Anthony Vassiliou

Acoustic tomography for monitoring enhanced oil recovery

In 1984, Professor Amos Nur of Stanford University presented a paper at the SEG Annual International Meeting in which he reported results from work carried out by his group to measure compressional and shear wave velocities, as well as attenuation, in samples of heavy oil sands from several reservoirs. Although the explanation for the observed effect was not clear, the results suggested that acoustic velocities and attenuation in sands saturated with heavy oils are strongly dependent on the degree of heating of the sample. To many who heard that presentation, it was apparent that these observations might provide the basis for applying geophysical technology to the problem of monitoring the progression of flood fronts in thermal enhanced oil recovery (EOR) projects in heavy oil reservoirs. Since that time, a number of important projects have been carried out to test the theory and to attempt to verify the results predicted by the laboratory experiments. It is reasonable to say that results, to date, have b...

Detection and extraction of fault surfaces in 3D seismic data

We propose an efficient method for detecting and extracting fault surfaces in 3D-seismic volumes. The seismic data are transformed into a volume of local-fault-extraction (LFE) estimates that represents the likelihood that a given point lies on a fault surface. We partition the fault surfaces into relatively small linear portions, which are identified by analyzing tilted and rotated subvolumes throughout the region of interest. Directional filtering and thresholding further enhance the seismic discontinuities that are attributable to fault surfaces. Subsequently, the volume of LFE estimates is skeletonized, and individual fault surfaces are extracted and labeled in the order of decreasing size. The ultimate result obtained by the proposed procedure provides a visual and semantic representation of a set of well-defined, cleanly separated, one-pixel-thick, labeled fault surfaces that is readily usable for seismic interpretation.

Low bit-rate efficient compression for seismic data

Compression is a relatively new introduced technique for seismic data operations. The main drive behind the use of data compression in seismic data is the very large size of seismic data acquired. Some of the most recent acquired marine seismic data sets exceed 10 Tbytes, and in fact there are currently seismic surveys planned with a volume of around 120 Tbytes. Thus, the need to compress these very large seismic data files is imperative. Nevertheless, seismic data are quite different from the typical images used in image processing and multimedia applications. Some of their major differences are the data dynamic range exceeding 100 dB in theory, very often it is data with extensive oscillatory nature, the x and y directions represent different physical meaning, and there is significant amount of coherent noise which is often present in seismic data. Up to now some of the algorithms used for seismic data compression were based on some form of wavelet or local cosine transform, while using a uniform or quasiuniform quantization scheme and they finally employ a Huffman coding scheme. Using this family of compression algorithms we achieve compression results which are acceptable to geophysicists, only at low to moderate compression ratios. For higher compression ratios or higher decibel quality, significant compression artifacts are introduced in the reconstructed images, even with high-dimensional transforms. The objective of this paper is to achieve higher compression ratio, than achieved with the wavelet/uniform quantization/Huffman coding family of compression schemes, with a comparable level of residual noise. The goal is to achieve above 40 dB in the decompressed seismic data sets. Several established compression algorithms are reviewed, and some new compression algorithms are introduced. All of these compression techniques are applied to a good representation of seismic data sets, and their results are documented in this paper. One of the conclusions is that adaptive multiscale local cosine transform with different windows sizes performs well on all the seismic data sets and outperforms the other methods from the SNR point of view. All the described methods cover wide range of different data sets. Each data set will have his own best performed method chosen from this collection. The results were performed on four different seismic data sets. Special emphasis was given to achieve faster processing speed which is another critical issue that is examined in the paper. Some of these algorithms are also suitable for multimedia type compression.

Dynamic Programming For Multichannel Blind Seismic Deconvolution

We present an algorithm for multichannel blind deconvolution of seismic signals, which exploits lateral continuity of earth layers by dynamic programming approach. We assume that reflectors in consecutive channels, related to distinct layers, form continuous paths across channels. We introduce a quality measure for evaluating the quality of a continuous path, and iteratively apply dynamic programming to find the best continuous paths. The improved performance of the proposed algorithm and its robustness to noise, compared to a competitive algorithm, are demonstrated using simulated and real seismic data examples.

Geophysical diffraction tomography

Abstract Regions of the earths subsurface to be imaged tomographically frequently exhibit large variations in refractive index so that the assumption of straight ray paths is invalid and often leads to inferior reconstructions. The use of curved raypaths in diffraction tomography, however, significantly complicates the reconstruction problem. We develop an algorithm for diffraction tomography which involves iterations on successive ray tracing and tomographic inversion. Singular value decomposition as well as conjugate gradients provide alternative approaches to the inversion step at each iteration. Each procedure can give good results but singular value decomposition can be costly to apply. In diffraction tomography, partial singular value decomposition using a very small number of singular values and singular vectors at each inversion step can yield very accurate reconstructions in a few iterations.

Low-bit-rate efficient compression for seismic data

The main drive behind the use of data compression in seismic data is the very large size of seismic data acquired. Some of the most recent acquired marine seismic data sets exceed 10 Tbytes, and in fact there are currently seismic surveys planned with a volume of around 120 Tbytes. Nevertheless, seismic data are quite different from the typical images used in image processing and multimedia applications. Some of their major differences are the data dynamic range exceeding 100 dB in theory, very often it is data with extensive oscillatory nature, the x and y directions represent different physical meaning, and there is significant amount of coherent noise which is often present in seismic data. The objective of this paper is to achieve higher compression ratio, than achieved with the wavelet/uniform quantization/Huffman coding family of compression schemes, with a comparable level of residual noise. The goal is to achieve above 40dB in the decompressed seismic data sets. One of the conclusions is that adaptive multiscale local cosine transform with different windows sizes performs well on all the seismic data sets and outperforms the other methods from the SNR point of view. Comparison with other methods (old and new) are given in the full paper. The main conclusion is that multidimensional adaptive multiscale local cosine transform with different windows sizes perform well on all the seismic data sets and outperforms other methods from the SNR point of view. Special emphasis was given to achieve faster processing speed which is another critical issue that is examined in the paper. Some of these algorithms are also suitable for multimedia type compression.

Full-wave-equation Depth Migration Using Multiple Reflections

We present a migration method in the temporal frequency domain that can propagate both downgoing and upgoing waves. The method can handle arbitrary velocity model, and we demonstrate its ability to image overturned events and steep reflectors. Our algorithm has the advantage of migrating the data sequentially in depth and frequency, leading to significant advantages compared to Reverse Time Migration when combined with migration velocity analysis. We also show that the method can easily generate offset and angle gathers.

Multichannel Seismic Modeling And Inversion Based On Markov-Bernoulli Random Field

We introduce a multichannel blind deconvolution algorithm for seismic signals based on Markov-Bernoulli random field modeling. The proposed model accounts for layer discontinuities resulting from splitting, merging, starting or terminating layers within the region of interest. We define a set of reflectivity states and legal transitions between reflector configurations of adjacent traces, and subsequently extract sequences of reflectors that are connected across the traces by legal transitions. The improved performance of the proposed algorithm and its robustness to noise, compared to a competitive algorithm, are demonstrated using simulated and real seismic data examples.

Acoustic And Elastic Modeling Using Bases For Bandlimited Functions

In this paper we describe a novel algorithm for solving acoustic and elastic wave equations. The algorithm utilizes prolate spheroidal wavefunctions for representing bandlimited functions. Key features of the method are high accuracy, elimination of numerical dispersion, and close to optimal sampling rates. Numerical results in two and three dimensions are presented which demonstrate the accuracy of the method and its ability to handle complex media such as the SEG salt model.

Applications of adapted waveform analysis for spectral feature extraction and denoising

While tackling tough problems in processing and analysis of seismic data we exploit the rich resources of Adapted Waveform Analysis, AWA. Wavelets, wavelet packets, and local trigonometric waveforms are among the many tools made available by AWA to augment the yield of information from seismic data. In this paper we illustrate AWA utilization with two examples, pertaining to spectral feature extraction and application to difficult noise scenarios.