95 results about "Bayesian framework" patented technology

The invention discloses an intelligent fishing point dynamic prediction method and system based on multi-source marine environment data fusion. The method comprises the following steps: step 1, access, space-time alignment and pre-screening of multi-source heterogeneous marine environment data; step 2, priori knowledge base construction and suitability modeling based on target fish ecological habits; 3, constructing a fishing point prediction model fusing the multi-time-sequence environmental characteristics and deep learning; step 4, fusing two-channel prediction results under the Bayesian framework and quantifying uncertainty; 5, generating a dynamic mask of a real-time sea condition safety threshold value and fishery regulation space constraint; 6.1, constructing a comprehensive scoring function of the risk perception function. According to the method, multi-source heterogeneous data is constructed, a target fish ecological suitability model and a depth time sequence prediction model are combined, the fishing point posterior probability is generated through a Bayesian fusion mechanism, the prediction uncertainty is quantified, and dynamic fishing point recommendation with risk perception and compliance safety is realized.

The invention belongs to the field of artificial intelligence, particularly relates to an agent strategy generation and online optimization method based on dynamic scene perception, and aims to solve the problems that in a dynamic environment, strategy response is slow, optimization is difficult under sparse rewards, and online learning is unstable. The method comprises the following steps: constructing a multi-modal fused dynamic scene semantic perception module, and generating a high-dimensional scene representation; historical behaviors and environment contexts are fused through an attention mechanism, and an initial probabilistic strategy is output; executing an action and collecting real-time feedback to form an experience tuple; performing incremental strategy updating by adopting a non-parametric Bayesian framework and combining with a multi-peak exploration operator; a Lyapunov stability criterion and a strategy distillation mechanism are embedded to guarantee convergence robustness. According to the scheme, strategy generation within 50 milliseconds is achieved, the convergence speed of sparse reward tasks is increased by three times, strategy fluctuation is controlled within 8%, the system availability reaches 99.5%, and the real-time decision-making capacity and group cooperation efficiency of an intelligent agent in a complex dynamic scene are remarkably improved.

The invention discloses a single cell activity evaluation method based on cell nucleus geometric parameters and a Bayesian framework, which comprises the following steps: establishing a HeLa cell activity gradient model through adriamycin induction, carrying out cell nucleus segmentation by adopting Cell pose 3.0 and extracting 26 geometric features, screening a high-confidence sample in combination with a Bayesian deep learning framework, and evaluating the activity of a single cell according to the high-confidence sample. And constructing a multi-modal feature fusion model to integrate geometric features and deep semantic features, and finally realizing unmarked and high-precision single cell activity evaluation. The method overcomes the limitation that a traditional technology depends on biochemical markers, has the advantages of being easy and convenient to operate, high in flux and high in interpretability, and provides an innovative technical means for tumor liquid biopsy and precise medical treatment.

The invention provides a debris flow risk assessment method and system based on debris flow parameter probability inversion calibration, and relates to the technical field of geological disaster risk assessment. According to the technical scheme provided by the embodiment of the invention, an effective means is provided for scientifically identifying debris flow parameters, and the method is particularly suitable for setting single gullies which are limited in detailed observation and cannot directly measure the parameters. A traditional method depends on statistical parameters or deterministic back analysis, the former introduces errors due to poor regional adaptability, and the latter has the problem of non-uniqueness of parameters; according to the scheme, the parameter posteriori distribution is updated by combining the Bayesian framework with historical observation data (debris flow influence areas), so that the standard deviation of key parameters is generally reduced. In the embodiment of the invention, the influence area is taken as core observation information, the practical limitation that the flow depth and the flow velocity are difficult to observe in real time in the debris flow event is avoided, the observation information can be obtained through post-event field investigation or satellite images, and the scheme is high in practicability.

The invention relates to the technical field of unconventional oil-gas exploration, in particular to a well-seismic integrated geological model intelligent construction and reservoir gas-bearing prediction system, which is characterized in that a data acquisition and preprocessing module generates multi-dimensional feature mapping through wavelet transform denoising and principal component analysis; the well-seismic integrated modeling module adopts a Transform and BiGRU hybrid model to fuse well-seismic data, and constructs a three-dimensional geologic model adaptive to a fracture network; the dynamic gas-bearing prediction module is used for predicting an adsorbed gas / free gas ratio and gas saturation based on a Monte Carlo-Bayesian framework; the real-time iteration module corrects model parameters through online optimization and multi-source data feedback; and the output and visualization module generates a dynamic distribution diagram and an uncertainty interval, and through efficient multi-source data processing, construction of a high-precision three-dimensional geologic model, dynamic prediction of gas content and quantification of uncertainty, prediction precision, timeliness and decision reliability of exploration are improved in combination with real-time iterative optimization of the model and visualization output.

The invention discloses a hybrid antenna array direction of arrival estimation method based on noise marginalization SBL. The method comprises the following steps: initializing system parameters and a sampling grid set; establishing a hybrid antenna array receiving signal model, and initializing a hybrid beam forming matrix into a block diagonal matrix meeting constant modulus constraint; constructing a hierarchical probability model under a Bayesian framework, and introducing a noise precision parameter into signal prior; integral operation is carried out on the noise precision parameter, so that the signal posterior distribution is converted into student t distribution; an objective function is constructed, an expectation maximization algorithm is adopted to iteratively update a signal energy spectrum, and the process does not involve noise parameter estimation; an alternating iteration strategy is adopted, the signal energy is fixedly updated through inner circulation, and optimization is performed through a gradient descent method after outer circulation is fixed; after convergence, constructing a target function of off-grid estimation by reconstructing a covariance matrix; and searching the off-grid direction of the maximized objective function near a spectrum peak to obtain a final DOA estimation value.

The invention relates to the technical field of smart agriculture, and discloses a smart agricultural crop demand-driven multi-source linkage method and a water and fertilizer intelligent allocation system, the smart agricultural crop demand-driven multi-source linkage method comprises the following steps: constructing an adaptive sparse sensor network and planning a mobile inspection path; based on a Bayesian framework, a data credibility weight mechanism is introduced, and spatial-temporal resolution unification is realized by using a variational Kriging interpolation method; variety-adaptive crop stress state recognition is realized through the health state correction factor and a multi-state classifier; generating a crop water and fertilizer demand prediction scheme considering the yield loss risk in combination with the value-at-risk model and future weather prediction; generating an optimal water and fertilizer blending strategy; multi-source linkage closed-loop feedback control is realized, and self-adaptive evolution is realized by continuously optimizing system parameters through an online learning mechanism; through the adaptive sparse sensor network construction technology, the number of sensor nodes is reduced on the premise of ensuring the monitoring coverage rate.

The invention belongs to the technical field of battery residual life prediction, and particularly relates to a lithium ion battery residual life prediction method considering dynamic temperature influence, and the method specifically comprises the steps: building a lithium ion battery capacity degradation model, and obtaining a state space model under the dynamic temperature influence; a filtering method based on a Bayesian framework is used for predicting the capacity degradation state estimation value and the residual life of the lithium ion battery, a time-varying fading factor is introduced in the importance sampling process, and adaptive kernel density estimation is used in the resampling process. According to the method, a Wiener process is adopted to model a battery capacity degradation process in a nonlinear and time-varying temperature environment, and meanwhile, a time-varying fading factor and adaptive kernel density estimation are introduced to construct an importance density function and a resampling process of a filtering algorithm based on a Bayesian framework, so that the capacity degradation state change tracking capability of the filtering is improved, and the filtering efficiency is improved. And the battery residual life prediction error under the influence of the external environment is reduced.

The invention discloses a foundation pit horizontal displacement probability prediction method and system based on a sparse Bayesian extreme learning machine, and belongs to the technical field of civil engineering monitoring and artificial intelligence crossing. According to the method, a probability model containing input and output noise is constructed, feature selection and uncertainty quantification are automatically carried out by using a sparse Bayesian framework, and probability prediction of horizontal displacement at the position where a sensor is not arranged is realized. The method can output the prediction mean value and the confidence interval, effectively solves the problems of data sparsity and noise, and improves the prediction reliability and the engineering decision support capability. The method has the advantages of being high in automation degree, high in anti-interference capacity, suitable for actual engineering monitoring and the like.

The application discloses a scanning radar two-dimensional super-resolution imaging method based on Bayesian sparse learning, first constructs a bearing-pitch two-dimensional scanning radar signal model, then, according to a maximum posteriori criterion under a Bayesian framework, establishes a sparse optimization target function about target scattering and environmental noise, finally, utilizes a conjugate gradient algorithm and Kronecker product properties to accelerate iterative estimation of target scattering and noise power, realizes adaptive sparse two-dimensional super-resolution imaging of the scanning radar. The method solves the problems of high complexity and poor noise robustness of prior art means, compared with prior art two-dimensional super-resolution methods, has lower calculation complexity, is more robust, has excellent noise adaptive capacity, and can realize two-dimensional scanning radar super-resolution imaging under a low signal-to-noise ratio condition.

This invention provides a germline mutation detection method and system with a low false positive rate. The system is computer-executed and includes: first, performing a PCR repeat cluster consistency test on sequence alignment files generated from high-throughput sequencing reads, down-regulating the base count weights of inconsistent sites within the cluster; then, based on the sample-specific background error baseline, calculating the variation confidence index of each genomic site using an empirical Bayesian framework to obtain candidate single nucleotide variants (SNPs); obtaining candidate insertion / deletion variants through read clustering and physical verification of insertion fragment lengths; subsequently, performing a dual-engine cross-feedback iteration on the two candidate types until convergence, integrating and filtering, and outputting a structured mutation detection report. This invention significantly reduces the false positive rate of both SNPs and insertion / deletion variants while maintaining sensitivity, and improves the detection capability of complex insertion / deletion variants.

The application discloses a kind of ground and satellite-borne measurement precipitation data fusion algorithm under bayesian framework, comprising the following steps: selecting time and space matching satellite-borne measurement precipitation and ground radar measurement precipitation data, when matching, set certain space window and time window, time window is within ±6min with the time difference that ground radar starts once body scanning time that satellite-borne measurement sweeps matching space window, space window is the region that with ground radar as center, the circular region of r radius and the region that satellite-borne radar survey strip is intersected, the ground and satellite-borne measurement precipitation data fusion algorithm under bayesian framework of the application, based on hierarchical bayesian method, the fusion of ground radar and satellite-borne measurement precipitation data, improve satellite-borne measurement instantaneous precipitation precision, obtain the high-precision, high-resolution precipitation estimation result of comprehensive multi-source precipitation observation, and can quantitatively give the uncertainty size that fusion result contains, to better be applied to hydrological model.

The invention discloses a noise enhancement nonlinear system joint detection and estimation method under a Bayesian framework, and belongs to the field of signal processing. Firstly, independent additive noise is added to a nonlinear system input signal, and noise-corrected nonlinear system output is obtained after the signal passes through a nonlinear system. And secondly, under the Bayesian criterion, judging which hypothesis in the binary hypotheses is established by utilizing the output of a nonlinear system of noise correction, and estimating unknown parameters in the signal of which the judgment result is H1. On the premise that the detection performance is not reduced, a noise enhancement nonlinear system joint detection and estimation model which minimizes the estimation risk is constructed. The additive noise is the optimal solution of the model and is random distribution formed by not more than two constant vectors. According to the method, noise enhancement and nonlinear system joint detection and estimation under the Bayesian framework are combined, and the Bayesian estimation risk is further reduced under the condition that the Bayesian detection cost is not increased.

The invention relates to the technical field of laser-induced breakdown spectroscopy data processing, in particular to an LIBS spectrum processing method and device based on residual skewness estimation and a storage medium. The invention discloses an LIBS (Laser-induced Breakdown Spectroscopy) processing method based on residual skewness estimation. The method comprises the following steps: step S10, acquiring an observation signal of an LIBS; s20, on the basis of morphological analysis of the LIBS spectrum, constructing a spectrum model by using prior distribution of a Bayesian framework; step S30, based on the updated hidden variable observation signal, analyzing the updated hidden variable spectrum model by using variational inference to obtain iteration deviation and posterior analysis data; s40, judging whether the updating hidden variable iteration deviation meets an iteration termination requirement or not; if yes, iteration is stopped, and actual spectrum data is obtained according to the updated hidden variable posteriori analysis data; and if not, executing the step S20. According to the method, the spectrum model is analyzed by utilizing variational inference, the optimal convergence point is judged, effective separation of observation signals is realized, posterior analysis data is obtained, and the spectrum estimation precision and the calculation efficiency are greatly improved.

The present invention pertains to a method for determining DNA sequence variation in a fetus from samples of the fetus and parents using a Bayesian framework. The method comprising the following steps: receiving the samples comprise genomic sequence data covering one or more genomic regions of interest in relation to the fetus and the parents; processing the received samples at least in part based on a reference genome; computing a probability of fetal genotypes based on the samples using the Bayesian framework, where the Bayesian framework computes a posterior probability of whether a DNA sequence variant is present in the samples based on a prior probability and a likelihood function; and determining, based on the posterior probability of fetal genotypes, whether the DNA sequence variant is present in the fetus.

The application provides a kind of motorized platform forward-looking super-resolution imaging method of combined structured sparse and Bayesian framework, it is related to remote sensing technical field, comprising: establishing high dynamic platform forward-looking imaging signal processing model;Receive echo signal, according to echo signal and high dynamic platform forward-looking imaging signal processing model, obtain the preprocessed signal of echo signal in two-dimensional time domain.Construct phase compensation factor, according to phase compensation factor and the preprocessed signal of two-dimensional time domain, obtain multi-domain joint phase correction signal.The multi-domain joint phase correction signal is expressed as the form of overcomplete dictionary and signal scattering coefficient convolution, and the variable estimation result of the preprocessed signal of two-dimensional time domain in all distance gates is determined;According to the variable estimation result under all distance gates, generate the super-resolution forward-looking imaging of two-dimensional scene.Make the high-order motion of motorized platform be considered and the target structure with block sparse characteristics, realize the forward-looking super-resolution imaging of motorized platform to the surface target scene with structured sparse.

The invention provides a carbon emission reduction accounting method and system based on multi-modal uncertainty quantization, and relates to the technical field of carbon emission reduction accounting, and the method comprises the following steps: collecting and preprocessing the multi-modal data of a reconstructed building, and obtaining a preprocessed multi-modal data set; uncertainty modeling is carried out on the preprocessed multi-modal data set, and uncertainty distribution is obtained; based on the preprocessed multi-modal data set and uncertainty distribution, performing inversion by adopting a Bayesian framework to obtain a multi-modal data posterior probability; and performing Monte Carlo simulation on the posterior probability of the multi-modal data to obtain an uncertainty weight, and calculating the carbon emission reduction based on the uncertainty weight. According to the method, quantitative modeling is carried out on the uncertainty of the multi-modal data, the probability transmission and fusion of the uncertainty are realized by using the Bayesian framework, and finally the carbon emission reduction amount accounting result with the confidence interval is output, so that the scientificity and the credibility of the accounting are remarkably improved.

This invention relates to the field of wind power equipment fault diagnosis technology, and provides a method for extracting fault features from wind turbine gearboxes based on Gaussian mixture modeling. The method includes: modeling a mathematical model of the wind turbine gearbox observation signal by superimposing impact fault feature vectors and multi-source noise vectors, wherein the fault feature vector is the product of a redundant dictionary D and a sparse coefficient vector; modeling the multi-source noise vector as a Gaussian mixture distribution; constructing an objective function within a Bayesian framework to solve for the sparse coefficient vector in the mathematical model using maximum a posteriori probability estimation; simplifying the objective function; and using the EM algorithm and ADMM algorithm in a joint alternating iterative solution to obtain the optimized sparse coefficient vector; and reconstructing the impact fault feature vector in the wind turbine gearbox observation signal. This method improves the accuracy and robustness of extracting fault features from the observation signal of offshore wind turbine gearboxes.

The present application belongs to the technical field of image processing, and particularly relates to a multi-light source estimation method based on human visual color perception mechanism. In order to solve the problems of spectral coupling and edge blur which are extremely challenging in multi-light source estimation, the feedforward and feedback mechanisms in the human visual'retina-LGN-V1' color perception pathway are modeled as energy function optimization problems under the Bayesian framework, and the semi-quadratic splitting (HQS) algorithm is used for expansion solution, so as to realize the integration of human visual mechanism into the design of data-driven model. In combination with the carefully designed multi-scale opponent color initialization and color cross-attention optimization module, the framework significantly improves the estimation accuracy of complex spatial light distribution.

The application discloses a power distribution insulation thermal stress monitoring method based on dynamic change of pyrolysis particles, and relates to the technical field of smart grid monitoring, and specifically comprises the following steps: detecting characteristic gas concentration and micro pressure difference through multiple sensors, and tracking particle distribution of the characteristic gas; a double-chip architecture is established; a random field is defined, a priori is established by using Gaussian distribution, a conditional relationship of the random field is defined in a Bayesian framework according to a physical relationship, and a multivariate joint probability model is constructed; a posteriori of attention distribution of different space-time is automatically learned by using a Bayesian attention mechanism, a reliable interval is calculated based on drawing an attention graph, insulation materials in a power distribution box are divided into multiple regions, and thermal stress states of the regions are determined; a quantitative relationship model is constructed, and a probability distribution of a posteriori thermal stress numerical value is dynamically calculated by Bayesian inference. Through multiple sensor detection, Bayesian random field modeling and an attention mechanism, the application dynamically monitors and quantifies thermal stress distribution of insulation materials in the power distribution box under overheating conditions.

The invention discloses an uncertain quantization and propagation method for constitutive parameters of a viscoelastic composite material, which comprises the following steps of: developing a multi-scale micro-mechanical model on the basis of a fractional order differential and average field homogenization theory so as to reveal a nonlinear relation among component material performance, microstructure parameters and macroscopic viscoelasticity. And secondly, the Levenberg-Marquardt optimization algorithm and the Physics-Informed Neural Network proxy model are used for realizing the self-adaptive optimization of the sampling initial conjecture and the improvement of the calculation efficiency. Therefore, a Bayesian probability model is established, and model parameter posterior distribution is calculated by adopting a Markov chain Monte Carlo method so as to evaluate uncertainty, correlation and model errors of parameters. And finally, calculating posterior prediction distribution of the overall response, the constitutive response and the noise response. According to the method, the provided multi-scale Bayesian framework provides an effective way for optimizing component material performance and microstructure parameters, and the method has an important guiding effect on design of advanced viscoelastic composite materials with higher bearing capacity.

This invention proposes a safety assessment method for delaminated composite materials based on the principle of damage non-propagation, belonging to the fields of composite material delaminated damage modeling and composite structure safety assessment. The method includes: constructing ultimate load envelopes that meet the damage non-propagation requirement under different damage sizes; for readily available binary observation data in practical engineering, i.e., whether damage propagates under specific damage sizes and load conditions, connecting discrete observations with a continuous parameter space through a latent variable model; using the Probit link function to map the predicted difference between the load and the ultimate load into a damage propagation probability; and constructing a likelihood model using a Bernoulli likelihood function; and optimizing the ultimate load envelope within a Bayesian framework. This invention can effectively support safety assessment and maintenance decisions for composite material structures in aerospace and other fields.

The invention belongs to the technical field of radar signal sorting, and particularly discloses an environment adaptive optimization method for radar signal sorting under a Bayesian framework, which comprises the following steps of: acquiring a plurality of characteristic parameters of a radar signal, grouping the characteristic parameters into a plurality of characteristic domains according to physical attributes of the characteristic parameters, and then calculating statistical dispersion of the characteristic parameters in each domain, obtaining a complexity index of each feature domain; performing cross-domain weighted fusion on the complexity indexes of the feature domains to obtain a comprehensive electromagnetic environment complexity quantitative score; and dynamically mapping the electromagnetic environment complexity quantization score into a probability parameter for controlling generation of a new cluster in the CRP model under the Bayesian framework, and realizing adaptive optimization of the CRP model parameter. According to the method, adaptive adjustment of the CRP model parameters can be realized, and the robustness and accuracy of a sorting algorithm in a dynamic electromagnetic environment are improved.

The application discloses a ship radiation noise line spectrum estimation method based on multi-snapshot sparse Bayesian learning, and belongs to the field of underwater acoustic signal processing; the method utilizes the sparse characteristics of the ship radiation noise line spectrum, that is, the line spectrum is not uniformly distributed in the whole frequency range, but is discrete and sparse, converts the line spectrum estimation problem into a sparse signal recovery problem, so as to solve the problems in the background technology, under the condition that the data sample length is limited and the sparsity is unknown, the Bayesian framework utilizes probability modeling to adaptively adjust the hyperparameters from the global optimization angle, can effectively reduce the sidelobe, realizes high resolution, reduces the background noise fluctuation variance through multiple observation snapshots statistical average, enhances the signal-to-noise ratio of the line spectrum output, and improves the line spectrum feature detection performance.

The invention discloses a traffic sign recognition method based on a random Fourier feature visual state space model, and belongs to the technical field of computer vision, deep learning and intelligent traffic systems. The objective of the invention is to solve the problems of spectrum deviation, poor multi-scale adaptability and lack of uncertainty perception of the existing visual state space model. A hierarchical probabilistic network architecture (MS-RFF-VSSM) is constructed, a traffic sign image is mapped to a high-dimensional random feature space by using a kernel approximation technology through a hierarchical variational random Fourier feature embedding module, and meanwhile, the uncertainty of prediction is explicitly quantized based on a Bayesian framework; secondly, designing a multi-scale attention fusion module and a multi-scale receptive field visual state space backbone network, and considering global context modeling and adaptive extraction of local multi-scale features while keeping linear calculation complexity; and finally, carrying out dynamic weighting on the sampling features through a class attention prediction module to output a classification result. According to the method, the recognition precision and robustness of the model in a complex dynamic environment can be remarkably improved under the condition of relatively low parameter quantity, and the balance between the precision and the calculation efficiency is realized.

The invention relates to the technical field of cardiac electrophysiology noninvasive detection. The invention discloses a heart abnormal pulsation source positioning method, device and equipment and a medium. The method comprises the following steps: synchronously acquiring an MCG signal and an ECG signal; based on a preset feature extraction model, performing feature extraction on the MCG signal and the ECG signal to obtain an ECG time sequence feature sequence and an MCG space-time feature set; obtaining an MCG sensor coordinate and an ECG electrode coordinate, and carrying out space alignment processing on the MCG sensor coordinate and the ECG electrode coordinate based on a preset space coordinate mapping model; and on the basis of a preset heart geometry and conductance model, according to a mapping relationship among the ECG time sequence feature sequence, the MCG space-time feature set, the MCG sensor coordinates and the ECG electrode coordinates, determining an abnormal pulsation source through a Bayesian framework model, a deep learning model and a sparse inversion model. According to the invention, high temporal-spatial resolution, high stability and individualized noninvasive cardiac electrical activity source positioning are realized.

The invention belongs to the field of ecological disaster risk assessment, and particularly discloses a vegetation loss risk assessment method under drought and flood sudden change stress, and the method comprises the steps: processing the month-by-month rainfall data and NDVI data of a target region, and obtaining a rainfall data and NDVI data season scale sequence; based on the rainfall data seasonal scale sequence, calculating the occurrence probability of the dry-wet composite event between the adjacent seasons in different scenes by using a Copula function, and calculating the loss probability of vegetation under the stress of the dry-wet composite event between the adjacent seasons in different scenes by using the combination of a Bayesian framework and the Copula function; based on the NDVI data seasonal scale sequence, calculating the exposure degree of the vegetation system to the dry-wet composite event by adopting a seasonal average normalized vegetation index; and according to the occurrence probability, the loss probability and the exposure degree, calculating the vegetation loss risk under the stress of the dry-wet composite event. The vegetation loss risk can be evaluated more accurately, and a scientific basis is provided for ecological protection and disaster management.

This invention relates to the field of smart agriculture technology, and discloses a multi-source linkage method and an intelligent water and fertilizer allocation system driven by crop demand in smart agriculture. The multi-source linkage method includes: constructing an adaptive sparse sensor network and planning mobile inspection paths; using a Bayesian framework and introducing a data credibility weighting mechanism, and employing variational Kriging interpolation to achieve unified spatiotemporal resolution; achieving crop stress state identification adaptively by using a health status correction factor and a multi-state classifier; generating a crop water and fertilizer demand prediction scheme that considers yield loss risk by combining a risk value model and future weather forecasts; generating an optimal water and fertilizer allocation strategy; realizing multi-source linkage closed-loop feedback control, and continuously optimizing system parameters through an online learning mechanism to achieve adaptive evolution. This invention reduces the number of sensor nodes while ensuring monitoring coverage through adaptive sparse sensor network construction technology.

The application discloses a kind of front-looking scanning radar frequency domain fast imaging methods based on Gaussian prior, first obtains echo data and carries out pre-processing, then based on Gaussian prior Bayesian framework, carries out Bayesian inference, again constructs time-domain deconvolution problem, adds smoothing filter operator in equivalent regularization process, and the time-domain deconvolution problem is converted to frequency domain by Fourier transform, and fast frequency domain is solved.The method of the application adds smoothing filter operator when processing time-domain deconvolution problem, can smooth the noise and details of image, while retaining the basic structure of image, improve imaging quality, by Fourier transform into frequency domain, using the property that Fourier transform time-domain convolution is equal to frequency multiplication, successfully convert inverse operation into division operation, greatly reduce the calculation complexity, with higher imaging efficiency under the condition of not losing imaging quality.

The present application relates to the field of geophysical prestack seismic inversion, and provides a tight sandstone reservoir physical parameter inversion method and system. The tight sandstone reservoir physical parameter inversion method comprises the following steps: a rock physics model considering rock consolidation degree and pore structure is established, and a consolidation parameter and a pore structure parameter are calculated by combining known logging data with the rock physics model; the consolidation parameter and the pore structure parameter are fitted with porosity and shale content in the logging data to obtain a fitting relationship; the rock physics model is brought into an Aki-Richard reflection coefficient equation, and a target function is constructed under a Bayesian framework; and the target function is optimized by adopting a method of alternately updating reservoir physical parameters, consolidation parameters and pore structure parameters under the constraint of the fitting relationship to obtain the reservoir physical parameters. The present application can realize accurate prediction of a reservoir.