38 results about "Bayesian inversion" patented technology

The invention relates to a medical X-ray device 5 arrangement for producing three-dimensional information of an object 4 in a medical X-ray imaging medical X-ray device arrangement comprising an X-ray source 2 for X-radiating the object from different directions and a detector 6 for detecting the X-radiation to form projection data of the object 4. The medical X-ray device 5 arrangement comprises:means 15 for modelling the object 4 mathematically independently of X-ray imagingand means 15 for utilizing said projection data and said mathematical modelling of the object in Bayesian inversion based on Bayes' formulap⁢(⁢x⁢m)=ppr⁡(x)⁢p(m⁢x)p⁡(m)to produce three-dimensional information of the object, the prior distribution ppr(x) representing mathematical modelling of the object, the object image vector x, which comprise values of the X-ray attenuation coefficient inside the object, m representing projection data, the likelihood distribution p(m|x) representing the X-radiation attenuation model between the object image vector x and projection data m, p(m) being a normalization constant and the posteriori distribution p(x|m) representing the three-dimensional information of the object 4.

A method of locating subsurface hydrocarbon reservoirs includes acquiring seismic data, extracting seismic data attributes from the seismic data, calculating exemplar data group probability density function values using distribution values associated with the seismic attributes and applying Bayesian inversion to determine the likelihood of subsurface hydrocarbons.

A medical X-ray device 5 arrangement for producing three-dimensional information of an object 4 in a medical X-ray imaging comprises an X-ray source 2 for X-radiating the object from at least two different directions; a detector 6 for detecting the X-radiation to form projection data of the object 4; a computational device 15 for modelling the object 4 mathematically utilizing the projection data to solve the imaging geometry and / or the motion of the object, where the solving concerns either some or all parts of the imaging geometry and / or the motion of the object. The computational device 15 utilizes said projection data and said mathematical modelling of the object in Bayesian inversion based on Bayes' formulap⁡(x,θ❘m)=ppr⁡(θ)⁢ppr⁡(x)⁢p⁡(m❘x,θ)p⁡(m)to produce three-dimensional information of the object.

The invention discloses a lithofacies-constrained reservoir physical parameter inversion method and device, wherein the method includes: according to the Bayesian inversion framework, combined with lithofacies information, establishing a target inversion function based on lithofacies constraints; introducing lithofacies information, establish a statistical petrophysical lithofacies model; use stochastic simulation technology to generate elastic parameters and reservoir physical parameter training sample sets containing lithofacies information based on the statistical petrophysical lithofacies model; input pre-stack seismic inversion results data and lithofacies Data, use the training sample set to solve the target inversion function based on lithofacies constraints. The present invention uses lithofacies information as inversion constraint information, and by adding lithofacies constraints, the inversion principle is more rigorous, the inversion process is more reasonable, and the ambiguity of inversion results can be greatly reduced; the derived target inversion function Incorporating lithofacies information, the solution process is more targeted, and the results are more reliable; the favorable location of the reservoir can be accurately identified.

The invention provides a Bayesian linear AVA and AVF retrieval method of a sticky sound medium and belongs to the field of geophysical prospecting for petroleum. The method comprises the following steps: S1, inputting angle-domain seismic data, frequency-domain seismic data and log data; S2, establishing prior information and a likelihood function of the log data; S3, carrying out Bayesian linear AVA and AVF retrieval by using the prior information and the likelihood function so as to obtain a velocity and a quality factor.

The present invention provides a method for dynamically constructing an uncertain landslide model based on the fusion of multi-source heterogeneous data, including: joint use of geological survey, data collection, indoor mechanical test, CT scanning and numerical sample random reconstruction technology to obtain landslide rock and soil Spatial variation information of body mechanics parameters; on the basis of statistics on the spatial laws of rock and soil mechanics parameters, construct an initial uncertain landslide model; use non-invasive random finite element method to carry out numerical simulation of landslides under corresponding working conditions; construct different time The intelligent response surface model of point landslide output information and random variables, based on the intelligent response surface model and the reliability Bayesian inversion method of adaptive conditional sampling, uses the landslide direct information and monitoring indirect data to update the uncertain model in real time, thereby establishing a multi- A landslide uncertainty model for data fusion from heterogeneous sources. The beneficial effect of the invention is that a fine landslide geological model is established, which provides an effective means for subsequent mechanism analysis and stability evaluation of landslides.

The invention discloses a facies-controlled porosity inversion method based on Bayesian classification and belongs to the technical field of petroleum geology exploration. The facies-controlled porosity inversion method includes the following steps: 1. perform cross analysis on a well logging curve of rock porosity and the well logging curve of a plurality of rock elastic parameters which is acquired through experiment tests to acquire the rock elastic parameter data sensitive to the rock porosity; 2. acquiring the screened porosity and the screened elastic parameter data; 3. establishing a borehole-seism initial model and also acquiring the prior probability distribution of the screened porosity; 4. acquiring resample porosity and also acquiring fitting screening elastic parameter data and fitting resampling elastic parameter data; 5. acquiring resampling errors; 6. acquiring reconstructed elastic parameter data; 7. acquiring the prior sample series and conducting Bayesian classification to acquire the prior probability distribution and posterior probability distribution; and 8. conducting Bayesian inversion to transform a known elastic parameter data volume to a porosity data volume.