229 results about "Microseism" patented technology

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 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 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 invention relates to the technical field of tunnel engineering, in particular to a tunnel rockburst risk assessment method and prevention method, and the assessment method comprises the steps: installing a plurality of first micro-seismic sensors in the circumferential direction of a tunnel wall within a preset distance range behind a tunnel face, determining an initial rockburst range based on the micro-seismic data collected by the first micro-seismic sensor; and a plurality of second micro-seismic sensors are annularly installed on the tunnel wall corresponding to the initial rockburst range, the monitoring precision of the second micro-seismic sensors is higher than that of the first micro-seismic sensors, and the final rockburst position is determined based on micro-seismic data collected by the second micro-seismic sensors. The tunnel rock burst occurrence position can be finely determined, and hydraulic slotting pressure relief is guided.

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 discloses a method and a system for identifying a micro-seismic signal mode of a surrounding rock rockburst risk, particularly relates to the technical field of deep underground engineering rockburst monitoring, and is used for solving the problem that an existing micro-seismic early warning model lags in identifying a rock mass damage evolution critical state. The method comprises the following steps: collecting a surrounding rock micro-seismic event signal in real time, intercepting an event sequence based on a sliding time window, and calculating a space-time fractal dimension; extracting an event space density field in the window to calculate an energy gradient, and marking a strong interaction cluster of which the energy gradient exceeds a threshold value; storing the fractal dimension change sequence in real time; performing characteristic frequency band filtering on the signals in the window, calculating a ratio of an energy ratio entropy descent rate to a fractal dimension descent rate, and determining a damage acceleration state when the ratio is a supercritical coefficient; generating a rockburst high-risk instruction when the strong interaction cluster and the damage acceleration state coexist and the fractal dimension descent rate is supercritical threshold; the multi-dimensional collaborative identification of the rockburst precursor is realized, and the early warning accuracy and timeliness are remarkably improved.

The invention discloses an HE event early warning method, device and equipment, a medium and a product, and relates to the field of mine earthquake early warning. The method comprises the following steps: analyzing space-time correlation of mine earthquake data energy by adopting a semi-variation function according to acquired mine earthquake monitoring data, and determining a historical data window; constructing a dynamic sliding mechanism; based on a dynamic sliding mechanism, adopting a principal component analysis method and a kernel density estimation method to extract a kernel density peak value of the micro-seismic event, and adopting a fractal dimension analysis method to determine a fractal dimension index so as to analyze and quantify the geometric complexity change condition and obtain a quantification result; based on a quantification result, optimizing early warning parameters by adopting grid search, and determining a sliding window joint early warning model; and monitoring a kernel density peak value and a fractal dimension index in real time by adopting a sliding window combined early warning model based on the optimized early warning parameters so as to realize early warning of the HE event. The invention aims to improve the accuracy and timeliness of HE event prediction.

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 discloses a micro-seismic monitoring piezoelectric acceleration sensor optimization method based on an NSGA-II algorithm, provides a piezoelectric sensor based on a triangular shear structure, and relates to the technical field of piezoelectric acceleration sensors. According to the micro-seismic monitoring piezoelectric acceleration sensor optimization method based on the NSGA-II algorithm, the NSGA-II genetic algorithm is adopted to optimize the structural size of the designed sensor, and an optimal structural parameter combination is obtained, that is, when the height of a mass block is 10.6 mm, the thickness of a piezoelectric patch is 3.29 mm, the height of the piezoelectric patch is 8.1 mm and the height of a central column is 19 mm, the sensor design is optimal, and the optimal structural parameter combination is obtained. The working frequency of the optimized sensor is improved by 2.68%, and the voltage sensitivity is improved by 8.96%. And finally, respectively carrying out a calibration experiment and a micro-seismic signal test on the designed sensor by building an experiment platform. Experimental results show that the working frequency range of the designed sensor is 0.1-2123 Hz, and the voltage sensitivity is 173.49 mv / g.

The invention discloses a shale reservoir fracturing casing deformation calculation method, and relates to the technical field of shale oil and gas development. The method comprises the following steps: fusing microseism, logging, rock core and drilling and logging data, and constructing a fracture network-geomechanics coupling model; calculating a direction cosine matrix according to the fracture three-dimensional stress and the inclination angle, evaluating an activation risk, and screening out high-risk fractures; the triangular mesh boundary element solves the slippage as a displacement boundary, and an eight-node hexahedron elastic-plastic casing mesh is utilized to iteratively calculate a casing variable and draw a cloud picture; and a micro-seismic data real-time iteration model is newly added along with construction, and the perforation cluster distance, the fracturing scale, the displacement and the well spacing are dynamically optimized. According to the method, a large amount of on-site microseismic data is fully utilized, reservoir fracture fine description and slippage risk analysis are achieved, shaft multi-point casing variable prediction is completed, and key technical support is provided for deep shale gas development fracturing scheme decision and risk avoidance.

The invention provides a micro-seismic P-wave first arrival pickup method based on deep learning and an adaptive time window, and the method comprises the steps: (1) collecting and preprocessing a micro-seismic signal, making a data set, and dividing the data set into a training set and a test set; (2) building an MSC-NET deep learning neural network, taking a data set as input for training, and adjusting model parameters to achieve an optimal model effect; (3) performing a micro-seismic signal classification task by applying the trained deep learning model, and dividing a large number of micro-seismic signals into an effective available class and an ineffective unavailable class; and (4) finally, carrying out P-wave first arrival time pickup on the effective micro-seismic signals based on an energy ratio algorithm of an adaptive time window to obtain a relatively reliable pickup result. According to the method, the deep learning technology and the improved traditional signal processing technology are combined, the reliability and accuracy of the automatic pickup result of the micro-seismic signal P wave first arrival time are greatly improved, and meanwhile the method has the advantages of being high in robustness and applicability and the like.

The invention relates to the technical field of tunnel blasting, in particular to a method and system for monitoring accumulated damage of reserved rock mass after blasting of a section tunnel. The monitoring method comprises the steps that S1, sensing equipment is arranged and used for collecting rock mass parameter data including micro-seismic data and strain data; s2, preprocessing the data acquired by each sensing device, and mapping the rock mass parameter data acquired by each sensing device to the same three-dimensional coordinate system; and S3, calculating a damage index according to the strain energy density, the strain energy density initial value, the micro-seismic event frequency and the geological adjustment coefficient, and performing early warning according to the damage index and a preset early warning rule. According to the method, the micro-seismic data and the strain data of the reserved rock mass are collected and processed, then the damage index is calculated, and early warning is performed according to the damage index and the preset early warning rule, so that the accuracy of monitoring the blasting damage of the surrounding rock is improved, the damage condition of the surrounding rock can be rapidly evaluated, and therefore protection measures can be taken in time, and the safety of the surrounding rock is improved. The stability of the tunnel structure is ensured.

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.

A comprehensive monitoring and handling method for a destruction region after an extremely strong time-delayed rockburst occurs, which method relates to the technical field of deep-underground hard-rock tunnel engineering. By means of providing micro-seismic monitoring systems in a destruction region of an extremely strong time-delayed rockburst, carrying out a crustal stress test (1), an acoustic wave test and borehole imaging (2), and blasting vibration monitoring of the surface and interior of surrounding rock, and performing long-term disturbance stress monitoring on the destruction region of the extremely strong time-delayed rockburst. Therefore, with regard to the aftermath of an extremely strong time-delayed rockburst, the monitoring efficiency is improved, and a rational and effective support system is established, thereby ensuring later-stage safety.

The invention discloses a fracturing crack fine characterization method and system based on microseism monitoring data, and relates to the technical field of oil and gas field development, and the method comprises the steps: obtaining basic parameters of a target well; according to the well completion data of the target well, the micro-seismic event points of all the fracturing sections of the target well are preprocessed, and the preprocessed micro-seismic event points of all the fracturing sections are obtained; noise reduction processing is carried out on the preprocessed micro-seismic event points of all the fracturing sections, and the micro-seismic event points of all the fracturing sections after noise reduction are obtained; clustering the microseismic event points after noise reduction according to the number of perforation clusters of the fracturing section, and determining effective microseismic event points of each perforation cluster; on the basis of the effective microseismic event points of each perforation cluster, through a crack surface fitting method, respectively obtaining characterization parameters of each perforation cluster crack; and according to the characterization parameter of each perforation cluster crack in the target well, obtaining a fracturing crack fine characterization result of the target well. According to the invention, fine characterization of the fractured crack can be realized.

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 provides a micro-seismic positioning method and device based on an artificial bee colony algorithm, equipment and a storage medium. Relates to the technical field of dry micro-seismic data processing. The method comprises the following steps: acquiring micro-seismic data, identifying the micro-seismic data by adopting a long-short time window ratio method to obtain micro-seismic data of a micro-seismic event, and performing noise reduction processing and first arrival pickup to obtain an arrival time sequence of the micro-seismic event; taking the cosine similarity as a target function, taking the coordinates of each sensor as initial parameters, randomly selecting a plurality of initial points, starting inversion based on a set iteration number, calculating a time difference sequence from each initial point to each sensor as an arrival time sequence of an inversion waveform, and calculating the arrival time sequence of the inversion waveform; and if the cosine similarity of the arrival time sequence of the inversion waveform is higher than the value before iteration, replacing the arrival time sequence of the previous inversion waveform with the arrival time sequence of the current inversion waveform, otherwise, continuing to search until the set number of iterations is reached, and outputting the optimal seismic source point. According to the invention, the accuracy of micro-seismic positioning can be obviously improved.

The invention relates to the technical field of geothermal pipe network monitoring, in particular to a geothermal pipe network intelligent monitoring method and system based on edge calculation. The method comprises the following steps: acquiring a sound pressure signal, a vibration signal and a pressure signal in real time, and performing first-order difference calculation based on the pressure signal to obtain a pressure difference change sequence; inputting the sound pressure signal and the vibration signal into a micro-seismic intelligent identification model, outputting a micro-seismic risk quantitative scoring index, generating an execution regulation and control instruction, and recording execution feedback data to form a micro-seismic scoring feedback data set; performing time sequence reconstruction on the pressure difference change sequence by using a neural differential equation model, identifying an abnormal propagation source point and a shortest propagation path, and constructing a water hammer propagation response structural body; and the microseismic score feedback data set and the water hammer propagation response structural body are transmitted to a cloud platform, and the state of the geothermal pipe network is monitored and early warned in real time. According to the invention, intelligent monitoring and tracking of the micro-seismic abnormal state and the water hammer effect of the geothermal pipe network are realized.

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 provides a micro-seismic event P-wave first arrival pickup method and device, and the method comprises the steps: obtaining micro-seismic monitoring data, and selecting a time window containing a wave crest in a P-wave first arrival preset range; establishing an initial Gaussian model; and inputting the selected time window data into the initial Gaussian model, performing iterative fitting, obtaining a final Gaussian model curve when iteration meets a preset condition, and determining the P-wave first arrival time according to the final Gaussian model curve. The invention provides a high-precision micro-seismic event P-wave first arrival pickup scheme based on Gaussian curve iterative fitting, realizes automatic and accurate pickup of P-wave first arrival time in micro-seismic signals, has relatively strong anti-noise capability, and is suitable for analysis of complex waveform data.

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 discloses a deep ore body three-dimensional positioning method and a deep ore body three-dimensional positioning system, belongs to the technical field of ore body detection, and aims to solve the problems of low resolution and large positioning error in traditional single physical field inversion. Comprising the following steps: collecting broadband waveform data acquired by an underground microseismic sensor and high-precision magnetic field data of a ground quantum magnetometer; respectively extracting travel time characteristics from the micro-seismic waveform and extracting magnetic anomaly gradient characteristics from the magnetic field data; inputting the two physical field features into a graph convolutional network, and generating a three-dimensional velocity field model through cross-modal coupling; inputting the model into a generative adversarial network to generate synthetic vein topological structure data; and finally, jointly transmitting the velocity field model and the synthetic data to a wave-magnetic fusion inversion engine, calculating an ore body three-dimensional structure through a joint optimization algorithm, carrying out dynamic matching verification by utilizing real-time micro-seismic travel time data, and outputting a final ore body three-dimensional structure model when the matching degree meets a preset threshold value.

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.

The invention provides a tunnel micro-seismic blasting targeted protection control method and device, and relates to the technical field of tunnel blasting, and the method comprises the steps: obtaining the distance between a protected building and a tunnel, and building a blasting vibration wave propagation model based on the distance and an initial attenuation coefficient; calculating predicted vibration data of seismic waves generated by each blasting section in a preset blasting scheme, wherein the seismic waves are transmitted to the protected building; taking the minimum theoretical peak vibration velocity of the protected building as a target, and calculating by adopting a numerical optimization algorithm to obtain a non-equal-difference detonation delay sequence of each blasting section; when the detonating time interval of the adjacent detonating sections is not equal to the integral multiple of the inherent main earthquake period, detonating is carried out according to the non-equal-difference detonating delay sequence; and comparing the actually measured vibration data with the predicted vibration data, and adjusting the attenuation coefficient based on the comparison result, thereby effectively preventing the resonance effect, preventing the vibration energy from being amplified, and ensuring the construction safety.

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 relates to the technical field of coal mine underground geophysical exploration, and provides a coal mine underground micro-seismic source and speed simultaneous inversion method and system, and the method comprises the steps: collecting micro-seismic original data through a micro-seismic detector and a seismograph; picking up the direct wave first arrival travel time t real in the micro-seismic record; assuming an initial speed model and a seismic source position, and carrying out travel time forward modeling and ray path forward modeling to obtain theoretical travel time t < theoretical k > and a Fresnel body ray path; constructing a travel time residual term and a nonlinear objective function of a regularization constraint term based on travel time data, a speed model and a seismic source position; and solving a corresponding normal equation, inverting a speed model and a seismic source position, and outputting a final result through iterative convergence. According to the method, the coupling relation between the speed model and the seismic source position is utilized, and the joint objective function is directly optimized through inversion, so that the detection precision of the underground coal mine speed model and the seismic source position is improved.