Surface Wave Analysis in Laterally Varying Media

This work studies the possibility of using surface wave analysis as a tool for a robust estimation of the S-wave velocity behaviour in laterally varying media. The surface wave method, in fact, can be effectively adopted for different purposes and at different scales, but I focused on the geo-engineering and geotechnical applications of surface wave analysis and also on the production of near-surface models for deep exploration: in both cases the aim is to retrieve the trend of the S-wave velocity in the first tens up to hundreds meters of depth of the subsoil. The surface wave method exploits the geometric dispersion proper of surface waves: in a non-homogeneous medium every frequency is characterized by a different phase velocity, as every frequency component travels through a portion of medium whose thickness is proportional to its wavelength. The curve associating every frequency component to its phase velocity is called dispersion curve, and it constitutes the experimental datum one uses for the solution of an inverse problem to estimate the behaviour of S-wave velocity in the subsurface. The inversion is performed by assuming a 1D forward modelling simulation and suffers from equivalence problems, leading to the non uniqueness of the solution. Despite its great ductility, the main limitation of surface wave method is constituted by its 1D approach, which has proved to be unsatisfactory or even misleading in case of presence of lateral variations in the subsoil. The aim of the present work is to provide data processing tools able to mitigate such limitation, so that the surface wave method can be effectively applied in laterally varying media. As far as the inadequacy of surface wave method in case of 2D structures in the subsoil, I developed two separate strategies to handle smooth and gradual lateral variations and abrupt subsurface heterogeneities. In case of smooth variations, the approach I adopted aims at "following" the gradual changes in subsoil materials properties. I therefore propose a procedure to extract a set of neighbouring dispersion curves from a single multichannel seismic record by applying a suitable spatial windowing of the traces. Each curve corresponds to a different subsoil portion, so that gradual changes in subsoil seismic parameters can be reconstructed through the inversion of dispersion curves. The method was tested on synthetic and real datasets, but proved its reliability in processing the data from a small scale seismic experiment as well. In the context of characterizing smooth 2D structures in the subsurface via the surface wave method, I also developed a procedure to quantitatively estimate the (gradual) lateral variability of model parameters by comparing the shape of local dispersion curves, without the need to solve a formal inverse problem. The method is based on a sensitivity analysis and on the applications of the scale properties of surface wave. The procedure can be devoted to different applications: I exploited it to extend a priori local information to subsoil portions for which an experimental dispersion curve is available and for an estimation of the lateral variability of model parameters for a set of neighboring dispersion curves. The method was successfully applied to synthetic and real datasets. To characterize sudden and abrupt lateral variations in the subsurface, I adopted another strategy: the aim is to estimate the location and embedment depth of sharp heterogeneities, to process separately the seismic traces belonging to quasi-1D subsoil portions. I adapted several methods, already available in literature but developed for different purposes and scales, to the detection of sudden changes in subsoil seismic properties via the analysis of anomalies in surface wave propagation. I got the most promising results when adapting these methods, originally developed for single shot configurations, to multifold seismic lines, exploiting their data redundancy to enhance the robustness of the analyses. The outcome of the thesis is therefore a series of processing tools that improve the reliability and the robustness of surface wave method when applied to the near surface characterization of laterally varying medi

Quantification of the effects of fracture properties on seismic data

Fractures have significant impact on hydrocarbon production planning and management. Their properties directly determine the well location selection, drilling design and oil/gas productivity. The goal of this research is twofold. The first part is to explore and find an efficient modeling method that can describe fractures explicitly embedded in elastic media under for wave propagation modeling. The second part is to establish correlations between fracture properties and seismic response quantitatively using the modeling results. The results will provide essential information for developing a systematic characterization procedures for fractures. In the first part, the discontinuous Galerkin method (DG) is first explored for fracture modeling. Within this method, the displacement discontinuity is incorporated by using a jump function included within the shape functions commonly used in the finite element method. A single fracture model is explored using the DG method. The results are compared with the analytical solutions and found to be in close agreement. From the displacement fields, it is observed that the wave scattering is the main effect of fractures observed in seismic data. However, the expensive computational effort gives rise to challenges in conducting parametric study for several realistic models using DG methods. This poses problems in systematically understanding the effect of fractures on seismic waves. In the second part, an integral based method is implemented for the parametric studies to investigate the effect of fractures on seismic waves in elastic media. This integral based method ensures accuracy at the nodes of the elements and has greater computational efficiency. Using this algorithm, the effects of fracture spacing, density, and azimuth are investigated in a three-dimensional setting. The scattering index is used to evaluate the extent of wave scattering induced by fractures. The quantitative relationships between fracture spacing, azimuth and scattering index are established. These results provide valuable information for future fracture characterization procedures.Geological Science

Concurrent Multiscale Computing of Deformation Microstructure by Relaxation and Local Enrichment with Application to Single-Crystal Plasticity

This paper is concerned with the effective modeling of deformation microstructures within a concurrent multiscale computing framework. We present a rigorous formulation of concurrent multiscale computing based on relaxation; we establish the connection between concurrent multiscale computing and enhanced-strain elements; and we illustrate the approach in an important area of application, namely, single-crystal plasticity, for which the explicit relaxation of the problem is derived analytically. This example demonstrates the vast effect of microstructure formation on the macroscopic behavior of the sample, e.g., on the force/travel curve of a rigid indentor. Thus, whereas the unrelaxed model results in an overly stiff response, the relaxed model exhibits a proper limit load, as expected. Our numerical examples additionally illustrate that ad hoc element enhancements, e.g., based on polynomial, trigonometric, or similar representations, are unlikely to result in any significant relaxation in general

A 1-D modelling of streaming potential dependence on water content during drainage experiment in sand

The understanding of electrokinetics for unsaturated conditions is crucial for numerous of geophysical data interpretation. Nevertheless, the behaviour of the streaming potential coefficient C as a function of the water saturation Sw is still discussed. We propose here to model both the Richards' equation for hydrodynamics and the Poisson's equation for electrical potential for unsaturated conditions using 1-D finite element method. The equations are first presented and the numerical scheme is then detailed for the Poisson's equation. Then, computed streaming potentials (SPs) are compared to recently published SP measurements carried out during drainage experiment in a sand column. We show that the apparent measurement of DV / DP for the dipoles can provide the SP coefficient in these conditions. Two tests have been performed using existing models for the SP coefficient and a third one using a new relation. The results show that existing models of unsaturated SP coefficients C(Sw) provide poor results in terms of SP magnitude and behaviour. We demonstrate that the unsaturated SP coefficient can be until one order of magnitude larger than Csat, its value at saturation. We finally prove that the SP coefficient follows a non-monotonous behaviour with respect to water saturation. Key words: Electrical properties; Electromagnetic theory; Hydrogeophysics; Hydrology; Permeability and porosity; electrokinetic; streaming potential; self-potential; water content; water saturation; unsaturated condition; finite element modelin

Tackling Lateral Variability Using Surface Waves: A Tomography-Like Approach

Lateral velocity variations in the near-surface reflect the presence of buried geological or anthropic structures, and their identification is of interest for many fields of application. Surface wave tomography (SWT) is a powerful technique for detecting both smooth and sharp lateral velocity variations at very different scales. A surface-wave inversion scheme derived from SWT is here applied to a 2-D active seismic dataset to characterize the shape of an urban waste deposit in an old landfill, located 15 km South of Vienna (Austria). First, the tomography-derived inverse problem for the 2-D case is defined: under the assumption of straight rays at the surface connecting sources and receivers, the forward problem for one frequency reduces to a linear relationship between observed phase differences at adjacent receivers and wavenumbers (from which phase velocities are straightforwardly derived). A norm damping regularization constraint is applied to ensure a smooth solution in space: the choice of the damping parameter is made through a minimization process, by which only phase variations of the order of the average wavelength are modelled. The inverse problem is solved for each frequency with a weighted least-squares approach, to take into account the data error variances. An independent multi-offset phase analysis (MOPA) is performed using the same dataset, for comparison: pseudo-sections from the tomography-derived linear inversion and MOPA are very consistent, with the former giving a more continuous result both in space and frequency and less artefacts. Local dispersion curves are finally depth inverted and a quasi-2-D shear wave velocity section is retrieved: we identify a well-defined low velocity zone and interpret it as the urban waste deposit body. Results are consistent with both electrical and electromagnetic measurements acquired on the same line

Time series morphological analysis applied to biomedical signals events detection

Dissertation submitted in the fufillment of the requirements for the Degree of Master in Biomedical EngineeringAutomated techniques for biosignal data acquisition and analysis have become increasingly powerful, particularly at the Biomedical Engineering research field. Nevertheless, it is verified the need to improve tools for signal pattern recognition and classification systems, in which the detection of specific events and the automatic signal segmentation are preliminary processing steps. The present dissertation introduces a signal-independent algorithm, which detects significant events in a biosignal. From a time series morphological analysis, the algorithm computes the instants when the most significant standard deviation discontinuities occur, segmenting the signal. An iterative optimization step is then applied. This assures that a minimal error is achieved when modeling these segments with polynomial regressions. The adjustment of a scale factor gives different detail levels of events detection. An accurate and objective algorithm performance evaluation procedure was designed. When applied on a set of synthetic signals, with known and quantitatively predefined events, an overall mean error of 20 samples between the detected and the actual events showed the high accuracy of the proposed algorithm. Its ability to perform the detection of signal activation onsets and transient waveshapes was also assessed, resulting in higher reliability than signal-specific standard methods. Some case studies, with signal processing requirements for which the developed algorithm can be suitably applied, were approached. The algorithm implementation in real-time, as part of an application developed during this research work, is also reported. The proposed algorithm detects significant signal events with accuracy and significant noise immunity. Its versatile design allows the application in different signals without previous knowledge on their statistical properties or specific preprocessing steps. It also brings added objectivity when compared with the exhaustive and time-consuming examiner analysis. The tool introduced in this dissertation represents a relevant contribution in events detection, a particularly important issue within the wide digital biosignal processing research field

An Incremental Navigation Localization Methodology for Application to Semi-Autonomous Mobile Robotic Platforms to Assist Individuals Having Severe Motor Disabilities.

In the present work, the author explores the issues surrounding the design and development of an intelligent wheelchair platform incorporating the semi-autonomous system paradigm, to meet the needs of individuals with severe motor disabilities. The author presents a discussion of the problems of navigation that must be solved before any system of this type can be instantiated, and enumerates the general design issues that must be addressed by the designers of systems of this type. This discussion includes reviews of various methodologies that have been proposed as solutions to the problems considered. Next, the author introduces a new navigation method, called Incremental Signature Recognition (ISR), for use by semi-autonomous systems in structured environments. This method is based on the recognition, recording, and tracking of environmental discontinuities: sensor reported anomalies in measured environmental parameters. The author then proposes a robust, redundant, dynamic, self-diagnosing sensing methodology for detecting and compensating for hidden failures of single sensors and sensor idiosyncrasies. This technique is optimized for the detection of spatial discontinuity anomalies. Finally, the author gives details of an effort to realize a prototype ISR based system, along with insights into the various implementation choices made

Using a Time-based Subarray Method to Extract and Invert Noise-derived Body Waves at Long Beach, California

The reconstruction of body waves from the cross‐correlation of random wavefields has recently emerged as a promising approach to probe the fine‐scale structure of the Earth. However, because of the nature of the ambient noise field, the retrieval of body waves from seismic noise recordings is highly challenging and has only been successful in a few cases. Here, we use seismic noise data from a 5,200‐node oil‐company survey to reconstruct body waves and determine the velocity structure beneath Long Beach, California. To isolate the body wave energy from the ambient noise field, we divide the entire survey into small‐aperture subarrays and apply a modified double‐beamforming scheme to enhance coherent arrivals within the cross‐correlated waveforms. The resulting beamed traces allow us to identify clear refracted P waves traveling between different subarray pairs, which we then use to construct a high‐resolution 3D velocity model of the region. The inverted velocity model reveals velocity variations of the order of 3% and strong lateral discontinuities caused by the presence of sharp geologic structures such as the Newport‐Inglewood fault (NIF). Additionally, we show that the resolution that is achieved through the use of high‐frequency body waves allows us to illuminate small geometric variations of the NIF that were previously unresolved with traditional passive imaging methods

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