Signal processing techniques for the enhancement of marine seismic data

This thesis presents several signal processing techniques applied to the enhancement of marine seismic data. Marine seismic exploration provides an image of the Earth's subsurface from reflected seismic waves. Because the recorded signals are contaminated by various sources of noise, minimizing their effects with new attenuation techniques is necessary. A statistical analysis of background noise is conducted using Thomson’s multitaper spectral estimator and Parzen's amplitude density estimator. The results provide a statistical characterization of the noise which we use for the derivation of signal enhancement algorithms. Firstly, we focus on single-azimuth stacking methodologies and propose novel stacking schemes using either enhanced weighted sums or a Kalman filter. It is demonstrated that the enhanced methods yield superior results by their ability to exhibit cleaner and better defined reflected events as well as a larger number of reflections in deep waters. A comparison of the proposed stacking methods with existing ones is also discussed. We then address the problem of random noise attenuation and present an innovative application of sparse code shrinkage and independent component analysis. Sparse code shrinkage is a valuable method when a noise-free realization of the data is generated to provide data-driven shrinkages. Several models of distribution are investigated, but the normal inverse Gaussian density yields the best results. Other acceptable choices of density are discussed as well. Finally, we consider the attenuation of flow-generated nonstationary coherent noise and seismic interference noise. We suggest a multiple-input adaptive noise canceller that utilizes a normalized least mean squares alg orithm with a variable normalized step size derived as a function of instantaneous frequency. This filter attenuates the coherent noise successfully when used either by itself or in combination with a time-frequency median filter, depending on the noise spectrum and repartition along the data. Its application to seismic interference attenuation is also discussed

Digital Signal Processing

Contains summary of research and reports on sixteen research projects.U.S. Navy - Office of Naval Research (Contract N00014-75-C-0852)National Science Foundation FellowshipNATO FellowshipU.S. Navy - Office of Naval Research (Contract N00014-75-C-0951)National Science Foundation (Grant ECS79-15226)U.S. Navy - Office of Naval Research (Contract N00014-77-C-0257)Bell LaboratoriesNational Science Foundation (Grant ECS80-07102)Schlumberger-Doll Research Center FellowshipHertz Foundation FellowshipGovernment of Pakistan ScholarshipU.S. Navy - Office of Naval Research (Contract N00014-77-C-0196)U.S. Air Force (Contract F19628-81-C-0002)Hughes Aircraft Company Fellowshi

Digital Signal Processing

Contains research objectives and reports on sixteen research projects.U.S. Navy - Office of Naval Research (Contract N00014-75-C-0852)National Science Foundation FellowshipNational Science Foundation (Grant ENG76-24117)U.S. Navy - Office of Naval Research (Contract N00014-77-C-0257)U.S. Air Force (Contract F19628-80-C-0002)U.S. Navy - Office of Naval Research (Contract N00014-75-C-0951)Schlumberger-Doll Research Center FellowshipHertz Foundation FellowshipGovernment of Pakistan ScholarshipU.S. Navy - Office of Naval Research (Contract N00014-77-C-0196

Digital Signal Processing

Contains a research summary and reports on fifteen research projects.National Science Foundation FellowshipJoint Services Electronics Program (Contract DAAG29-78-C-0020)National Science Foundation (Grant ENG76-24117)U.S. Navy - Office of Naval Research (Contract N00014-75-C-0951)National Science Foundation (Grant ENG76-24117)Schlumberger-Doll Research Center FellowshipHertz Foundation FellowshipNational Aeronautics and Space Administration (Grant NSG-5157)U.S. Navy - Office of Naval Research (Contract N00014-77-C-0196

Nonparametric estimation of ultrasound pulses

An algorithm for non-parametric estimation of one-dimensional ultrasound pulses in echo sequences from human tissues is derived. The technique is a variation of the homomorphic filtering technique using the real cepstrum, and the underlying basis of the method is explained. The algorithm exploits a priori knowledge about the structure of rf line echo data and can employ a number of adjacent rf lines from an image. The prime application of the algorithm is to yield a pulse suitable for deconvolution algorithms. This will enable these algorithms to properly take into account the frequency dependence of the attenuation and its variation within a patient and among patients. It is also possible to use the estimated pulse for attenuation estimation, and the consistency of the assumptions underlying the proposed technique is demonstrated by its ability to recover low variance attenuation estimates in the normal liver from in vivo pulse-echo data. Estimates are given for eight different patien..

On the inclusion of channel's time dependence in a hidden Markov model for blind channel estimation

In this paper, the theory of hidden Markov models (HMM) is applied to the problem of blind (without training sequences) channel estimation and data detection. Within a HMM framework, the Baum–Welch(BW) identification algorithm is frequently used to find out maximum-likelihood (ML) estimates of the corresponding model. However, such a procedure assumes the model (i.e., the channel response) to be static throughout the observation sequence. By means of introducing a parametric model for time-varying channel responses, a version of the algorithm, which is more appropriate for mobile channels [time-dependent Baum-Welch (TDBW)] is derived. Aiming to compare algorithm behavior, a set of computer simulations for a GSM scenario is provided. Results indicate that, in comparison to other Baum–Welch (BW) versions of the algorithm, the TDBW approach attains a remarkable enhancement in performance. For that purpose, only a moderate increase in computational complexity is needed.Peer Reviewe

Blind deconvolution of sparse pulse sequences under a minimum distance constraint: a partially collapsed Gibbs sampler method

For blind deconvolution of an unknown sparse sequence convolved with an unknown pulse, a powerful Bayesian method employs the Gibbs sampler in combination with a Bernoulli–Gaussian prior modeling sparsity. In this paper, we extend this method by introducing a minimum distance constraint for the pulses in the sequence. This is physically relevant in applications including layer detection, medical imaging, seismology, and multipath parameter estimation. We propose a Bayesian method for blind deconvolution that is based on a modified Bernoulli–Gaussian prior including a minimum distance constraint factor. The core of our method is a partially collapsed Gibbs sampler (PCGS) that tolerates and even exploits the strong local dependencies introduced by the minimum distance constraint. Simulation results demonstrate significant performance gains compared to a recently proposed PCGS. The main advantages of the minimum distance constraint are a substantial reduction of computational complexity and of the number of spurious components in the deconvolution result

A room acoustics measurement system using non-invasive microphone arrays

This thesis summarises research into adaptive room correction for small rooms and pre-recorded material, for example music of films. A measurement system to predict the sound at a remote location within a room, without a microphone at that location was investigated. This would allow the sound within a room to be adaptively manipulated to ensure that all listeners received optimum sound, therefore increasing their enjoyment. The solution presented used small microphone arrays, mounted on the room's walls. A unique geometry and processing system was designed, incorporating three processing stages, temporal, spatial and spectral. The temporal processing identifies individual reflection arrival times from the recorded data. Spatial processing estimates the angles of arrival of the reflections so that the three-dimensional coordinates of the reflections' origin can be calculated. The spectral processing then estimates the frequency response of the reflection. These estimates allow a mathematical model of the room to be calculated, based on the acoustic measurements made in the actual room. The model can then be used to predict the sound at different locations within the room. A simulated model of a room was produced to allow fast development of algorithms. Measurements in real rooms were then conducted and analysed to verify the theoretical models developed and to aid further development of the system. Results from these measurements and simulations, for each processing stage are presented