50 results about "Microearthquake" patented technology

The invention relates to the technical field of petroleum and natural gas engineering, and discloses an oil and gas reservoir optimized mining method based on multi-modal data, which comprises the following steps: constructing an oil and gas reservoir multi-modal data acquisition system, seismic wave field data, logging interpretation data, production dynamic data, micro-seismic monitoring data, underground temperature and pressure time sequence data and shaft structure parameter data are obtained through the acquisition system; and performing space-time alignment processing on the acquired multi-modal data, establishing a unified geological coordinate system and a time reference, and eliminating data isomerism caused by different acquisition frequencies and spatial resolutions. A six-dimensional heterogeneous data acquisition system covering a seismic wave field, well logging interpretation, production dynamics, micro-seismic monitoring, an underground temperature and pressure time sequence and shaft structure parameters is constructed, so that the depiction precision of a reservoir porosity field, a permeability field, a saturation field and a pressure field is essentially improved.

The invention provides a karst collapse early warning method and device, electronic equipment and a storage medium. The method provided by the invention comprises the steps of determining a first time sequence feature of surface displacement data, a second time sequence feature of groundwater dynamic data, a spatial feature of geological radar image data and a vibration feature of microseismic signal data, and generating a multi-modal fusion feature vector according to the first time sequence feature, the second time sequence feature, the spatial feature and the vibration feature; according to the multi-modal fusion feature vector and an early warning model, an early warning signal is obtained, and the early warning model is obtained by updating an early warning threshold value through a historical multi-modal fusion feature vector and a Transform-LSTM hybrid network by using a time sequence difference learning strategy; and performing validity judgment on the early warning signal according to the surface displacement data, the groundwater dynamic data and the micro-seismic signal data, and determining whether to perform early warning according to a judgment result. According to the method, the accuracy of karst collapse early warning can be improved.

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 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 top plate hydraulic fracturing pressure relief effect pre-evaluation method based on microseism monitoring, which comprises the following steps of: firstly, acquiring a rock stratum distribution condition in a fracturing range, determining the position of a fractured rock stratum, determining an overburden load according to the position of the fractured rock stratum and physical and mechanical parameters of the rock stratum, and calculating a fracture step pitch of the fractured rock stratum; further calculating the maximum bending deformation energy when the fractured rock stratum is broken; the fracturing effect evaluation step pitch is determined according to the roof fracturing construction scheme; counting micro-seismic events in the fracturing period in each evaluation step range, and calculating the ultimate bending deformation energy accumulated in the fractured rock stratum in the evaluation step range; and finally, calculating the hydraulic fracturing pressure relief degree based on the micro-seismic event and the ultimate bending deformation energy in the fracturing period in each evaluation step range, and determining the hydraulic fracturing pressure relief effect in combination with an evaluation standard. In the process, monitoring sensors do not need to be arranged underground, and under the condition that the working face is not mined, the middle-high-position hard rock stratum fracturing effect can be accurately pre-evaluated.

1. Name of the product in this design: Long-lasting probe. 2. Applications of this design: Real-time intelligent monitoring of microseismic events, earthquake background noise imaging, etc. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The invention discloses a micro-earthquake dry and wet event separation method and device based on a Gaussian mixture model, and the method comprises the steps: S1, building a corresponding Gaussian mixture model based on a micro-earthquake detection result, carrying out the random initialization of parameters of the Gaussian mixture model, and setting the number of mixed components; s2, calculating the posterior probability of each microseismic event point relative to each Gaussian component; s3, updating Gaussian mixture distribution parameters based on the posterior probability of each microseismic event point relative to each Gaussian component; s4, based on the updated Gaussian mixture distribution parameters, calculating a maximum likelihood function and evaluating whether the maximum likelihood function accords with a threshold value, if not, returning to S2, and if yes, executing S5; and S5, outputting the Gaussian mixture model after parameter updating and a dry and wet event classification result. According to the method, effective transformation events corresponding to fracturing construction and fracture activity events caused by ground stress changes can be rapidly distinguished.

This invention relates to the field of unconventional shale oil and gas exploration and development, and proposes a multi-scale three-dimensional fracture modeling method and system. The method includes: acquiring geological data, well logging data, seismic data, and microseismic data of the study block; establishing a natural fracture DFN model based on the acquired geological, well logging, and seismic data, and establishing a microseismic pressure fracture DFN model based on the acquired microseismic data; and fusing the natural fracture DFN model and the microseismic pressure fracture DFN model to obtain a multi-scale three-dimensional fracture model. This invention efficiently integrates various geological and engineering information and rapidly establishes fracture models, showing great application potential in unconventional oil and gas reservoir fracturing monitoring and numerical simulation of oil and gas reservoir development.

The application discloses a microseismic source positioning method, device, equipment and medium, and relates to the field of earthquake monitoring, and comprises the following steps: acquiring initial seismic data, converting the initial seismic data into an envelope signal; determining a reference channel, identifying P-wave peaks and S-wave peaks on the envelope signal of the reference channel to determine a reference P-S separation point; aligning the envelope signal by using a dynamic time warping algorithm to obtain an alignment path; mapping the reference P-S separation point to each seismic channel according to the alignment path to obtain an aligned P-S separation point, and determining a wave phase travel time offset according to the alignment path; based on the aligned P-S separation point, extracting P-wave signals and S-wave signals from the envelope signal respectively to determine a P / S wave double-difference correlation function; constructing a weighted imaging operator based on the P / S wave double-difference correlation function and the wave phase travel time offset, and scanning the weighted imaging operator to determine a microseismic source position in a three-dimensional grid map. The source position can be accurately positioned under the 3D grid.

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 utility model discloses a replaceable buckling restrained brace device, particularly relates to buckling restrained brace technical field, including core material, reinforcing sleeve and packing layer, the packing layer is provided between core material and reinforcing sleeve, core material outer wall surface and reinforcing sleeve inner wall surface are both provided with the non-adhesive layer, the packing layer is provided with the non-adhesive layer. Fixing grooves are formed in the top and the bottom of the reinforcing sleeve, fixing covers are detachably connected into the two fixing grooves, through holes matched with the core material are formed in the tops of the two fixing covers, and the core material penetrates through the two through holes. The buckling restrained brace device is composed of the core material, the reinforcing sleeve, the non-bonding layer, the filling layer and the fixing cover, can resist slight earthquakes, medium-grade earthquakes, high-grade earthquakes and wind loads, can also serve as an efficient energy dissipation damper, reduces response of the structure in the earthquakes through energy dissipation, accordingly improves the anti-seismic performance of the structure, and reduces the construction cost. And the two fixing covers are disassembled, so that the core material is convenient to replace and the sleeve is convenient to reinforce.

1. The name of the design product: probe (high precision). 2. The use of the design product: monitoring microseismic events, seismic background noise imaging, etc. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view. 5. The bottom of the design product is not easy to see or not visible during use, omit the top view.

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.

This invention provides a method and system for automatic vertex picking of DAS-based microseismic events, relating to the field of microseismic location technology. The method includes acquiring multi-channel data corresponding to microseismic events and performing demodulation analysis to form multi-channel waveform data; performing correlation analysis on the waveform data from different channels to establish an automatic picking correlation model; performing image transformation based on the automatic picking correlation model to form corresponding grayscale images; and performing location discrimination analysis based on the grayscale images to determine the event vertex channel. This method improves the interpretation accuracy and reliability of geological analysis of seismic images by removing noise while preserving geological structural features such as faults and strata.

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.

A method of leaching copper from a porphyry copper ore body within a subsurface volume of rock can include forming an injection well in the ore body and stimulating the injection well in a hybrid stimulation phase including a first hydraulic fracturing phase at an injection rate and pressure exceeding Shmin to form hydraulic fractures that intersect with a first set of natural fractures; a first hydroshearing phase holding the injection rate until hydraulic fracture growth is allowed to arrest; mapping the first set of natural fractures using a micro-seismic array to form a stimulated natural fracture map; and emplacing a proppant in the fractures. A production well can be drilled into the stimulated propped fractures, and the production well can be stimulated in a second hydroshearing phase to stimulate a portion of the first set of natural fractures intersecting with the production well.

The application discloses a kind of microseismic event precision positioning method and system based on knowledge enhanced model, comprising the following steps: collecting the waveform signal of downhole and surface multi-array geophone, obtaining spatial coordinates and synchronous clock information, generating propagation time field representing seismic wave propagation;Determine the spatial constraint range, set continuity and velocity condition to form knowledge constraint domain;Geometric energy model of propagation time distribution is established in the constraint domain, and the propagation time field is iteratively updated by variational solution, and converges to stable space-time manifold;Identify the point that meets the condition on the manifold as the location and time of microseismic event, and according to local residual and noise level, grid refinement and recalculation are carried out, and the reliability evaluation of event positioning result is completed.The application is based on knowledge enhanced propagation time field modeling and variational manifold solution, and realizes high-precision positioning and reliability evaluation of microseismic event in complex geological environment.

The invention relates to a rock mass instability stage early warning method, system and equipment and a medium. The method comprises the following steps: acquiring microseism event monitoring data of a target rock mass area; calculating and obtaining magnitude frequency distribution index sampling time sequence data, accumulated energy index sampling time sequence data and accumulated micro-seismic event number sampling time sequence data of the target rock mass area based on the micro-seismic event monitoring data of each monitoring time sequence point; according to the magnitude frequency distribution index sampling time sequence data, the accumulated energy index sampling time sequence data and the accumulated microseismic event number sampling time sequence data, calculating to obtain magnitude frequency distribution index increment sampling time sequence data, accumulated energy index increment sampling time sequence data and accumulated microseismic event number increment sampling time sequence data; and generating global instability stage classification information of the target rock mass area of each monitoring time sequence point in combination with the earthquake magnitude frequency distribution index early warning threshold. By adopting the method, multi-dimensional monitoring characteristics can be integrated, and the accuracy of rock mass instability stage division is improved.

The invention relates to the technical field of shale gas intelligent wellbores, and particularly discloses a multi-source data-driven shale gas well casing deformation risk dynamic intelligent prediction method, which comprises the following steps of: collecting block data of a built shale gas well to construct an original data set; carrying out abnormal value processing, missing value filling, data normalization, one-hot coding and data enhancement on the original data set in sequence; a threshold coefficient is set for data screening, and a final sample data set is obtained; establishing a casing deformation intelligent prediction model based on the CBT model and the BLSTM model; the intelligent casing deformation prediction model is adopted to predict the shale gas well casing deformation risk; and correcting seismic attribute data by using a newly monitored micro-seismic event, and iterating the casing deformation intelligent prediction model by adopting an incremental learning method in combination with well drilling and completion data and casing deformation data of a new well. The dynamic and intelligent prediction method for the deformation of the casing pipe is constructed, and real-time and accurate prediction of the deformation of the fracturing casing pipe is achieved.

This invention belongs to the field of petroleum seismic exploration technology, specifically disclosing a spring-type multi-wave pulse excitation system and method. The system includes an excitation device, a hammering device, a transmitting device, a geophone, a fixed support for fixing the transmitting device, and a multi-wave velocity determination device. The geophone is connected to the multi-wave velocity determination device. The excitation device is set parallel to the ground, and the hammering device is detachably mounted on the transmitting device. The excitation method involves installing the hammering device, placing the transmitting device and the excitation device, releasing the hammering device, and striking the excitation device to generate seismic waves. The geophone collects the seismic wave signals, forming a seismic record which is uploaded to the multi-wave velocity determination device. The multi-wave velocity determination device determines the P-wave and S-wave velocities and the near-surface thickness. This invention enables the reception of multi-wave pulses in the well or on the surface, as well as the determination of P-wave and S-wave velocities and near-surface thickness, improving the efficiency of multi-wave microseismic logging and ensuring the accuracy of multi-wave microseismic logging data. This invention is applicable to the excitation and detection of seismic waves.

The application discloses a method and device for predicting seismic fractures, and relates to the technical field of reservoir geophysics. The method comprises the following steps: obtaining three-dimensional seismic and imaging logging data of a first geological area, and modeling a fracture prediction model based on the three-dimensional seismic data and the imaging logging data; predicting seismic fractures based on the three-dimensional seismic data and the imaging logging data to obtain a first fracture prediction result in the first geological area; obtaining drilling data of the first geological area; obtaining microseismic data of the first geological area; correcting the first fracture prediction result based on the drilling data and the microseismic data to obtain a second fracture prediction result; training the fracture prediction model through the second fracture prediction result to obtain a trained fracture prediction model, and the trained fracture prediction model is used for predicting seismic fractures in a geological area, iteratively updating the prediction results of the early stage, and improving the accuracy of the seismic fracture prediction result based on a dynamic and static combination method.

The invention discloses a microseismic crosstalk noise elimination method and device based on cross-correlation. The method comprises the following steps: S1, inputting microseismic monitoring data; s2, calculating a correlation coefficient of each channel of monitoring data, and constructing a standard crosstalk set based on each channel of correlation coefficient and a crosstalk identification threshold; s3, calculating a maximum crosstalk channel, and obtaining a standard crosstalk channel based on the correlation coefficient of the maximum crosstalk channel and the standard crosstalk set; s4, calculating a correlation coefficient of the standard crosstalk channel and the data, and constructing a crosstalk elimination factor based on the correlation coefficient of the standard crosstalk channel and the data; s5, constructing crosstalk of each channel through the standard crosstalk channel and the crosstalk elimination factor, and eliminating the crosstalk of each channel from the original data of each channel to obtain new microseism monitoring data; and S6, repeatedly executing the steps S2-S5 on the new micro-seismic monitoring data until the standard crosstalk set cannot be constructed, and outputting the micro-seismic monitoring data after crosstalk elimination. According to the invention, the crosstalk of the microseism monitoring data can be automatically eliminated.

The invention discloses a microseism event point plane distribution identification method, electronic equipment, a storage medium and a microseism event point plane distribution identification device. The method comprises the following steps: defining a correlation matching relationship between microseism event attributes and rhombus attributes; on the premise that microseism event points diffuse outwards from a shaft along with time, a sequential link rule between the event points is made, and the diamond direction of the microseism event points is defined; and carrying out microseism event point plane distribution identification on a microseism event positioning result based on the correlation matching relationship, the successive link rule between the event points and the diamond direction of the microseism event points. According to the micro-seismic event point plane distribution identification method, the micro-seismic event ball is replaced by the rhombus to carry out micro-seismic event point plane distribution identification, the dynamic fracture size and direction of the micro-seismic event point are identified completely and vividly, the actual interpretation requirement is met, and micro-seismic event positioning result interpretation is facilitated.

The invention discloses an attenuation-considered scattered wave reverse time imaging seismic positioning method and system for a complex structure, and the method comprises the steps: building a frequency domain viscous sound wave equation, and representing a stratum absorption attenuation effect through a complex velocity model; introducing a contrast source based on a scattering theory, and separating a background wave field from a scattering wave field; inverse time continuation considering attenuation compensation is carried out, and energy focusing is realized through reverse wave field propagation and filtering processing; determining a focus position by applying imaging conditions; the invention specifically relates to an earthquake event positioning method based on a scattering theory, in particular to an inverse time continuation earthquake source positioning method under the condition of considering stratum heterogeneity and energy attenuation compensation, which can be applied to micro-earthquake monitoring related to industrial activities, hydrofracture effect evaluation and earthquake event positioning in complex geological structures. The technical problems of a traditional earthquake positioning method in a complex structure and an attenuation medium are effectively solved, and a new technical support is provided for underground space fine detection and engineering safety monitoring.

The present application relates to a kind of microseismic velocity inversion methods of deep learning and wave equation combined drive, underground model is dispersed, and setting detector;Establish underground velocity model, utilize finite difference algorithm to calculate the waveform information of random seismic source at each detector position;Waveform record, velocity model and seismic source coordinate are batched into Unet neural network;Pure data driven calculation obtains preliminary velocity structure, physical drive calculation, uses finite difference to solve wave equation, obtains seismic record;Loss function is composed of two parts of velocity error and waveform error;The structural similarity between the predicted velocity and the real velocity is calculated, and the average value is set as the dynamic weight of physical drive loss value;The mapping relationship between waveform feature and velocity model is extracted using neural network, and the underground velocity structure is inverted to predict.The present application realizes artificial intelligence real-time prediction microseismic velocity model, and the precision of inversion velocity model is high, and the interpretability is strong.

The invention relates to a method and device for predicting a crack based on seismic data under microseismic crack constraint, and the method comprises the steps: determining a first seismic attribute through employing the density data, obtained through logging data, of the development of a crack around a well; determining the fracture density of the research area through an XGBoost extreme gradient reinforcement learning algorithm and the first seismic attribute; determining a second seismic attribute by using the azimuth data of the fracture around the horizontal well determined by the microseismic data; performing random fracture orientation modeling by utilizing Mises distribution according to the second seismic attribute to determine the fracture orientation of the research area; and establishing a discrete fracture network model according to the fracture density of the research area and the fracture orientation of the research area, and obtaining a prediction result of the fracture of the research area by using the discrete fracture network model.

The invention relates to the technical field of underground monitoring equipment, in particular to permanent underground microearthquake monitoring equipment resistant to high temperature and high pressure. Comprising a sleeve and an industrial personal computer, a sensing control device is arranged in the sleeve, an environment detection device is arranged outside the sleeve, a clamping partition device is arranged in the sleeve, the clamping partition device is used for fixing the sensing control device and dividing a cavity between the sleeve and the sensing control device into a plurality of parts, and a fluid inlet and outlet regulation and control device is arranged at the top of the sleeve. The fluid inlet and outlet regulating device is connected with the air source device through a pipeline, and the pressure and the flow speed of fluid flowing in the sleeve are controlled through the fluid inlet and outlet regulating device according to the environmental data in the sleeve, so that cooling and pressure supplementing in the sleeve are achieved. Cooling and pressure supplementing in the sleeve are achieved, so that the sleeve is in a high-temperature and high-pressure environment, the temperature in the sleeve is kept within a set range, the pressure difference inside and outside the sleeve is smaller than a pressure difference set value, the stress of the sleeve is reduced, and the overall service life is prolonged.

The application belongs to the field of seismic signal detection and estimation, and is a seismic phase automatic identification method based on the combination of dense connection and U-shaped structure. One-dimensional microseismic signals are saved as an image, target objects in the picture are framed and calibrated according to image calibration information, a data set is obtained, a one-dimensional neural network model for seismic phase picking is designed and trained, the data set is trained, the one-dimensional neural network model is obtained, the seismic signal to be detected is input for intuitive testing, and the seismic occurrence time and position are obtained according to the obtained test results. Therefore, accurate identification and time estimation of seismic phases are realized, and accurate seismic event position and time are obtained.

The invention discloses a ground monitoring microseism event identification method, electronic equipment, a storage medium and a device. The method comprises the following steps: respectively carrying out dynamic correction on perforation data and ground micro-seismic monitoring data; constructing a microseism seed channel sequence based on the perforation data after dynamic correction and ground microseism monitoring data; constructing micro-seismic wavelets based on the micro-seismic data of the micro-seismic seed channel sequence; calculating the maximum cross correlation coefficient of each seed channel and each microseism wavelet in the microseism seed channel sequence; constructing superposed single-channel data based on the maximum cross correlation coefficient; and ground monitoring microseism event identification is carried out through a long-short time window energy ratio method based on superposed single-channel data. According to the invention, the method achieves the accurate recognition of the ground microseism event through the construction of the microseism wavelet, the calculation of the maximum cross correlation coefficient of the seed channel and the microseism wavelet, the construction of a new seed channel sequence, the formation of a new superposition channel, and the utilization of a long and short time window energy ratio method, and improves the recognition accuracy of the ground microseism event.