144 results about "Microseism" patented technology

The invention discloses a deep rock mass fracture imaging method and system based on micro-seismic monitoring, and belongs to the technical field of geophysical exploration, and the method comprises the steps: collecting multi-channel micro-seismic event waveform data, obtaining a main seismic phase direct waveform and a scattering tail waveform through wave field separation, and carrying out the imaging of a deep rock mass fracture. Determining a three-dimensional space coordinate and an energy distribution form of the seismic source through joint inversion, carrying out clustering analysis, and constructing an initial three-dimensional space distribution form; each seismic source is regarded as an active excitation source, a scattering tail wave waveform is regarded as a reflection signal, scatterer three-dimensional point cloud data is generated through reverse time migration processing, analysis is carried out in combination with an initial three-dimensional space distribution form, and an enhanced fracture structure is formed; and finally, through topology analysis and orientation clustering, discrete scatterers are eliminated, and a fracture development map is generated. According to the method, a technical path of combining a main seismic phase direct wave inversion seismic source and a scattering tail wave inversion scatterer is adopted, high-precision and high-fidelity imaging of a deep rock mass movable fracture can be realized, and physical activity is revealed.

The invention relates to the field of mine disaster prediction, and discloses an AI-based mine disaster prediction and risk assessment method, which comprises the steps of deploying a multi-source sensor network which comprises a microseismic monitoring sensor, a gas concentration sensor, a stress sensor and a hydrological sensor, the data acquisition module is used for acquiring micro-seismic waveform data, gas concentration time sequence data, stress data and hydrological parameter data of a mining working face, a roadway and a geological structure area; and performing space-time alignment processing on the microseismic waveform data, the gas concentration time sequence data, the stress data and the hydrological parameter data to generate a space-time aligned heterogeneous data set, and performing encryption feature interaction with an external system through a federated learning framework. According to the method, a multi-modal deep learning model is constructed, microseismic waveforms, gas concentration time sequences and geologic structure space correlation characteristics are extracted, rock mechanics equation constraint conditions are injected in model training, and the recognition capacity of rock stratum fracture precursor signals and the disaster chain coupling process is remarkably enhanced.

The invention provides a rockburst intelligent early warning method based on cross-modal fusion of while-drilling sensing data and micro-seismic monitoring data, and belongs to the technical field of tunnel and underground engineering safety and disaster prevention and control. The rockburst intelligent early warning method comprises the steps that while-drilling parameters are acquired in real time, and drilling three-dimensional track coordinates are recorded; microseismic event signals induced by drilling disturbance are collected in real time, and seismic source parameters are inverted; preprocessing and time-space alignment are carried out on the collected while-drilling parameters and the microseismic event signals, and an aligned multi-modal feature sequence is generated; inputting the aligned multi-modal feature sequence into a trained rockburst risk prediction model, and outputting a rockburst risk grade probability distributed along the axis of each drilling track; and performing spatial interpolation on the rockburst risk level probability distributed along the axis of each drilling track, generating a three-dimensional rockburst risk probability field in front of the tunnel face, performing three-dimensional visual reconstruction on the three-dimensional rockburst risk probability field, performing risk level judgment according to a three-dimensional risk cloud picture in front of the tunnel face, and automatically issuing a corresponding early warning signal.

The invention discloses a slope safety early warning method, device and system and a medium, and the method comprises the steps: obtaining the detonation time of an electronic detonator in a target slope and first micro-seismic waveform data; second microseismic waveform data and a three-dimensional model of the target slope are obtained; recognizing and eliminating collaborative residual sound signals in the second micro-seismic waveform data to obtain target second micro-seismic waveform data; removing a common-mode blasting signal in the target second micro-seismic waveform data to obtain third micro-seismic waveform data; generating a blasting signature representing the blasting event and storing the blasting signature in a blasting noise dictionary; performing event detection on the third micro-seismic waveform data to obtain a plurality of to-be-analyzed events, removing blasting residual sound events from the plurality of to-be-analyzed events to obtain to-be-alarmed events, performing seismic source positioning and closing detection, and if the seismic source positioning and closing detection is passed, determining that the to-be-alarmed events are real micro-seismic events and performing danger alarm; and if not, determining that the event is an interference micro-seismic event. According to the invention, the accuracy of slope safety early warning can be improved.

The invention belongs to the technical field of intelligent monitoring and safety early warning, and provides an intelligent separation grouting opportunity decision system and method.The system comprises a multi-source monitoring data synchronous acquisition and preprocessing module, an InSAR deformation field spatio-temporal feature extraction module and a micro-seismic event cluster multi-parameter analysis module; according to the method, an InSAR macroscopic deformation field and a microseismic microcosmic fracture sequence are cooperatively utilized by the modules, the real-time evolution stage of mining separation is accurately diagnosed through multi-parameter feature extraction and machine learning intelligent judgment, and a quantitative and scientific optimal grouting opportunity decision is provided accordingly. And effective technical support is provided for prevention and control of mine disasters.

The invention relates to a mining trackless rubber vehicle centralized control method, which comprises the steps of obtaining an elastic wave signal generated by surrounding rock fracture at the position of a mining trackless rubber vehicle, obtaining surrounding rock three-dimensional principal stress component data at the position of the mining trackless rubber vehicle, processing the elastic wave signal through an edge processing module to obtain micro-seismic event parameters, and outputting the micro-seismic event parameters. The micro-seismic event parameters comprise a seismic source position and an energy characteristic of a micro-seismic event; converting the surrounding rock three-dimensional principal stress component data into a strain energy density field, fusing the strain energy density field with energy characteristics, and calculating to obtain a total energy value of a micro-seismic event; generating a surrounding rock health state evaluation result based on the micro-seismic event total energy value; determining a rockburst risk level and a high-risk area boundary based on the seismic source position of the micro-seismic event and a surrounding rock health state evaluation result; calculating a safe driving track based on the rockburst risk level and the high-risk area boundary; and sending the safe driving track to a vehicle control system of the mining trackless rubber vehicle.

The invention relates to the technical field of underground microseism monitoring, in particular to a method for correcting three-dimensional parameter values of an underground microseism monitoring fracturing horizontal fracture network, which comprises the following steps of: constraining the height of a fracture network by using the thickness of a single sand body of a target stratum; a single sand body constraint fracture height is obtained, an interval with the width being a constrained fracture network height value is selected in the horizontal axis direction of the fracture monitoring microseismic event height normal distribution diagram, the number of effective microseismic events with invalid noise points removed is obtained, and the number of the effective microseismic events with the invalid noise points removed and the effective ringing area fracture length are obtained; correcting the height of the post-fracturing fracture network and the length of the post-fracturing fracture network; according to the method, the seam height is restrained through the thickness of the single sand body of the target layer, the seam length and the seam width are determined in combination with the number of effective microseism events, the difference between a microseism monitoring crack result and a fracturing effective crack is determined, and the precision of three-dimensional parameters of a microseism monitoring crack network is remarkably improved.

The invention discloses a tight oil and gas reservoir horizontal well volume fracturing fracture network wave and volume prediction method, which comprises the following steps of: based on geological engineering parameters of a fractured horizontal well and microseism monitoring fracture volume data, calculating weight factors of each geological and engineering parameter by establishing a multi-factor evaluation model; aiming at a to-be-predicted fracturing section, combining the solved weight factor and the geological parameters of the perforation positions of the fracturing clusters in the fracturing section, and calculating a fracturing crack-making efficiency coefficient representing the distribution uniformity of fracturing fluid; multiplying the original micro-seismic monitoring fracture volume of the fractured section by the calculated fracture forming efficiency coefficient to obtain a corrected effective fracture network wave and volume; according to the method, the fracture forming efficiency coefficient is introduced to correct the original microseismic monitoring volume, the problem that prediction results of an existing method are generally large is solved, accurate quantitative prediction of the effective fracture network waves and the volume is achieved, and a scientific basis is provided for fracturing effect evaluation and scheme optimization.

The invention relates to the technical field of tunnel and underground engineering safety construction, in particular to a deeply-buried hard rock tunnel micro-seismic monitoring and segmented advanced blasting collaborative pressure relief method which comprises the steps that a pre-constructed micro-seismic monitoring system is arranged along the tunnel wall so as to collect and analyze seismic source parameters of a micro-seismic event; whether a preset early warning threshold value is exceeded is judged according to the judgment result; if yes, segmented advanced blasting pressure relief design is carried out based on the seismic source parameters of the micro-seismic event, so that blasting parameters are obtained; according to the blasting parameters, drilling wall protection and charging blasting are conducted on the tunnel hole wall, so that the blasted tunnel hole wall is obtained; the current surrounding rock response of the tunnel wall after blasting is observed, whether the current surrounding rock response exceeds a preset early warning threshold value or not is judged, if yes, it is judged that pressure relief is effective and tunneling is normal, and if not, blasting parameters are adjusted or pressure relief is supplemented in the next cycle. Therefore, the problems of passive rock burst risk early warning, poor pressure relief effect, extensive parameter control and the like under the high ground stress condition in drilling and blasting method construction of the deeply-buried hard rock tunnel are solved.

To improve estimation accuracy of an underground structure.SOLUTION: In the underground structure estimation method, micromotion of the soil at observation points P1 to P6 is observed by a micromotion array 10, and at least an S-wave speed Vs1 in a base layer 1 and a depth H to an upper surface 1a of the base layer 1 are calculated by inverse analysis using a relationship between a frequency and a phase speed of a wave propagating through the soil G from the observed micromotion, thereby estimating an underground structure at the observation points P1 to P6. In the subsurface structure estimation method, predetermined observation points among the plurality of observation points P1 to P6 are grouped as group observation points Pg, inverse analysis is performed assuming that the S-wave velocities Vs1 in the base layer 1 at all the grouped group observation points Pg are the same, and the S-wave velocities Vs1 in the base layer 1 and the depth H to the upper surface 1a of the base layer 1 at each of the group observation points Pg are calculated.SELECTED DRAWING: Figure 1

This invention relates to the field of mine slope technology and discloses a mine slope micro-seismic monitoring device, including a monitoring instrument body. A transmission line is plugged into one side of the monitoring instrument, and a sensing probe is electrically connected to one end of the transmission line. An embedded transmission mechanism is provided at the bottom of the sensing probe. The embedded transmission mechanism includes a positioning plate, and the top of the positioning plate has an installation groove for use with the sensing probe. By setting the embedded transmission mechanism, this invention can press the positioning plate downwards to allow the conical guide head and the embedded rod to enter the underground of the slope until the bottom of the positioning plate contacts the ground surface, compacting the soil. Then, the sensing probe is fixed to the positioning plate by a clamping mechanism. When the vibration force generated by blasting is transmitted, the vibration force is transmitted to the positioning plate by the two embedded rods, and then to the sensing probe by the positioning plate, thereby monitoring accurate and effective data and improving the accuracy of monitoring.

The application discloses a microseismic positioning method based on blasting seismic source wave velocity CT inversion results, first, a plurality of microseismic sensors are arranged in the crossheading on both sides of a working face to be mined, roof pre-splitting blasting holes are arranged between every two adjacent microseismic sensors; then, roof pre-splitting blasting is carried out on the working face to be mined, and a seismic source generated by pre-splitting blasting is taken as an active source to carry out vibration wave CT inversion, so that the wave velocity distribution of the working face to be mined is determined, and the method has high accuracy; microseismic information generated in the production process of the working face is recorded, including the microseismic waveform and P-wave arrival time data received by each microseismic sensor; then, a displacement-wave velocity-time equation group is established, the vibration wave velocity CT inversion result is brought into the equation group, and the positioning result of each sensor combination is solved, the positioning coordinates are solved for multiple times, the abnormal positioning values are eliminated, the central coordinates are taken as the final coordinate points, the positioning error is further reduced, and the microseismic positioning precision is ensured.

The invention discloses a grouting monitoring method and system based on distributed optical fibers, the optical fibers are laid in a grouting area in a distributed mode, and the method comprises the steps that micro-seismic events and micro-seismic data corresponding to the micro-seismic events are determined according to optical fiber sensing data; the micro-seismic data, the grouting parameters and the geological information are fused to obtain micro-seismic fusion features, the micro-seismic fusion features are input into a trained micro-seismic feature analysis model to be analyzed to obtain a stress change coefficient and a fracture mechanism probability, a geomechanical simulation model is constructed according to the geological information, dynamic inversion is carried out, a grouting process evolution diagram is generated, and the grouting process evolution diagram is analyzed. And further determining grouting effect information and / or grouting state information as a grouting monitoring result of the grouting area. According to the method and the device, real-time dynamic inversion of the grouting process can be realized, a visual map for visually displaying the grouting process is generated, and an accurate and reliable grouting monitoring result is obtained.

The present application provides a kind of complex stratum structure and topographic effect considering slope microseismic event 3D positioning method, the technical scheme includes S1: the theoretical travel time model suitable for complex slope environment is built;S2: the actual travel time difference is extracted from slope monitoring data;S3: based on the 3D positioning of theoretical and observed travel time difference matching, the ambient noise surface wave tomography technology is applied to slope velocity modeling, without artificial seismic source, high resolution velocity structure can be obtained;Realize 3D ray tracing under the constraint of digital elevation model, ensure that wave propagation path meets physical law, positioning accuracy reaches 3 m, can reach 1.5 m in dense array coverage area, compared with traditional method, it is improved by one order of magnitude;Successfully applied to complex environment with more than 700m topographic relief and extreme velocity variation, can be applied to microseismic monitoring in various complex geological environments.

The application provides a real-time wave fluctuation migration positioning method for microseismic signals and belongs to the technical field of seismic signal processing; solves the problems of complex data preprocessing, insufficient calculation efficiency and low timeliness of existing migration positioning processing technologies; and comprises the following steps: arranging multiple geophones as receiving points of microseismic data according to different depths in the range of a region to be monitored on a coal mine working face floor; counting the number N of triggered geophones, and distributing the data of the N geophones to different GPUs to carry out seismic wave field simulation of the N geophones in parallel; obtaining imaging results of longitudinal waves and transverse waves by using directional aperture constraints and storage-free imaging conditions, fusing the imaging results of the longitudinal waves and the transverse waves, and extracting imaging results with the most focused energy and consistent imaging point positions as microseismic information of microseismic events; and the application is applied to coal mine roof and floor monitoring.

This invention relates to the field of safety monitoring and disaster early warning technology for deep underground engineering, and discloses an intelligent rockburst dynamic risk prediction method integrating real-time microseismic data. This method first constructs a high-precision medium model reflecting the real-time stress and attenuation characteristics of the rock mass based on background noise interferometric imaging and the adjoint state method inversion. Then, it reconstructs a four-dimensional energy density field, defines and calculates the waveguide locking index to accurately screen key energy localization regions. Subsequently, it extracts the energy residual sequence of the key regions, calculates the lag first-order autocorrelation coefficient and time-domain variance, and uses the critical slowing theory to determine the precursors of system dynamic instability and trigger graded early warnings. This invention corrects imaging biases caused by energy dissipation through physics-driven medium parameter inversion, and combined with data-driven nonlinear dynamic analysis, achieves accurate spatiotemporal positioning of the rockburst incubation core area and early identification of disaster risks.

The invention relates to a mine rock mass instability early warning method, system and equipment based on a microseismic strength index and a medium. According to the method, an event list is generated by collecting underground micro-seismic waveform data, then multi-dimensional parameters such as a b value and accumulated energy are calculated, and the rock mass instability stage is automatically recognized; for each event, a micro-seismic intensity index is calculated by fusing the magnitude and the propagation distance, and a three-dimensional space intensity distribution gridding graph is constructed; and comparing daily parameters with a preset threshold to generate a risk precursor index set, determining a dynamic risk level in combination with an instability stage, and finally fusing the level and a spatial distribution diagram to generate an early warning report. According to the scheme, full-chain early warning from multi-parameter fusion analysis and intelligent judgment in the instability stage to risk space visual output is achieved, and the real-time performance, accuracy and decision support capacity of mine rock mass instability early warning are remarkably improved.

The invention discloses a deep-buried tunnel disaster early warning method, system and equipment, and the method comprises the following steps: S1, initial screening of original micro-seismic data: firstly, carrying out the relative coordinate conversion of MS events, and carrying out the initial screening of the MS events participating in the subsequent clustering analysis according to the division range of an early warning unit; s2, extracting effective MS events: performing clustering analysis on the MS events in the early warning unit by using a WOA-DBSCAN clustering algorithm, and identifying the effective MS events and clusters to which the effective MS events belong; s3, calculating an energy density logarithm logED value: calculating the convex hull volume of the main MS event cluster through a Quickhull3D algorithm, and calculating the logED value in combination with the total energy and the volume; s4, triggering graded early warning: according to comparison between the logED value and a preset threshold value, identifying a disaster breeding stage and outputting an early warning signal; according to the method, effective micro-seismic events can be efficiently extracted, and the distribution characteristics of the micro-seismic events in the three-dimensional space can be quantified by calculating the energy density logED in the early warning unit.

The invention discloses a micro-seismic bad trace elimination method and device based on data statistics. The method comprises the following steps: inputting micro-seismic monitoring data; counting the maximum value and the minimum value of each channel of monitoring data in the microseism monitoring data; on the basis of the maximum value and the minimum value of the monitoring data of each channel, constructing a bad channel elimination factor of each channel; rejecting bad trace data in the microseismic monitoring data through each bad trace rejecting factor; and outputting the microseism monitoring data after the bad traces are removed. According to the invention, automatic elimination of bad traces of micro-seismic monitoring data can be realized.

The invention discloses a coal seam floor water guide crack identification method based on micro-seismic Krauklis waves, and belongs to the technical field of coal seam floor water disaster detection.The method comprises the steps that firstly, micro-seismic original signals are collected and segmented, and continuous wavelet transformation and time-frequency domain conversion are sequentially carried out on each signal segment to obtain a time-frequency spectrogram; according to the time-frequency spectrogram, separating and extracting the time-frequency characteristics of the Krauklis waves; for each frequency, determining an energy extreme point corresponding to the frequency and Krauklis wave energy packet arrival time; a frequency dispersion curve is obtained by calculating the phase velocity of each frequency, and the slopes of different positions of the frequency dispersion curve are obtained through calculation; establishing a quantitative mapping model between the crack opening and the frequency dispersion curve slope on the basis of the propagation theory of Krauklis waves in the slab crack; and the slope is substituted into the quantitative mapping model for inversion, so that the average opening degree of the water guiding crack in the current detection area is accurately obtained, and data support is provided for accurate monitoring and early warning of water inrush of a deep mining coal seam floor.

This invention discloses a microseismic monitoring and early warning system for rockbursts during tunnel / tunnel operation, relating to the field of rockburst microseismic monitoring technology. Sensors are installed in a cross-distribution manner between the left and right tunnel sections to ensure effective acquisition of microseismic signals during tunnel operation. Sensor grouping using cross-numbering and adjacent numbering ensures that the monitoring ranges between monitoring sections do not overlap. Cross-verification of signals from multiple sensors improves the accuracy of seismic source location. The system extracts spectral characteristic parameters of the microseismic signals using Fourier transform, time-frequency analysis, and other methods, and performs microseismic activity intensity analysis to obtain the activity intensity index of the monitoring area. This index is then de-distanced to obtain the source activity index for each monitoring area. Based on the development trend of the source activity index of microseismic events in each monitoring area, the system assesses the rockburst risk of each monitoring area, obtains a risk warning value, and automatically triggers risk warnings or encryption processing accordingly, improving the safety and stability of tunnel operation.

This invention discloses a method and system for early warning of rockbursts based on automatic machine learning and the fusion of seismic, acoustic, and electrical signals. The method includes: collecting multi-source monitoring data such as microseismic events, acoustic emission, and electromagnetic radiation from a field-deployed microseismic and acoustic-electrical monitoring system to provide data support for model construction; proposing two modules based on an automatic machine learning framework: automatic seismic, acoustic, and electrical modeling and prediction, and multi-parameter integrated early warning; the MAEP module automatically identifies key temporal features in the multi-source monitoring data, constructs a high-performance prediction model integrating multiple algorithms, accurately identifies the nonlinear and co-evolutionary characteristics of multi-source signals, and realizes the prediction of high-energy rockburst events; the MAEW module constructs an early warning index based on the prediction results, adopts an ensemble learning algorithm, comprehensively reflects the abrupt change characteristics and abnormal enhancement trends of seismic, acoustic, and electrical signals, and can automatically optimize the model structure and parameters according to data changes to achieve efficient, intelligent, and real-time early warning without human intervention.

The application discloses a three-dimensional positioning method of a seismic source based on a multi-channel amplitude ratio. The method obtains waveform data of the same event on at least four sensor channels and spatial coordinates of the sensors, pre-processes the waveforms, determines an event window corresponding to the event, extracts amplitude indicators of each channel in the event window and performs channel consistency correction, constructs an amplitude logarithm ratio observation on an arbitrary channel based on an exponential decay model to eliminate a source intensity factor, and further forms a constraint related to a distance difference of the seismic source and the sensor. A redundant channel constraint set is further constructed, and an abnormal channel is down-weighted or removed. A weighted nonlinear least square is used in combination with an iterative reweighted least square or a robust loss function to solve the problem, a residual threshold is determined and is updated with iteration. A plurality of candidate solutions are obtained by solving a multi-initial value or a coarse grid initial value in a preset feasible region, and an optimal solution is output. The method reduces the dependence on the arrival time picking, and can be used for microseismic monitoring, acoustic emission positioning and underground engineering safety monitoring.

The invention discloses a micro-seismic time sequence prediction method and system based on energy filling, and relates to the technical field of mine safety monitoring and geological disaster early warning. The method comprises the following steps: performing time difference positioning analysis on a non-uniform wave velocity model through acquired microseismic waveform data and microseismic sensor coordinates to obtain three-dimensional coordinates and initial time of a seismic source; correcting the original energy based on the path characteristics, the bedding attenuation law and the spherical diffusion effect to obtain corrected seismic source energy; calculating a correction factor and a confidence coefficient based on the corrected seismic source energy and the original energy, and screening the corrected seismic source energy according to the confidence coefficient to obtain representative energy; based on the initial time, the representative energy and the confidence coefficient, constructing a microseismic event sequence feature tensor; inputting the micro-seismic event sequence feature tensor into a pre-trained micro-seismic energy prediction model to obtain a maximum energy prediction value; according to the method, the problem of energy deviation caused by rock anisotropy is solved, and the accuracy of micro-seismic monitoring and early warning is improved.

The invention discloses a microseism synchronous inversion method based on multi-task learning, and belongs to the field of microseism inversion, and the method comprises the steps: firstly constructing a ternary data set corresponding to seismic data, a speed model and seismic source position probability distribution; then a double-branch U-Net network model is designed, a shared encoder of the double-branch U-Net network model is integrated with a multi-scale convolution module to extract multi-scale features, and double decoders realize feature decoupling and reinforcement through a task-adaptive guided attention gate; meanwhile, based on wave equation Frechet derivative analysis, a physically-driven dynamic weight adjustment strategy is provided, and optimization of focus positioning and speed modeling tasks is adaptively balanced in the training process. According to the method, the seismic source position and the underground velocity model can be synchronously and precisely inverted from the microseismic data, the problems of parameter coupling, multiplicity, unstable training and the like in a traditional method are effectively solved, and the inversion precision and robustness are remarkably improved.

The invention discloses a bedding rock slope monitoring and early warning method based on micro-seismic and acoustic emission cooperative perception, and relates to the technical field of slope monitoring and early warning. Micro-seismic signals and acoustic emission signals in a micro-seismic and acoustic emission sensing array are collected in real time according to preset sampling frequencies corresponding to different sensor types; the collected signals are preprocessed, and the preprocessed signals are divided into a natural fracture event, a construction disturbance event and an environmental noise event based on a pre-trained noise recognition model. By capturing precursor behaviors such as microcrack initiation and interlayer friction slippage, effective early warning can be given out several hours to several days earlier than traditional displacement monitoring; full-scale damage monitoring from millimeter-scale cracks to meter-scale fracture surfaces is realized through cooperation of micro-shock and acoustic emission; meanwhile, the method can provide a spatial migration criterion for layer direction control, and improves the special recognition capability for bedding slopes.

The invention relates to the technical field of hydrofracture micro-seismic event monitoring, in particular to a distributed optical fiber micro-seismic event positioning method and device, and the corresponding method comprises the steps: determining a vertical foot corresponding to a micro-seismic event according to the form of an event of the micro-seismic event in a seismic section corresponding to the micro-seismic event in a fractured well; determining the monitoring well and the perpendicular foot radius of the perpendicular foot in a cylindrical coordinate system; wherein the original point of the cylindrical coordinate system is the vertex of the event; and according to the radius of the perpendicular foot and the length of the fractured well, performing energy scanning on each grid under the cylindrical coordinate system in a plane which passes through the perpendicular foot and is perpendicular to the fractured well so as to position the microseism event. According to the method, the plane where the seismic source occurs is determined, and then seismic source scanning is performed only in the plane, so that the method has the advantages of high calculation precision and high calculation efficiency. And rapid real-time positioning of the distributed optical fiber fracturing microseism event can be ensured.

The application discloses a rock burst global monitoring and dynamic-static near and far field cooperative early warning method and system, and the early warning method comprises the following steps: based on the far field generation and propagation mechanism of dynamic load stress wave, determining the key monitoring area of the stope space and the stress wave type, delineating the high stress area of the working face surrounding rock and analyzing the structure characteristics of the rock stratum; setting a microseismic sensor in the far field key area, obtaining parameters such as the source rupture size, energy and magnitude through waveform inversion; arranging an acoustic-electric system in the near field high stress area, dynamically monitoring the coal rock rupture characteristics and quantitatively analyzing the static load energy storage state of the surrounding rock. Combined with the stress wave propagation attenuation coefficient, the near and far field fusion early warning criterion under the superposition action of dynamic and static energy is established, and through the cooperative analysis of multi-source monitoring data, the rock burst advanced precise early warning is realized. The application proposes a disaster gestating space global monitoring technology fusing seismic, acoustic and electric, considers the chain evolution disaster-causing process of dynamic load generation, cross-space propagation and working face surrounding rock damage, and is helpful to significantly improve the accuracy of rock burst early warning.

The invention relates to the technical field of engineering investigation, and discloses a rock mass quality evaluation method based on single-hole sound wave detection, which comprises the following steps: firstly, obtaining a rock core through drilling construction, calculating an RQD value, measuring a longitudinal wave velocity reference value, and recording geological information; then, rock mass wave velocity, waveform data and hole wall images are collected and structural surface features are extracted by using a dual-frequency-conversion sound wave test and a high-protection hole television imaging technology; then calculating fractal box dimension and a rock mass integrity coefficient, and constructing and training a three-layer BP neural network; only the two tests are carried out during subsequent drilling without coring, data are transmitted to an intelligent platform in real time to be automatically processed, a grading result is output, and the model is corrected when the geological condition difference reaches the standard; and finally, outputting a visual report optimization engineering scheme, and deploying micro-seismic monitoring for the poor-level rock mass. According to the method, multi-dimensional data fusion and intelligent evaluation are realized, the classification precision and efficiency are improved, full coverage from static evaluation to dynamic early warning is achieved, and a full-life-cycle solution is provided for complex geological engineering.

The application provides a kind of ion type rare earth ore base rock crack detection method and system, wherein the detection method includes the preset position of target detection area is laid active seismic source, active seismic source emits the elastic wave of preset frequency and preset amplitude;Microseismic sensor network laid in the target detection area collects the sound wave signal scattered and polarized after the crack of active seismic source emission;From the base rock crack microseismic sound wave signal, a plurality of crack characteristic parameters related to the base rock crack are extracted, the possible crack area is obtained by preliminary analysis, and the preliminary key parameter information of crack;Based on a plurality of crack characteristic parameters, a crack model containing crack geometric parameters and physical parameters is constructed by using a multi-parameter joint inversion method, and the crack model is optimized by regularization;Finally, based on the optimized crack model, the high-precision spatial position and distribution of the key parameters of the crack are obtained by inversion;The application can improve the detection accuracy of the base rock crack without damaging the ore body.