884 results about "Wave field" patented technology

The invention belongs to the technical field of marine environment prediction, and discloses a ship navigation sea wave dynamic space-time prediction method and system based on deep learning. The method comprises the following steps: carrying out space-time alignment, missing value repair and standardization processing on acquired ship AIS data and an ERA5 reanalysis data set, and generating node feature vectors containing latitudes and longitudes, timestamps, wind speeds and significant wave heights; through node feature coding, centrality coding, space coding and time coding, ship trajectory node importance and time-space interaction relation are quantified. Constructing a SeaGraph model, and outputting an effective wave height prediction value of a target waypoint; and performing verification. According to the method, the space-time constraint of a traditional static modeling framework is broken through, the advantages of a self-attention mechanism and a graph network are integrated, the predictive modeling capability of dynamic evolution of a wave field in front of a ship navigation track is enhanced, and high-precision sea wave forecasting support is provided for intelligent ship navigation under complex sea conditions.

The invention discloses a multi-agent-based gas insulated switchgear fault diagnosis method and system, and relates to the technical field of intelligent operation and maintenance of power equipment, and the method comprises the steps: obtaining signal data of target equipment, carrying out the feature extraction of the signal data, and constructing a multi-modal feature matrix; time delay features of acoustic and electromagnetic signals are extracted from the multi-modal feature matrix, a GIS propagation model is established, and the space coordinate position of a liberated power source is solved through a wave field inversion algorithm; combining the space coordinate position and the multi-modal feature matrix into a complete fusion feature vector, inputting the fusion feature vector into a dynamic Bayesian model, and outputting a fault type label and a corresponding confidence coefficient; migrating the dynamic Bayesian model based on a migration learning mechanism, and dynamically updating a classification threshold value; inputting the diagnosis history sequence into a time sequence prediction model, and predicting a future operation state; through multi-modal fusion and intelligent reasoning, GIS fault accurate positioning and prediction are realized, and the problems of low precision and poor adaptability of traditional diagnosis are solved.

The invention provides a multi-physics field cooperative regulation and control method in coal mine rock burst prevention and control, and relates to the technical field of coal mine rock burst prevention and control, and the method comprises the steps: synchronously collecting stress field, fracture field and vibration wave field data in real time through a distributed optical fiber sensor network and a micro-seismic monitoring system, and constructing a dynamically updated three-dimensional geomechanical model; based on an adaptive Kalman filtering algorithm, noise reduction multi-source data are fused, a dynamic stress concentration factor and an energy accumulation critical index are extracted, and a rock burst risk probability model is established; a multi-objective optimization algorithm is adopted to generate graded regulation and control instructions of grouting reinforcement, mining speed adjustment and pressure relief drilling, and the graded regulation and control instructions are executed in real time; and iteratively optimizing the model weight by using a transfer learning algorithm in combination with a historical case library to form a closed-loop feedback adaptive prevention and control system. According to the method, the analysis precision of rock mass fracture evolution under complex geological conditions is improved through multi-physics field collaborative perception and dynamic modeling, and risk quantification and real-time regulation and control are realized through multi-algorithm coupling analysis.

The invention relates to the field of floating type fan load simulation, in particular to a floating type fan simulation analysis method considering atmospheric stability in a stormy wave environment, which comprises the following steps of: simulating WRF wind field data; carrying out WRF-SWAN wave field simulation; calculating key parameters of wind field simulation; carrying out WRF-TurbSim wind field simulation; and carrying out OpenFAST simulation and analysis. On the basis of traditional wind field driving, a Richardson number calculation and atmospheric stability classification method is introduced, a spectrum inversion and adaptive fitting mechanism is combined, key parameters of an engineering wind speed spectrum model are extracted, and a parameter mapping relation between WRF output and TurbSim input is established. A disturbance construction module driven by Richardson numbers is developed in TurbSim, adaptive generation of wind speed disturbance under the multi-stability condition is achieved, and the physical consistency and engineering adaptability of a simulated wind speed field on the aspects of a section structure and spectrum characteristics are improved. And finally, finishing floating type fan load analysis under wind wave coupling input by combining with an OpenFAST platform, and supporting system response evaluation and structural design optimization under complex sea conditions.

The invention discloses an extreme sea condition parameter identification system based on deep learning, and relates to the technical field of ship navigation auxiliary equipment, in particular to a self-adaptive sea condition identification device which is used for acquiring image data and inertial measurement data of a current sea condition; the wave field visual depth estimation module is used for extracting visible light image features and infrared image features of a wave area from image data of the current sea condition, fusing the extracted visible light image features and infrared image features, using an encoder-decoder architecture and fusing an energy function to obtain a pixel-level wave height field, and outputting the pixel-level wave height field. The three-dimensional reconstruction of the wave surface is realized; the multi-modal data fusion module uses a filter dynamic model and a cost function to eliminate space-time asynchronous errors between inertial measurement data and visual perception data, performs multi-modal data fusion, and outputs wave field real-time parameterization information. According to the invention, the sea condition parameter real-time high-precision identification capability of the autonomous unmanned ship or the offshore carrying platform can be improved.

The invention discloses a road cavity volume calculation method and device based on a three-dimensional FK migration algorithm, equipment and a medium, and relates to the technical field of road cavity detection, and the method comprises the steps: obtaining three-dimensional ground penetrating radar data, and carrying out the preprocessing of the data, and obtaining target radar data; converting the target radar data to a frequency wavenumber domain to obtain frequency wavenumber domain radar data; establishing a road hierarchical velocity model, and performing hierarchical wave field extrapolation and double weight correction based on the frequency wave number domain radar data and the road hierarchical velocity model to obtain corrected wave field data; performing sparse constraint optimization imaging processing on the corrected wave field data to obtain a target imaging result; and carrying out boundary extraction processing on the target imaging result to obtain a three-dimensional cavity boundary point set, and carrying out voxelization processing based on the three-dimensional cavity boundary point set to obtain a road cavity volume. According to the invention, high-precision restoration of the shape of the road cavity and automatic extraction of the volume can be realized in three-dimensional GPR imaging.

The invention discloses an intelligent analysis system for ultrasonic phased array detection data of a welding seam, and relates to the technical field of nondestructive testing, and the intelligent analysis system is characterized in that a four-dimensional wave field tensor is constructed by collecting full-waveform ultrasonic data under multi-channel, multi-path and multi-angle conditions; extracting reflected signals which keep time coherence in a discontinuous path, and generating a high-dimensional coherence feature matrix; inputting the features into a self-supervised contrast learning model to obtain a defect semantic embedding vector; recognizing a suspected weak defect area based on the distribution density and the boundary change trend, and performing reverse beam focusing in combination with original data to obtain a defect three-dimensional positioning map; the shape bifurcation index and the boundary stability are calculated through topological analysis, and intelligent discrimination of artifacts and microcrack defects is achieved; the method does not need label data, has high automation and robustness, and is suitable for weak defect identification of complex structure welding seams.

The invention discloses an urban karst water guide channel detection system and method based on earthquake Krauklis waves. The urban karst water guide channel detection system comprises a piezoelectric type vibroseis, an array type earthquake receiving system and a multi-wave seismograph. The piezoelectric vibroseis is used for exciting seismic waves; and the multi-wave seismograph is used for acquiring seismic data acquired by each seismic acceleration sensor in the array type seismic receiving system. The structure can ensure the precision of seismic wave data acquisition each time; separating and extracting Krauklis waves from the acquired seismic wave data by adopting an energy-frequency double-selection method; then, based on the frequency dispersion characteristic of Krauklis waves, a mapping model of crack opening and wave field response is established; and finally, determining the opening degree of the urban karst water guide channel according to the mapping model, and performing inversion to obtain the dynamic flow of the urban karst water guide channel. Through treatment in the mode, the opening degree and the dynamic flow of the urban karst water guiding channel can be monitored, and finally prevention and control over hidden geological disasters are effectively improved.

The invention relates to the technical field of deep and far sea small island health monitoring, and discloses a deep and far sea small island health monitoring system and method. A non-intrusive data acquisition system composed of a sensor network, a shore-based camera device and a vibration sensor is adopted, and total-factor real-time monitoring of open sea environment parameters, reef images and the vibration state of a protective building is achieved. Through joint operation of a wave field distribution model and a structural damage prediction model, actually measured data and numerical simulation are combined, and the temporal-spatial resolution of wave parameters of the reef and the evaluation precision of the health state of a protective building are improved; the early warning threshold value is dynamically adjusted through deviation analysis of real-time association of historical disaster data and the current monitoring value, and the timeliness and accuracy of early warning of extreme events are improved; finally, through an automatic triggering mechanism of a multi-stage early warning signal, all-weather and high-reliability safety monitoring guarantee is provided for deep and far sea islands, meanwhile, the manual maintenance cost is reduced, and a remarkable technical synergistic effect is achieved.

The invention discloses a hybrid nonlinear floating fan hydrodynamic load coupling calculation method. The method comprises the steps of obtaining a velocity potential of a wave field in a computational domain through OceanWave3D numerical simulation (a wave field calculation model), and performing post-processing to obtain wave field data; dividing an instantaneous wet surface according to the six-degree-of-freedom motion, calculated based on an OpenFAST (floating fan calculation model), of the floating fan in each time step, and performing pressure integration to obtain a Front-Krylov force; updating the hydrostatic stiffness matrix according to the real-time position change of the buoyancy center of the fan, and calculating hydrostatic restoring force; linear radiation force and diffraction force are calculated through time domain convolution of frequency domain data, nonlinearity of the radiation force and the diffraction force is considered based on an instantaneous wetting volume change coefficient, and hydrodynamic load and structural motion response calculation in the current time step is completed after the OpenFAST is coupled. According to the method, coupling calculation between the open source code OpenFAST and OceanWave3D is achieved, and compared with commercial software at the present stage, the method is more accurate in wave load calculation under the extreme wave sea condition.

The invention relates to a complex medium inversion imaging method and system guided by a physical information neural network, and the method comprises the steps: constructing a two-dimensional grid model of a block structure with different medium distributions, obtaining out-of-plane displacement data at grid points through staggered grid finite difference, and forming a wave field full-waveform data set; constructing a physical information neural network, wherein the physical information neural network comprises a first feed-forward network for modeling a mapping relation of a displacement field of the sample, a second feed-forward network for modeling a velocity field of the sample, and a residual network; based on the wave field full-waveform data set, training a physical information neural network by taking minimization of compound loss as a target; and taking the space-time coordinates of the sample as input, and utilizing the trained physical information neural network to obtain predicted displacement and sound velocity of the corresponding position of the space-time coordinates, so as to realize inversion imaging of sound velocity medium distribution. According to the method, the ultrasonic propagation model of the complex medium can be quickly and accurately established under the condition that a small amount of marked data is used.

The invention provides an electromagnetic ultrasonic bolt stress detection system based on a space-time complementation algorithm, and the system comprises the steps: carrying out the multi-channel ultrasonic signal collection of a to-be-detected bolt, and obtaining a multi-channel echo signal group; performing time-varying attenuation compensation and adaptive entropy weight denoising on the multi-channel echo signal group to obtain a denoised echo signal group; extracting echo time-domain features from the de-noised echo signal group, performing segmented time-frequency feature extraction on the de-noised echo signal group based on a short-time sliding window to obtain echo time-frequency features, and fusing the echo time-domain features and the echo time-frequency features into ultrasonic time-frequency features; performing wave field reconstruction on the de-noised echo signal group to obtain a spatial ultrasonic energy diagram, and extracting ultrasonic spatial features of the spatial ultrasonic energy diagram based on a self-attention map convolutional network; performing correlation detection and space-time complementary modeling on the ultrasonic time-frequency features and the ultrasonic space features to obtain ultrasonic space-time joint features, and performing stress analysis on the ultrasonic space-time joint features to obtain bolt stress data.

The invention belongs to the technical field of intelligent sensing, and relates to a micro-seismic monitoring method for a ground moving target in a sensitive area, which comprises the following steps: collecting five types of moving target seismic signals, and constructing a data set; preprocessing the data; constructing an interpretable convolution kernel based on a scalar wave field equation, and realizing adaptive extraction of key physical parameters and nonlinear dimensionality reduction of a high-dimensional feature space by embedding a partial differential operator to form compact feature representation with physical traceability; forward diffusion is adopted to carry out progressive Gaussian noise enhancement on the noisy features; designing a Transform architecture with a multi-head self-attention mechanism and position coding, capturing time-space correlation characteristics of vibration signals through context modeling, and realizing end-to-end mapping from physical characteristics to classification decision in combination with a multi-layer perceptron; and carrying out training and event decision making on the model, and outputting a decision classification result. According to the method, the identification accuracy of the seismic oscillation signal of the moving target can reach 95.2%, and the robustness is remarkably improved.

The invention belongs to the technical field of photovoltaic power generation, and particularly relates to a floating type photovoltaic array power generation performance evaluation method, which comprises the steps of constructing an offshore area environment model; a Boussinesq model is adopted to calculate a real near-shore wave field; constructing a matching surface on the outer side of the array, extracting wave field time domain information, and converting the wave field time domain information into a frequency domain component; solving the six-degree-of-freedom motion response of the array through a boundary element method by taking the component as a boundary condition; calculating the dynamic attitude of the assembly, the solar incident angle, the out-of-plane irradiance, the working temperature and the instantaneous output power in real time based on the motion response; and finally evaluating the wave-induced power generation loss. According to the method, the Boussinesq model and the boundary element method are coupled in a domain nested mode, balance is achieved between calculation efficiency and precision, key environmental factors such as non-constant water depth, topographic relief and shoreline effect are synchronously considered, 'hydrodynamic force-light receiving-power generation 'chain coupling is achieved for the first time, and reliable support is provided for engineering design and performance evaluation of a near-shore floating type photovoltaic array.

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 present invention relates to the field of nondestructive testing technology and provides a composite material damage imaging method based on the spatiotemporal cross-correlation of ultrasonic guided wave wavefields. The method comprises the following steps: S1, acquiring a wavefield signal of the plate being tested; S2, performing narrowband signal extraction on the wavefield signal; S3, filtering the wavefield signal after narrowband signal extraction; S4, shifting the wavefield signal to obtain wavefield signal cross-correlation features; and S5, using a convex hull algorithm to obtain damage imaging results. This method innovatively extracts the difference coefficient between wavefield signals as a basis for damage location, providing a new approach to feature extraction algorithms based on the time-frequency domain. This method enables automated identification of composite material delamination damage and is sensitive to both surface and internal damage.

The invention belongs to the technical field of advanced detection, and provides a shield tunnel optical fiber earthquake forecasting method and system, and the method comprises the steps: obtaining the carrying position and number of seismic wave excitation seismic source equipment based on a shield tunneling machine structure type, installing a pneumatic hammering seismic source, calculating the dynamic correlation between the air pressure and the hammering force, and determining the shield air pressure range. Determining the relative spatial position of the seismic source and the optical fiber sensor, and establishing an observation mode; according to the relative spatial position and the observation mode, arranging an optical fiber sensor in a spatial range behind the shield tail of the shield tunnel, controlling a pneumatic hammering seismic source to hammer surrounding rock to generate seismic waves to obtain geological forecast data, preprocessing the obtained geological forecast data to obtain geological wave field data, and calculating the geological wave field data according to the geological wave field data. And calculating rock mass mechanical parameters, a reflection coefficient and a stress gradient of the shield excavation surface, obtaining a three-dimensional image, carrying out feature analysis, obtaining a disaster-causing structure position, a disaster-causing structure scale and an underground water occurrence condition, and completing prediction of the shield tunnel optical fiber earthquake.

The invention provides a cable fault diagnosis method and system based on multi-modal composite fusion. The method comprises the following steps: synchronously acquiring and performing time-space alignment on distributed acoustic, temperature and high-frequency pulse signals along a cable path; the acoustic signal is decoupled into an internal event channel and an external interference channel by time domain polarization analysis. And calculating the acoustic nonlinear index of the candidate event in the internal channel, dynamically correcting the judgment threshold value of the candidate event by using the real-time temperature signal, and performing cross-modal correlation verification with the high-frequency pulse signal so as to confirm that the candidate event is an electrical discharge event. And if the judgment result is true, an acoustic wave field data array of the event is extracted, and a passive synthetic aperture focusing algorithm is applied to reconstruct a two-dimensional morphological acoustic image of the fault source. And finally, integrating all information to generate a comprehensive diagnosis report. The technical problem that a traditional distributed sensing method can only provide one-dimensional fault positioning and cannot perform high-confidence qualitative and multi-dimensional morphological representation on the fault is solved.

The invention provides a mine vibration safety detection method and system during mining blasting. The method comprises the following steps: acquiring three-component signals of explosion fluctuation at a plurality of sampling points in a warning range through an intelligent sensor network during mine blasting; decomposing each three-component signal into a plurality of attenuation waves, determining cross correlation coefficients of the attenuation waves in the same component direction among different sampling points, and performing reverse time back propagation on each sampling point according to all the cross correlation coefficients to obtain a reverse time wave field of blasting vibration at each sampling point; inputting all the reverse time wave fields into a pre-trained multi-seismic-source positioning model to obtain a plurality of explosion points; and determining the risk value of miss-fire of each blast hole in the detonation network according to the coordinate positions of all the explosion points, and performing explosion aftershock evaluation on the mine according to all the risk values. According to the scheme, the miss-fire risk of each blast hole can be evaluated after mine blasting.

The invention provides a karst cave group detection method and device in shield construction, and the method achieves the comprehensive detection of karst caves through a transient electromagnetic and sound wave array composite wave field detection mode, and improves the detection accuracy and detection efficiency of the karst cave group. And feature extraction is performed on the composite wave field detection data through a cross-physical coupling model, a karst cave probability model is established, and quantitative evaluation of a karst cave detection result is realized. When the karst cave possibly exists in the construction direction of the shield tunneling machine, the distribution prediction direction of the karst cave is further determined, and the shield tunneling machine is prevented from entering the karst cave area blindly in the tunneling process. Through the multi-source sensing array carried on the shield tunneling machine, equipment parameters during operation of the shield tunneling machine are collected in real time, potential construction anomalies can be found easily, and the accuracy and timeliness of karst cave detection are improved. Combined analysis is carried out on the composite wave field detection data and the operation parameters of the shield tunneling machine through the risk assessment model, a karst cave risk prediction value is determined, and the accuracy of karst cave risk assessment is improved.

The invention discloses an ultra-deep reflection imaging method based on passive source seismic data and a related device, and relates to the technical field of earth depth structure imaging, and the method comprises the following steps: firstly, for original continuous seismic waveform data, intercepting seismic waveform data with a first arrival of a P wave and a non-arrival of an S wave, and carrying out the preprocessing of the data, so as to obtain a far earthquake event data segment; the method comprises the following steps: firstly, analyzing and identifying a far earthquake event with a high signal-to-noise ratio by using a time-frequency-wavenumber domain, removing data which are greatly influenced by noise, and then, reconstructing a reflection wave field of a high-quality far earthquake data fragment by using a multi-dimensional deconvolution technology, and carrying out convolution on a seismic source wavelet to obtain ultra-deep reflection wave field data. And finally, performing reflection imaging processing in combination with velocity analysis and superposition imaging technologies, optimizing the analysis capability of a deep reflection interface, and finally obtaining an earth deep reflection imaging profile, so that a continuous structure interface extending from the shallow earth crust to the deep mantle can be clearly revealed, and coupling characteristics between shallow and deep earth structures can be effectively captured.

Disclosed are methods, systems, and computer programs for dynamically generating a development plan for a resource site. The methods for example, include receiving seismic data associated with a subsurface of the resource site. The seismic data may be associated with a propagated wavefield within the subsurface of the resource site and can include at least structural geological data associated with the resource site. The methods also include directionally determining a plurality of rate of change data based on the propagated wavefield within the subsurface. The methods further include executing an averaging operation using the plurality of rate of change data and thereby generate an impedance model. The impedance model may be used to generate a development plan which is then used for energy development operations at the resource site.

The invention discloses a delta-beach bar composite sand body prediction method, which comprises the following steps of S1, acquiring geological background data of a target work area, and dividing delta and beach bar deposition to obtain a beach bar deposition thickness; s2, carrying out source and sink system analysis, establishing a sediment transport channel path, and obtaining a sand body transport path plane distribution diagram; s3, the ancient landform, ancient wind field features and ancient wave field features of the target work area in the deposition period are restored, the ancient landform height and the distance to the source area of the underwater ancient protrusion are obtained according to the ancient landform, and the features include the ancient wind direction, the ancient wind path, the ancient water depth and the ancient wave height; s4, determining a favorable development zone, a favorable development layer section and a favorable development target zone of the delta-beach bar composite sand body; and S5, obtaining distribution characteristics of the delta-beach bar composite sand body of the target work area according to the favorable development zone, the favorable development layer section and the favorable development target area. According to the method, the delta-beach bar composite sand body can be accurately predicted, and technical support is provided for oil exploration and development.

The invention discloses a wave field rapid prediction method based on an Encoder-Only Transform model, and the method comprises the following specific steps: collecting and preparing a data set, and carrying out the data preprocessing; the method comprises the following steps of: constructing an Encoder-Only Transform model; initializing model parameters and sliding window method parameters; inputting the preprocessed training set data and the verification set into a built network model through a sliding window method for model training; inputting test set data into the model through a sliding window method, and evaluating the prediction capability of the model by using a test set; the parameters of the Transform model and the parameters of the sliding window method are initialized again; and the prediction precision is optimized. According to the method, the original decoder layer is removed, the training and reasoning speed is greatly improved, and the sliding window method is introduced to update historical data in time, so that the rapid prediction of the wave field is realized.

The invention discloses a least square reverse time migration method and device for a three-dimensional anisotropic medium, and the method comprises the steps: building a forward equation of the three-dimensional anisotropic medium for a seismic block to be subjected to reverse time migration, and obtaining a background wave field under the three-dimensional anisotropic medium through regional forward modeling; determining a three-dimensional anisotropic medium adjoint wave field by using a pre-established wave equation of the adjoint wave field; according to the adjoint wave field and the background wave field, determining an initial gradient by using a pre-established least square reverse time migration cross-correlation equation, and obtaining a migration result according to the initial gradient; and updating an imaging result through a conjugate gradient method according to the offset result to obtain a reflected wave record.

The invention relates to the technical field of data processing, and discloses a wind turbine generator optimization design method based on tidal current-wave-sediment coupling simulation. The method comprises the following steps: carrying out adaptive time step adjustment on marine environment data through a physical process intensity discrimination algorithm to obtain tidal current field, wave field and sediment transportation data; carrying out fluid-solid coupling calculation on the structural parameters of the wind turbine generator to obtain structural vibration response and ocean load distribution data, and predicting pile foundation scouring depth distribution; carrying out time-varying coefficient correction on the pile foundation bearing capacity parameters to obtain dynamic bearing capacity evaluation data; inputting a multi-objective optimization algorithm to obtain a Pareto optimal solution set; and obtaining a wind turbine generator optimization design scheme through engineering feasibility verification and screening. The technical problems that in existing offshore wind turbine generator design, tidal current-wave-sediment coupling simulation space-time scales are not matched, the pile foundation scouring prediction precision is insufficient, bearing capacity evaluation lacks time-varying characteristic consideration, and the multi-target optimization design capacity is insufficient are solved.

The invention discloses a concrete chloride ion erosion detection system and method. The system comprises a terahertz time-domain spectroscopy module, a laser-induced breakdown spectroscopy module and a sample bearing platform, the transmitting end of the terahertz time-domain spectroscopy module and the transmitting end of the laser-induced breakdown spectroscopy module face the sample bearing platform; the terahertz time-domain spectroscopy module is used for acquiring terahertz wave field information of a to-be-tested sample; and the laser-induced breakdown spectroscopy module is used for acquiring a spectral signal of the to-be-detected sample. In the aspect of detection precision, terahertz imaging can achieve preliminary positioning and LIBS accurate measurement, matrix interference can be avoided through cooperation of the terahertz imaging and the LIBS accurate measurement, and low-content chloride ions are accurately quantified; in a detection range, terahertz can detect an internal deeper area, an LIBS focusing surface and a near surface, and full-depth detection is realized after combination; in information acquisition, terahertz imaging provides macroscopic distribution image information, LIBS provides element content chemical information, and data are richer and more accurate due to complementation of the macroscopic distribution image information and the LIBS.

The invention relates to the technical field of wave compensation, in particular to an ocean wave compensation method and system for ship stabilization, and the method comprises the following steps: collecting omnibearing ocean wave monitoring parameters, carrying out multi-time window period division processing and wave energy density quantitative analysis, and generating the wave energy density of each time window; performing nonlinear dynamic behavior mining and dynamic sea wave digital perception modeling according to the wave energy density of each time window, and constructing a sea wave field dynamic model; multi-scale wave feature analysis is carried out based on the sea wave field dynamics model, wave trend evolution prediction is carried out, and a wave trend prediction map is constructed; and identifying current ship state parameters. According to the invention, the gravity center position is automatically adjusted according to the data monitored in real time, and the self-adaptive stability optimization of the ship attitude under different wave conditions is realized.

The invention discloses a microseismic signal denoising method fusing multi-scale features and global-local attention. The method comprises the following steps: constructing a large-scale pure microseismic signal data set based on elastic wave field simulation; carrying out noise-containing sample set generation and data standardization preprocessing in combination with real environment noise; carrying out scientific division and preparation on the noisy / pure sample pair; an MSF-GLANet layered coding and decoding network framework based on the U-Net + + topology is constructed; constructing a composite loss function which takes the Charbonier loss as a core, and combining a multi-level depth supervision strategy; performing initialization and iterative training based on a composite loss function on the MSF-GLANet network by using the training set; the model performance is monitored in real time through the verification set, and the optimal model weight is stored; a real environment microseismic signal to be denoised is preprocessed and then input into the trained model for reasoning; and performing element-by-element addition reconstruction on a residual signal predicted by the network through a residual connection mechanism to realize high-fidelity recovery of a de-noised signal.

The invention belongs to the technical field of seismic exploration, and particularly discloses an elastic wave vector seismic exploration data static correction method, which comprises the following steps: S1, establishing a longitudinal wave micro-logging surface velocity model; s2, establishing a three-component micro-logging shear wave surface velocity model; s3, performing low-frequency static correction on the longitudinal wave seismic data; s4, performing low-frequency static correction on the transverse wave seismic data; s5, performing low-frequency static correction on the converted wave seismic data; s6, calculating residual static correction values of shot points and detection points of the longitudinal wave field, the converted wave field and the transverse wave field; and S7, calculating total static correction values of shot points and detection points of the longitudinal wave field, the converted wave field and the transverse wave field. According to the method, the static correction problem of various wave field seismic data in elastic wave vector seismic exploration is effectively solved, and the calculation efficiency and the imaging precision are improved. The method is suitable for static correction of seismic exploration data.