Approximate Finite Rate of Innovation Based Seismic Reflectivity Estimation

IF 1.8 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
P. Sudhakar Reddy, B. S. Raghavendra, A. V. Narasimhadhan
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引用次数: 0

Abstract

Reflectivity inversion is an important deconvolution problem in reflection seismology that helps to describe the subsurface structure. Generally, deconvolution techniques iteratively work on the seismic data for estimating reflectivity. Therefore, these techniques are computationally expensive and may be slow to converge. In this paper, a novel method for estimating reflectivity signals in seismic data using an approximate finite rate of innovation (FRI) framework, is proposed. The seismic data is modeled as a convolution between the Ricker wavelet and the FRI signal, a Dirac impulse train. Relaxing the accurate exponential reproduction limitation given by generalised Strang-Fix (GSF) conditions, we develop a suitable sampling kernel utilizing Ricker wavelet which allows us to estimate the reflectivity signal. The experimental results demonstrate that the proposed approximate FRI framework provides a better reflectivity estimation than the deconvolution technique for medium-to-high signal-to-noise ratio (SNR) regimes with nearly 18% of seismic data.

Abstract Image

基于近似有限创新率的地震反射率估算
反射率反演是反射地震学中一个重要的解卷积问题,有助于描述地下结构。一般来说,解卷积技术通过对地震数据进行迭代来估算反射率。因此,这些技术计算成本高,收敛速度慢。本文提出了一种利用近似有限创新率(FRI)框架估算地震数据反射率信号的新方法。地震数据被建模为 Ricker 小波与 FRI 信号(Dirac 脉冲序列)之间的卷积。我们放宽了广义斯特朗-菲克斯(GSF)条件给出的精确指数再现限制,利用 Ricker 小波开发了一种合适的采样核,使我们能够估计反射率信号。实验结果表明,对于信噪比(SNR)接近 18% 的中高地震数据,所提出的近似 FRI 框架比去卷积技术提供了更好的反射率估计。
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来源期刊
Circuits, Systems and Signal Processing
Circuits, Systems and Signal Processing 工程技术-工程:电子与电气
CiteScore
4.80
自引率
13.00%
发文量
321
审稿时长
4.6 months
期刊介绍: Rapid developments in the analog and digital processing of signals for communication, control, and computer systems have made the theory of electrical circuits and signal processing a burgeoning area of research and design. The aim of Circuits, Systems, and Signal Processing (CSSP) is to help meet the needs of outlets for significant research papers and state-of-the-art review articles in the area. The scope of the journal is broad, ranging from mathematical foundations to practical engineering design. It encompasses, but is not limited to, such topics as linear and nonlinear networks, distributed circuits and systems, multi-dimensional signals and systems, analog filters and signal processing, digital filters and signal processing, statistical signal processing, multimedia, computer aided design, graph theory, neural systems, communication circuits and systems, and VLSI signal processing. The Editorial Board is international, and papers are welcome from throughout the world. The journal is devoted primarily to research papers, but survey, expository, and tutorial papers are also published. Circuits, Systems, and Signal Processing (CSSP) is published twelve times annually.
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