208 results about "Full waveform" patented technology

The invention discloses an underground anti-seepage structure leakage monitoring method and system based on discrete time, and relates to leakage detection. The method comprises the following steps: collecting transient electromagnetic response data generated by an excitation signal; according to the initial conductivity parameter and the electrode position parameter of the monitoring area, establishing a three-dimensional geophysical model of the monitoring area through a full waveform inversion algorithm; obtaining electromagnetic abnormal data by using a finite element forward modeling algorithm; according to the temperature data, a distributed temperature field analysis method is adopted to calculate the space gradient and the time change rate of the temperature, and temperature abnormal data are obtained; according to the pressure data and time sequence change characteristics of the pressure data, determining pressure abnormal data; fusing the electromagnetic abnormal data, the temperature abnormal data and the pressure abnormal data by using a Bayesian algorithm to obtain fused data; and inputting the fusion data into a pre-trained machine learning model to obtain a leakage detection result. Aiming at the low dynamic leakage monitoring precision of the underground anti-seepage structure, the dynamic monitoring precision is improved.

The invention relates to the technical field of atmospheric remote sensing, in particular to a satellite-borne laser radar-based multi-feature attention cloud top height estimation algorithm, which comprises the following steps of: full-waveform laser radar data acquisition and preprocessing: acquiring an original echo signal through a satellite-borne full-waveform laser radar system; performing time domain alignment, noise suppression and saturated signal elimination on the signals; a median filtering and wavelet threshold denoising algorithm is combined to eliminate high-frequency noise, and background radiation interference is eliminated through dynamic baseline correction; according to the method, a multi-stage noise reduction strategy combining sliding window median filtering and self-adaptive wavelet denoising is introduced in the waveform feature extraction stage, pulse interference and signal oscillation are effectively restrained, meanwhile, through a terrain matching error compensation mechanism, the detection accuracy is improved, and the detection accuracy is improved. The physical consistency of the elevation residual error under the real terrain background is enhanced, and the authenticity and the checkability of the point elevation return are improved.

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 relates to the technical field of seismic inversion, and particularly discloses a variational reasoning full-waveform inversion method and device based on geological prior information, and the method comprises the steps: constructing a plurality of filters; acting the plurality of filters obtained by construction on a seismic migration section to calculate a correlation coefficient matrix; according to a logging information fitting theory variation function, performing low-rank approximation on the calculated correlation coefficient matrix to obtain a correlation coefficient matrix after low-rank approximation; randomly generating a plurality of speed samples based on the correlation coefficient matrix after low-rank approximation; calculating the gradient of the objective function according to the plurality of speed samples; according to the gradient of the objective function, calculating the gradient of posteriori distribution by using a Bayesian formula; and based on the gradient of the full waveform inversion posteriori distribution, utilizing a Stein variational gradient descent algorithm to carry out multiple times of iterative updating on the plurality of speed samples, and outputting a target speed model obtained by inversion of the plurality of speed samples. According to the scheme, the multiplicity of inversion is reduced, and the inversion efficiency is improved.

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.

Systems and methods for predicting seismic velocities for a subsurface formation include obtaining seismic data from a subsurface formation. The seismic data includes an amount of information about the seismic formation. A reduced seismic dataset is generated by applying an unsupervised machine learning model to cluster the seismic data. The reduced seismic dataset has less data than the seismic data, and the reduced seismic dataset includes the amount of information about the subsurface formation. Inversion models and forward modeling data are generated by performing a full waveform inversion for the reduced seismic dataset. The inversion models specify velocities in the subsurface formation and the forward modeling data specify response values of the inversion models. A supervised machine learning model is trained using the inversion models and the forward modeling data; and seismic velocities are predicted by providing the seismic data as input to the trained supervised machine learning model.

The invention discloses a stratigraphic water content layering inversion method based on a shallow seismic full waveform, and relates to the technical field of geophysical exploration data processing, and the method comprises the following steps: 1, under the same survey line condition, synchronously obtaining a seismic wave full waveform, complex impedance and an induced polarization frequency spectrum; 2, constructing a layered initial model containing a layer boundary line and an in-layer water content initial value; based on the layered initial model, executing dual-domain quantile alignment interface keeping iteration until convergence, and obtaining a final layer boundary line and an in-layer water content field; the double-domain quantile alignment interface maintaining iteration comprises the following steps: updating an in-layer moisture content field and a layer boundary line according to a seismic wave full waveform; and performing total variation regularization processing on the updated in-layer water content field to maintain the transition characteristic of the layer boundary line. According to the method, an efficient and reliable solution with actual guidance value is provided for mineral resource exploration, geological structure detection, groundwater survey and engineering geology evaluation.

A method is described for generating high-resolution images of transversely isotropic mechanical properties and attenuation properties of tissues associated with measured compressional waves. A pressure wave is transmitted to a region containing fibers that are oriented with transverse isotropy. Waves are received that propagate after the pressure wave has impinged upon the fibers. An initial velocity and attenuation model is created that takes into account the transverse isotropy of the fibers. Full Waveform Inversion (FWI) is used to iteratively minimize a difference between the received data and the initial velocity and attenuation model to create an image of the region that takes into account the transverse isotropy of the fibers. The image has a higher resolution than if created using the initial velocity and attenuation model.

The invention relates to the technical field of seismic wave full-waveform inversion, in particular to a seismic wave full-waveform inversion method and system based on an RBF-FD fusion difference method, and the method comprises the steps: setting an artificial seismic source and a detection point, triggering a seismic source signal, and collecting actual measurement seismic waveform data; scanning a tunnel of a to-be-detected coal seam to generate point cloud data; constructing a tunnel three-dimensional geometric model according to the point cloud data, and dividing a regular surface area, an irregular surface area and a transition area; performing partition discrete solution to obtain a partition discrete solution result; presetting a medium parameter field of the to-be-measured coal seam; executing seismic wave forward modeling based on the seismic source signal, the medium parameter field of the current to-be-measured coal seam and the partition discrete solution result, generating a forward modeling wave field and extracting a forward modeling waveform; and performing residual comparison on the forward simulation waveform and the actually measured seismic waveform, calculating a residual norm, and updating the medium parameter field through an optimization algorithm until the precision requirement is met. According to the invention, high-fidelity seismic wave full-waveform inversion can be realized.

A method can include receiving seismic data of a subsurface region; performing an iterative full waveform inversion using at least a portion of the seismic data to generate a model of the subsurface region, where the performing includes, after one or more iterations of the full waveform inversion, automatically selecting one or more parts of the seismic data for inclusion in the at least a portion of the seismic data based on data classification using one or more quality attributes; and outputting the model of the subsurface region.

The invention discloses a method for quantitatively evaluating the three-dimensional shape of a thinning defect in a plate under ultrasonic guided wave field undersampling scanning, belongs to the field of industrial nondestructive testing, and particularly relates to high-precision three-dimensional shape reconstruction and thickness loss quantification of a corrosion defect in a metal plate shell structure (such as aerospace skin, a pressure vessel wall, an oil pipeline and the like). According to the method, sparse sampling guided wave field reconstruction, full waveform inversion (FWI) and priori knowledge constraint are fused, Shannon sampling theorem limitation is broken through, efficient measurement of corrosion depth, contour and distribution under the conditions of a single excitation source and a limited view angle is achieved, and the method is suitable for rapid online detection in a complex industrial environment.

The invention relates to the technical field of coal seam inversion and imaging, in particular to a reflection channel wave imaging method and system considering the layering attenuation characteristic of a thick coal seam, and the method comprises the steps: constructing a coal seam layering geologic model, setting the initial wave velocity and initial quality factor of each layer, and forming an initial parameter field; setting an artificial seismic source and a plurality of detection points, exciting a seismic source signal at the artificial seismic source, recording seismic source information, and collecting a seismic wave response signal of each detection point; performing forward modeling by using a fractional order viscoelastic wave equation considering a spatial variable quality factor; carrying out full waveform inversion to obtain an inversion model; and on the basis of the inversion model, a level set function and a self-adaptive grid division strategy are adopted to express a barrier boundary in the coal seam to execute pre-stack wave field separation reverse time migration imaging, and a coal seam structure image is generated. The method can significantly improve the imaging resolution and calculation efficiency of the coal seam structure, and achieves the high-precision recognition of the boundary of the obstacle.

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.

The invention discloses a plate structure defect Lamb wave full waveform inversion method based on U-Net neural network regularization, and belongs to the field of nondestructive testing. Firstly, a two-dimensional sound wave equation is solved through a frequency domain finite difference method, and analog signals of all receiving points are obtained; the method comprises the following steps: constructing a target function to represent a relative deviation between an analog signal and a sensor actual measurement signal, and converting an inversion problem of defects in a metal plate into an optimization problem of the target function; performing derivation on the target function by using an adjoint state method to obtain a gradient, updating the velocity field along the negative direction of the gradient, and performing regularization processing on the velocity field through a specific coding-decoding structure of the U-Net neural network to suppress noise and artifacts; a frequency serial inversion strategy is adopted, and inversion is carried out step by step from low frequency to high frequency. According to the method, artifacts in the inversion process are suppressed by using the U-Net neural network, dependence of inversion on initial model precision is reduced by using a frequency domain serial inversion strategy, and high-precision inversion of plate structure defects is realized.

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.

Method and apparatus for performing a traveltime-based seismic full waveform inversion using the frequency-dependent Hann window and the source-independent scheme to generate a final velocity model of subsurface formations are provided. The method includes positioning seismic data recording sensors in the survey region; blasting at points of incidence in the survey region to generate seismic waves; and sensing and recording the seismic waves using the seismic data recording sensor. The recorded seismic waves are seismic data. The method further includes transmitting the seismic data to a computer system including one or more memories and storing the seismic data in one or more memories; storing a source wavelet in the one or more memories; performing, by the computer system, a forward modeling operation using the source wavelet; and generating, by the computer system, the final velocity model using the seismic full waveform inversion with the forward modeling operation.

The invention discloses a weighted exponential decay instantaneous phase inversion method based on automatic differentiation. Under the framework of automatic differential, the underground velocity model can be inversed by using phase and waveform information at the same time, and the advantages of constructing an initial model at the early stage of inversion and improving the resolution of the model at the later stage of inversion are combined. The method comprises the following steps: firstly, performing Hilbert transform on seismic wave data, extracting index instantaneous phase information of observation data and simulation data, and constructing a weighted instantaneous phase inversion objective function by introducing an attenuation coefficient and combining waveform information; secondly, carrying out back propagation on the loss of the objective function based on an automatic differential framework, and solving an update gradient; and finally, updating the speed model by using an SDG optimizer. According to the method, a layered multi-scale inversion effect can be realized by adjusting attenuation factor values, an objective function is constructed by weighting exponential attenuation phase information and waveform information, a better initial model is constructed before a conventional multi-scale full-waveform inversion process, the problem of cycle jump of full-waveform inversion is relieved, and the inversion efficiency is improved. And finally, a high-precision inversion result is obtained.

The present application relates to the technical field of flow measurement and industrial monitoring, in particular to a full waveform diagnosis and adaptive gas flow measurement system; comprising full waveform acquisition, feature extraction, diagnostic analysis, dynamic weight calculation and health degree evaluation modules; the system obtains acoustic channel characteristics through digital sampling and multi-dimensional analysis of ultrasonic echoes; the core is to dynamically adjust the weight of each acoustic channel according to the waveform similarity and sound velocity deviation index to obtain the standard flow and generate the measurement health index; the present application realizes the transformation from single feature analysis to full waveform physical state deconstruction, can accurately distinguish scale, noise and turbulence anomalies, and overcomes the defect that the traditional technology cannot identify the root cause of the fault.

Systems and methods for using cross-well seismic data to image reservoir structures illuminated with sources located in a lateral well and receivers located in one or more nearby lateral wells. The systems and methods include numerical FWI algorithms and workflows based on an elastic vertical transverse isotropy (VTI) anisotropic full waveform inversion (EFWI) engine. The EFWI engine is used for inversions based on the cross-well seismic data and additional data (e.g., well logs, vertical seism profiles). Inversion results include 2D / 3D maps of elastic properties (e.g., velocity, anisotropy, density, attenuation) of subsurface. The EFWI engine includes a wave propagation modeling accounting for various seismic waves propagating in the subsurface.

The invention discloses a waveform feature extraction method and system for irregular waveforms of a hyperspectral laser radar, and aims at solving the problem that asymmetric waveforms, weak echoes and overlapped echoes in multi-channel full-waveform laser radar signals are difficult to accurately decompose, modeling full-waveform signals collected by multiple spectral channels into superposition of echo components and background noise. A multi-channel waveform is preprocessed through noise threshold and pulse width combined constraint, the waveform position is determined by adopting a gravity center discrimination method, and time synchronization calibration of each channel signal is realized in combination with a multi-echo quality index. The method further utilizes a cubic B-spline function to carry out modeling on transmitted waveforms of all spectrum channels, carries out weighted accumulation on multi-channel echo signals to enhance weak echo signals, and adopts a multi-channel interconnection decomposition optimization strategy to realize accurate extraction of irregular waveform features under the constraint condition of keeping time parameters of sub-echoes of all the channels consistent.

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 application discloses a two-dimensional distributed optical fiber sensing full waveform simulation method based on a discrete elastic lattice, and belongs to the technical field of seismic wave field simulation and distributed optical fiber sensing. The application comprises the following steps: receiving seismic wave data of a target geological medium layer; setting a two-dimensional calculation region according to the spatial distribution of the longitudinal wave velocity, the transverse wave velocity and the density parameters of the seismic wave, and taking the two-dimensional calculation region as a numerical simulation carrier of seismic wave propagation; discretizing the two-dimensional calculation region into a plurality of particle nodes, and connecting adjacent particle nodes through elasticity to form a discrete elastic lattice, so as to represent the mechanical properties of the stratum medium; and according to the density parameters, the longitudinal wave velocity, the transverse wave velocity or the equivalent elastic parameters of the stratum medium, the elastic connection between the particle nodes is endowed with corresponding elastic coefficients. The application provides a reliable numerical tool for DAS deployment design, data interpretation and seismological inversion, and can be widely applied to the fields of earthquake monitoring, oil and gas exploration, geological disaster warning and the like.

The invention provides a Sigmoid modulation phase correlation surface wave full waveform inversion method and system. The method comprises the following steps: acquiring a surface wave seismic record and constructing an initial S wave velocity model; performing forward modeling to generate a synthetic record; calculating instantaneous phase correlation of observation and synthetic records to construct an amplitude-independent target function; modulating the target function by using a Sigmoid function, and mapping the value of the target function to a predetermined interval; and calculating a gradient based on the modulated target function and iteratively updating the speed model until a termination condition is met, and outputting a final model. According to the method, the smooth saturation and bounded output characteristics of the Sigmoid function are utilized, the target function response is converted into a self-adaptive gradient scaling mechanism, a self-stabilization optimizer is introduced for the inversion process, and the convergence domain is widened by applying smooth constraint, so that the dependence on the initial model precision is reduced, and the first success rate and the result reliability of inversion under complex conditions are improved.

This disclosure relates to a method, apparatus, and product for full-waveform reflection inversion based on dynamic graphic warping, applicable to the field of seismic exploration technology. In this disclosure, seismic data of the target area is acquired; wavefield simulation is performed based on the seismic data to determine the background wavefield and scattered wavefield. Based on dynamic graphic warping, full-waveform reflection inversion is performed on the background wavefield and scattered wavefield to determine the inversion target model. The inversion target model is optimized to determine the target image of the target area. Determining the inversion target model through the dynamic graphic warping algorithm can improve the resolution of the inversion target model and reduce the model's ambiguity and computational load. Furthermore, iterative optimization of the inversion target model can improve imaging quality. Simultaneously, using the dynamic graphic warping algorithm can reduce the accuracy requirements of high wavenumber models, enabling more accurate imaging of complex geological features. Therefore, the accuracy of imaging is improved.

The invention provides a deep learning algorithm for full-waveform inversion of a coal (rock) layer isomer region, and the algorithm comprises the steps: guiding a resampling process through the gradient information of a stress wave field, constructing a resampling matching point data set, reducing the input data size, and improving the sample quality; a self-adaptive sampling physical information neural network model is constructed, a loss weighting strategy is introduced to dynamically adjust the weight, and the convergence speed and precision of the model are optimized. Through example verification, the inversion precision of the method is superior to that of traditional full waveform inversion, the data dependence is effectively reduced, the model performance is improved, and the method can be used for inversion analysis of a coal (rock) layer isomer region.

The invention discloses a multi-scale full-waveform inversion method and device based on dynamic least square mapping, electronic equipment and a storage medium. The method comprises the following steps: acquiring expected low-frequency seismic data based on a dynamic least square mapping method; and carrying out multi-scale full-waveform inversion based on the expected low-frequency seismic data. According to the method, the low-frequency information of the seismic data is expanded through a dynamic least square mapping method, the missing low-frequency component of the seismic data is made up, multi-scale full-waveform inversion of frequency bands is carried out on the basis, and the inversion accuracy is improved. The problem of cycle jump caused by insufficient precision of an initial model or loss of low-frequency information of seismic data in full-waveform inversion can be effectively avoided, and the precision of full-waveform inversion is improved.

The application discloses a hydraulic fracturing fracture monitoring method and device based on Krauklis wave full waveform inversion, and comprises the following steps: obtaining a forward data set containing Krauklis wave and an actual data set of Krauklis wave; using full waveform inversion to invert the hydraulic fracturing fracture; periodically using full waveform inversion to image the fracturing fracture and periodically monitoring the hydraulic fracturing fracture. The application fully utilizes the relationship between the waveform characteristics of Krauklis wave, such as propagation along the fluid layer in the fracture, strong amplitude, strong frequency dispersion and low frequency propagation speed, and the fracture geometry, is based on the numerical solution of the elastic wave equation, adopts full wave field information, and thus provides a high-resolution and high-precision fracture fine structure image, so as to serve quantitative fracturing monitoring and fracture effect evaluation.

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.