73 results about "Waveform inversion" patented technology

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 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 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 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 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.

The invention discloses a converter valve current waveform inversion method and apparatus, and an equipment medium. The method comprises the steps of obtaining a three-phase alternating current on a converter valve side; performing variational mode decomposition on the three-phase alternating current to obtain a plurality of mode components corresponding to each phase of alternating current; inputting a plurality of modal components of each phase of alternating current into a multi-branch MIMO network for current inversion to obtain a current waveform of each valve in the converter; the multi-branch MIMO network comprises a spatial feature extraction branch, a time sequence feature extraction branch and a first feature fusion module. The modal component of each phase of alternating current is input into the multi-branch MIMO network for current inversion, rich spatial feature information contained in the three-phase alternating current is considered, the time sequence features of the three-phase alternating current are also considered, the features of the three-phase alternating current are deeply excavated to obtain more accurate valve current waveforms, and the accuracy of valve current inversion is improved. Therefore, the operation state of the converter can be monitored more effectively.

The invention belongs to the technical field of underground engineering geophysical exploration, and provides a transverse inhomogeneous medium front and back traveling wave separation and waveform inversion method and system, and the method comprises the steps: obtaining an observation waveform and a forward modeling waveform; carrying out Tau-p conversion on the observation waveform and the forward modeling waveform, extracting a target wave field in a set range from the data after Tau-p conversion, extracting backward traveling wave information, and then carrying out inverse conversion to obtain a time domain wave field; performing normalization processing on the time domain wave field, calculating an L2 norm between observation data and forward modeling data after processing as a slave objective function, taking an L2 norm between a forward modeling waveform and an observation waveform as a master objective function, and taking a comprehensive objective function fused by the two objective functions as a final objective function, and performing iterative inversion; and judging whether the iterative inversion meets a termination condition or not. According to the invention, high-resolution imaging results with clear boundaries and controlled artifacts can be obtained in a complex environment.

The invention discloses a focused sound field generation system and method based on three-dimensional transcranial ultrasonic imaging, relates to the technical field of ultrasonic imaging and sound focusing, and solves the technical problem that significant spatial deviation exists between a focusing focus and a target spot. A three-dimensional full-waveform inversion and / or tomographic inversion algorithm is adopted to jointly invert parameters such as sound velocity, attenuation and density of the skull and the brain tissue, and an individualized three-dimensional acoustic atlas is constructed. And on the premise of meeting the safety threshold parameter, performing multi-target optimization on the array element phase, amplitude, frequency band and emission time sequence of the multilayer ring array according to the three-dimensional acoustic atlas to obtain an individualized focusing parameter and an acoustic dose parameter for the predetermined brain region. Individualized modeling and imaging-regulation closed loop integration based on ultrasonic self-imaging are achieved, and low-cost, repeatable and real-time three-dimensional transcranial imaging and accurate nerve regulation guidance can be provided in a bedside environment.

The invention discloses a rapid inversion method based on a radar electromagnetic model in a high-cold and high-altitude geological environment, and the core idea of the method is that a two-parameter wave field forward modeling model is constructed based on a Maxwell electromagnetic field equation, a time window-based convolution type objective function is utilized, and a real source function is not needed. Source-independent full-waveform inversion of single-offset GPR data is realized, an SVD method is introduced to automatically extract a reference waveform and determine the length of a time window, direct wave and reflected wave information is effectively separated, and the inversion calculation overhead is remarkably reduced in cooperation with a multi-scale frequency and random sub-source sampling strategy. According to the method, high-precision reconstruction can be performed on the underground dielectric constant and the conductivity on the premise that accurate source waveforms are not needed, and the practicability and the real-time performance of plateau area non-contact radar detection are remarkably improved.

This application proposes a full-waveform inversion method and apparatus based on local rapid matching decomposition. According to this application, accurate subscaled seismic data can be obtained through local rapid matching seismic data decomposition, with the advantages of high time-frequency resolution, no aliasing, low computational effort, and low storage requirements. Furthermore, the subscaled seismic data is used for step-by-step full-waveform inversion, enabling the inversion of velocity models from large to small scales, avoiding the problem of full-waveform inversion easily falling into local extremes, and obtaining a high-precision velocity model.

The invention discloses a construction method, system, equipment and medium of an oil and gas reservoir velocity-temperature model, and particularly relates to the technical field of model construction, and the technical key points are as follows: obtaining environmental thermodynamic parameters of an underground oil and gas reservoir medium and sound pressure components of seismic waves; constructing a thermal-acoustic coupling medium seismic wave propagation model based on an elastic theory and an acoustic approximation theory; based on a thermal-acoustic coupling medium seismic wave propagation model, forward modeling is carried out by using a staggered grid finite difference method, and a simulated sound pressure component of a seismic wave is obtained through simulation; acquiring an actual seismic sound pressure component, and constructing a target function by minimizing an error between the actual seismic sound pressure component and the simulated sound pressure component; implementing full-waveform inversion by using a gradient descent method, and solving an objective function to obtain an optimal model parameter; and performing parameter updating on the thermal-acoustic coupling medium seismic wave propagation model by using the optimal model parameters to obtain an updated oil and gas reservoir velocity-temperature model.

The invention discloses a tunnel face and borehole geological radar combined advanced forecasting detection method and system. The method mainly comprises the steps that a tunnel face and drilling geological radar integrated host is developed, joint collection of a tunnel face and a drilling radar is achieved, a ground penetrating radar of the tunnel face is used for transmitting electromagnetic waves, an antenna on one side of the drilling radar is used for transmitting the electromagnetic waves, and an antenna on the other side of the drilling radar and the ground penetrating radar of the tunnel face receive the electromagnetic waves. The tunnel face ground penetrating radar and drilling radar combined observation system is designed, all-directional and multi-angle ray coverage is achieved, and the observation data volume is increased; deriving calculation formulas such as tunnel ground penetrating radar forward modeling, an adjoint state method, a target function, an error inverse function and the like to realize tunnel ground penetrating radar full-waveform inversion; and quasi-three-dimensional fine detection imaging of unfavorable geologic bodies such as karst caves and broken bodies in front of the tunnel face can be realized.

The application discloses an ultrasonic CT waveform inversion method and device based on a dynamic aperture, and belongs to the technical field of ultrasonic image processing, and comprises the following steps: acquiring an observed sound pressure signal at a position of a receiving element of an ultrasonic CT data acquisition system; inputting a current sound velocity distribution into a wave equation to obtain a synthesized sound pressure signal at the position of the receiving element; for each transmitting element, extracting the synthesized sound pressure signals at the positions of M receiving elements away from the transmitting element; calculating the difference between the synthesized sound pressure signals at the positions of the M receiving elements corresponding to all the transmitting elements and the corresponding observed sound pressure signals; calculating the gradient of the current sound velocity distribution by using an adjoint state method according to the difference, and then obtaining an updated sound velocity distribution; re-substituting the updated sound velocity distribution into the wave equation for iteration, and simultaneously increasing the value of M until the iteration is stopped, and outputting the updated sound velocity distribution. The application can improve the accuracy of a reconstructed image under the condition of missing low-frequency information.

The application discloses a rock burst global monitoring and dynamic-static near and far field cooperative early warning method and system, and the early warning method comprises the following steps: based on the far field generation and propagation mechanism of dynamic load stress wave, determining the key monitoring area of the stope space and the stress wave type, delineating the high stress area of the working face surrounding rock and analyzing the structure characteristics of the rock stratum; setting a microseismic sensor in the far field key area, obtaining parameters such as the source rupture size, energy and magnitude through waveform inversion; arranging an acoustic-electric system in the near field high stress area, dynamically monitoring the coal rock rupture characteristics and quantitatively analyzing the static load energy storage state of the surrounding rock. Combined with the stress wave propagation attenuation coefficient, the near and far field fusion early warning criterion under the superposition action of dynamic and static energy is established, and through the cooperative analysis of multi-source monitoring data, the rock burst advanced precise early warning is realized. The application proposes a disaster gestating space global monitoring technology fusing seismic, acoustic and electric, considers the chain evolution disaster-causing process of dynamic load generation, cross-space propagation and working face surrounding rock damage, and is helpful to significantly improve the accuracy of rock burst early warning.

Provided is a gate drive circuit, including a plurality of shift registers and buffers in cascade, wherein each of the shift registers is configured to output a first gate scanning signal stage by stage according to a preset scanning timing; and each of the buffers is configured to perform waveform inversion on the first gate scanning signal for a plurality of times to convert the first gate scanning signal into a second gate scanning signal, wherein falling edge time of the second gate scanning signal is less than falling edge time of the first gate scanning signal.

This invention discloses a knowledge-guided method for separating P-waves and S-waves in complex geological structures, applied to the field of seismic data processing. It addresses the problem that existing technologies struggle to effectively separate P-waves and S-waves in complex geological structures. First, this invention utilizes a recurrent neural network for elastic wave full-waveform inversion to obtain knowledge representations of P-waves and S-waves in complex structures. Second, using these knowledge representations, a dual-branch autoencoder network is constructed. One branch separates P-waves under the guidance of the P-wave knowledge representation, while the other branch separates S-waves under the guidance of the S-wave knowledge representation. Finally, an autoencoder network architecture is constructed that comprehensively constrains wavefield reconstruction error, P-wave knowledge-guided error, and S-wave knowledge-guided error. Theoretical analysis and numerical calculations demonstrate that the proposed method can effectively separate P-waves and S-waves in complex geological structures, reducing dependence on data samples and exhibiting a certain degree of robustness.

This invention relates to a tunnel full-waveform inversion method based on spectral element forward modeling and physical information neural network, comprising: collecting engineering geological data and obtaining physical property parameters; establishing a two-dimensional finite element model and obtaining spatial information; constructing a wavefield model and performing wavefield forward modeling simulation to obtain synthetic full-waveform data; collecting measured waveform data and preprocessing it, performing time registration and normalization processing on the preprocessed measured waveform data and synthetic full-waveform data, and extracting multiple features; constructing a parameter training set; constructing and training a physical information neural network model, constructing a comprehensive loss function during the training process; and using the physical information neural network model to perform full-waveform inversion. This invention deeply integrates finite element forward modeling, full-waveform inversion, and physical information neural network, taking into account both data fitting and physical consistency, significantly improving the quantitative accuracy and reliability of tunnel advanced geological prediction.

This invention discloses a high-precision three-dimensional elastic wave full-waveform inversion method. First, several discrete frequency points are selected from the observation data and processed using a conventional band-limited wavelet source to form a new source. Then, the forward and reverse propagating wave fields are obtained using the new source, and a gradient is constructed using Lamé parameters as model parameters. Finally, the L-BFGS method, which has second-order convergence, is used to calculate the inverse of the Hessian matrix and continuously update the initial velocity field model, obtaining the final velocity field model parameters, which represent the high-precision three-dimensional imaging of the desired region. This method enhances the energy of low-frequency signals during inversion, effectively increasing the contribution of low-frequency signals to the low wavenumber information of the model, thereby effectively reducing the dependence of the inversion on the initial velocity field model and improving the inversion accuracy, achieving the goal of high-precision inversion.

The present invention relates to a method, system, device and medium for simulating Rayleigh surface waves of a high-speed railway earthquake source, and belongs to the technical field of numerical simulation of earthquake waves. A high-speed train is a moving line source in a fixed direction. Based on the train moving load model, the loads applied by the four pairs of axles of each carriage to the sleepers under the track are taken as point sources, and the moving line source is decomposed into a superposition of a series of point sources. Considering the elastic effect of the track rails, a time function of the high-speed railway earthquake source is constructed based on the Euler-Bernoulli elastic beam theory. Considering the absorption and attenuation of seismic waves by the underground medium, the staggered grid finite difference method is used to solve the Kelvin viscoelastic wave equation, and at the same time, considering the free surface boundary, the wave field of the earthquake Rayleigh surface wave excited by the high-speed railway moving earthquake source is numerically simulated. It provides the basis and data for the subsequent waveform inversion based on the high-speed railway earthquake source and the inversion of the high-speed railway earthquake source surface wave dispersion curve.

The application provides an anchor cable slip risk prediction method and system based on deep learning, and relates to the technical field of risk monitoring.The method comprises the following steps: filtering and denoising a time sequence vibration waveform of a target object to obtain an effective waveform segment; performing waveform inversion on the effective waveform segment to obtain event space features, and sequentially encoding the event space features to obtain an event joint vector; performing prototype clustering on the event joint vector to obtain a common mode of the event joint vector, and performing adaptive prototype mapping on the common mode to obtain a prototype memory matrix of the event joint vector; performing similarity aggregation on a current vibration signal of the target object to obtain a risk representation of the current vibration signal; quantifying a critical state risk of the risk representation to obtain a slip risk index; and grading the slip risk index to obtain a slip risk grade of the current vibration signal.The application can improve the efficiency of the anchor cable slip risk prediction based on deep learning.

The invention discloses a regularized neural network-based substation broadband transient voltage reverse solving method, system, equipment and medium, and relates to the field of broadband overvoltage measurement, and the method comprises the steps: measuring a space electric field signal around a high-voltage charged body in a substation, and obtaining a signal frequency spectrum; solving an electric field-voltage mapping matrix under a frequency domain by using a regularization least square method, and taking the mapping matrix as a training target of a neural network; extracting the frequency and amplitude of a main frequency point in each layer through multi-scale wavelet decomposition; predicting an electric field-voltage mapping matrix by adopting a feedforward neural network model; and performing coefficient correction on the mapping matrix by taking a power frequency voltage signal monitored in real time as a reference. And obtaining the frequency spectrum of the voltage through inversion calculation, and carrying out inverse Fourier transform to obtain the time domain full waveform of the broadband overvoltage. According to the method, the insulation technology difficulty and the safety risk of high voltage measurement are reduced, and full-waveform inversion measurement of various transient overvoltage of an offshore flexible direct current system is realized.

The present invention provides a reflection wave waveform inversion method based on an envelope error function, a computer-readable storage medium, and an electronic device. The method comprises: obtaining a background wave field through migration and demigration; updating the background velocity field using a reflection wave full waveform inversion technique based on the envelope error function; and using the inverted background velocity field as the initial velocity field and updating the initial velocity field using a reflection wave full waveform inversion technique based on the L2 error function to obtain a disturbance velocity model. When applied to model inversion, the present invention can produce a more accurate velocity model, significantly improving the quality of the migration profile.

This disclosure relates to the field of oil and gas exploration technology, and in particular to a method, apparatus, equipment, medium, and product for imaging domain reflection wave waveform inversion. The method includes: calculating the gradient corresponding to each offset and a pre-constructed velocity model; defining an objective function for imaging domain reflection wave waveform inversion based on the gradients corresponding to all offsets; calculating the update gradient of the objective function corresponding to the velocity model; updating the velocity model based on the update gradient; and performing imaging domain reflection wave waveform inversion based on the converged velocity model. By defining the objective function for imaging domain reflection wave waveform inversion and calculating the update gradient of the objective function corresponding to the velocity model, and updating the velocity model, imaging domain reflection wave waveform inversion is achieved. This directly improves the imaging quality in the depth domain and compensates for the shortcomings of reflection wave waveform inversion applications with depth domain imaging as the ultimate goal.

The application relates to a shield tunnel wall rear cavity combined detection and intelligent identification method and system, solves the core technical problems of high misjudgment rate and single information dimension of shield tunnel wall rear cavity detection caused by dependence on a single data source, and the method comprises the following steps: suspected cavity areas are screened through high-frequency pulse electromagnetic wave scanning, and normal-time micro-motion signals are used for verification. Then, multi-dimensional features of the electromagnetic wave and the micro-motion signals are extracted and fused, and input into a pre-trained hybrid deep learning model. The model synchronously outputs the profile position, quantitative size and structure influence grade of the cavity through parallel image recognition, waveform inversion and vibration analysis branches, and finally generates a structured disease diagnosis report containing multi-dimensional information. The application has the following technical effects: the reliability of cavity detection is significantly improved, and complete structured disease information containing position, profile, size and influence grade can be automatically generated.