12588results about "Seismic signal processing" patented technology

The present invention discloses a battery SOC and SOH comprehensive evaluation system and a predictive maintenance method thereof, the system comprising: a multi-source data acquisition module, a data preprocessing module, a multi-source data fusion module, a deep learning SOC estimation module, a transfer learning SOH evaluation module, a graph neural network battery pack state evaluation module, a reinforcement learning predictive maintenance decision module, a big data analysis platform, an edge computing node, an explainable AI module, an adaptive online learning module, a digital twin module, a quantum machine learning module, a federated learning framework, a self-supervised learning anomaly detection module, a multimodal deep learning module, an adaptive sampling strategy module, a causal reasoning fault diagnosis module, etc. The present invention realizes a comprehensive and accurate evaluation of the battery state by integrating a variety of advanced artificial intelligence and big data technologies, and formulates a predictive maintenance strategy based on the evaluation results to extend the battery life and improve the system reliability.

The invention provides an emergency broadcast intelligent triggering system based on multi-source seismic data fusion, and relates to the technical field of seismic monitoring and early warning, and the system comprises a data receiving module which is used for receiving multi-source seismic early warning information in real time, including seismic source parameter data, strong vibration observation data and real-time early warning parameters, and transmitting the multi-source seismic early warning information through a built-in communication protocol; and outputting the standardized multi-source seismic data. And the localization calculation module is used for carrying out real-time analysis on the standardized multi-source seismic data, extracting a P-wave initial parameter, a seismic motion acceleration predicted value and a seismic source fracture feature, carrying out dynamic calibration on an analysis result by combining actual measurement data of a built-in intensity meter, and outputting a multi-dimensional seismic data stream verified by the intensity meter. Through multi-source earthquake data fusion, real-time analysis and calibration, dynamic risk modeling and intelligent broadcast scheduling, generation and efficient propagation of earthquake early warning information are realized, emergency response timeliness and pertinence are improved, and earthquake disaster risks are reduced.

The invention discloses a loess tunnel surrounding rock deformation monitoring method, and relates to the technical field of civil engineering and geological engineering, and the method comprises the steps: integrating a spiral winding type optical fiber sensor, a double-cavity humidity compensation vibrating wire sensor and a microseismic array, and capturing surrounding rock strain, vibration and geological activities; the vibrating wire sensor suppresses humidity interference through a silicone oil damping medium and self-adaptive excitation frequency, and the optical fiber sensor is fixed through a pre-embedded silica gel sleeve to adapt to surrounding rock deformation; edge computing nodes are deployed on the inner wall of the tunnel, an FPGA chip and a lightweight GRU model are integrated, optical fiber strain, a micro-seismic energy spectrum and laser point cloud displacement field data are fused in real time, the strain gradient is analyzed, and a crack propagation probability cloud picture is generated; redundant optical fiber link switching is combined with a six-degree-of-freedom mechanical arm to realize breakpoint self-repairing, and a self-cleaning air curtain is integrated to inhibit dust adhesion; and constructing a geological parameter library based on a BIM-GIS fusion platform, driving a finite element-discrete element coupling model to dynamically update boundary conditions, and predicting surrounding rock deformation and collapse risks.

The invention provides an underground cavern surrounding rock parameter collaborative sensing method and system based on multi-source data fusion, and the method comprises the steps: firstly deploying a sensor group comprising an optical fiber strain sensor, a micro-seismic monitoring array, a three-dimensional laser scanner and an acoustic emission sensor, the optical fiber strain sensor is arranged along the axis of a cavern, the micro-seismic monitoring array is deployed according to a triangular network, and the three-dimensional laser scanner is arranged along the axis of the cavern; performing time synchronization on the sensor group through a synchronization pulse signal, establishing a local coordinate system at a central point of a cavern entrance, uniformly collecting data, generating a time-aligned data stream and a space-unified data set, and triggering associated sensors to enter a hierarchical response collection mode according to a strain rate and a vibration signal; and generating a multi-modal monitoring data set, finally carrying out dynamic weight distribution and conflict resolution processing on the multi-modal monitoring data set, extracting surrounding rock deformation characteristics and fracture event density, and outputting a surrounding rock stability evaluation result, thereby realizing underground cavern surrounding rock parameter collaborative perception and stability evaluation.

The present invention relates to the technical field of tunnel rockburst microseismic monitoring, and provides a strain-structural plane slip-type rockburst failure mechanism analysis method for a drill-and-blast method tunnel. The present invention collects strain-structural plane slip-type rockburst microseismic data during tunnel construction; in light of engineering geological survey data, performs microseismic monitoring and data analysis on strain-structural plane slip-type rockburst that has occurred in a tunnel, divides a strain-structural plane slip-type rockburst risk area, and determines a microseismic rockburst early warning area; and, by means of these data, deeply understands a microseismic activity law of strain-structural plane slip-type rockburst of deep-buried drill-and-blast method tunnels, discriminates respective functions of tensioning and shearing in rock failure processes, comprehensively analyzes the failure mechanism of strain-structural plane slip-type rockburst, and further obtains the failure mechanism of strain-structural plane slip-type rockburst of the deep-buried drill-and-blast method tunnels, providing an important theoretical basis for predicting and preventing rockburst disasters.

The invention provides an urban underground space high-precision detection method, system and device and a storage medium, and relates to the technical field of geological exploration. The method comprises the following steps: acquiring reflection seismic data, high-resolution electromagnetic response data, environmental noise data, well-side seismic data and logging data of a target area, and performing refined processing on the reflection seismic data, the high-resolution electromagnetic response data, the environmental noise data, the well-side seismic data and the logging data to obtain multi-source multi-scale detection data; performing constraint condition extraction based on the multi-source multi-scale detection data to obtain a multi-source constraint condition, and constructing an urban underground space three-dimensional information model based on the multi-source constraint condition and the multi-source multi-scale detection data; and performing data mining based on the urban underground space three-dimensional information model to obtain three-dimensional geological data. According to the scheme, the accuracy of urban underground space detection can be improved.

The invention discloses a shield tunnel active and passive source combined geological detection method and system. The method comprises the following steps: acquiring an active source seismic wave reflection signal and a passive source acoustic wave reflection signal; according to the active source seismic wave reflection signal, obtaining an active source geological detection imaging result by adopting a longitudinal and transverse wave combined imaging principle; according to the passive source sound wave reflection signal, a frequency dispersion extraction-shear wave velocity inversion method fusing spatial autocorrelation and a half-wavelength model is adopted to obtain a passive source geological exploration imaging result; and taking an active source geological detection imaging result as a main result, taking a passive source geological detection imaging result as an auxiliary result, and combining adaptive adjustment coefficients between the two results to obtain an unfavorable geological risk level of the to-be-detected area. Shield tunneling vibration noise with high energy and wide frequency band is introduced as a passive detection source, the detection depth and resolution are improved, and support is provided for accurate geological imaging.

The invention discloses a vibration table test device and a test method for seismic dynamic response of a fault-crossing mountain tunnel. The test device comprises an earthquake simulation vibration table, a laminated shear model box, surrounding rock, a fault fracture zone, a tunnel model and a monitoring and measuring system. Wherein the earthquake simulation vibration table inputs earthquake waves and provides earthquake power; the bottom of the laminated shear model box is fixed on the earthquake simulation vibration table, and the laminated shear model box is filled with surrounding rocks and fault fracture zones; the fault dip angle is 90 degrees, and left and right surrounding rock elevations are inconsistent; the tunnel model comprises a non-shock-absorption layer and a shock-absorption layer which are buried in the surrounding rock in the model box in parallel and perpendicularly intersect with a fault fracture zone. The monitoring and measuring system is used for measuring tunnel acceleration and strain, surrounding rock acceleration, fault acceleration and the like in the seismic oscillation process. The test device can simulate the dynamic response process of the fault-crossing mountain tunnel under the action of an earthquake, and time evolution and spatial distribution characteristics of tunnel earthquake response are determined.

The invention provides a geotechnical engineering earthquake sand liquefaction discrimination method and system, and relates to the technical field of geotechnical engineering, and the method comprises the steps: collecting site basic data (a site parameter table, earthquake acceleration and pore pressure time history data); establishing a preliminary numerical model and applying boundary conditions to correct the preliminary numerical model into a complete numerical model; the pore pressure ratio and the shear strain distribution are obtained through nonlinear dynamic analysis, and the liquefaction risk is comprehensively evaluated; a key soil layer observation scheme is made according to the prediction result, field verification is carried out, and a final judgment result is obtained by combining actual measurement and numerical simulation. According to the method, numerical analysis and actual measurement data are combined, the sand liquefaction risk is rapidly and accurately evaluated, and field testing and observation are guided.

The invention provides an earth surface anomaly monitoring data fusion system based on vibration signal processing, and relates to the technical field of earthquake precursor monitoring, and the system comprises a data collection module which is used for carrying out the real-time monitoring of the earth surface through a distributed multi-parameter sensor network, and obtaining the time sequence monitoring data related to the earthquake precursor; the signal processing module is used for processing the time sequence monitoring data by adopting a two-parameter joint optimization algorithm and outputting a separation signal component containing precursor information; the data fusion and state inversion module is used for carrying out fusion processing on the separated signal components by adopting a dual-time scale analysis framework and outputting a multi-scale precursor state estimation result and an uncertainty quantitative index; and the monitoring and early warning module is used for outputting earthquake precursor grading early warning information through a three-layer anomaly recognition architecture and probabilistic risk assessment. According to the invention, high-precision separation and identification of earthquake precursor related signals can be realized, and the false alarm phenomenon is reduced through an intelligent early warning mechanism.

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

The invention relates to an earthquake fracture inversion method based on multi-source observation data. The method comprises the following steps: acquiring a seismic source mechanism solution of a specified earthquake event; according to the seismic source mechanism solution, geometric parameters of a fault of the seismic event are obtained; generating a three-dimensional grid model of a fault plane to which the fault belongs according to a pre-constructed geometric shape model of the fault and the geometric parameters of the fault; according to the limiting condition of each sub-fault in the three-dimensional grid model, obtaining the slippage of the sub-fault to which the seismic fracture belongs; and according to the observation data to which the seismic event belongs and the slippage of the sub-faults, performing inversion verification by means of an inversion model, and if the verification is passed, taking the slippage of all the sub-faults as a seismic fracture inversion result when the verification is passed. According to the method, the stability and reliability of an inversion result are improved, the sliding vector at each sub-fault can be better estimated, and the sliding process of the whole seismic fault can be more comprehensively understood.

The invention discloses a quantitative relation chart construction method based on fault characteristics and stress disturbance in the technical field of oil-gas exploration and development. The method comprises the following steps: constructing a geological structure model; the geologic structure model is converted into a mechanical model, and stratum three-dimensional heterogeneous geomechanical attribute modeling is completed through single-point test analysis, single-well logging interpretation and plane distribution simulation; importing the established three-dimensional heterogeneous rock mechanical parameter model by using modeling software, endowing rock mechanical parameters, and performing grid division on the imported model to generate a grid mathematical model; taking a three-dimensional heterogeneous rock mechanical parameter model as a parameter model for crustal stress prediction, and setting a boundary load loading mode; through finite element calculation, the current crustal stress distribution characteristics of the research area are obtained. According to the scheme, through the technical means of single-point test analysis, single-well logging interpretation, plane distribution simulation and the like, the mechanical properties of the rock under different conditions can be described more accurately.

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

The invention provides a method for quickly establishing a three-dimensional near-seafloor speed model in a shallow sea area; the method comprises the following steps of extracting initial informationand extracting a common-center-point gather; calculating the slope of the initial travel time in the common-center-point gather; judging whether a shot point is located in the seawater layer or not,and if yes, correcting the shot point to the seafloor; calculating the speed and the depth of inflection points corresponding to respective offset distances; performing linear interpolation on the speed at each depth to obtain a one-dimensional speed model of the common center point; after all the common-center point domain gathers are processed, adopting the linear interpolation method, calculating the speed values of the rest grid points by utilizing the one-dimensional speed model of all common center points, and a speed model is output. By adoption of the method for quickly establishing the three-dimensional near-seafloor speed model in the shallow sea area, the influence of the seawater layer on ray propagation is overcome, and the influence of multiple excitation receiving modes on an inversion result is eliminated, and the problem that the conventional method needs to carry out multi-ray tracing forward modeling is avoided.

A method for extracting and displaying desired information from a set of tables includes storing a set of tables including information associated with a subsurface of the Earth; receiving a user query; selecting a table from the set of tables based on an embedding search performed for the user query, on a vector database of table-column questions of the set of tables; selecting one or more columns from the table based on a likelihood estimation performed in an embedding space, between (1) the user query and (2) a table summary and descriptions of columns for the selected table; determining one or more rows associated with the one or more columns; and displaying one or more answers in response to the user query.

The invention relates to a surface collapse monitoring system based on multi-parameter distributed optical fiber sensing, and the system comprises a monitoring module which comprises a plurality of fully distributed optical fiber sensors laid in a monitoring area, and an optical signal transmission and demodulation module which comprises a continuous spectrum laser, an optical switch, an optical amplifier, an optical filter, and a demodulation module. The data processing and analysis center comprises a data acquisition module and a collapse model modeling module, and the data acquisition module is used for performing acquisition and digital conversion on the optical signal demodulated by the demodulation module; the collapse model modeling module constructs a model according to the monitoring data and can predict the development trend of surface collapse; and the early warning and communication module communicates with a remote monitoring center. The monitoring system provided by the invention has the advantages of multi-parameter monitoring, high monitoring precision, suitability for large-scale laying, real-time response and the like, and can comprehensively and accurately reflect the real condition of surface collapse.

The invention relates to a multi-dimensional seismic risk assessment method based on seismic oscillation space-time distribution. The method comprises the following steps: S1, selecting IM indexes; s2, key structure parameters are extracted; s3, recording an engineering demand parameter EDP; s4, selecting an optimal IM index; s5, establishing an agent model of structural seismic response; s6, establishing an earthquake prediction equation; s7, establishing an aftershock risk analysis model; s8, obtaining aftershock intensity samples of the positions of the target structures; s9, obtaining an aftershock vulnerability curve; and S10, determining repair time and repair cost according to the repair of the structure and the repair of the structure function so as to finally determine the total repair cost of the structure of the regional line and the anti-seismic capacity after repair. Compared with the prior art, the method has the advantages of improving the accuracy of earthquake risk estimation and the like.

The invention discloses a multi-factor prospecting prediction method suitable for tin polymetallic ore, and relates to the technical field of geological science. The prospecting prediction method comprises the following steps: collecting data; fusing multi-source data; extracting and analyzing mineralization information; constructing a comprehensive prediction model; and predicting mineralization. According to the method, a potential tin polymetallic ore deposit area is preliminarily selected by comprehensively considering multi-source information such as geology, geophysics, geochemistry and remote sensing, drilling data and profile data are collected in the area, the drilling data and the profile data are utilized to construct a comprehensive prediction model, and the model is combined with advanced algorithms such as geostatistics and machine learning, so that the prediction accuracy of the tin polymetallic ore deposit area is improved. The multi-source data is efficiently integrated, and a three-dimensional visualization result is provided, so that the position and the scale of a potential ore body can be more accurately predicted, and the success rate of prospecting is improved.

The invention relates to the technical field of coal mine exploration, in particular to a coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, which comprises the steps of S1, integrating multi-source geological data; s2, data processing and feature enhancement; s3, machine learning modeling; s4, target spot optimization and dynamic verification; and S5, geological modeling and risk grading. According to the coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, a depth domain joint data set is constructed, multi-dimensional features such as seismic amplitude, lithology coding, fracture index and fault distance field are integrated, and multi-source data fusion and physical constraint machine learning are realized through a feature enhancement technology; locking a high-uncertainty region based on the prediction variance, and dynamically updating a target spot through a Gaussian process proxy model to realize targeted drilling and dynamic closed-loop optimization; a multi-attribute risk fusion model is constructed, a four-level risk map is output through the risk factors, grouting resources are guided to be preferentially put into a high-risk area, and risk grading early warning processing is achieved.

The invention belongs to the technical field of geological information processing, and discloses a geological mineral exploration data extraction system which comprises the following steps: constructing an air-ground-hole three-in-one acquisition network, and respectively acquiring earth surface mineral spectrum data, seismic longitudinal wave and transverse wave reflection data and rock core main quantity and trace element composition data; processing the collected data to form a five-dimensional data cube containing space coordinates, spectrum parameters, physical property parameters, element parameters and time indexes, performing physical layer attribute extraction on the five-dimensional data cube, performing analysis to obtain a wave impedance value and a lithology category probability array, performing semantic layer attribute extraction on the five-dimensional data cube, and performing semantic layer attribute extraction on the five-dimensional data cube; an alteration type probability array is obtained through analysis, an alteration zoning graph layer is generated, the mineralization potential index and a five-dimensional data cube are fused, a four-dimensional dynamic database is constructed, a mineralization potential prediction graph layer and a drilling priority graph are generated based on the four-dimensional dynamic database, and therefore the decision-making level of ore body drilling deployment is improved.

The invention relates to the technical field of geotechnical engineering safety monitoring, in particular to a river valley area bedding slope stability monitoring method and related products, and the method comprises the steps: building a three-dimensional geology-mechanics numerical model of a slope; acquiring monitoring data of the slope state in real time; dynamically correcting mechanical parameters in the three-dimensional geology-mechanical numerical model through inversion calculation; evaluating the stability of the slope and outputting early warning information; according to the method, multi-source monitoring data are fused into the numerical model in real time, dynamic inversion and tracking of internal mechanical parameters of the slope are achieved, comprehensive indexes such as the real-time safety coefficient, the main sliding direction deviation angle and the damage evolution rate are calculated, graded early warning is triggered according to the indexes, and the perspectiveness and reliability of early warning are improved.

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

The invention relates to the technical field of dynamic monitoring, in particular to an AI-based coal mine underground and roadway three-dimensional earthquake dynamic monitoring system, which comprises an underground edge computing node cluster module, an underground edge computing node cluster module and an underground edge computing node cluster module, the multi-source heterogeneous data fusion processing module is used for receiving the original signal data set, performing space-time alignment and feature decoupling processing, and generating a standardized seismic-electric feature matrix; the three-dimensional geological dynamic modeling module is used for generating a three-dimensional geological dynamic model updated in real time through a dynamic voxel compensation algorithm based on the standardized seismoelectric characteristic matrix; the abnormal characteristic evolution analysis module is used for receiving time sequence data of the three-dimensional geological dynamic model and constructing a space-time two-dimensional abnormal degree evolution graph; and the visual early warning decision module is used for generating graded early warning signals and a three-dimensional visual interface. According to the invention, the six-level progressive processing architecture is innovatively designed, so that the performance of the three-dimensional earthquake dynamic monitoring system for the underground coal mine and the roadway is remarkably improved.

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

The present disclosure discloses a seismic inversion method based on joint constraint of a physical model and priori information. The method includes: extracting seismic wavelets based on seismic data, and determining an amplitude scaling factor of the wavelets; counting priori information of impedance parameters; establishing an initial impedance parameter model by using seismic structural interpretation information and logging data; obtaining a simplified approximate equation based on an interface weak elasticity difference hypothesis, forward modeling a seismic gather by using the simplified equation, and calculating an inversion residual; rewriting an objective function into a function related to the impedance parameters by using a generalized linear inversion idea, solving the impedance parameters by using an iterative reweighted least squares algorithm, and updating the impedance parameters; and repeating the above steps until the inversion residual reaches the requirements or reaches the maximum number of iterations, and outputting a final processing result.

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

The invention relates to the field of seismic state recognition, and provides a converter station vector seismic state recognition method and system based on distributed optical fibers, and the method comprises the steps: collecting multi-dimensional vibration signal data of a converter station, and obtaining seismic monitoring data through processing; performing phase demodulation processing, time difference analysis and space vector decomposition algorithm processing on the seismic monitoring data to obtain seismic wave propagation vector characteristic data, and performing characteristic integration and optimization to obtain a seismic wave comprehensive state characteristic data set; identifying and classifying the seismic wave comprehensive state feature data set to obtain a seismic wave form classification result; performing analysis based on the seismic wave form classification result to obtain a converter station seismic state evaluation result; and carrying out comparative analysis on the evaluation result and a threshold value, generating earthquake early warning information of the converter station, and providing a corresponding emergency response suggestion according to the earthquake early warning information. According to the invention, accurate identification and classification of seismic waves are realized, and the earthquake safety early warning capability of the converter station is improved.

The invention discloses an orthogonal fractured reservoir frequency variation AVO inversion and parameter prediction method, and relates to the technical field of exploration geophysics, and the method comprises the steps: deducing and reconstructing a fluid saturation horizontal-vertical orthogonal fractured medium rock physical model based on a multi-group fracture-pore medium Chapman model, realizing fluid saturation rock physical modeling containing two groups of orthogonal fracture media; based on an anisotropic reflectivity algorithm and a fluid saturation horizontal-vertical orthogonal fractured medium rock physical model, realizing frequency variation AVO forward modeling of the fractured reservoir, and calculating a frequency variation AVO reflection coefficient and a synthetic seismic record of the fractured reservoir under the interface reflection model; a frequency-varying AVO inversion theory based on a Bayesian framework is constructed by taking a forward modeling process of the frequency-varying AVO as a drive, and the frequency-varying AVO inversion based on the Bayesian inversion framework is realized and reservoir parameter prediction is carried out by utilizing pre-stack seismic AVO data and introducing a simulated annealing-particle swarm algorithm in an inversion process. The method provides a new method and a new means for fine description and prediction of the fractured reservoir.

The invention relates to the technical field of high-speed rail platform safety monitoring, and discloses a high-speed rail platform area intrusion detection and early warning alarm system, which comprises an acoustic and vibration sensor network deployed in a platform area and used for collecting environment signals in real time, the edge calculation unit extracts signal features and compares the signal features with a dynamically maintained environmental rhythm feature baseline to generate a detuning event mark, and the detuning mode analysis device identifies an abnormal mode through space-time correlation and triggers an alarm. According to the method, early perception of subtle anomalies is realized by constructing a multi-modal environment rhythm baseline, active perturbation injection and differential response analysis technologies are combined, the recognition capability of a silent target is remarkably improved, meanwhile, the monitoring continuity under an extreme working condition is ensured by using an elastic baseline adaptive mechanism, and the detection accuracy is improved. And a closed-loop security and protection system from passive sensing to active discrimination is formed.