Seismic Full-Waveform Inversion of 3D Field Data – From the Near Surface to the Reservoir

The theory of FWI is well-established. However its practical application to 3D seismic datasets is still a subject of intense research. This technique has shown spectacular results in quantitatively extracting P-wave velocities in the shallow near surface at depths of less than 1 km, using wide-angle OBC datasets. This study deals with establishing a robust methodology for the application of FWI that can be routinely applied to analogous field datasets, both in the shallow near surface and at deeper reservoir depths. A practical strategy for anisotropic 3D acoustic FWI was developed and implemented. The stratergy is tested on a series of 3D datasets: (1) a synthetic Marmousi dataset, (2) an OBC field data and (3) a streamer data. A 3D synthetic Marmousi data is used to compare FWI implementations in both the time domain and the frequency domain. In both domains, it was possible to recover an almost ‘perfect’ model with complete data coverage, no noise, and few iterations. Both approaches were useful and competitive, and ideally both should be available within a comprehensive suite of inversion tools. The anisotropic time-domain FWI strategy was successfully implemented to complex OBC field data set with long offsets, full-azimuthal coverage and low frequencies. The FWI quantitatively recovered p-wave velocities in the shallow near surface, at intermediate depths where the sediments are gas bearing, and at deeper reservoir depths. The velocities are indeed realistic and are consistent with an independent reflection PSDM volume, well data and pressure data. The synthetic FWI data better match the field data, with the phase residuals between the two datasets significantly reduced to low values. The gathers are flatter and the depth-migrated images are more resolved and focused. The strategy was also successfully implemented to complex streamer field data set with short offsets, narrow-azimuthal coverage and reduced signal at the low frequencies. The FWI quantitatively recovered P-wave velocities down to depths of 750 m. A complex series of high and low velocity channels are recovered. These are consistent with an independent reflection PSTM volume. The synthetic FWI data better match the field data, with the phase residuals between the two datasets significantly reduced to low values. The depth-migrated images are more resolved and focused in the shallow section. Open Acces

Advances in Seismic First-arrival Tomography

Seismic first-arrival tomography is a technique currently experiencing a renaissance in popularity due to the simplicity of implementation and promising results for delineating a variety of subsurface targets. The purpose of this study is to investigate seismic first-arrival tomography in a variety of settings and applications, and thus to provide a solid framework for future work. The study largely consists of two separate themes, hydrogeophysics and low-velocity anomaly detection. Hydrogeophysics is an emerging field whereby measured geophysical properties are used as proxies for physical properties of the subsurface. This study represents one of the first high-resolution hydrogeophysical investigations in the upper three meters of the subsurface using seismic first-arrival tomography, and consists of detecting shallow high-velocity zones that are interpreted to be perched water bodies on the basis of geophysical and hydrologic evidence. The delineation and imaging of the perched water bodies is further advanced using trend-analysis techniques. A second theme of this dissertation is the optimization of methods for delineating low-velocity anomalies at depth using seismic first-arrival tomography. In order to locate a low-velocity zone at Oak Ridge, Tennessee, multiple seismic lines were collected and correlated with site-wide geology. The integration of geologic and geophysical data-sets assisted in developing a comprehensive transport conceptual model, and provided a predictive framework for future geophysical investigations at Oak Ridge. As a second component of this theme, a systematic methodology for detecting and delineating shallow low-velocity zones is developed

Quasi-Newton inversion of seismic first arrivals using source finite bandwidth assumption: Application to subsurface characterization of landslides

International audienceCharacterizing the internal structure of landslides is of first importance to assess the hazard. Many geophysical techniques have been used in the recent years to image these structures, and among them is seismic tomography. The objective of this work is to present a high resolution seismic inversion algorithm of first arrival times that minimizes the use of subjective regularization operators. A Quasi-Newton P-wave tomography inversion algorithm has been developed. It is based on a finite frequency assumption for highly heterogeneous media which considers an objective inversion regularization (based on the wave propagation principle) and uses the entire source frequency spectrum to improve the tomography resolution. The Fresnel wavepaths calculated for different source frequencies are used to retropropagate the traveltime residuals, assuming that in highly heterogeneous media, the first arrivals are only affected by velocity anomalies present in the first Fresnel zone. The performance of the algorithm is first evaluated on a synthetic dataset, and further applied on a real dataset acquired at the Super-Sauze landslide which is characterized by a complex bedrock geometry, a layering of different materials and important changes in soil porosity (e.g. surface fissures). The seismic P-wave velocity and the wave attenuation are calculated, and the two tomographies are compared to previous studies on the site

OMP-type Algorithm with Structured Sparsity Patterns for Multipath Radar Signals

A transmitted, unknown radar signal is observed at the receiver through more than one path in additive noise. The aim is to recover the waveform of the intercepted signal and to simultaneously estimate the direction of arrival (DOA). We propose an approach exploiting the parsimonious time-frequency representation of the signal by applying a new OMP-type algorithm for structured sparsity patterns. An important issue is the scalability of the proposed algorithm since high-dimensional models shall be used for radar signals. Monte-Carlo simulations for modulated signals illustrate the good performance of the method even for low signal-to-noise ratios and a gain of 20 dB for the DOA estimation compared to some elementary method

Auto-Calibrated MIMO-OFDM Channel Sounder for 3D Spatial Channel

This paper presents an improved test-bed designed for analyzing the spatial behaviour of wideband indoor channels using MIMO-OFDM systems. A 3D antenna positioning system (3-DAPS) is specifically designed for obtaining 3D spatial data. Also, it allows carrying out some measurements in the range of correlation distance where fading of the radio channel link is significant for indoor scenarios. Special emphasize is made on the RF calibration module, which is designed to track the frequency response of all RF chain of transmitter and receiver and apply those responses to the channel measurements. The average of the pseudo-spectrum MUSIC over all frequencies improves the resolution of the spatial spectrum giving clear peaks where a signal source is and smoothing fake peaks coming from scatters

Mitigating non-linearity in full waveform inversion using scaled-Sobolev norms

Seismic full waveform inversion (FWI) is a non-linear problem. The Born approximation provides a way to linearize FWI and obtain a gradient in a computationally efficient manner. However, this linearization is only valid if the background velocity is sufficiently known, which often is not possible in practice. There have been various attempts at solving problems associated with the non-linearity of FWI by separating the problems of background and scatterer inversion. Most of the methods, however either depend on the availability of low frequencies and large offsets in the data, or separate the spatial scales completely, which removes the scattered information from the gradient. A complete separation of scales can fail to solve the problem of false local minima. Constrained scale separation methods have also been proposed, however these either require extra computational cost or a priori information about the reflectivity. Cycle-skipping in FWI is an offset dependent phenomenon; a differential semblance approach has been used to take this offset dependance into account. However differentiating the residuals with offset creates a preferred weighting on large offset arrivals, which generally correspond to longer path lengths. In this thesis, I propose scaled-Sobolev methods, which can be applied with negligible extra computational cost per iteration. To this end, I will define a scaled Sobolev inner product (SSIP) to take the scaled derivatives of a function into account when defining a norm, and use it to derive scaled-Sobolev pre-conditioners (SSP) for model and data domain pre-conditioning. The model domain SSP provides a constrained scale separation. The offset dependance of cycle-skipping is taken into account by a scaled-Sobolev objective function (SSO). I apply the scaled-Sobolev methods in both model and data domains using 2D synthetic examples within the acoustic approximation. Finally, I apply the scaled-Sobolev methods to the ocean bottom wide-angle velocity experiment (OBWAVE). The OBWAVE inversion results show that the scaled-Sobolev methods managed to correct some large traveltime errors and suppress the artifacts in the gradient, thereby mitigating the non-linearity in the FWI. The results revealed deeper structures interpreted as the Moho discontinuity and showed good agreement with previous studies for the shallow structures