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Estimation of the subsurface electromagnetic velocity distribution from diffraction hyperbolas by means of a novel automated picking procedure: Theory and application to glaciological ground-penetrating radar data sets
Dossi, Matteo (Roma Tre University) | Forte, Emanuele (University of Trieste) | Cosciotti, Barbara (Roma Tre University) | Lauro, Sebastian Emanuel (Roma Tre University) | Mattei, Elisabetta (Roma Tre University) | Pettinelli, Elena (Roma Tre University) | Pipan, Michele (University of Trieste)
ABSTRACT We have developed an auto-picking algorithm that is designed to automatically detect subsurface diffractors within ground-penetrating radar (GPR) data sets, to accurately track the hyperbolic diffractions originating from the identified scatterers, and to recover the subsurface electromagnetic (EM) velocity distribution, among other possible analyses. Our procedure presents several advantages with respect to other commonly applied diffraction tracking techniques because it can be applied with minimal signal preprocessing, thus making it more versatile and adaptable to local conditions; it requires only limited input from the interpreter in the form of a few thresholds for the tracking parameters, thus making the results more objective; and it does not involve pretraining as opposed to machine-learning algorithms, thus removing the need to gather a large and comprehensive image database of all possible subsurface situations, which would not necessarily be limited to only examples of diffractions. The presented algorithm starts by identifying those signals that are likely to belong to diffraction apexes, which are then used as initial seeds by the auto-tracking process. The horizontal search window used during the auto-tracking process is locally adapted through a rough preliminary estimate of the size of each diffraction. In addition, multiple seeds within the same apex can produce several acceptable hyperbolas tracking the same diffraction phase. The algorithm thus selects the best-fitting ones by assessing several signal attributes while also removing redundant hyperbolas and the expected false positives. The algorithm is applied to two glaciological GPR profiles, and it is able to accurately track the vast majority of the recorded diffractions, with very few false positives and negatives. This produces a statistically sound EM velocity distribution, which was used to assess the state of the surveyed alpine glacier.
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Electromagnetic Surveying (1.00)
Posterior sampling with convolutional neural network-based plug-and-play regularization with applications to poststack seismic inversion
Izzatullah, Muhammad (King Abdullah University of Science and Technology (KAUST)) | Alkhalifah, Tariq (King Abdullah University of Science and Technology (KAUST)) | Romero, Juan (King Abdullah University of Science and Technology (KAUST)) | Corrales, Miguel (King Abdullah University of Science and Technology (KAUST)) | Luiken, Nick (King Abdullah University of Science and Technology (KAUST)) | Ravasi, Matteo (King Abdullah University of Science and Technology (KAUST))
ABSTRACT Uncertainty quantification is a crucial component in any geophysical inverse problem, as it provides decision makers with valuable information about the inversion results. Seismic inversion is a notoriously ill-posed inverse problem, due to the band-limited and noisy nature of seismic data; as such, quantifying the uncertainties associated with the ill-posed nature of this inversion process is essential for qualifying the subsequent interpretation and decision-making processes. Selecting appropriate prior information is a crucial โ yet nontrivial โ step in probabilistic inversion because it influences the ability of sampling-based inference algorithms to provide geologically plausible posterior samples. However, the necessity to encapsulate prior knowledge into a probability distribution can greatly limit our ability to define expressive priors. To address this limitation and following in the footsteps of the plug-and-play (PnP) methodology for deterministic inversion, we develop a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence loss โ a measure of the distance between the approximated and target probabilistic distributions โ with a convolutional neural network-based denoiser. We call this new algorithm PnP Stein variational gradient descent and determine its ability to produce high-resolution trustworthy samples that realistically represent subsurface structures. Our method is validated on synthetic and field poststack seismic data.
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > ร sgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Svarte Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Sleipner Formation (0.99)
- (17 more...)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.93)
ABSTRACT A novel recursive, self-supervised machine-learning (ML) inversion scheme is developed. It is applied for fast and accurate full-waveform inversion of land seismic data. ML generalization is enhanced by using virtual super gathers (VSGs) of field data for training. These are obtained from midpoint-offset sorting and stacking after applying surface-consistent corrections from the decomposition of the transmitted wavefield. The procedure implements reinforcement learning concepts by adopting an inversion agent to interact with the environment and explore the model space under a data misfit optimization policy. The generated parameter distributions and related forward responses are used as new training samples for supervised learning. The active learning (AL) paradigm is further embedded in the procedure, for which queries on data diversity and uncertainty are used to generate fully informative reduced sets for training. The procedure is recursive. At each cycle, the physics-based inversion is coupled to the ML predictions via penalty terms that promote a long-term data misfit reduction. The resulting self-supervised, AL, physics-driven deep-learning inversion generalizes well with field data. The method is applied to perform full-waveform inversion (FWI) of a complex land seismic data set characterized by transcurrent faulting and related structures. High signal-to-noise VSGs are inverted with a 1.5D Laplace-Fourier FWI scheme. The AL inversion procedure uses a small fraction of data for training while achieving sharper velocity reconstructions and a lower data misfit when compared with previous results. AL FWI is highly generalizable and effective for land seismic velocity model building and for other inversion scenarios.
- North America > United States (1.00)
- Asia > Middle East > Saudi Arabia (0.46)
- Geology > Structural Geology (0.46)
- Geology > Rock Type > Sedimentary Rock (0.46)
- Geology > Geological Subdiscipline > Geomechanics (0.46)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Clustering (0.93)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (0.88)
This topic presents information on fuel system design, providing guidelines for determining the facility fuel requirements and information on selecting equipment needed to treat fuels prior to use. Technical explanations on Fired heaters and their uses, along with guidelines for selecting and specifying a fired heater is presented. The next tutorial provides methods for determining the amount of waste heat energy available, and presents information and procedures on the design of a waste heat recovery system. Then, various components of refrigeration and heating systems are described, and a procedure is established for sizing evaporator duties, condenser duties, compressor horsepower, fan horsepower and duct sizes. In the last two subtopics, a basic understanding of reciprocating and centrifugal pumps is provided.
The learner will learn how best practices and procedures, including workflow management, are applied to Petroleum exploration and how they lead to streamlined, predictable and efficient use of the companies resources and improved business performance. The learner will be able to: learn the essentials of the five-stage life cycle of a petroleum project that are an integral part of the corporate planning and exploration management process, learn the standard documents and procedures that should be applied to petroleum exploration, learn the key components of Stage One: Assessment of Exploration Opportunities, and learn a typical exploration workflow and best practices used in petroleum exploration.
These presentations provide an understanding of two-phase and three-phase separators, describe how they work and what the design procedures are for sizing them. Then, different methods and procedures are described for oil treating and associated equipment design. Oil desalting is the process of removing water-soluble salts from an oil stream. This presentation describes the equipment commonly used and provides references for sizing the equipment. Crude oil or condensate stabilization describes the various processes used to stabilize a crude oil or condensate stream, and presents a preliminary method for determining liquid recoveries through stabilization.
Upon completing this Learning Module assignment, the participant should be able to define the following reservoir properties and understand their importance in the overall reservoir development scheme: rock properties (porosity, permeability, fluid saturation, compressibility, anisotropy), fluid properties (phase behavior, PVT relationships, density, viscosity, compressibility, formation volume factor, gas-oil ratio), rock/fluid interactions (wettability, interfacial tension, capillary pressure, relative permeability), read and understand wellsite descriptions of recovered core material, evaluate the core handling and preservation techniques employed, and select sample intervals for laboratory analysis, generate a procedure for preparing and analyzing selected core samples, specifying the tests to be run and the information to be obtained, describe the laboratory techniques and perform the calculations used for determining rock properties, design procedures for obtaining representative surface and subsurface formation fluid samples, describe procedures for generating PVT analyses of reservoir fluid samples, and interpret the resulting reports, and use published correlations to estimate reservoir fluid properties.
This means each frequency component travels at a different speed; namely, the horizontal phase velocity. The dispersive character of guided waves is most pronounced in shallow water environments (less than 100 m). Depending on various water-bottom conditions, such as a mud layer with variable thickness or a hard bottom, the character of these waves may vary from shot to shot (Figure 6.0-3). They also can cause linear noise on stacked data (Figure 6.2-8a) and are easily confused with the linear noise that is associated with side scatterers (Figure 6.0-4). McMechan and Yedlin [4] proposed a way to obtain phase velocity information from field data.
- Information Technology > Knowledge Management (0.76)
- Information Technology > Communications > Collaboration (0.76)