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A case study on homogeneous and heterogeneous reservoir porous media reconstruction by using generative adversarial networks

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Energy Procedia 2019年 158卷 6164-6169页

作者： Siyan Liu Zhi Zhong Ali Takbiri-Borujeni Mohammad Kazemi Qinwen Fu Yuhao Yang The University of Kansas Department of Chemical & Petroleum Engineering Lawrence Kansas USA 66045 Bureau of Economic Geology The University of Texas at Austin Texas USA 78713 West Virginia University Department of Petroleum and Natural Gas Engineering Slippery Rock University Department of Petroleum and Natural Gas Engineering

The advancing of modern X-ray computer tomography technology provides a powerful tool for us to illustrate the details inside the reservoir rock in three-dimensional space. Pore-scale rock characterization, modeling, and related fluid flow simulation can be challenging due to the high complexity of various rock samples. Conventional pore scale structure modeling methods such as various stochastic methods were developed for reservoir rock 3D microscopic structure reconstruction in order to generate representative realizations for numerical simulations and property upscaling approaches. In this work, generative adversarial networks (GANs) is used for generating the synthetic micro representations of porous rock by acquiring non-linear statistical information from the real 3D rock images in an unsupervised learning scheme. The related 3D image pre-processing, network training and adjusting as well as data post-processing procedures are addressed. The network prediction results from a homogeneous Berea sandstone and a heterogeneous Estaillades carbonate demonstrated the capability of GANs for high-resolution porous rock image representations reconstruction, generated and real images are compared via various visualizations and inspections. The study also illustrated the importance of the training image preprocessing, which indicating the data augmentation techniques can be one of the promising improvements in terms of capturing the sparsely distributed features from heterogenous 3D images and reconstructing the synthetic realizations, meanwhile, the robustness of the model during training process is enhanced when limited real data is available.

关键词： porous media image reconstruction generative adversarial networks neural networks

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Estimated edge's points position. Optimized model for polarimetric SAR imagery

Estimated edge's points position. Optimized model for polari...

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Institute-of-Electrical-and-Electronics-Engineers (IEEE) Biennial Congress of Argentina

作者： Monferran, Daniel Sartarelli, Andres Univ Nacl Gen Sarmiento Inst Ind Dept Sistemas Informac Juan Maria Gutierrez 1150 RA-1613 Buenos Aires DF Argentina Univ Nacl Gen Sarmiento Inst Desarrollo Humano Juan Maria Gutierrez 1150 RA-1613 Buenos Aires DF Argentina Univ Tecnol Nacl FRD San Martin 1171 RA-2804 Buenos Aires DF Argentina

ISBN: (纸本)9781538650325

The Polarimetric Synthetic Aperture Radar is a special type of radar widely used in remote sensing since it allows capturing high resolution long-range terrestrial images. PolSAR images contain a large volume of information and are also contaminated with speckle noise which is neither Gaussian nor additive, for this reason the automatic interpretation of PolSAR images is a very difficult task. The characteristics of this noise make it necessary to use statistical methods for processing and digital analysis in this type of images. The complex Wishart distribution is the statistical distribution model most used to work with images generated from polarimetric multilook data. This distribution is indexed by two parameters: Sigma the covariance matrix and L the equivalent number of looks used to form the image. It is necessary to work with a very precise L value, this has led to research works aimed at reducing the bias in their estimation. The importance of processing times in estimating the edge points position has led to analyze and compare the processing times consumed using different estimation methods. In this work, different edge points identification techniques using the complex Wishart distribution, maximum likelihood, stochastic distances and active contours are analyzed, with the aim of reducing the computational cost in the estimation of the position of the edge points for the segmentation of PolSAR images. By applying the signal model in discrete time, optimized analytical expressions are obtained to estimate the edge points position with a lower computational cost. For the graphic visualization of the results, the region edges with B-spline curves are delineated. As a significant fact, in the particular case of complex Wishart distribution, the new analytical expression developed does not include the equivalent number of looks L. With Montecarlo experiments in simulated and real PolSAR images, the reduction of processing times is evaluated.

关键词： Covariance matrices image edge detection Synthetic aperture radar image segmentation Computational modeling Radar imaging

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stochastic Speckle Discrimination with Time-tagged Photon Lists: Digging below the Speckle Noise Floor

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PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC 2019年第1005期131卷 1-1页

作者： Walter, Alex B. Bockstiegel, Clinton Brandt, Timothy D. Mazin, Benjamin A. Univ Calif Santa Barbara Dept Phys Santa Barbara CA 93106 USA

We present an algorithm that uses the distribution of photon arrival times to distinguish speckles from incoherent sources, like planets and disks, in high-contrast images. Using simulated data, we show that our approach can overcome the noise limit from fluctuating speckle intensity. The algorithm is likely to be most advantageous when a coronagraph limits the coherent diffraction pattern in the image plane but the intensity is still strongly modulated by fast-timescale uncorrected stellar light, for example from atmospheric turbulence. These conditions are common at small inner working angles of highly corrected adaptive optics images and will allow probing of exoplanet populations at smaller angular separations. The technique requires a fast science camera that can temporally resolve the speckle fluctuations, and the detection of many photons per speckle decorrelation time. Because the algorithm directly extracts the incoherent light, standard differential imaging postprocessing techniques can be performed afterwards to further boost the signal.

关键词： methods: data analysis methods: statistical planets and satellites: detection techniques: high angular resolution techniques: image processing

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Soft Dropout And Its Variational Bayes Approximation

Soft Dropout And Its Variational Bayes Approximation

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IEEE Workshop on Machine Learning for Signal processing

作者： Jiyang Xie Zhanyu Ma Guoqiang Zhang Jing-Hao Xue Zheng-Hua Tan Jun Guo Pattern Recognition and Intelligent Systems Lab Beijing University of Posts and Telecommunications China School of Electrical and Data Engineering University of Technology Sydney Australia Department of Statistical Science University College London United Kingdom Department of Electronic Systems Aalborg University Denmark

Soft dropout, a generalization of standard “hard” dropout, is introduced to regularize the parameters in neural networks and prevent overfitting. We replace the “hard” dropout mask following a Bernoulli distribution with the “soft” mask following a beta distribution to drop the hidden nodes in different levels. The soft dropout method can introduce continuous mask coefficients in the interval of [0, 1], rather than only zero and one. Meanwhile, in order to implement the adaptive dropout rate via adaptive distribution parameters, we respectively utilize the half-Gaussian distributed and the half-Laplace distributed variables to approximate the beta distributed masks and apply a variation of variational Bayes optimization called stochastic gradient variational Bayes (SGVB) algorithm to optimize the distribution parameters. In the experiments, compared with the standard soft dropout with fixed dropout rate, the adaptive soft dropout method generally improves the performance. In addition, the proposed soft dropout and its adaptive versions achieve performance improvement compared with the referred methods on both image classification and regression tasks.

关键词： Neural networks Probability density function Standards Training Bayes methods Approximation algorithms Optimization

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Robust Optimisation Monte Carlo

arXiv

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arXiv 2019年

作者： Ikonomov, Borislav Gutmann, Michael U. School of Informatics University of Edinburgh

This paper is on Bayesian inference for parametric statistical models that are defined by a stochastic simulator which specifies how data is generated. Exact sampling is then possible but evaluating the likelihood function is typically prohibitively expensive. Approximate Bayesian Computation (ABC) is a framework to perform approximate inference in such situations. While basic ABC algorithms are widely applicable, they are notoriously slow and much research has focused on increasing their efficiency. Optimisation Monte Carlo (OMC) has recently been proposed as an efficient and embarrassingly parallel method that leverages optimisation to accelerate the inference. In this paper, we demonstrate an important previously unrecognised failure mode of OMC: It generates strongly overconfident approximations by collapsing regions of similar or near-constant likelihood into a single point. We propose an efficient, robust generalisation of OMC that corrects this. It makes fewer assumptions, retains the main benefits of OMC, and can be performed either as post-processing to OMC or as a stand-alone computation. We demonstrate the effectiveness of the proposed Robust OMC on toy examples and tasks in inverse-graphics where we perform Bayesian inference with a complex image renderer. Copyright © 2019, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Ultrasound image Despeckling Using stochastic Distance-Based BM3D

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IEEE TRANSACTIONS ON image processing 2017年第6期26卷 2632-2643页

作者： Santos, Cid A. N. Martins, Diego L. N. Mascarenhas, Nelson D. A. Univ Fed Sao Carlos Dept Comp BR-13565905 Sao Carlos Brazil Israelite Hosp Albert Einstein Dept Intervent Radiol BR-05651901 Sao Paulo Brazil Fac Campo Limpo Paulista BR-13231230 Campo Limpo Paulista Brazil

Ultrasound image despeckling is an important research field, since it can improve the interpretability of one of the main categories of medical imaging. Many techniques have been tried over the years for ultrasound despeckling, and more recently, a great deal of attention has been focused on patch-based methods, such as non-local means and block-matching collaborative filtering (BM3D). A common idea in these recent methods is the measure of distance between patches, originally proposed as the Euclidean distance, for filtering additive white Gaussian noise. In this paper, we derive new stochastic distances for the Fisher-Tippett distribution, based on well-known statistical divergences, and use them as patch distance measures in a modified version of the BM3D algorithm for despeckling log-compressed ultrasound images. State-of-the-art results in filtering simulated, synthetic, and real ultrasound images confirm the potential of the proposed approach.

关键词： Despeckling ultrasound imaging stochastic distances BM3D patch-based filtering

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PARALLELIZED stochastic GRADIENT MARKOV CHAIN MONTE CARLO ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION

PARALLELIZED STOCHASTIC GRADIENT MARKOV CHAIN MONTE CARLO AL...

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IEEE International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Simsekli, Umut Durmus, Alain Badeau, Roland Richard, Gael Moulines, Eric Cemgil, A. Taylan Univ Paris Saclay Telecom ParisTech LTCI F-75013 Paris France Ecole Polytech Ctr Appl Math Palaiseau France Bogazici Univ Dept Comp Engn TR-34342 Istanbul Turkey

ISBN: (纸本)9781509041176

stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods have become popular in modern data analysis problems due to their computational efficiency. Even though they have proved useful for many statistical models, the application of SG-MCMC to non-negative matrix factorization (NMF) models has not yet been extensively explored. In this study, we develop two parallel SG-MCMC algorithms for a broad range of NMF models. We exploit the conditional independence structure of the NMF models and utilize a stratified sub-sampling approach for enabling parallelization. We illustrate the proposed algorithms on an image restoration task and report encouraging results.

关键词： stochastic Gradient MCMC Non-Negative Matrix Factorization Tweedie Distribution Beta Divergence Richardson-Romberg Extrapolation

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D4L: Decentralized Dynamic Discriminative Dictionary Learning

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION processing OVER NETWORKS 2017年第4期3卷 728-743页

作者： Koppel, Alec Warnell, Garrett Stump, Ethan Ribeiro, Alejandro Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA US Army Res Lab Computat & Informat Sci Directorate Adelphi MD 20783 USA

We consider discriminative dictionary learning in a distributed online setting, where a network of agents aims to learn, from sequential observations, statistical model parameters jointly with data-driven signal representations. We formulate this problem as a distributed stochastic program with a nonconvex objective that quantifies the merit of the choice of model parameters and dictionary. We consider the use of a block variant of the Arrow-Hurwicz saddle point algorithm to solve this problem, which exploits factorization properties of the Lagrangian to yield a protocol in that only requires exchange of model information among neighboring nodes. We show that decisions made with this saddle point algorithm asymptotically achieve a first-order stationarity condition on average. The learning rate depends on the signal source, network structure, and discriminative task. We illustrate the algorithm performance for solving a large-scale image classification task on a network of interconnected servers and observe that practical performance is comparable to a centralized approach. We further apply this method to the problem of a robotic team seeking to autonomously navigate in an unknown environment by predicting unexpected maneuvers, demonstrating the proposed algorithm's utility in a field setting.

关键词： Optimization methods multi-robot systems autonomous systems signal representation stochastic systems distributed processing

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A Survey of stochastic Simulation and Optimization methods in Signal processing

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL processing 2016年第2期10卷 224-241页

作者： Pereyra, Marcelo Schniter, Philip Chouzenoux, Emilie Pesquet, Jean-Christophe Tourneret, Jean-Yves Hero, Alfred O., III McLaughlin, Steve Univ Bristol Sch Math Bristol BS8 1TW Avon England Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Univ Paris Est Lab Informat Gaspard Monge F-77454 Marne La Vallee France Univ Toulouse INP ENSEEIHT IRIT TeSA F-31071 Toulouse France Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Heriot Watt Univ Engn & Phys Sci Edinburgh EH14 4AS Midlothian Scotland

Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, requiring ever more sophisticated computational inference techniques. This has driven the development of statistical SP methods based on stochastic simulation and optimization. stochastic simulation and optimization algorithms are computationally intensive tools for performing statistical inference in models that are analytically intractable and beyond the scope of deterministic inference methods. They have been recently successfully applied to many difficult problems involving complex statistical models and sophisticated (often Bayesian) statistical inference techniques. This survey paper offers an introduction to stochastic simulation and optimization methods in signal and image processing. The paper addresses a variety of high-dimensional Markov chain Monte Carlo (MCMC) methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms. It also discusses a range of optimization methods that have been adopted to solve stochastic problems, as well as stochastic methods for deterministic optimization. Subsequently, areas of overlap between simulation and optimization, in particular optimization-within-MCMC and MCMC-driven optimization are discussed.

关键词： Bayesian inference Markov chain Monte Carlo proximal optimization algorithms variational algorithms for approximate inference

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基于鲁棒最优控制的在线机器学习方法

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中国科学：数学 2018年第9期48卷 1181-1202页

作者：宁瀚文李占风中南财经政法大学统计与数学学院武汉430073

基于再生核Hilbert空间(reproducing kernel Hilbert space,RKHS)的统计学习模型被广泛应用于函数逼近、图像处理、模式识别和回归分析等领域,并且也在非线性随机动力学系统的辨识问题中有着很好的表现.本文提出一个基于鲁棒最优控制的R... 详细信息

基于再生核Hilbert空间(reproducing kernel Hilbert space,RKHS)的统计学习模型被广泛应用于函数逼近、图像处理、模式识别和回归分析等领域,并且也在非线性随机动力学系统的辨识问题中有着很好的表现.本文提出一个基于鲁棒最优控制的RKHS模型学习方法,来实现对非线性随机动力学系统的高效在线建模.利用本文得到的关于再生核空间的一些理论结果,本文将随机动力学系统的在线学习问题转化为一组具有有界随机扰动的离散时变线性系统的输出反馈镇定问题,并利用模型预测控制技术来设计相应的控制算法和学习算法.与现有的RKHS模型学习方法相比,在不引入任何数据窗口原理、剪枝技术、学习步长的调整机制以及对噪声统计性质的假设的情形下,新方法可以在保证模型参数快速且鲁棒收敛的同时,实现对动力学系统的自适应高精度建模.此外,本文首次从最优控制的视角出发,研究动力学系统的在线核学习问题.在本文提出的研究框架下,现有各种控制技术可以被利用起来开发新的鲁棒学习方法,这也为核学习理论的研究和算法的开发提供一些新的思路.本文亦给出了数值算例和对比结果,用来说明新方法的有效性.

关键词：统计学习在线学习再生核空间随机动力学系统鲁棒最优控制模型预测控制

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