Nonhyperbolic normal moveout stretch correction with deep learning automation

Normal-moveout (NMO) correction is a fundamental step in seismic data processing. It consists of mapping seismic data from recorded traveltimes to corresponding zero-offset times. This process produces wavelet stretching as an undesired by-product. We have addressed the NMO stretching problem with two methods: (1) an exact stretch-free NMO correction that prevents the stretching of primary reflections and (2) an approximate post-NMO stretch correction. Our stretch-free NMO produces parallel moveout trajectories for primary reflections. Our post-NMO stretch correction calculates the moveout of stretched wavelets as a function of offset. Both methods are based on the generalized moveout approximation and are suitable for application in complex anisotropic or heterogeneous environments. We use new moveout equations, modify the original parameter functions to be a constant over the primary reflections, and then interpolate the seismogram amplitudes at the calculated traveltimes. For fast and automatic modification of the parameter functions, we use deep learning. We design a deep neural network (DNN) using convolutional layers and residual blocks. To train the DNN, we generate a set of 40,000 synthetic NMO-corrected common-midpoint gathers and the corresponding desired outputs of the DNN. The data set is generated using different velocity profiles, wavelets, and offset vectors, and it includes multiples, ground roll, and band-limited random noise. The simplicity of the DNN task - a 1D identification of primary reflections - improves the generalization in practice. We use the trained DNN and find successful applications of our stretch-correction method on synthetic and different real data sets

Multiazimuth velocity analysis using velocity-independent seismic imaging

textMultiazimuth seismic data contains information about how the Earth’s seismic response changes with azimuthal direction. Directional-dependence of the seismic response can be caused by anisotropy or heterogeneity, associated with subsurface features such as fractures, stresses, or structure. Characterizing azimuthal variations is done through velocity analysis, which provides a link between an acquired data set and its image, as well as between the image and subsurface geology. At the stage which conventional velocity analysis is applied, it is difficult to distinguish the geologic cause of observed azimuthal velocity variations. The inability to distinguish the similar effects of anisotropy and heterogeneity leads to positioning errors in the final image and velocity estimates. Regardless of the cause, azimuthally variable velocities require at least three parameters to characterize, as opposed to the conventional single-parameter isotropic velocity. The semblance scan is the conventional tool for seismic velocity analysis, but it was designed for the isotropic case. For multiple parameters, the semblance scan becomes computationally impractical. In order to help address the xiissues of geologic ambiguity and computational efficiency, I develop three methods for multiazimuth seismic velocity analysis based on “velocity-independent” imaging techniques. I call this approach, velocity analysis by velocity-independent imaging, where I reverse the conventional order of velocity estimation followed by image estimation. All three methods measure time-domain effective-velocity parameters. The first method, 3D azimuthally anisotropic velocity-independent NMO, replaces the explicit measurement of velocity with local slope detection. The second method, time-warping, uses local slope information to predict traveltime surfaces without any moveout assumption beforehand, and then fit them with a multiparameter velocity model. The third method, azimuthal velocity continuation, uses diffraction image focusing as a velocity analysis criterion, thereby performing imaging and velocity analysis simultaneously. The first two methods are superior to the semblance scan in terms of computational efficiency and their ability to handle multi-parameter models. The third method is similar to a single multi-parameter semblance scan in computational cost, but it helps handle the ambiguity between structural heterogeneity and anisotropy, which leads to better positioned images and velocity estimates.Geological Science

Theory, Software and Testing Examples for Decision Support Systems

Research in methodology of Decision Support Systems is one of the activities within the System and Decision Sciences Program which was initiated seven years ago and is still in the center of interests of SDS. During these years several methodological approaches and software tools have been developed; among others the DIDAS (Dynamic Interactive Decision Analysis and Support) and SCDAS (Selection Committed Decision Analysis and Support). Both methodologies gained a certain level of popularity and have been successfully applied in other IIASA programs and projects as well as in many scientific institutions. Since development and testing the software and methodologies on real life examples requires certain -- rather high -- resources, it was decided to establish a rather extensive international collaboration with other scientific institutions in various NMO countries. This volume presents the result of the second phase of such a cooperation between the SDS Program and the four scientific institutions in Poland. The research performed during this stage related mostly to converting the decision support software developed during the previous phase, from the mainframe to the microcomputer, ensuring simultaneously high level of rebustness, efficiency and user friendliness. Several new theoretical developments, like new non-simplex algorithm for linear programming, new algorithms for mixed-integer programming and job shop scheduling are also described in the volume. Finally, it presents also new theoretical developments relating to supporting the processes of negotiations as well as the methodological issues on application the Decision Support Systems in industry management

Genetic full waveform inversion to characterise fractures

Active seismic methodologies provide a non-invasive tool to remotely characterise the physical properties of fractures at a wide range of scales, and have a positive impact in helping to solve rock engineering problems in a variety of geo-industrial applications. With current advances in seismic processing tools, such as full waveform inversion (FWI), and accurate models of seismic wave interaction with fractures, seismic characterisation of fractures can be tackled by utilising the entire seismic wavefield recorded at the receiver locations. A two-step strategy, using the genetic algorithm (GA) for global optimisation and the Neighbourhood Algorithm (NA) for evaluating uncertainties, was developed to simultaneously estimate the fracture properties (both fracture specific stiffness and equivalent fracture stiffness) and the background material properties directly from seismic waveforms. The optimisation involves minimising the difference between the observed (measured) and forward-modelled full waveforms through the finite difference code WAVE3D. The development, named Genetic Algorithm Full-Waveform Fracture Inversion (GAFWFI), looks beyond conventional seismic methods which focus on characterising fracture-induced anisotropy, by reducing the need to manually condition the data (e.g. manual picking of seismic phases), and by providing a robust means to explore multiple solutions. The development also allows the gap between different representations of fracturing to be bridged within a comprehensive method which can employ both discrete fracture and effective fracture models. GA-FWFI is tested initially on synthetic ultrasonic experiments with parallel fractures. Results confirm that the method can effectively invert for physical properties such as fracture stiffness, location, background material properties, while the posterior probability density (PPD) show that inversions are very well constrained. GA-FWFI is then applied to waveforms from a laboratory experiment investigating fracture slip and again results show high degree of accuracy. GA-FWFI is then utilised to unveil the coupling between discrete fracture networks (DFNs) and their equivalent fracture zone properties. The results reveal that the transition from a medium with open cracks to one with welded interfaces leads to the equivalent media having the equivalent medium stiffness non-linearly related to the crack specific stiffness. An attribute χ is proposed which helps guide the interpretation of a cracked medium by giving a range of likely values for crack size and crack stiffness. This work paves the way for novel strategies to seismically characterise fractures

Quantitative MRI and machine learning for the diagnosis and prognosis of Multiple Sclerosis

Multiple sclerosis (MS) is an immune-mediated, inflammatory, neurological disease affecting myelin in the central nervous system, whose driving mechanisms are not yet fully understood. Conventional magnetic resonance imaging (MRI) is largely used in the MS diagnostic process, but because of its lack of specificity, it cannot reliably detect microscopic damage. Quantitative MRI provides instead feature maps that can be exploited to improve prognosis and treatment monitoring, at the cost of prolonged acquisition times and specialised MR-protocols. In this study, two converging approaches were followed to investigate how to best use the available MRI data for the diagnosis and prognosis of MS. On one hand, qualitative data commonly used in clinical research for lesion and anatomical purposes were shown to carry quantitative information that could be used to conduct myelin and relaxometry analyses on cohorts devoid of dedicated quantitative acquisitions. In this study arm, named bottom-up, qualitative information was up-converted to quantitative surrogate: traditional model-fitting and deep-learning frameworks were proposed and tested on MS patients to extract relaxometry and indirect-myelin quantitative data from qualitative scans. On the other hand, when using multi-modal MRI data to classify MS patients with different clinical status, different MR-features contribute to specific classification tasks. The top-down study arm consisted in using machine learning to reduce the multi-modal dataset dimensionality only to those MR-features that are more likely to be biophysically meaningful with respect to each MS phenotype pathophysiology. Results show that there is much more potential to qualitative data than lesion and tissue segmentation, and that specific MRI modalities might be better suited for investigating individual MS phenotypes. Efficient multi-modal acquisitions informed by biophysical findings, whilst being able to extract quantitative information from qualitative data, would provide huge statistical power through the use of large, historical datasets, as well as constitute a significant step forward in the direction of sustainable research

Advanced geophysical studies of accretion of oceanic lithosphere in Mid-Ocean Ridges characterized by contrasting tectono-magmatic settings

Thesis (Ph. D.)--Joint Program in Oceanography (Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences; and the Woods Hole Oceanographic Institution), 2012.Cataloged from PDF version of thesis.Includes bibliographical references.The structure of the oceanic lithosphere results from magmatic and extensional processes taking place at mid-ocean ridges (MORs). The temporal and spatial scales of the variability of these two processes control the degree of heterogeneity of the oceanic lithosphere, represented by two end-member models: the classical Penrose Model exemplified by layered magmatic crust formed along fast-spreading MORs, e.g., East Pacific Rise (EPR); and the recently defined Chapman Model describing heterogeneous mafic and ultramafic lithosphere formed in settings of oceanic detachment faulting common along slow-spreading MORs, e.g., Mid-Atlantic Ridge (MAR). This thesis is using advanced marine geophysical methods (including finite-difference wave propagation modeling, 3D multi-channel seismic reflection imaging, waveform inversion, streamer tomography, and near-bottom magnetics) to study lithospheric accretion processes in MORs characterized by contrasting tectono-magmatic settings: the magmatically dominated EPR axis between 9°30'-10°00°N, and the Kane Oceanic Core Complex (KOCC), a section of MAR lithosphere (23°20°-23°38°N) formed by detachment faulting. At the EPR study area, I found that the axial magma chamber (AMC) melt sill is segmented into four prominent 2-4-km-long sections spaced every -5- 10 km along the ridge axis characterized by high melt content (>95%). In contrast, within the intervening sections, the AMC sill has a lower melt content (41-46%). The total magma volume extracted from the AMC sill was estimated of ~46 x 106 M3, with ~24 x 106 M3 left unerupted in the upper crust as dikes after 2005-06 eruption. At the KOCC, I used streamer tomography to constrain the shallow seismic velocity structure. Lithological interpretation of the seismic tomographic models provides insights into the temporal and spatial evolution of the melt supply at the spreading axis as the KOCC formed and evolved. Investigation of a magnetic polarity reversal boundary in crosssection at the northern boundary of KOCC suggests that the boundary (representing both a frozen isotherm and an isochron) dips away from the ridge axis along the Kane transform fault scarp, with a west-dipping angle of ~45° in the shallow (<1 km) crust and <20° in the deeper crust.by Min Xu.Ph.D

Seismic Waves

The importance of seismic wave research lies not only in our ability to understand and predict earthquakes and tsunamis, it also reveals information on the Earth's composition and features in much the same way as it led to the discovery of Mohorovicic's discontinuity. As our theoretical understanding of the physics behind seismic waves has grown, physical and numerical modeling have greatly advanced and now augment applied seismology for better prediction and engineering practices. This has led to some novel applications such as using artificially-induced shocks for exploration of the Earth's subsurface and seismic stimulation for increasing the productivity of oil wells. This book demonstrates the latest techniques and advances in seismic wave analysis from theoretical approach, data acquisition and interpretation, to analyses and numerical simulations, as well as research applications. A review process was conducted in cooperation with sincere support by Drs. Hiroshi Takenaka, Yoshio Murai, Jun Matsushima, and Genti Toyokuni