Wail A. Mousa

Designing stable extrapolators for explicit depth extrapolation of 2D and 3D wavefields using projections onto convex sets

We have developed a robust algorithm for designing explicit depth extrapolation operators using the projections-onto-convex-sets (POCS) method. The operators are optimal in the sense that they satisfy all required extrapolation design characteristics. In addition, we propose a simple modification of the POCS algorithm (modified POCS, or MPOCS) that further enhances the stability of extrapolated wavefields and reduces the number of iterations required to design such operators to approximately 2% of that required for the basic POCS design algorithm. Various synthetic tests show that 25-coefficient 1D extrapolation operators, which have 13 unique coefficients, can accommodate dip angles up to 70°. We migrated the SEG/EAGE salt model data with the operators and compare our results with images obtained via extrapolators based on modified Taylor series and with wavefield extrapolation techniques such as phase shift plus interpolation (PSPI) and split-step Fourier. The MPOCS algorithm provides practically stable...

Lung nodule classification utilizing support vector machines

Lung cancer is one of the deadly and most common diseases in the world. Radiologists fail to diagnose small pulmonary nodules in as many as 30% of positive cases. Many methods have been proposed in the literature such as neural network algorithms. Recently, support vector machines (SVMs) had received increasing attention for pattern recognition. The advantage of SVM lies in better modeling the recognition process. The objective of this paper is to apply support vector machines SVMs for classification of lung nodules. The SVM classifier is trained with features extracted from 30 nodule images and 20 non-nodule images, and is tested with features out of 16 nodule/non-nodule images. The sensitivity of SVM classifier is found to be 87.5%. We intend to automate the pre-processing detection process to further enhance the overall classification.

Processing of Seismic Reflection Data Using MATLAB

This short book is for students, professors and professionals interested in signal processing of seismic data using MATLAB. The step-by-step demo of the full reflection seismic data processing workflow using a complete real seismic data set places itself as a very useful feature of the book. This is especially true when students are performing their projects, and when professors and researchers are testing their new developed algorithms in MATLAB for processing seismic data. The book provides the basic seismic and signal processing theory required for each chapter and shows how to process the data from raw field records to a final image of the subsurface all using MATLAB. Table of Contents: Seismic Data Processing: A Quick Overview / Examination of A Real Seismic Data Set / Quality Control of Real Seismic Data / Seismic Noise Attenuation / Seismic Deconvolution / Carrying the Processing Forward / Static Corrections / Seismic Migration / Concluding Remarks

An algorithm for petro-graphic colour image segmentation used for oil exploration

We propose a new heuristic algorithm for porosity segmentation for the coloured petro-graphic images. The proposed algorithm automatically detects the porosities that represent the presence of oil, gas or even water in the analyzed thin section rock segment based on the colour of the porosity area filled with dies in the analyzed sample. For the purpose of the oil exploration the thin section fragments are died in order to emphasize the porosities that are analyzed under the microscope. The percentage of the porosity is directly proportional to the probability of the oil, gas or even water presence in the area where the drilling is performed, i.e. the increased porosity indicates the higher probability of oil existence in the region. The proposed automatic algorithm shows better results to the existing K-means segmentation method.

Image coding using entropy-constrained reflected residual vector quantization

Residual vector quantization (RVQ) is a structurally constrained vector quantization (VQ) paradigm. RVQ employs multipath search and has higher encoding cost as compared to sequential single-path search. Reflected residual vector quantization (Ref-RVQ), a design with additional symmetry on the codebook, was developed later to a jointly optimized RVQ structure with single-path search. The constrained Ref-RVQ codebook exhibits an increase in distortion. However, it was conjectured that the Ref-RVQ codebook has a lower output entropy than that of the multipath RVQ codebook. Therefore, the Ref-RVQ design was generalized to include noiseless entropy coding. We apply it to image coding. The method is referred to as entropy-constrained Ref-RVQ (EC-Ref-RVQ). Since the RVQ scheme is able to implement very large dimensional vector quantization designs like 16/spl times/16 and 32/spl times/32 VQs, it is found highly successful in extracting linear and non-linear correlation among image pixels. We intend to implement these large dimensional vectors with the EC-Ref-RVQ scheme to realize a computationally less demanding image-RVQ design. Simulation results demonstrate that EC-Ref-RVQ, while maintaining single path search, provides 1 dB improvement in PSNR for image data over the multipath EC-RVQ.

Design and analysis of entropy-constrained reflected residual vector quantization

Residual vector quantization (RVQ) is a vector quantization (VQ) paradigm which imposes structural constraints on the encoder in order to reduce the encoding search burden and memory storage requirements of an unconstrained VQ. Jointly optimized RVQ (JORVQ) was introduced as an effective design algorithm for minimizing the overall quantization error. Reflected residual vector quantization (RRVQ) was introduced as an alternative design algorithm for RVQ structure with smaller computation burden. RRVQ works by imposing an additional symmetry constraint on the RVQ code book design. Savings in computation was accompanied by increase in distortion. However, it is expected that an RRVQ codebook being structured in nature, will provide lower output entropy. Therefore, we generalize RRVQ to include noiseless entropy coding. The method is referred to as Entropy-Constrained RRVQ (EC-RRVQ). Simulation results show that EC-RRVQ outperforms RRVQ by 4-dB for memoryless Gaussian and Laplacian sources. In addition, for the same synthetic sources, EC-RRVQ provided an improvement over other entropy-constrained designs, such as entropy-constrained JORVQ (EC-JORVQ). The design performed equally well on image data. In comparison with EC-JORVQ, EC-RRVQ is simpler and outperforms the EC-JORVQ.

Seismic Migration: A Digital Filtering Process Reducing Oil Exploration Risks [Applications Corner]

Humans consider oil and gas to be important natural energy resources: they are used to fuel our cars, generate power, and produce plastics and fertilizers, among other things. To produce oil or gas, we need first to determine the subsurface structure. This can be done by the reflection seismology method. This geophysical technique relies on the generation of artificial seismic waves and the recording of their reflections from different geological layers. However, such acquired seismic data does not reveal an accurate image of the subsurface unless we use appropriate signal processing such as frequency and/or wavenumber filtering, multichannel and/ or multidimensional filtering, deconvolution, and so on [1].

Experiences of a Multidisciplinary Course on Geo-Signal Processing From a DSP Perspective Offered in Electrical Engineering at King Fahd University of Petroleum and Minerals

The purpose of this paper is to share the experience gained in, and the efforts made toward, introducing and implementing a new course in the challenging and important area of geophysical signal processing at the Electrical Engineering (EE) Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia. The new course, titled “Geo-Signal Processing,” was offered both at the graduate level and as a special topics course to undergraduates. The course was developed in collaboration with the Earth Sciences Department at KFUPM. This paper contributes new information because it stresses the multidisciplinary aspects of digital signal processing (DSP) technologies when applied to estimating the Earths layered structure on the basis of seismic data. Unlike many Earth sciences seismic data processing courses, this Geo-Signal Processing course also emphasizes that the perspective taken by those working in DSP is different from that taken by geophysicists. The course presents DSP with particular emphasis on seismic data signals and the artifacts accompanying them while covering the principles and algorithms needed for processing seismic signals in both deterministic and statistical fashion. Topics include, but are not limited to, basic seismic theory, acquisition of seismic data, analysis of seismic signals and noise, deterministic filtering of seismic data, and statistical processing of seismic data.

Geo-Signal Processing…A DSP course experience in Electrical Engineering at King Fahd University of Petroleum & Minerals

This paper presents the great and challenging experience of offering a new course for Electrical Engineering students. The course, which is offered in two versions one for senior level and the other is for graduate level students, is titled: Geo-Signal Processing, with emphasis on processing of seismic data that are used to reveal the Earths layered structure from a digital signal processing prospective. The two versions of the course were offered at the department of Electrical Engineering in collaboration with the Earth Sciences Department both at King Fahd University of Petroleum & Minerals.

Accurate & efficient wavefield extrapolators using IIR F - X filters

We propose a new way of performing stable and more efficient F - X wavefield extrapolation for the application of seismic imaging and datuming via Infinite Impulse Response (IIR) F - X filters. To prove the concept, we used model reduction techniques to design the complex-valued IIR F - X extrapolation filters. Simulation results indicate that we can obtain stable designs using the proposed filters. Compared with the results obtained using FIR F - X extrapolation filters, we obtained a saving of 62% in the order of the IIR F - X extrapolation filters.