Download or read book Joining Statistics and Geophysics for Assessment and Uncertainty Quantification of Three dimensional Seismic Earth Models written by Carène Larmat and published by . This book was released on 2017 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seismic inversions produce seismic models, which are 3-dimensional (3D) images of wave velocity of the entire planet retrieved by fitting seismic measurements made on records of past earthquakes or other seismic events. Computing power of the TeraFlop era, along with the dataflow from new, very dense, seismic arrays, has led to a new generation of 3D seismic Earth models with an unprecedented level of resolution. Here we compare two recent models of western United States from the Dynamic North America (DNA) seismic imaging effort. The two models only differ in the wave propagation that was used for their inversion: one is based on ray theory (RT), and the other on finite frequency (FF). We evaluate the two models using an independent numerical method and statistical tests. We show that they differ in how they produce seismic signals from a subset of earthquakes that were used in the original inversion and were recorded on the US array. This is especially true for measurements done in the Yellowstone area which has a large negative seismic anomaly. This result is of importance for seismologists who have been debating on the practical benefit of using FF in ill-posed Earth inversions. Model evaluation, such as the one reported here, represents an opportunity for collaboration between geophysical and statistical communities. More opportunities should arise with the upcoming Exascale era, which will provide enough computational power to explore together several sources of errors in models with thousands of parameters, opening the way of uncertainty quantification of seismic models.
Download or read book Estimating the Uncertainty and Predictive Capabilities of Three Dimensional Earth Models written by and published by . This book was released on 2009 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many three-dimensional models of seismic velocity structure in Eurasia have been developed in recent years by the seismic nuclear monitoring community. Most of these models are not accompanied by quantitative estimates of uncertainty, either in the model velocities themselves or in geophysical observables predicted by the models (e.g., body-wave travel times). Moreover, the various 3D models produced by these studies have not been compared to one another for their predictive capabilities in any meaningful way. We have recently begun a new effort to address these issues, which will culminate in a comprehensive evaluation of the current generation of 3D seismic velocity models. In this paper we show the results of applying two familiar validation techniques, or model evaluation metrics, to three seismic velocity models. The evaluation metrics are regional travel-time prediction and event relocation, each using a ground-truth (GT) dataset that includes events with epicenters known to 7 km or better and regional P and S arrivals within the footprint of the model region. The models include the Joint Weston/MIT (JWM) crust and upper-mantle velocity model for south-central Asia, which was derived by jointly inverting a large set of body-wave Q travel times and surface-wave group velocities in a coupled nonlinear procedure. We also derived models from the body-wave and surface-wave datasets separately, using the same initial model, inversion grids, constraints and regularization employed in the joint inversion. To make comparisons with the JWM model possible, we applied the Poisson's ratio of the initial model to convert the P velocity model constructed with travel times to an S velocity model (and vice versa). The results of these exercises reveal many factors that complicate the straightforward evaluation of the models.
Download or read book Elements of 3D Seismology third edition written by Christopher L. Liner and published by SEG Books. This book was released on 2016-10-15 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Elements of 3D Seismology, third edition is a thorough introduction to the acquisition, processing, and interpretation of 3D seismic data. This third edition is a major update of the second edition. Sections dealing with interpretation have been greatly revised in accordance with improved understanding and availability of data and software. Practice exercises have been added, as well as a 3D seismic survey predesign exercise. Discussions include: conceptual and historical foundations of modern reflection seismology; an overview of seismic wave phenomena in acoustic, elastic, and porous media; acquisition principles for land and marine seismic surveys; methods used to create 2D and 3D seismic images from field data; concepts of dip moveout, prestack migration, and depth migration; concepts and limitations of 3D seismic interpretation for structure, stratigraphy, and rock property estimation; and the interpretation role of attributes, impedance estimation, and AVO. This book is intended as a general text on reflection seismology, including wave propagation, data acquisition, processing, and interpretation and will be of interest to entry-level geophysicists, experts in related fields (geology, petroleum engineering), and experienced geophysicists in one subfield wishing to learn about another (e.g., interpreters wanting to learn about seismic waves or data acquisition).
Download or read book Earthquake Data in Engineering Seismology written by Sinan Akkar and published by Springer Science & Business Media. This book was released on 2011-01-03 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses current activities in strong-motion networks around the globe, covering issues related to designing, maintaining and disseminating information from these arrays. The book is divided into three principal sections. The first section includes recent developments in regional and global ground-motion predictive models. It presents discussions on the similarities and differences of ground motion estimations from these models and their application to design spectra as well as other novel procedures for predicting engineering parameters in seismic regions with sparse data. The second section introduces topics about the particular methodologies being implemented in the recently established global and regional strong-motion databanks in Europe to maintain and disseminate the archived accelerometric data. The final section describes major strong-motion arrays around the world and their historical developments. The last three chapters of this section introduce projects carried out within the context of arrays deployed for seismic risk studies in metropolitan areas. Audience: This timely book will be of particular interest for researchers who use accelerometric data extensively to conduct studies in earthquake engineering and engineering seismology.
Download or read book Three Dimensional Space Time Analysis Theory of Geotechnical Seismic Engineering written by Changwei Yang and published by Springer Nature. This book was released on 2019-11-30 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by respected experts, this book presents essential findings on the Wenchuan earthquake. It establishes a series of time–frequency analysis methods, and subsequently applies them to the layered site, slope, and earth-retaining wall. Further, it examines various cases and their solutions, and shares the results of numerous shaking-table tests and numerical simulations. As such, it is a valuable resource for researchers and engineers in the fields of geotechnical engineering and anti-seismic engineering.
Download or read book Statistical Learning and Inference of Subsurface Properties Under Complex Geological Uncertainty with Seismic Data written by Anshuman Pradhan and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Attempting to characterize, image or quantify the subsurface using geophysical data for exploration and development of earth resources presents interesting and unique challenges. Subsurface heterogeneities are the result of abstract paleo geological events, exhibiting variability that is spatially complex and existent across multiple scales. This leads to significantly high-dimensional inverse problems under complex geological uncertainty, which are computationally challenging to solve with conventional geophysical and statistical inference methods. In this dissertation, we discuss these challenges within the context of subsurface property estimation from seismic data. We discuss three specific seismic estimation problems and propose methods from statistical learning and inference to tackle these challenges. The first problem we address is that of incorporating constraints from geological history of a basin into seismic estimation of P-wave velocity and pore pressure. In particular, our approach relies on linking velocity models to the basin modeling outputs of porosity, mineral volume fractions, and pore pressure through rock-physics models. We account for geologic uncertainty by defining prior probability distributions uncertain basin modeling parameters. We have developed an approximate Bayesian inference framework that uses migration velocity analysis in conjunction with well and drilling data for updating velocity and pore pressure uncertainty. We apply our methodology in 2D to a real field case from the Gulf of Mexico. We demonstrate that our methodology allows for building a geologic and physical model space for velocity and pore-pressure prediction with reduced uncertainty. In the second problem, we investigate the applicability of deep learning models for conditioning reservoir facies models, parameterized by geologically realistic geostatistical models such as training-image based and object-based models, to seismic data. In our proposed approach, end-to-end discriminative learning with convolutional neural networks (CNNs) is employed to directly learn the conditional distribution of model parameters given seismic data. The training dataset for the learning problem is derived by defining and sampling prior distributions on uncertain parameters and using physical forward model simulations. We apply our methodology to a 2D synthetic example and a 3D real case study of seismic facies estimation. Our synthetic experiments indicate that CNNs are able to almost perfectly predict the complex geological features, as encapsulated in the prior model, consistently with seismic data. For real case applications, we propose a methodology of prior falsification for ensuring the consistency of specified subjective prior distributions with real data. We found modeling of additive noise, accounting for modeling imperfections and presence of noise in the data, to be useful in ensuring that a CNN, trained on synthetic simulations, makes reliable predictions on real data. In the final problem, we present a framework that enables estimation of low-dimensional sub-resolution reservoir properties directly from seismic data, without requiring the solution of a high dimensional seismic inverse problem. Our workflow is based on the Bayesian evidential learning approach and exploits learning the direct relation between seismic data and reservoir properties to efficiently estimate reservoir properties. The theoretical framework we develop allows incorporation of non-linear statistical models for seismic estimation problems. Uncertainty quantification is performed with approximate Bayesian computation. With the help of a synthetic example of estimation of reservoir net-to-gross and average fluid saturations in sub-resolution thin sand reservoir, several nuances are foregrounded regarding the applicability of unsupervised and supervised learning methods for seismic estimation problems. Finally, we demonstrate the efficacy of our approach by estimating posterior uncertainty of reservoir net-to-gross in sub-resolution thin sand reservoir from an offshore delta dataset using pre-stack seismic data.
Download or read book Interpretation of Three dimensional Seismic Data written by Alistair R. Brown and published by Aapg. This book was released on 2004 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book 3D Seismic Imaging written by Biondo Biondi and published by SEG Books. This book was released on 2006 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accompanying CD-ROM includes PDF slides for teaching the material in the book and the C3-narrow-azimuth classic data set.
Download or read book Elements of 3 D Seismology written by Christopher L. Liner and published by Pennwell Books. This book was released on 1999 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: The author presents: 1-D and 2-D seismic concepts; historical and current 3-D shooting geometries; 3-D seismic survey pre-design techniques; concepts of seismic data processing, including migration and dip movement; and concepts and limitations of 3-D seismic interpretation for structure, stratigraphy, and rock properties."--BOOK JACKET.
Download or read book Three Dimensional Seismic written by Geophysical Society of Oklahoma City and published by . This book was released on 1978* with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Quantitative Seismic Interpretation written by Per Avseth and published by Cambridge University Press. This book was released on 2010-06-10 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative Seismic Interpretation demonstrates how rock physics can be applied to predict reservoir parameters, such as lithologies and pore fluids, from seismically derived attributes. The authors provide an integrated methodology and practical tools for quantitative interpretation, uncertainty assessment, and characterization of subsurface reservoirs using well-log and seismic data. They illustrate the advantages of these new methodologies, while providing advice about limitations of the methods and traditional pitfalls. This book is aimed at graduate students, academics and industry professionals working in the areas of petroleum geoscience and exploration seismology. It will also interest environmental geophysicists seeking a quantitative subsurface characterization from shallow seismic data. The book includes problem sets and a case-study, for which seismic and well-log data, and MATLAB® codes are provided on a website (http://www.cambridge.org/9780521151351). These resources will allow readers to gain a hands-on understanding of the methodologies.
Download or read book 3 D Seismic Survey Design written by Gijs J. O. Vermeer and published by SEG Books. This book was released on 2002 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computationally Efficient Methods for Uncertainty Quantification in Seismic Inversion written by Georgia K. Stuart and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Full waveform inversion is an iterative optimization technique used to estimate subsurface physical parameters in the earth. A seismic energy source is generated in a borehole or on the surface of the earth which causes a seismic wave to propagate into the underground material. The transmitted wave then reflects off of material interfaces (rocks and fluids) and the returning wave is recorded at geophones. The inverse problem involves estimating parameters that describe this wave propagation (such as velocity) to minimize the misfit between the measured data and data we simulate from our mathematical model. The seismic velocity inversion problem is difficult because it contains sources of uncertainty, due to the instruments used to record the data and our mathematical model for seismic wave propagation. Using uncertainty quantification (UQ), we construct distributions of earth velocity models. Distributions give information about how probable an Earth model is, given the recorded seismic data. This rich information impacts real-world decision making, such as where to drill a well to produce oil and gas. UQ methods based on repeated sampling to construct estimates of the distribution, such as Markov chain Monte Carlo (MCMC), are desirable because they do not impose restrictions on the shape of the distribution. How ever, MCMC methods are computationally expensive because they require solving the wave equation repeatedly to generate simulated seismic wave data. This dissertation focuses on techniques to reduce the computational expense of MCMC methods for the seismic velocity inversion problem. Two-stage MCMC uses an inexpensive filter to cheaply reject unacceptable velocity models. The operator upscaling method, an inexpensive surrogate for the wave equation, is one such filter. We find that two-stage MCMC with the operator upscaling filter is effective at producing the same uncertainty information as traditional one-stage MCMC, but reduces the computational cost by between 20% and 45%. A neural network, in conjunction with operator upscaling, is another choice of filter. We find that the neural network filter reduces the computational cost of MCMC by 65% for our experiment, which includes the time needed to generate the training set and the neural network. The size of the problem we can solve using two-stage MCMC is limited by the random walk sampler. Hamiltonian Monte Carlo (HMC) and the No-U-Turn sampler (NUTS) use gradient information and Hamiltonian dynamics to steer the sampler, thereby eliminating the inefficient random walk behavior. Discretizing Hamiltonian dynamics requires two user specified parameters: trajectory length and step size. The NUTS algorithm avoids setting the trajectory length in advance by constructing variable-length paths. We find that the NUTS algorithm for seismic inversion results in superior decrease in the residual over traditional HMC while removing the need for costly tuning runs. However, constructing the gradient for the seismic inverse problem is computationally expensive. In two-stage, neural network-enhanced HMC we replace the costly gradient computation with a neural network. Additionally, we use the neural network to reject unacceptable samples as in two-stage MCMC. We find that the two-stage neural network HMC scheme reduces the computational cost by over 80% when compared to traditional HMC for a 100-unknown layered problem.
Download or read book Epistemic Uncertainty Quantification of Seismic Damage Assessment written by Hesheng Tang and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The damage-based structural seismic performance evaluations are widely used in seismic design and risk evaluation of civil facilities. Due to the large uncertainties rooted in this procedure, the application of damage quantification results is still a challenge for researchers and engineers. Uncertainties in damage assessment procedure are important consideration in performance evaluation and design of structures against earthquakes. Due to lack of knowledge or incomplete, inaccurate, unclear information in the modeling, simulation, and design, there are limitations in using only one framework (probability theory) to quantify uncertainty in a system because of the impreciseness of data or knowledge. In this work, a methodology based on the evidence theory is presented for quantifying the epistemic uncertainty of damage assessment procedure. The proposed methodology is applied to seismic damage assessment procedure while considering various sources of uncertainty emanating from experimental force-displacement data of reinforced concrete column. In order to alleviate the computational difficulties in the evidence theory-based uncertainty quantification analysis (UQ), a differential evolution-based computational strategy for efficient calculation of the propagated belief structure in a system with evidence theory is presented here. Finally, a seismic damage assessment example is investigated to demonstrate the effectiveness of the proposed method.
Download or read book Earth s Structure from a Bayesian Analysis of Seismic Signals and Noise written by Mallory Kay Young and published by . This book was released on 2014 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: The prevailing drive of modern seismology is to improve our knowledge of the Earth's structure, composition, and dynamics through an analysis of seismic waveforms. With increasing computing power, number and quality of seismic stations, and length of data records, the resolution and spatial coverage of current Earth models has improved substantially over the past few decades. Yet many limitations remain. The advent of ambient noise seismology has provided the solution to many issues, such as the irregular distribution of earthquakes, biases from structures outside the model region, earthquake location errors, and lack of near-surface resolution. Despite improvements to data quality and quantity and the introduction of unconventional datasets such as ambient seismic noise, a persisting shortcoming of many tomographic inversions is ad-hoc error estimation, parameterization, and regularization, which prevent a meaningful portrayal of model complexity and uncertainty. With the rapid increase in computing power, non-linear techniques based on densely sampling favorable regions of model space are now becoming tractable for real-world tomographic problems and directly address these shortcomings. One such recently introduced and promising method is transdimensional and hierarchical Bayesian inference. This alternate approach allows model parameterization and resolution to be driven by the data. This thesis presents a collection of seismic inverse problems using real world datasets, some of which are tackled using fully non-linear Bayesian statistics. The benefits of a probabilistic approach are demonstrated for datasets targeting the uppermost crust down to the core through the development of novel methods of inversion and uncertainty quantification. To begin, an unconventional methodology for studying earthquake focal mechanisms in intraplate settings is presented through the inversion of ambient noise, receiver functions, and dispersion curves. The ambient seismic noise imaging approach of this study is subsequently applied to Tasmania - to which it is highly suited - and the resulting group and phase velocity maps help decipher Tasmania's enigmatic tectonic history. The same ambient noise dataset is further manipulated to yield a 3D shear velocity model of the region using a two-step transdimensional, hierarchical ensemble inference approach. Two prominent low-velocity anomalies offer insight into the Paleozoic evolution of the east Gondwana margin and support a connection between Tasmania and mainland Australia since the Cambrian. This approach is also applied to a larger dataset encompassing much of mainland southeast Australia. The Bayesian approach is also applied to a global dataset of differential body wave travel times in an effort to reveal P-wave velocity heterogeneity in the lowermost mantle. Another deep Earth application is demonstrated through an inversion for the time-dependent differential rotation of the inner core with respect to the rest of the mantle using careful measurements of earthquake doublets. The transdimensional nature of the inversion problem means that the data drive the number of free parameters constraining the differential rotation pattern, which exhibits much more complexity than the simple linear trend long-promoted by previous studies. The contents of this thesis help augment the diverse and wide-reaching applications for Bayesian statistics, which will continue to improve with future increases in computational power.
Download or read book Industrial Applications of Fuzzy Control written by Michio Sugeno and published by North Holland. This book was released on 1985 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume focuses on the practical applications of fuzzy control, which is one of the most promising research fields in fuzzy engineering. Control engineers in many fields can benefit from these case studies, which include the control of trains, aircraft, robots, and various industrial processes. Also featured is a comprehensive ''Annotated Bibliography of Fuzzy Control''.
Download or read book Geophysical Inversion written by J. Bee Bednar and published by SIAM. This book was released on 1992-01-01 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of papers on geophysical inversion contains research and survey articles on where the field has been and where it's going, and what is practical and what is not. Topics covered include seismic tomography, migration and inverse scattering.
