70 results about "Density model" patented technology

The invention relates to the technical field of construction surrounding rock stability evaluation, discloses a tunnel surrounding rock stability evaluation method and system based on multi-physics field parameter inversion, and aims to solve the problems that an existing method is insufficient in data collaboration, high in parameter inversion multiplicity and poor in surrounding rock stability evaluation accuracy and timeliness. According to the scheme, the method mainly comprises the steps that an acousto-optic electromagnetic vibration drill multi-mode sensing array is arranged, and time-space synchronization is implemented; establishing a mutual interference entropy spectral density model to realize multi-physics field collaborative excitation and acquisition; a unified feature vector is obtained through data correction, feature extraction and weighted fusion; a joint inversion objective function embedded with rock physical constraints is constructed, a three-dimensional physical property parameter field is obtained through inversion, and a dynamic permeability field is calculated in combination with acoustic emission energy; and finally, dynamically updating the model by utilizing ensemble Kalman filtering, and obtaining a final risk probability based on updated parameters and seepage-uncertainty coupling correction. According to the method, the accuracy, the real-time performance and the reliability of the stability evaluation of the surrounding rock of the deep-buried tunnel are improved.

The invention relates to the technical field of pet behavior data processing, and discloses a time-space sequence data processing method and system for pet abnormal behavior recognition, and the method comprises the steps: 1, obtaining multi-modal data, carrying out the time alignment, building a multi-scale scene semantic graph, and generating a grid occupancy frequency and a region transfer matrix; 2, constructing a normal behavior template library, and generating window-level spatio-temporal features; 3, establishing group normal behavior distribution by using a generative density model, calculating a residual error in combination with a time sequence prediction model, and obtaining individual model parameters; 4, performing statistics on historical rhythm distribution and calculating differences of the day to obtain rhythm deviations; 5, fusing multi-component anomalies to obtain a comprehensive anomaly score; step 6, introducing an Internet of Things event to generate a gating coefficient and adjusting an abnormal score; and 7, comparing the abnormal score after gating with a threshold value, and outputting an abnormal alarm. According to the invention, accurate identification and stable alarm of the abnormal behavior of the pet are realized.

Methods and systems for fully quantifying predictive uncertainty for recommender systems are disclosed. Embodiments include providing a fully Bayesian multiway neural network model, assigning a prior distribution over a plurality of weight parameters of a model parameter set of the neural network model such that a corresponding posterior distribution represents an epistemic uncertainty of the neural network model; incorporating a mixture density model for a target layer of the neural network model, wherein the mixture density model models aleatoric uncertainty of input data; estimating the posterior distribution of the weight parameters given training data by updating the prior distribution based on data likelihood of the training data; sampling a plurality of empirical samples from the estimated posterior distribution of the weight parameters; performing inference for each stochastic realization of the neural network model with the sampled weight parameters on an inference sample to generate one or more predictive distributions; drawing samples from each of the one or more predictive distributions as final predictions for the inference sample to obtain a matrix of predictions for the inference sample, wherein the matrix represents a joint distribution of both the aleatoric and epistemic uncertainty of the recommender system.

The embodiment of the specification discloses a self-adaptive variable grid forward modeling method for middle-deep dry hot rock mass. p , a transverse wave velocity V s and a medium density p model, pre-setting forward modeling parameters; performing self-adaptive variable grid processing on the velocity model, determining a mapping relationship between coordinate systems, and determining a wave equation under variable grid based on the mapping relationship; performing self-adaptive variable grid processing on the velocity model, determining a mapping relationship between coordinate systems, obtaining the underground medium longitudinal wave velocity, transverse wave velocity and density after variable grid, determining a wave equation under variable grid based on the mapping relationship, and generating final regular grid horizontal direction longitudinal wave field, horizontal direction transverse wave field, vertical direction longitudinal wave field and vertical direction transverse wave field information by using the mapping relationship reversely according to the wave field information in each direction at a specific moment after the variable grid, so that the memory occupation is small and the calculation speed is faster.

The invention provides a Bayesian fusion-based 4D millimeter wave radar point cloud enhancement and target detection method, and the method comprises the steps: 1, projecting a 4D millimeter wave radar point cloud to an image plane, carrying out the matching of an image instance segmentation result, and obtaining an image semantic category and image segmentation confidence; 2, constructing a class conditional probability density model based on the physical characteristics of the radar points; based on a Bayesian probability framework, carrying out weighted fusion on the image segmentation confidence and the class probability density to obtain fusion confidence; 3, taking the fusion confidence and semantic category codes as augmented features, and performing feature splicing on the augmented features and the 4D millimeter-wave radar point cloud to generate a semantic enhanced point cloud; and sampling the semantic enhanced point cloud by adopting a sampling method fusing confidence guidance, and inputting the sampled semantic enhanced point cloud into a detection network for training and reasoning. According to the method, the technical problems of unidirectional dependence, shallow geometric matching and insufficient utilization of radar physical characteristics in the existing image and radar point cloud fusion technology are solved.

The invention belongs to the technical field of geophysical inversion, and relates to a gravity anomaly three-dimensional inversion method based on multi-scale feature fusion, which comprises the following steps: randomly generating density anomaly bodies with different numbers, densities, sizes and shapes to obtain an underground medium three-dimensional density model; carrying out forward modeling on the three-dimensional density model of the underground medium to obtain two-dimensional gravity abnormal data of the earth surface; a multi-scale feature fusion model is trained with the underground medium three-dimensional density model as output and the two-dimensional gravity anomaly data as input, and the multi-scale feature fusion model comprises a two-dimensional encoder, a three-dimensional decoder, a dimension converter and a multi-scale feature fusion device, wherein the dimension converter and the multi-scale feature fusion device are located between the two-dimensional encoder and the three-dimensional decoder; and inputting actually measured two-dimensional gravity anomaly data into the trained multi-scale feature fusion model, and performing inversion to obtain a three-dimensional density body constrained by a physical range. According to the method, the expression capability of the multi-scale features of the underground structure is enhanced, and the multi-solution problem and the deep resolution reduction problem of inversion can be relieved.

The application discloses a land geophysical exploration method, an electronic device and a readable storage medium, and belongs to the technical field of land geophysical exploration. The land geophysical exploration method comprises the following steps: detecting a first seismic wave generated by vibration at a preset position of the underground or the ground and transmitted to the ground; detecting a second seismic wave transmitted by the vibration to the air above the ground; calculating a ground parameter in a set range of the preset position according to the first seismic wave and the second seismic wave; and calculating an underground velocity model and a density model according to the ground parameter. The second seismic wave transmitted to the air from the ground is detected by utilizing the characteristics that the sound speed and the density of the air are consistent under the same meteorological condition, so that the subsequent inversion algorithm is more accurate, and the underground velocity model and the density model are more accurate.

The invention discloses a method for simulating particle transport and related equipment, and relates to the technical field of radiotherapy, and the method comprises the steps: constructing an equivalent density model along a particle transport direction based on the structure parameters of an energy regulator combination, and building a matched boundary information table, the boundary information table at least records the position information of the downstream boundary of each energy regulator piece in the energy regulator combination, the equivalent water thickness accumulated from the incident end to the boundary, and the air gap length between the boundary and the upstream boundary of the next energy regulator piece; obtaining a particle current position and boundary information table; the medium area where the current free path of the particles is located is judged, and differential calculation is carried out on offset generated by movement of the particles in different areas according to the medium area until the particles pass through the energy regulator combination to be transported; according to the method, rapid and accurate judgment and differential offset calculation of the boundary are realized, so that the boundary processing complexity is remarkably reduced and the calculation efficiency is improved on the premise of ensuring the physical simulation precision when the particles pass through the energy regulator.

The invention relates to a negative Poisson's ratio metamaterial enhanced seamless expansion joint design analysis method, which comprises the steps of collecting geometric construction parameters and mechanical property data of a negative Poisson's ratio metamaterial, and constructing a training data set; training a deep learning reverse design model and a mechanical property prediction model; the design parameters of the expansion joint device are optimized through a multi-objective optimization algorithm; an optimized expansion joint device is manufactured by adopting a 3D printing technology, the durability of the expansion joint device is evaluated by combining mechanical property data and a fatigue life prediction model, and iterative optimization is performed through a deep learning reverse design model. The expansion joint device has the beneficial effects that the impact resistance and the energy absorption capacity of the expansion joint device are remarkably enhanced through the design of the concave hexagonal honeycomb structure of the negative Poisson's ratio metamaterial and the nonlinear strain energy density model optimization geometric parameters.

The embodiment of the application relates to the technical field of sandstone type uranium ore exploration, and particularly relates to a method for determining a favorable area of a sandstone type uranium ore, which comprises the following steps: collecting well logging data of an exploration area of the sandstone type uranium ore, and gradually determining argillaceous content, total porosity of the exploration area, sandstone content of a sand and shale mixed matrix, longitudinal wave time difference, transverse wave time difference and density, equivalent elastic modulus of dry rock of the exploration area, and conversion relationship between longitudinal wave velocity and transverse wave velocity of rock under saturated fluid condition and a transverse wave velocity prediction curve; then, longitudinal wave impedance, velocity ratio of longitudinal wave and transverse wave and a density model are established; according to the model, three-dimensional elastic data bodies of impedance, density and shear modulus of longitudinal wave and transverse wave are determined by inversion; and according to the three-dimensional elastic data bodies, the favorable area of the sandstone type uranium ore is determined. The method for determining the favorable area of the sandstone type uranium ore according to the embodiment of the application can effectively improve the prediction accuracy of a favorable area of a sandstone type uranium ore.

Approaches presented herein provide for the reconstruction of implicit multi-dimensional shapes. In one embodiment, oriented point cloud data representative of an object can be obtained using a physical scanning process. The point cloud data can be provided as input to a trained density model that can infer density functions for various points. The points can be mapped to a voxel hierarchy, allowing density functions to be determined for those voxels at the various levels that are associated with at least one point of the input point cloud. Contribution weights can be determined for the various density functions for the sparse voxel hierarchy, and the weighted density functions combined to obtain a density field. The density field can be evaluated to generate a geometric mesh where points having a zero, or near-zero, value are determined to contribute to the surface of the object.

The invention relates to a method and apparatus for single epoch position bounding. A method for determining a protection level for a position estimate using a single epoch of GNSS measurements, the method comprising: specifying a prior probability density of states x P(x); specifying a system model h(x) relating states x to measured observations z; quantifying a quality metric q associated with the measurements; specifying a non-Gaussian residual error probability density model f(r|θ,q) and fitting the model parameters θ using a set of experimental data; defining a posterior probability density P(x|z,q,θ); estimating states x; and calculating the protection level by integrating the posterior probability density P(x|z,q,θ) over states x.

The invention relates to the technical field of oil and gas resource exploration data processing, in particular to a three-dimensional gravity gradient inversion method for oil and gas resource exploration, a medium and equipment. The method comprises the following steps: acquiring gravity gradient plane two-dimensional observation data of an exploration target area; setting an inversion constraint function; and performing inversion iterative operation to obtain an underground three-dimensional density structure, and delineating a space boundary of a salt hill, a rock cover or an oil storage structure according to the structure. According to the method, a regularization objective function is constructed based on the L1 norm of the model and the total variation TV1 of the model, an L1-TV1 weight factor is introduced and reasonably selected, and a density model vector after depth weighting is used in the total variation of the model. The method can effectively overcome the shielding effect of the shallow rock cover on the deep salt nucleus in salt hill detection, improve the depth resolution and the boundary depiction definition, reduce the inversion multiplicity of solutions, and provide accurate guidance for oil-gas exploration and drilling risk avoidance.

The invention discloses a new energy output probability density modeling method, which comprises the following steps of: collecting and preprocessing data to obtain a new energy power curve modeling data set; performing abnormal data cleaning on the data to obtain a new energy power curve modeling data set after partition processing; taking the new energy power curve modeling data set as input, and establishing an autocorrelation new energy power generation power condition kernel density estimation model; according to a new energy power generation power condition kernel density estimation model, determining an optimal bandwidth through a mean square integral error between a minimum estimation density and an unknown real density; and according to the optimal bandwidth, in combination with the new energy power generation power condition kernel density estimation model of the autocorrelation, calculating to obtain an output probability density model of the new energy. According to the method, the time autocorrelation of the new energy power generation data is fully considered, abnormal data can be eliminated in time, and new energy power generation characteristics under different environmental conditions can be adapted.

The application belongs to the technical field of geophysical inversion, and relates to a gravity anomaly three-dimensional inversion method based on multi-scale feature fusion, comprising the following steps: randomly generating density anomaly bodies with different number, density, size and shape to obtain a three-dimensional density model of underground medium; forward modeling the three-dimensional density model of underground medium to obtain two-dimensional gravity anomaly data of the ground surface; training a multi-scale feature fusion model with the three-dimensional density model of underground medium as output and the two-dimensional gravity anomaly data as input, wherein the multi-scale feature fusion model comprises a two-dimensional encoder and a three-dimensional decoder, and a dimension converter and a multi-scale feature fusion device located between the two-dimensional encoder and the three-dimensional decoder; inputting the measured two-dimensional gravity anomaly data into the trained multi-scale feature fusion model to obtain a three-dimensional density body constrained by a physical range through inversion. The application enhances the expression capability of multi-scale features of underground structures, and can relieve the multi-solution problem of inversion and the problem of resolution decline in deep parts.

The application relates to a mountain stability evaluation method, device, computer equipment and storage medium, and relates to the technical field of mountain stability. The evaluation method comprises the following steps: dividing a mountain into a plurality of voxels; constructing an adjacency structure graph and a density field posterior probability distribution of the mountain, wherein the adjacency structure graph is constructed by muon data recorded by a muon detector arranged in a grotto, and the density field posterior probability distribution is a probability distribution of all density models of the mountain under the muon data and the adjacency structure graph; sampling from the density field posterior probability distribution according to the adjacency structure graph to obtain a plurality of density field samples of the mountain; obtaining a density value of each voxel according to the plurality of density field samples; and obtaining a stability abnormal area in the mountain according to the density values of the plurality of voxels. The mountain stability evaluation method can accurately determine the stability abnormal area in the mountain, and improves the accuracy of mountain stability evaluation.

The invention provides a muscle density measuring method, system and device and a storage medium, and relates to the technical field of medical imagines.The muscle density measuring method comprises the following steps that an ultrasonic probe is used for collecting scattering signals of muscles to be measured; constructing an initial sound velocity and density model; performing time domain forward modeling based on a wave equation; calculating a residual error between the analog signal and an actual signal and defining a target function; calculating a gradient by using an adjoint state method; updating the model by adopting a gradient descent method; iteration is repeated until convergence; the system comprises an ultrasonic probe, an initial model construction module, a forward modeling module, a residual calculation module, a gradient calculation module, a parameter updating module, an iteration control module and a result output module. By means of the wave velocity-density joint inversion technology, high-precision noninvasive measurement of muscle density is achieved, the imaging capacity of deep muscles is improved, and important technical support is provided for early diagnosis and precise intervention of sarcopenia.

The invention discloses a gravity measurement and calculation system, and relates to the technical field of geophysical measurement and precise instruments. The system comprises a gravity measurement module, a dynamic correction module, a calculation module and a result output and early warning module. The method comprises the following steps: acquiring 0.1 nGal-level gravity data through an atomic interference type quantum gravimeter array; dynamically updating an energy component correction term epsilon (g) in the empirical equation by using an adaptive learning algorithm based on a Lyapunov stability criterion, and ensuring asymptotic stability of the system under high-frequency data flow; combining a real-time density value of a CRUST1.0 density model after Bouge correction, and accurately calculating the earth radius variation; and finally, through 5G and satellite double-link redundant communication, triggering early warning when severe conditions that delta g is greater than or equal to 2 mu Gal for continuous 30 minutes, and SNR is greater than or equal to 6 dB are met. According to the invention, high-precision quantum sensing, a stability control algorithm and a real-time geophysical model are deeply fused, reliable monitoring of micron-sized change of the earth shape and effective early warning of geophysical events are realized, and the method has high precision, high stability and high engineering practicability.

The application provides a satellite orbit prediction method and device based on orbit data of a spherical satellite. The method comprises determining a target atmospheric density model according to a target space environment condition of a satellite to be predicted, wherein the target atmospheric density model is obtained by correcting a preset atmospheric density model according to orbit data and a dynamic model of the spherical satellite in the target space environment condition; identifying an atmospheric drag coefficient of the satellite to be predicted based on the target atmospheric density model; and performing orbit prediction on the satellite to be predicted according to the target atmospheric density model and the atmospheric drag coefficient. According to the embodiment of the application, the spherical satellite maintains a stable and high-precision surface value ratio and atmospheric drag coefficient when operating in an orbit, the orbit data of the spherical satellite can be used to better achieve high-precision correction of the preset atmospheric density model, and the atmospheric drag coefficient of the satellite to be predicted can be more accurately identified based on the corrected atmospheric density model, thereby improving the orbit prediction accuracy of the satellite to be predicted.

This invention provides a method for calculating the recommended clearance length between tunnels and interchange exits, comprising the following steps: constructing a VISSIM simulation model; obtaining the headway, speed, and instantaneous traffic flow density at different cross-sections in the model, and fitting the models to obtain headway models and speed distribution models for different road segments, constructing speed and density models and critical intercalation gap models; constructing a safe lane-changing probability model using the differential method; solving the safe lane-changing probability model based on the Monte Carlo simulation method according to various uncertainties, obtaining the target lane-changing success rate under different traffic volumes, large vehicle ratios, and clearance lengths, and providing a recommended clearance length value. This invention uses a deterministic approach to calculate the problem of large clearance values ​​required under traffic capacity, and utilizes a reliability design method to integrate traffic characteristics, driver characteristics, and traffic safety requirements of small clearance road segments, proposing a method for calculating the recommended clearance length value.

The application discloses a salt ore body density prediction method based on a double-branch and multi-attention mechanism, belongs to the technical field of machine learning and gravity inversion, and comprises the following steps: constructing a three-dimensional density model dataset based on gravity observation surface data and gravity field potential field migration theory; constructing a multi-attention mechanism fusion neural network with double-path feature fusion and training and optimizing the three-dimensional density model data; wherein the multi-attention mechanism fusion neural network with double-path feature fusion comprises two branches, one branch is a U-Net branch, and the other branch is a K-Net branch; the U-Net branch comprises a channel attention module, a self-attention mechanism module and a cross-resolution residual feature block; the gravity data of a salt ore body to be predicted is converted into corresponding three-dimensional body companion operators, and the deep learning network after training and optimization is processed to obtain a salt ore body density prediction result. The application has excellent effects in gravity field value recovery and deep structure boundary depiction.

The application provides a 4D millimeter wave radar point cloud enhancement and target detection method based on Bayesian fusion, comprising the following steps: step one: projecting the 4D millimeter wave radar point cloud to an image plane and matching the image plane with an image instance segmentation result to obtain an image semantic category and an image segmentation confidence; step two: constructing a conditional probability density model based on the physical characteristics of the radar points; weighting and fusing the image segmentation confidence and the category probability density based on a Bayesian probability framework to obtain a fusion confidence; step three: using the fusion confidence and the semantic category as augmented features to splice the features of the 4D millimeter wave radar point cloud to generate a semantic enhanced point cloud; and using a fusion confidence guided sampling method to sample the semantic enhanced point cloud and inputting the semantic enhanced point cloud into a detection network for training and reasoning. The application solves the technical problems of one-way dependence, shallow geometric matching and insufficient utilization of radar physical characteristics in the existing image and radar point cloud fusion technology.

The invention relates to the technical field of cable positioning, and discloses a cable positioning method, system and device and a medium, and the method comprises the steps: collecting a magnetic field signal excited by a target cable; based on an information geometry theory, constructing a parameterized probability density model according to the magnetic field signal and a preset signal parameter space so as to determine a three-dimensional statistical manifold, and further generating a Fisher information matrix; projecting the three-dimensional statistical manifold by adopting an alternating optimization strategy to obtain a two-dimensional sub-manifold and a corresponding Fisher information matrix after projection; constructing a log-likelihood function according to the signal parameter space and the parameterized probability density model, and iteratively solving the log-likelihood function by adopting a natural gradient descent method based on the Fisher information matrix after projection to obtain an amplitude estimation result of the magnetic field signal so as to determine the position of the target cable; according to the method, the information geometry theory, the alternate optimization strategy and the natural gradient descent method are combined, and efficient and accurate positioning of the cable position is achieved.

The invention discloses an oil and gas reservoir density parameter inversion method and device, electronic equipment and a storage medium, and the method comprises the steps: selecting near-angle and medium-angle superposed seismic data, carrying out the two-parameter inversion based on two Fatti approximation formulas, and obtaining the impedance of longitudinal and transverse waves; performing three-parameter forward modeling on the angle, the longitudinal and transverse wave impedance and the density initial model of the far-angle seismic data by using a Fatti three-term approximation formula to obtain forward modeling far-angle seismic data; performing conventional linear inversion by using the residual error of the far-angle original seismic data and the forward far-angle seismic data and taking the density parameter as a target to obtain a density parameter update quantity, and combining the density parameter update quantity with the density initial model to generate a density parameter inversion result; and taking the density parameter inversion result as prior information, and repeatedly executing the steps until the density parameter inversion result meets the requirement. According to the method, the density inversion accuracy can be improved, and description and interpretation of oil and gas reservoirs are facilitated.

The invention relates to the field of furniture material detection, and particularly discloses a furniture material detection and risk assessment system, which is characterized in that after spectral intensity data of furniture is acquired, an adaptive weighted fusion mechanism based on probability density modeling is adopted, and the credibility of each scanning is intelligently assessed to effectively suppress abnormal signals, so that the risk assessment accuracy is improved. And the spectral data input into the model can reflect the chemical nature of the material with high fidelity. On the basis, in order to overcome the problems of poor universality and insufficient field adaptability of an analysis model, a cloud-end collaborative transfer learning framework is further introduced. According to the framework, a universal basic model pre-trained at a cloud end is deployed to an end side, and a small number of local samples are used for rapid fine adjustment and personalized calibration, so that the model can rapidly adapt to the specificity of current equipment and environment, and accurate, rapid and robust on-site lossless screening of furniture materials is realized.

The invention relates to the technical field of intelligent traffic management systems, and discloses an expressway event detection method and device based on long video semantic analysis, and the method comprises the following steps: S1, collecting and preprocessing long video data of an expressway scene, and generating an event-level vector, a segment-level vector and a key frame vector based on the video data; the semantic vector is used for subsequent event detection and discrimination; and S2, based on the obtained semantic vector samples, establishing corresponding probability density models for abnormal event samples and non-event samples in different environment domains, and constructing a calibration cache based on the non-event samples. The long video data of the expressway scene is collected and preprocessed, and then the event-level vector, the segment-level vector and the key frame vector are generated, so that the problems that the traditional video data processing method mostly depends on a simple feature extraction mode, and multi-level feature information in the video is not fully considered, so that the video data processing efficiency is greatly improved are solved. Therefore, the problem of low detection precision is solved.

The invention belongs to the technical field of data processing, and particularly relates to a network analysis method and system for data acquisition, and the method comprises the steps: collecting flow features at a gateway layer through a kernel bypass technology, and extracting the physical time difference and load length of a data packet; constructing a flow rate rhythm density model, and evaluating the machine automation degree of the flow rate by using a variance suppression principle; constructing a space-time behavior drift model, and calculating a behavior mutation degree based on historical inertia; and constructing a dynamic judgment model based on group difference, and judging the final threat confidence in combination with an environment dynamic baseline. According to the method, through side channel dynamics analysis, abnormal attack behaviors are accurately identified without decryption, and the false alarm rate is effectively reduced.

This invention discloses a method, apparatus, equipment, and medium for constructing a three-dimensional geological model integrating multivariate data, applicable to the field of geological exploration. The method includes acquiring two-dimensional profile detection data of a target geological area and expanding it into three-dimensional training data; extracting spatial geological constraints; constructing a multivariate clustering pattern library based on the three-dimensional training data, velocity model, and density model; constructing a candidate pattern library based on a pre-defined simulation path and spatial geological constraints; calculating a probability matrix based on the candidate pattern library; selecting patterns based on the probability matrix and cross-entropy to obtain an initial three-dimensional geological model; and performing multi-scale iterative optimization on the initial three-dimensional geological model to obtain an optimized three-dimensional geological model. This invention effectively improves the accuracy and reliability of three-dimensional geological models, better handles data sparsity and geological complexity issues, and reduces the uncertainty of simulation results.

This invention relates to the field of seismic data processing and interpretation, and discloses a method, apparatus, equipment, and storage medium for analyzing the elastic effects of seismic waves. The method includes: collecting well logging data from target wells exhibiting well-seismic mismatch in a target work area; acquiring stratigraphic data from the target wells; establishing P-wave velocity, S-wave velocity, and density models to obtain seismic wavelets; conducting forward modeling based on acoustic wave equations to obtain first seismic shot records; conducting forward modeling based on elastic wave equations to obtain second seismic shot records; migrating and imaging the first and second seismic shot records respectively to obtain acoustic seismic data and elastic wave seismic data; and determining whether the well-seismic mismatch is caused by seismic wave elastic effects by comparing and analyzing the amplitude and waveform variations of the acoustic and elastic wave seismic data with synthesized well logging data and field-acquired seismic data. This invention solves the problem of reasonably interpreting the causes of well-seismic mismatch.