out statistical analysis to obtain a micro-seismic energy early warning index; the anchor rod supporting stress is analyzed, and then an anchor rod stress early warning index is determined; and carrying out information fusion on the micro-seismic energy early-warning index and the anchor rod stress early-warning index, establishing a combined early-warning method based on micro-seismic energy and support stress, and carrying out evaluation and early-warning on the rock burst grade of the surrounding rock. Rockburst occurrence risks can be effectively reduced, and safety of constructors and mechanical equipment is guaranteed.

The invention discloses a slope stability evaluation method based on micro-seismic signal double-dominant-frequency mechanism characteristics, and relates to material analysis and application. The method is based on the objective principle that rock acoustic emission dominant frequency characteristics and micro-seismic signal dominant frequency characteristics have consistency and relevance, and the phenomenon of double dominant frequency characteristics existing in acoustic emission tests and micro-seismic monitoring. A rock sample dominant frequency characteristic rule obtained by an acoustic emission test is used for processing micro-seismic signal waveform data obtained by micro-seismic monitoring, so that a dominant frequency characteristic evolution rule of slope rock damage can be obtained through a micro-seismic signal, and the stress condition of the slope rock is more accurately judged based on the dominant frequency characteristic evolution rule; the stability of the slope is evaluated, so that the accuracy of an evaluation result is remarkably improved; the evaluation method combines an acoustic emission test and micro-seismic monitoring, has the advantages of high evaluation accuracy and good universality, and is suitable for large-scale application in monitoring and prevention of geological disasters such as landslide and the like.

A method for monitoring a rock burst in ultra-thin coal seams, which method belongs to the technical field of fully mechanized coal mining in ultra-thin coal seams. The method comprises: respectively recording the acquired propagation times and propagation velocities of attenuated seismic waves from artificial seismic sources and the acquired propagation time and propagation velocity of a rock burst, and comparing different seismic wave signals and real-time rock burst signals passing through a mining area; converting the different seismic wave signals and the real-time rock burst signals into digital signals, and determining via comparison whether the digital signals have waveforms during the rock burst; when the digital signals have waveforms during the rock burst, identifying the waveforms of micro-seismic events, and determining the energy intensity of the rock burst; locating rock burst sources, and obtaining an intersection area of the rock burst sources, so as to obtain a seismic source position after a delineated range is reduced; and on the basis of acquired real-time pressure data in underground rock or coal seams, obtaining the positions of the maximum pressure and maximum pressure changes of a seismic source, and the numerical values thereof by means of analyzing a real-time pressure value and a real-time strain change rate. By means of the method, potential hazardous areas can be monitored. The present invention further relates to a system for monitoring a rock burst in ultra-thin coal seams.

The invention discloses a rock mass instability grading early warning method based on micro-seismic feature perception and a classification enhancement algorithm. The method comprises the following steps: acquiring rock mass instability case data information of different geotechnical engineering projects; selecting a plurality of microseismic parameters as rock mass instability risk candidate evaluation indexes, and performing correlation analysis to obtain rock mass instability grading early warning evaluation indexes; constructing a training data set; constructing a rock mass instability grading early warning primary model based on a classification enhancement algorithm and a genetic algorithm, and training to obtain a rock mass instability grading early warning model; and the obtained rock mass instability grading early warning model is adopted to complete rock mass instability grading early warning based on micro-seismic feature perception and a classification enhancement algorithm. The invention also discloses a system for realizing the rock mass instability grading early warning method based on the micro-seismic feature perception and classification enhancement algorithm. According to the method, rock mass instability grading early warning based on micro-seismic feature perception and a classification enhancement algorithm can be realized, the reliability is higher, and the accuracy is better.

The invention discloses a dynamic short-term and temporary intelligent forecasting system for mine time-delay rockburst, and belongs to the field of short-term and temporary forecasting of rockburst disasters. The system comprises a deep well three-dimensional micro-seismic field global sensing module, a micro-seismic characteristic parameter extraction module, a rockburst catastrophe evolution data set construction module, a data preprocessing module, a deep learning prediction module, an interpretability analysis module and a dynamic short-term and temporary intelligent forecasting module. A dynamic tracking type micro-seismic sensing network is constructed through a three-level annular nested layout strategy, micro-seismic event data are collected in real time, spatial-temporal characteristic parameters are extracted, a rockburst time sequence data set is constructed in combination with a sliding time window method, and the occurrence probability and intensity level of rockburst are predicted. The system adopts a grey wolf-simulated annealing hybrid algorithm to optimize model parameters, and improves the credibility of a prediction result through SHAP value analysis. The system can realize dynamic short-term and temporary early warning of rockburst disasters, and provides scientific decision support for mine safety management.

The invention provides an ionic type rare earth ore bed rock crack detection method and system, and the method comprises the steps: laying an active seismic source at a preset position of a target detection region, and enabling the active seismic source to emit an elastic wave with a preset frequency and a preset amplitude; a micro-seismic sensor network arranged in a target detection area collects sound wave signals which are emitted by the active seismic source and are scattered and polarized by the crack; extracting a plurality of crack characteristic parameters related to the bedrock crack from the bedrock crack microseismic sound wave signal, and performing preliminary analysis to obtain a possible crack region and preliminary key parameter information of the crack; based on the multiple fracture characteristic parameters, a multi-parameter joint inversion method is adopted to construct a fracture model containing fracture geometric parameters and physical parameters, and regularization optimization is carried out on the fracture model; and finally, based on the optimized crack model, carrying out inversion to obtain key parameters of high-precision spatial positions and distribution of the cracks. On the basis that the ore body is not damaged, the bedrock crack detection precision can be improved.

The invention relates to the technical field of earthquake prediction, in particular to a submarine seismic oscillation prediction method and device based on earthquake exploration, and the method comprises the following steps: selecting a monitoring region, collecting the submarine topographic data of the monitoring region, and drawing a topographic map; selecting a plurality of monitoring points according to the topographic map; geomagnetic field information, electric wave information, micro-seismic activity information and seabed deformation information of each monitoring point are collected and recorded; converting the collected information into acoustic signals and transmitting the acoustic signals to a sea surface buoy, and converting the acoustic signals into electric signals by the sea surface buoy and transmitting the electric signals to a ground data center; by optimizing site selection of monitoring points, integrating geomagnetic-electric wave-microseismic-deformation multi-source data, realizing high-efficiency data transmission by using an acoustic-electric staged communication chain, and finally realizing early recognition of earthquake precursor in combination with threshold analysis, the method can significantly improve the coverage range, data precision and early warning timeliness of seafloor earthquake monitoring, and has good application prospects. And a technical support is provided for marine geological disaster prevention and control.

The invention discloses a micro-seismic monitoring-based deep hole blasting crack evolution evaluation method and system. The method comprises the following steps of: obtaining a spatio-temporal evolution process of crack extension and a final crack distribution characteristic; microseismic signals in the blasting process are collected in real time, the spatial-temporal evolution process of crack expansion and the final crack distribution characteristics are combined, the microseismic signals are preprocessed, and preprocessed microseismic waveform data are obtained; calculating a spatial position coordinate of the micro-seismic event, and generating a complete three-dimensional micro-seismic positioning image; drawing a fracture density cloud picture according to the generated complete three-dimensional micro-seismic positioning image in combination with the acquired spatial distribution density and energy release rate of the micro-seismic event; a three-dimensional visualization model of fracture evolution is established, and evaluation indexes are obtained; and establishing a comprehensive index system for crack evolution evaluation, carrying out weight distribution and quantitative scoring on evaluation indexes, generating a quantitative evaluation report of crack evolution, and completing deep hole blasting crack evolution evaluation. The accuracy and reliability of evaluation are improved.

The invention relates to the technical field of safety management, in particular to a mine safety production risk prevention and control method based on multi-source data fusion, and the method comprises the following steps: constructing a coupling parameter set through a micro-seismic sensor and a temperature and humidity sensor, detecting and extracting displacement points through Bayesian regression, and drawing a deformation diagram; determining a displacement area, counting an airflow velocity gradient, extracting a main fracture direction, generating an airflow path diagram, calculating a curvature change rate, positioning a sudden change area, extracting a thermal strain rate and an axial strain rate, generating a smooth curve, identifying a risk point, and expanding a boundary when the overlapping rate exceeds 60%, and redrawing a mine thermal-mechanical coupling risk map. According to the method, a multi-parameter analysis framework is constructed through micro-seismic and temperature and humidity data coupling to extract a deformation active band, difference statistics airflow velocity gradient is combined with a crack direction to correct an airflow path, a sudden change area is positioned based on a curvature change rate mean threshold, and thermal strain and axial strain are extracted to screen risk points. And constructing thermal variable amplitude mapping to represent the rock mass instability risk.

According to the micro-seismic source intelligent positioning method and the model training method and system, physical laws such as a seismic wave equation are integrated, physical loss and data loss are comprehensively considered, the adaptability of the model to a complex geological environment is improved, micro-seismic source positioning challenges in the complex geological environment can be effectively dealt with, and the positioning accuracy of the micro-seismic source in the complex geological environment is improved. Noise immunity and positioning precision are improved, and more reliable technical support is provided for safety monitoring of underground engineering.

The invention provides a coal mine water inrush risk early warning method and system under multi-source information, and relates to the technical field of risk early warning, and the method comprises the steps: employing a multi-source information collection module to regularly collect geological structure data, hydrological data, microseismic signals and environment monitoring data in a coal mine operation area, and constructing a multi-source information database; performing data preprocessing to obtain a standard multi-source information database; carrying out multi-source data fusion, and constructing a water inrush risk comprehensive evaluation model in combination with a geological model, a hydrological model and a micro-seismic analysis model; analyzing the water inrush risk index in real time, and generating water inrush risk early warning information when the model output value is higher than a preset risk threshold value; and performing visual display of the water inrush risk early warning information through a visual interface. The technical problem that the prior art depends on a single data source and cannot comprehensively reflect complex condition changes in a coal mine operation area, so that accuracy of water inrush risk assessment is insufficient, and then early warning of the water inrush risk is missed is solved.

The invention relates to the technical field of side slope geological disaster monitoring and early warning, and discloses a Beidou-based side slope geological disaster monitoring and early warning method and system.The Beidou-based side slope geological disaster monitoring and early warning method comprises the steps that multi-scale geological signals based on Beidou system time service are acquired, the multi-scale geological signals comprise environmental noise signals, acoustic emission signals and micro-seismic signals; processing the multi-scale geological signals, and extracting environmental noise interference characteristics representing the change of a microstructure in the slope, acoustic emission signal characteristics representing the microscopic damage process of a material and micro-seismic signal characteristics representing the deformation of a macrostructure; based on the environmental noise interference features, the acoustic emission features and the micro-seismic features, constructing a slope instability risk assessment model, and outputting a risk assessment result; performing early warning according to a risk assessment result; according to the invention, high-precision, low-cost and full-coverage monitoring and early warning can be realized.

The invention provides a sandy stratum tunnel water burst risk prediction method. The method comprises the steps that a radar instantaneous energy spectrum in front of tunneling is obtained through a geological radar, a micro-seismic spectrum is obtained through a micro-seismic array, and a residual distance is obtained through laser ranging; aligning and coupling the two spectrums to obtain a water inrush risk index, inputting the water inrush risk index and the residual distance into a self-adaptive threshold comparator, and if the water inrush risk index and the residual distance exceed the limit, dynamically adjusting the rotating speed of a cutter head, propulsive force, the rotating speed of a screw conveyor and grouting pressure, and generating a risk avoidance path; and calling a water inrush risk model library constructed by LSTM-Attention, inputting a risk index sequence, a residual distance, cutterhead torque, a soil pressure gradient, a current change rate and other auxiliary variables, and outputting a future multi-step risk level. According to the method, accurate prediction and real-time avoidance of the water gushing risk of the sandy stratum tunnel can be realized, and the safety and efficiency of tunnel construction are improved.

The invention provides a landslide monitoring and early warning method based on air-sky-earthquake integration, and belongs to the technical field of landslide disaster monitoring. Air-sky-ground-earthquake monitoring data are integrated, multi-dimensional parameters such as surface deformation, topographic change, internal strain, crack propagation, rainfall intensity and micro-earthquake activity are covered, the limitation of a single monitoring means is solved, and a space-time complementary three-dimensional monitoring system is formed. According to a data analysis processing result, an early warning response rule is set, an early warning model is constructed through a multi-source data fusion technology in combination with an optimal weighted random forest method and a comprehensive scoring model, real-time evaluation and early warning grade judgment of landslide risks are realized, and visual display of monitoring data is realized. The model is iteratively trained by introducing reward and punishment factors, and weight distribution of different data sources is dynamically adjusted, so that the early warning model has adaptive ability, early warning requirements of long-term slow deformation and short-term accelerated deformation are considered, and the early warning accuracy of the model is improved.

The invention relates to the technical field of mine safety and geological environment evaluation, in particular to a mine goaf geological environment dynamic evaluation method based on digital twinning. The method comprises the following steps: constructing a digital twinborn model of a mine goaf, designing a data structure and a virtual-real mapping rule, collecting multi-source heterogeneous data, and performing spatio-temporal data indexing processing to form a spatio-temporal associated data stream; microseismic travel time data are extracted from the space-time associated data stream; performing parallel ray tracing and rapid inversion on the microseismic travel time data to obtain a wave velocity distribution initial value; carrying out wave velocity field self-adaptive grid refinement on the micro-seismic travel time data by utilizing the wave velocity distribution initial value to obtain a multi-resolution wave velocity field; and carrying out wave velocity change rate analysis on the multi-resolution wave velocity field to obtain a wave velocity variability tensor. According to the method, the abstract energy transfer process is concrete into a data field capable of being quantitatively analyzed, so that the geological environment of the goaf is accurately and dynamically evaluated.

The invention discloses a water injection induced earthquake monitoring and fault activation detection system, and belongs to the field of earthquake monitoring and fault activation detection. The earthquake observation module is used for obtaining earthquake monitoring data; the seismic phase data extraction module is used for carrying out micro-seismic identification according to the seismic monitoring data to obtain seismic phase data; the earthquake initial positioning module is used for obtaining an earthquake directory of initial positioning; the earthquake fine positioning module is used for carrying out secondary positioning to obtain an earthquake directory of fine positioning; the fault and structure modeling module is used for integrating the finely positioned seismic catalog into a three-dimensional geologic structure model and carrying out spatial positioning and visualization of seismic activities; and the fault activation and earthquake risk analysis module is used for analyzing the earthquake activity time change trend, b value change, fault activation and earthquake risk according to the three-dimensional geologic structure model. The method solves the problem that an existing method lacks monitoring of fault activation and weakening of the shale gas development area and water injection induced earthquakes.

The invention relates to the technical field of mine coal and rock dynamic disaster prevention and control, in particular to a micro-seismic-based deep coal and rock mass instability dynamic multi-parameter evaluation method. The method comprises the following steps: acquiring waveform data of each micro-seismic event of a deep coal and rock mass through a mine micro-seismic system; according to the waveform data, acquiring a micro-seismic characteristic index set of each micro-seismic event; according to the seismic source position and the micro-seismic characteristic index set of each micro-seismic event, acquiring an energy-based micro-seismic magnitude; according to the micro-seismic magnitude, obtaining a micro-seismic b value corresponding to a preset duration, and according to the micro-seismic characteristic index set, obtaining a micro-fracture type and change trend information of accumulated energy of each micro-seismic event; and according to the micro-seismic energy, the change trend information of the accumulated energy, the frequency, the ringing count, the micro-fracture type and the micro-seismic b value, the dynamic instability fracture risk information of the deep coal and rock mass is obtained. According to the invention, the determination accuracy and efficiency of the instability and fracture risk information can be improved.

The embodiment of the invention provides a micro-seismic monitoring and rockburst early warning method and device, and the method comprises the steps: deploying a sensor network to collect multi-source monitoring data which comprises acceleration data, speed data and strain data; constructing a dynamic system model represented by a Koopman operator based on historical multi-source monitoring data; combining the dynamic system model with real-time multi-source monitoring data, and performing state estimation through an improved Kalman filtering framework; based on the state estimation result, establishing a multi-scale prediction model for prediction by using a Koopman spectrum analysis method, and outputting a prediction result containing the frequency of the micro-seismic event, the vibration energy level and a Koopman spectrum characteristic index; a prediction result is corrected in real time through a tool variable method and a self-adaptive control method; and respectively comparing the microseismic event frequency, the vibration energy level and the Koopman spectrum characteristic index in the corrected prediction result with corresponding early warning thresholds, and outputting early warning information of different grades according to comparison results.

The invention provides a micro-seismic event identification method and device, and the method comprises the steps: monitoring a coal mine region, so as to obtain a to-be-identified micro-seismic signal; performing dynamic frequency slice wavelet transform (DFSWT) on the micro-seismic signal to be identified to obtain a corresponding target time-frequency diagram; performing feature extraction on the to-be-identified micro-seismic signal to obtain a corresponding target feature; based on the target time-frequency graph and the target features, adopting a target recognition model to obtain prediction probabilities of a plurality of microseismic events output by the target recognition model; wherein the prediction probability is used for indicating the probability of occurrence of the corresponding micro-seismic event; and determining a target micro-seismic event occurring in the coal mine area from the plurality of micro-seismic events according to the prediction probabilities of the plurality of micro-seismic events. According to the target time-frequency graph obtained by carrying out dynamic frequency slice wavelet transformation on the to-be-identified micro-seismic signals and the target features obtained by extracting the features of the to-be-identified micro-seismic signals, automatic identification of the micro-seismic events occurring in the coal mine area is realized, and the accuracy and effectiveness of micro-seismic event identification are improved.