Robust wavelet estimation and blind deconvolution of noisy surface seismics

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. V37-V46 ◽  
Author(s):  
Mirko van der Baan ◽  
Dinh-Tuan Pham
Keyword(s):  
Blind Deconvolution ◽  
General Purpose ◽  
Sufficient Accuracy ◽  
Wiener Filtering ◽  
Challenging Problem ◽  
Limited Data ◽  
Wavelet Estimation ◽  
Seismic Wavelet ◽  
Principal Frequency ◽  
Band Limited

Robust blind deconvolution is a challenging problem, particularly if the bandwidth of the seismic wavelet is narrow to very narrow; that is, if the wavelet bandwidth is similar to its principal frequency. The main problem is to estimate the phase of the wavelet with sufficient accuracy. The mutual information rate is a general-purpose criterion to measure whiteness using statistics of all orders. We modified this criterion to measure robustly the amplitude and phase spectrum of the wavelet in the presence of noise. No minimum phase assumptions were made. After wavelet estimation, we obtained an optimal deconvolution output using Wiener filtering. The new procedure performs well, even for very band-limited data; and it produces frequency-dependent phase estimates.

Regularized phase retrieval for seismic wavelet estimation and blind deconvolution

2020 ◽  
Vol 177 ◽  
pp. 104035
Author(s):  
Sepideh Vafaei Shoushtari ◽  
Ali Gholami ◽  
Hamidreza Siahkoohi
Keyword(s):  
Phase Retrieval ◽  
Blind Deconvolution ◽  
Wavelet Estimation ◽  
Seismic Wavelet

Architectural Pattern for Scheduling Multi-Component Applications in Distributed Systems

2016 ◽  
Vol 8 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Absalom El-Shamir Ezugwu ◽  
Marc Eduard Frincu ◽  
Sahalu Balarabe Junaidu
Keyword(s):  
Design Pattern ◽  
Heterogeneous Computing ◽  
Systems Design ◽  
General Purpose ◽  
Area Network ◽  
Challenging Problem ◽  
Wide Area Network ◽  
Computing Environment ◽  
Scheduling System ◽  
Design Goal

This paper presents a conceptual perspective on scheduling systems' design pattern for several classes of multi-component applications. The authors consider this scheduling problem in a wide-area network of heterogeneous computing environment. The heterogeneity in both the user application and distributed resource environments make this a challenging problem. In addition, the authors propose a component-based reference architectural model, which describes the design of a general purpose scheduling system targeted at the scheduling of multi-component applications. The design goal is to identify and map out the necessary ingredients required to effectively perform the scheduling of multi-component applications.

scholarly journals A Seismic Blind Deconvolution Algorithm Based on Bayesian Compressive Sensing

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Yanqin Li ◽  
Guoshan Zhang
Keyword(s):  
Compressive Sensing ◽  
Blind Deconvolution ◽  
Optimization Method ◽  
Seismic Signal ◽  
Unknown Parameters ◽  
Seismic Wavelet ◽  
Deconvolution Algorithm ◽  
Seismic Records ◽  
Real Value ◽  
Seismic Signal Processing

Compressive sensing in seismic signal processing is a construction of the unknown reflectivity sequence from the incoherent measurements of the seismic records. Blind seismic deconvolution is the recovery of reflectivity sequence from the seismic records, when the seismic wavelet is unknown. In this paper, a seismic blind deconvolution algorithm based on Bayesian compressive sensing is proposed. The proposed algorithm combines compressive sensing and blind seismic deconvolution to get the reflectivity sequence and the unknown seismic wavelet through the compressive sensing measurements of the seismic records. Hierarchical Bayesian model and optimization method are used to estimate the unknown reflectivity sequence, the seismic wavelet, and the unknown parameters (hyperparameters). The estimated result by the proposed algorithm shows the better agreement with the real value on both simulation and field-data experiments.

A new method for the time-variant seismic wavelet estimation

2014 ◽  
Author(s):  
Manman Zhang ◽  
Yongshou Dai ◽  
Yanan Zhang ◽  
Jinjie Ding ◽  
Rongrong Wang
Keyword(s):  
New Method ◽  
Wavelet Estimation ◽  
Seismic Wavelet

Time-varying wavelet estimation and deconvolution by kurtosis maximization

Geophysics ◽  
2008 ◽  
Vol 73 (2) ◽  
pp. V11-V18 ◽  
Author(s):  
Mirko van der Baan
Keyword(s):  
Field Measurements ◽  
Phase Changes ◽  
Data Driven ◽  
Wiener Filtering ◽  
Time Varying ◽  
Seismic Wavelet ◽  
Phase Rotation ◽  
Physical Laws ◽  
Kurtosis Maximization ◽  
Zero Phase

Phase mismatches sometimes occur between final processed sections and zero-phase synthetics based on well logs, despite best efforts for controlled-phase acquisition and processing. The latter are often based on deterministic corrections derived from field measurements and physical laws. A statistical analysis of the data can reveal whether a time-varying nonzero phase is present. This assumes that the data should be white with respect to all statistical orders after proper deterministic corrections have been applied. Kurtosis maximization by constant phase rotation is a statistical method that can reveal the phase of a seismic wavelet. It is robust enough to detect time-varying phase changes. Phase-only corrections can then be applied by means of a time-varying phase rotation. Alternatively, amplitude and phase deconvolution can be achieved using time-varying Wiener filtering. Time-varying wavelet extraction and deconvolution can also be used as a data-driven alternative to amplitude-only inverse-[Formula: see text] deconvolution.

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