Optimal seismic deconvolution: distributed algorithms

Abstract

Abstract-Deconvolution is one of the most important aspects of seismic signal processing. The objective of the deconvolution procedure is to remove the obscuring effect of the wavelet's replica making up the seismic trace and therefore obtain an estimate of the reflection coefficient sequence. This paper introduces a new deconvolution algorithm. Optimal distributed estimators and smoothers are utilized in the proposed solution. The new distributed methodology, perfectly suitable for a multisensor environment, such as the seismic signal processing, is compared to the centralized approach, with respect to computational complexity and architectural efficiency. It is shown that the distributed approach greatly outperforms the currently used centralized methodology offering flexibility in the design of the data fusion network.

Seismic data deconvolution using Kalman filter based on a new system model

“…The limitation of the method is that it is only a crude attempt to balance the amplitude spectrum of the seismic trace. Optimal seismic deconvolution (Lainiotis et al, 1988) and its distributed algorithms (Plataniotis et al, 1998) derive the new minimum variance deconvolution formulas that can use any filtering/smoothing scheme to obtain optimal estimates of the reflection coefficient sequence. This method is limited by the assumption that the reflection coefficient sequence is random zero-mean and white.…”

Adaptive Estimation of FCG Using Nonlinear State-Space Models

“…In this paper, an efficient adaptive nonlinear algorithm for estimation and identification, the so-called adaptive Lainiotis filter (ALF), proposed by Lainiotis [12][13][14][15][16], and investigated and extensively applied to important engineering problems by Katsikas and Lainiotis [17], Lainiotis and Papaparaskeva [18], Plataniotis et al [19], and Leros et al [20], is applied to the problem of FCG estimation, identification, and prediction of the final crack (failure). First, a stochastic nonlinear state-space model is presented, based on the most common FCG equations.…”