136 results about "Waveform inversion" patented technology

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 discloses a multi-satellite network converged communication link optimization and anti-interference method, which belongs to the technical field of wireless communication networks, and comprises the following steps: acquiring Doppler frequency shift parameters of satellite nodes, adjacent satellite radiation quantity parameters and preset geographical shielding coefficients, and generating a link dynamic quality coefficient; constructing a dynamic topological graph according to the link dynamic quality coefficient; performing radio frequency fingerprint extraction and frequency domain waveform phase reversal processing on the detected interference signal to generate an anti-interference compensation signal; and when the link dynamic quality coefficient is greater than or equal to a preset quality threshold value, selecting a target transmission path according to the dynamic topological graph, and injecting the anti-interference compensation signal into the target transmission path. According to the invention, a mechanism of cooperation of dynamic link quality evaluation, interference fingerprint feature matching and multi-path fragmentation transmission is adopted, and real-time optimization selection of communication paths, accurate suppression of interference signals and efficient distribution of network resources can be realized in a complex air and space environment.

The invention belongs to the technical field of seismic exploration, and discloses a robust full-waveform inversion method, system and device based on generative modeling, and a medium, and the method comprises the steps: obtaining seismic observation data; the method comprises the following steps: starting from random Gaussian noise, establishing a model space comprising a plurality of initial velocity models through an unconditional score model, and determining a global optimal initial velocity model of the model space by adopting a strategy search algorithm; de-noising is carried out on the global optimal initial velocity model based on back diffusion, seismological observation data are introduced as observation constraints, the velocity model is updated by calculating the mismatch gradient of forward modeling data of the current velocity model and the seismological observation data, and an implicit condition sample is obtained; performing forward diffusion processing on the implicit condition sample to obtain a speed model for a subsequent annealing time step; and when the annealing process reaches a preset condition, outputting a final speed model. According to the method, the robustness and accuracy of full-waveform inversion are improved, and the bottleneck problem of traditional full-waveform inversion is solved.

The invention discloses a sound wave full waveform inversion method and system based on optimization effective boundary storage, and the method comprises the steps: constructing an initial geological velocity model, and obtaining seismic wave parameter information; performing forward modeling according to the initial geological velocity model and the seismic wave parameter information to obtain seismic simulation data and forward seismic source wave field data; acquiring actual seismological observation data of the geologic model to be measured, and constructing an objective function according to the actual seismological observation data and the seismic simulation data; re-sampling the wave field data of the target area according to the forward seismic source wave field data; restoring the resampled wave field data of the target area to an original data state by using an interpolation method, and then reconstructing a forward transmission seismic source wave field; zero-delay cross-correlation is carried out according to the reconstructed forward transmission seismic source wave field and the reconstructed backward transmission residual wave field to obtain a gradient; and carrying out iterative updating on the target function by adopting a gradient optimization algorithm until a final geological velocity model is output.

The invention provides a Bayesian earth sound parameter inversion method. The method comprises the following steps: acquiring earth sound observation data and earth sound synthesis data; constructing a plurality of KRAKENC earthquake sound forward modeling models corresponding to the hierarchical model of the earthquake sound observation data; according to the earth sound observation data and the earth sound synthesis data, adopting a Wasserstein measurement form to determine the index likelihood distribution of a plurality of KRAKENC earth sound forward modeling models; according to the selected index likelihood distribution and prior distribution, calculating posterior probability distribution of the geoacoustic observation data by using a Bayesian algorithm; and carrying out random sampling processing on the posterior probability distribution by using a Markov chain Monte Carlo simulation algorithm to obtain random sampling data points, calculating sampling expectation values corresponding to the random sampling data points, and determining a Bayesian earth sound inversion result corresponding to the earth sound observation data according to the sampling expectation values. The accuracy of sedimentary layer attenuation inversion is improved, and the uncertainty of waveform inversion is remarkably reduced.

The invention relates to the technical field of tunnel engineering, and discloses a tunnel earthquake three-dimensional full-waveform inversion method, which comprises the following steps: S1, establishing a three-dimensional initial velocity model: establishing the three-dimensional initial velocity model based on a common reflection point migration method of ellipsoid expansion; s2, forward modeling three-dimensional full-space wave field characteristic analysis: performing forward modeling in the established typical unfavorable geologic body model, setting a seismic source point on a tunnel wall or a tunnel face, setting a detection point on the tunnel wall, and analyzing three-dimensional full-space wave field signal characteristics received at the positions of the tunnel wall and the tunnel face; s3, full-waveform inversion numerical simulation: realizing a full-waveform inversion numerical model experiment on the basis of the established initial velocity model to obtain simulation data; s5, performing three-dimensional full-waveform inversion imaging; and S6, result verification. The tunnel earthquake three-dimensional full-waveform inversion precision can be improved, and the tunnel earthquake three-dimensional advanced prediction method which is low in cost, safe, efficient and high in precision is achieved.

The invention provides a shale reservoir fracture prediction method and system based on three-dimensional full-waveform inversion and a medium, and relates to the technical field of data analysis, and the method comprises the steps: arranging a multi-channel seismic exploration instrument in a shale reservoir region, and collecting full-waveform seismic data, including longitudinal waves, transverse waves and reflected waves; performing three-dimensional full-waveform inversion based on the logging data, the geological structure map and the full-waveform seismic data to generate a shale reservoir attribute model; a multi-scale inversion strategy is constructed, fracture sensitivity analysis and fracture form dynamic prediction are carried out, and reservoir fracture distribution characteristics and reservoir fracture form characteristics are obtained; discrete fracture point connection and fracture network reconstruction are carried out, a reservoir three-dimensional fracture network model is constructed, and fracture prediction visualization display is carried out. The technical problems that in the prior art, only inference based on limited data can be provided, comprehensive capture of a complex fracture network is lacked, distribution and form of fractures cannot be comprehensively and accurately described, and reservoir analysis and mining decisions are affected are solved.

The invention belongs to the technical field of tunnel advanced detection, and particularly relates to a tunnel earthquake advanced detection waveform inversion method and system, and the method comprises the steps: carrying out the forward simulation according to an initial speed model and an earthquake source, and obtaining a simulated seismic record of a simulated seismic wave; establishing an objective function by minimizing the difference between the observed seismic data and the simulated seismic record; and performing inversion optimization on the target function by adopting an enhanced whale optimization algorithm, and updating the initial velocity model through repeated iteration until the inversion reaches the minimum value of the target function, so that the problem that the inversion precision is reduced along with the increase of the problem dimension in the tunnel space waveform inversion is solved, and the tunnel space waveform inversion accuracy is improved. According to the method, the global search and local development capabilities are improved, and the inversion precision and the convergence speed are improved.

The embodiment of the invention provides a full waveform inversion method based on surface wave dispersion inversion driving, a control device and a storage medium, and belongs to the technical field of seismic inversion. The full-waveform inversion method comprises the following steps: acquiring a Rayleigh surface wave observation signal through a preset detection point according to a three-station interference method; according to one-dimensional surface wave frequency dispersion inversion, through the obtained Rayleigh surface wave observation signal, obtaining a corresponding one-dimensional shear wave velocity representing a central area of the detection point pair; constructing a corresponding three-dimensional velocity model based on the one-dimensional shear wave velocity body; and performing full-waveform inversion on the three-dimensional velocity model to obtain a full-waveform inversion result of the region. According to the embodiment of the invention, aiming at passive source seismic signals, based on a three-station interference method, the high-signal-to-noise-ratio Rayleigh surface wave signals among the one-dimensional linear stations are extracted and enhanced, the surface wave signals are used as the input of the whole inversion process, the inversion result is not influenced by background noise quality, and the precision is higher.

The invention discloses a post-raining side slope seepage characteristic testing method based on waveform inversion of nano-iron concentration distribution, and belongs to the field of seepage characteristic determination in foundation pit side slope stability monitoring, the method comprises the following steps: spraying a mixture of nano-iron particles and a surfactant into cumulonimbus to make nano-iron permeate into a side slope soil body along with rainfall; a radio frequency coil is arranged in the orthogonal direction of the selected section of the slope, an electromagnetic field is applied to excite nano iron resonance, and electromagnetic wave signals are collected; dividing the selected section of the slope into a plurality of grid units, and inverting the concentration distribution of the nano-iron in each grid unit by using an iterative optimization algorithm based on the electromagnetic wave signal to obtain an inversion result; and generating a seepage cloud picture based on the inversion result and calculating the seepage speed. According to the method, economic loss caused by large-area laying of nano-iron and interference to follow-up monitoring are avoided, the seepage position and size are determined, and complete and clear information is provided for monitoring seepage changes in slope engineering.

The invention relates to the technical field of ground penetrating radar data processing, in particular to a ground penetrating radar fusion inversion fine imaging method, medium and equipment, and the method comprises the steps: scanning a certain detection region through a ground penetrating radar, and obtaining an original GPR data profile; analyzing wavefront continuity of the GPR data profile, and introducing a correction factor to carry out lossless separation on the GPR data profile to obtain a reflection data profile and a diffraction data profile; constructing an inversion initial model based on the hyperbolic characteristics of the diffraction data profile; constructing an inversion objective function based on the reflection data profile and the diffraction data profile; and taking the inversion initial model as an inversion initial solution, and solving an optimization problem of an inversion objective function by adopting an L-BFGS algorithm to obtain a diffracted wave-reflected wave fusion imaging result. According to the method, the imaging effect and precision of the ground penetrating radar processing result on the tiny diffraction anomalous body can be effectively improved, the inversion imaging performance is optimized, and the practicability of ground penetrating radar fine imaging and full-waveform inversion imaging is promoted.

The invention belongs to the technical field of seismic exploration and artificial intelligence, and relates to a physical perception collaborative fusion-based full waveform inversion neural network construction method, which comprises the steps of required model data construction, full waveform inversion neural network construction, loss function construction, network pre-training, and network fine tuning and application. Physical feature perception and collaborative fusion are carried out through a U-Net network and an adaptive residual learning module, so that the problem that traditional full-waveform inversion depends on synthetic data training and is not matched with an actually measured data field is solved. The designed pre-training strategy not only can get rid of dependence on a large-scale synthetic data set, but also can effectively relieve the domain offset problem through fine adjustment of measured data, thereby improving the generalization ability of the model. And the adaptive residual learning module enables the neural network to adaptively and dynamically optimize feature expression, and improves inversion precision through collaborative fusion of a physical perception mechanism. According to the method, the physical consistency and imaging reliability of speed model prediction can be remarkably improved while the labor cost is reduced.

The invention discloses a power transmission line lightning stroke current waveform inversion method and device, terminal equipment and a storage medium, and belongs to the field of electric pulses. The method comprises the steps of obtaining electrical parameters of a to-be-processed power transmission line and fault waveform data of a plurality of detection points; constructing a lossy transmission line equivalent circuit according to the electrical parameters and determining a telegraph equation; replacing a space differential in the telegraph equation with a central difference, and replacing a time differential in the telegraph equation with a forward difference to determine a lightning wave propagation model; inputting the fault waveform data into a lightning wave propagation model, and traversing each detection point according to a preset time step to obtain intermediate waveform data; the fault waveform data comprises a first voltage parameter and a first current parameter of the head end; the intermediate waveform data comprises a second voltage parameter and a second current parameter of the tail end; and determining the original waveform of the lightning stroke current based on the intermediate waveform data and a preset shunting coefficient. By implementing the invention, the problem that the real lightning waveform during the lightning stroke fault is difficult to accurately obtain in the prior art can be solved.

The invention discloses a full-waveform inversion method for enhancing boundary perception. The full-waveform inversion method comprises the following steps: acquiring seismic data; constructing a seismic waveform inversion network model; the seismic waveform inversion network model comprises an encoder, a decoder and a jump connection; carrying out the size adjustment of the seismic data through an encoder, and then extracting the advanced features of the reserved low-frequency information, and obtaining a multi-scale coding feature map; inputting the multi-scale encoding feature maps extracted by the encoder into different levels of the decoder by utilizing jump connection; a decoder is used for obtaining distinguishing information of the spatial relation contained in the multi-scale coding feature map, and a prediction speed model is generated; multi-scale analysis is executed through a wavelet convolution layer in an encoder, and low-frequency features are enhanced; then, distinguishing information is extracted by utilizing the spatial relationship of the features through a spatial attention module in a decoder so as to improve the resolution; finally, a reflection coefficient is introduced into the loss function to emphasize punishment on the low-contrast boundary; therefore, the full-waveform inversion precision is improved.

In view of the problems and the like of the prior art, the purpose of the present invention is to provide a method for imaging body tissue rapidly and robustly using waveform inversion from ultrasound examination data.　The method for imaging body tissue rapidly and robustly using waveform inversion from ultrasound examination data according to the present invention comprises: (a) a step for acquiring ultrasound examination data; (b) a step for generating one wave field by means of a plurality of computers (CPUs); (c) a step for integrating squared errors (which constitute fc in expression (5)) from all CPUs, each squared error being the error between a waveform recorded at a reception point from the wave field and an observed waveform obtained in step (a); (d) a step for re-dividing a correction gradient and a pseudo Hessian matrix into regions with an equal number of CPUs in the x-direction; (e) a step for re-dividing from the uniform regions in the x-direction into regions with an equal number of CPUs in the y-direction; (f) a step for calculating a step length required for model updating in full-waveform inversion using a quasi-Newton method while maintaining the division of regions in the x-direction; and (g) a step for repeating the steps (b) to (f) to obtain imaging of a viscosity Q value in addition to a sound wave propagation velocity. Expression 5: Here, fc (scalar quantity) is a cost function representing the sum of squared residuals between the observed data (d_obs) and the synthetic data (d_syn) calculated from the initial model at all times of all reception points, and Ns, Nr, Nt are respectively the number of transmission points, the number of reception points, and the number of time samples.

The invention discloses a full-waveform inversion detection method suitable for internal fractures of coal and rock mass.The method comprises the steps that observation data of a seismic source are obtained, then an objective function is constructed, synthetic wave field data are obtained through calculation, and the gradient of velocity field model parameters is obtained through solving with a Lagrange adjoint method; the method is advantaged in that an accurate updating direction of velocity field model parameters can be acquired, finally, the velocity field model parameters are iteratively updated through the deep convolutional network until the maximum training frequency or the objective function value satisfies the precision requirement, and a characterization result of the deep convolutional network model is outputted, namely, a final inversion model parameter is acquired. Carrying out full-waveform inversion by adopting a velocity field model of the parameter; according to the method, accurate inversion modeling and internal fracture accurate detection can be performed on the coal and rock mass model through the steps, and the effective boundary strategy is adopted to replace the memory consumption with the calculated amount, so that the memory consumption is reduced, and data support is provided for subsequent mining.

Disclosed herein is a method for facilitating waveform inversion using a Sifr optimizer, in accordance with some embodiments. The method includes receiving an observed data from a device. The method includes obtaining an initial model. The method includes obtaining a synthetic data from the initial model. The method includes determining a discrepancy between the observed data and the synthetic data. The method includes creating a cost function based on the determining of the discrepancy. The method includes updating the initial model iteratively using the Sifr optimizer until a condition is met. Further the Sifr optimizer provides an update for the updating of the initial model iteratively based on a regression typically a least squares resolution of a Sifr equation. The method includes generating a final model based on the updating. The method includes storing the final model.

The invention discloses a longitudinal wave data denoising and waveform inversion imaging method for acoustic logging remote exploration, and the method comprises the following steps: inputting original acoustic logging data, and carrying out the data conversion; inputting the converted data into a pre-trained network to obtain a signal mask and a noise mask; calculating a recovered time-frequency domain signal, converting the recovered time-frequency domain signal to a time domain, and carrying out denoising and frequency domain conversion; inputting initial longitudinal wave velocity data into the longitudinal wave velocity model, and constructing a discrete Helmholtz equation to obtain a forward modeling wave field and forward modeling prediction data; calculating a residual error between the forward modeling wave field data and the observation wave field data, and constructing a discrete dual Helmholtz equation to obtain a dual wave field; gradient information and iteration quantity are calculated respectively, and iteration is carried out on the longitudinal wave velocity model; and checking whether a convergence standard is reached. According to the method, reliable underground structure information can be provided for exploration and development of petroleum and natural gas, and powerful technical support can be provided for accurate and safe resource development.

The invention is suitable for the technical field of ground penetrating radar exploration, and provides a CUDA (Compute Unified Device Architecture) and PyTorch mixed compiled ground penetrating radar rapid full waveform inversion method, a PyTorch automatic differential function is self-defined by innovatively carrying out joint compilation on PyTorch and CUDA kernel functions, and a PyTorch platform is combined with CUDA mixed programming to realize automatic updating of two parameters of dielectric constant and conductivity. According to the method, GPU parallel calculation is used, the calculation efficiency of the ground penetrating radar data full-waveform inversion technology is remarkably improved, a two-parameter model can be quickly inverted, and the overall calculation efficiency is improved. The improvement of the efficiency is of great significance to the improvement of the overall effect of ground penetrating radar exploration and the practical application in the fields of urban underground space detection, road maintenance and the like.

Seismic data is processed using a full-waveform inversion using a model with a pseudo-δ layer. The presence of the pseudo-δ layer in the model enables handing the difference at the water bottom between acoustically generated synthetic data and seismic data that corresponds to an elastic medium. The pseudo-δ layer may be less than 100 m thick and / or may be located directly underneath the water bottom. The pseudo-δ layer may have a negative value for S and a null value for ϵ (δ and ϵ being Thomsen's anisotropy parameters).

The invention discloses a pre-stack waveform inversion method based on a viscoelastic layered medium, which relates to the field of geophysical exploration of petroleum, and comprises the following steps: carrying out forward modeling based on a viscoelastic reflection matrix method, and accurately simulating the propagation characteristics of seismic waves in the viscoelastic layered medium, thereby effectively improving the inversion prediction precision. According to the method, the elastic characteristic and the attenuation characteristic of the stratified medium are considered at the same time in the seismic wave propagation process, the P-wave speed, the S-wave speed, the density, the P-wave quality factor and the S-wave quality factor can be inverted at the same time, the precision of the inversion result is improved through fine simulation of the underground reservoir medium, and the method has the advantages of being high in practicability and the like. The multi-parameter inversion method has the advantages that strong support is provided for reservoir prediction, a Bayesian inversion framework is adopted, a multi-parameter inversion process is optimized by fusing prior information, robustness and noise immunity of the inversion method are improved, and stability and accuracy of the multi-parameter inversion method in actual seismic data application are guaranteed.

The invention discloses an intelligent exploration data analysis system and method, and relates to the technical field of geological exploration, and the system comprises a test module, an analysis module and a prediction module. According to the method, geological electromagnetic and stratum original data are collected in a multi-dimensional mode, forward modeling and inversion are carried out on electromagnetic data, the relation between underground conductivity and an electromagnetic field can be accurately simulated, data errors are effectively reduced, the electromagnetic data processing accuracy is improved, a geological unit calculates a seismic wave field based on wave equation numerical simulation, and the electromagnetic data processing accuracy is improved. The prediction wave field and the observation wave field are substituted into an objective function, the stratum velocity and density are accurately inverted, the geological data processing precision is guaranteed, the joint unit organically combines an electromagnetic inversion objective function, an earthquake full-waveform inversion objective function and a parameter correlation constraint term through a joint optimization formula, multi-source data depth fusion is achieved, and the geological data processing precision is guaranteed. The limitation of single data processing is avoided, and the underground geological condition is reflected more comprehensively.

A method for generating an image comprises providing an imaging device comprising a multi-channel radiofrequency data acquisition system comprising a plurality of receive elements and a transmission system, transmitting a signal pulse including content at first and second high frequencies from the transmission system, recording a time-domain channel waveform from each receive element in the multi-channel radiofrequency data acquisition system, calculating a set of frequency-difference signals for each receive element based on the first and second high frequencies, and applying a frequency-difference waveform inversion (FDWI) to the set of frequency difference signals to generate an image. An imaging system is also disclosed.

The invention provides a cross-correlation correction full-waveform inversion ultrasonic tomography method based on source coding. The method comprises the following steps of: 1, acquiring an actually measured sound pressure signal, setting an initial sound velocity model and the number of iterations of each stage, and initializing the number of iterations; step 2, calculating travel time difference between a prediction signal dm (xn, t) and a measured signal gm (xn, t) in the first iteration of each stage based on the initial sound velocity model in the step 1 and the number of iterations of each stage, generating an intermediate signal of the stage based on travel time, and not updating the travel time difference in the stage except the first iteration; 3, coding the intermediate signal and the prediction signal based on the intermediate signal and the prediction signal generated in the step 2; 4, calculating an adjoint wave field and gradient based on the intermediate signal and the prediction signal coded in the step 3, determining the step length of sound velocity model iteration, and completing model updating; and 5, obtaining a final model. According to the method, the stability of a traditional FWI-based ultrasonic tomography algorithm is improved while the calculated amount is not remarkably increased.

The invention belongs to the field of ocean vibroseis data imaging, and particularly relates to an ocean vibroseis data full-waveform inversion method for Doppler distortion, which comprises the following steps of: constructing a time-varying space offset operator # imgabs0 # for describing detector motion and a non-linear modeling operator # imgabs1 # which is based on a frequency sweeping-segmentation method and is used for describing focus motion; deploying a time-varying space offset operator for describing the motion of a detector and a nonlinear modeling operator which is used for describing the motion of a seismic source and is based on a frequency sweeping-segmentation method into a least square fitting frame, and forming a mismatch function; and iteratively solving the gradient of the velocity model parameter in the mismatch function, and obtaining an imaging result subjected to phase correction in the iteration process, thereby realizing the inversion of the ocean vibroseis data aiming at Doppler distortion. Effects can be achieved on OBN or towing cable ocean vibroseis data sets with Doppler distortion.

The invention relates to the technical field of elastic waves, and discloses an elastic wave full-waveform inversion method, which comprises the steps of inputting observed seismic data into a preset neural network encoder, and extracting to obtain high-dimensional feature representation; inputting the high-dimensional feature representation into a preset neural network decoder to obtain a gradient update value; obtaining an elastic parameter model based on the gradient update value and the initial elastic parameter model; obtaining Lame parameters based on the longitudinal wave velocity, the transverse wave velocity and the medium density; based on a Lame parameter and an elastic wave equation, obtaining synthetic seismic data; obtaining an objective function based on the observed seismic data and the synthesized seismic data; using the target function to iteratively update the preset neural network weight until the loss function reaches a first preset threshold value or the number of iterations reaches a second preset threshold value, and obtaining a target gradient update value; and based on the target gradient update value and the target elastic parameter model, obtaining a final target elastic parameter model so as to relieve discomfort caused by simultaneous inversion of multiple elastic parameters.

The invention relates to a biological tissue full-waveform inversion ultrasonic tomography method which comprises the following steps: placing ultrasonic transducers in a pure water medium with known sound velocity, and recording ultrasonic signal data of each transducer array element; setting a virtual source array in each transducer array element, constructing an optimization problem of sound source directivity and receiver response, and performing iterative optimization to obtain a final response coefficient and a virtual source signal of each transducer array element; the method comprises the following steps: fixing a biological tissue to be detected in an ultrasonic transducer, and collecting experimental observation signals received by each transducer array element; setting a sound velocity model to carry out forward simulation, taking the obtained virtual source signal as a sound source output, interacting with the sound velocity model, and multiplying by a response coefficient to obtain a forward simulation signal; and performing iterative updating on the sound velocity model according to the experimental observation signal and the forward simulation signal to obtain an ultrasonic tomography result. Compared with the prior art, the method has good flexibility and anti-noise capability, and the quality of the reconstructed image is remarkably improved.

The invention discloses a guided wave detection and quantitative evaluation method for submarine pipeline routing deviation. The method comprises the steps that S1, an excitation end module periodically injects stable guided wave signals into a pipeline; s2, an annular receiving module for synchronously receiving signals in the whole annular direction is installed at a terminal of the pipeline, and guided wave signals propagated for a long distance are accurately captured; s3, filtering and enhancing the originally collected noisy signals; s4, performing time-frequency analysis and waveform inversion on the received signal, and respectively extracting: calculating the equivalent curvature radius of the offset section according to the propagation velocity change; extracting phase lag information according to the phase change, and calculating a vibration path increment; calculating the overall transmission loss according to the amplitude attenuation degree; and S5, establishing a comprehensive identification criterion fusing the three types of feature parameters. The low-attenuation long-distance propagation characteristic of the L (0, 1) mode is utilized, the excitation device and the receiving array are arranged at the two ends of the pipeline, and online periodic remote monitoring under the non-stop production state is achieved.

The invention provides a radar intra-pulse modulation signal waveform inversion method and device, and relates to the technical field of radar signal processing, and the method comprises the steps: obtaining a signal parameter; generating an intermediate frequency signal for the signal parameter according to the signal generation function, and performing short-time Fourier transform on the intermediate frequency signal to generate a time-frequency diagram; respectively calculating a time range and a frequency range based on waveform parameters of the intermediate frequency signal, and respectively normalizing the time range and the frequency range into a coordinate range of a time-frequency diagram to obtain a detection label; according to the target detection model, training the time-frequency diagram through detection labels, and carrying out positive and negative sample distribution based on a Hungary matching strategy to obtain an improved target detection model; performing unknown parameter target detection on the time-frequency graph based on an improved target detection model to obtain a detection result; and performing inversion processing on the intermediate frequency signal based on the detection result to obtain an inversion result. According to the invention, the problem that the recognition accuracy is greatly reduced is solved.

The invention belongs to the technical field of seismic exploration, and relates to a strong interference seismic data seismic source function inversion method based on transverse local regularization, and the method comprises the steps: applying transverse local regularization constraint in a seismic source function waveform inversion process to guarantee the transverse continuity and precision of an inversion result; the nonlinearity of inversion and the degree of dependence on a speed model are reduced by using near-channel short-time data, and the overall calculation efficiency of the algorithm is ensured; seismic source function inversion is carried out under a full-waveform inversion framework, and an inversion result theoretically has the advantages of high precision and high resolution; the advantages of a cross-correlation objective function and a transverse local regularization algorithm are combined, a high-quality seismic source function inversion result can be obtained under the condition of strong interference, the transverse continuity of the inversion result is enhanced by utilizing mutual constraint between adjacent seismic source functions, and the problem of dependency of an initial seismic source is overcome to a large extent. According to the method, a seismic source function inversion result with high precision and good transverse consistency can be stably obtained under the condition that strong interference exists in seismic data.