Next Article in Journal
The New Moon: Major Advances in Lunar Science Enabled by Compositional Remote Sensing from Recent Missions
Next Article in Special Issue
Constrained Full Waveform Inversion for Borehole Multicomponent Seismic Data
Previous Article in Journal
Stratigraphy, Tectonics and Hydrocarbon Habitat of the Abadan Plain Basin: A Geological Review of a Prolific Middle Eastern Hydrocarbon Province
Previous Article in Special Issue
Modeling and Imaging of Multiscale Geological Media: Exploding Reflection Revisited
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle

High-Resolution Seismic Data Deconvolution by A0 Algorithm

Gazpromneft Science and Technology Centre, 75-79 liter D Moika River emb., St. Petersburg 190000, Russia
Author to whom correspondence should be addressed.
Geosciences 2018, 8(12), 497;
Received: 2 October 2018 / Revised: 5 December 2018 / Accepted: 12 December 2018 / Published: 18 December 2018
(This article belongs to the Special Issue Numerical Methods of Geophysical Fields Inversion)
PDF [3028 KB, uploaded 18 December 2018]


Sparse spikes deconvolution is one of the oldest inverse problems, which is a stylized version of recovery in seismic imaging. The goal of sparse spike deconvolution is to recover an approximation of a given noisy measurement T = W r + W 0 . Since the convolution destroys many low and high frequencies, this requires some prior information to regularize the inverse problem. In this paper, the authors continue to study the problem of searching for positions and amplitudes of the reflection coefficients of the medium (SP&ARCM). In previous research, the authors proposed a practical algorithm for solving the inverse problem of obtaining geological information from the seismic trace, which was named A 0 . In the current paper, the authors improved the method of the A 0 algorithm and applied it to the real (non-synthetic) data. Firstly, the authors considered the matrix approach and Differential Evolution approach to the SP&ARCM problem and showed that their efficiency is limited in the case. Secondly, the authors showed that the course to improve the A 0 lays in the direction of optimization with sequential regularization. The authors presented calculations for the accuracy of the A 0 for that case and experimental results of the convergence. The authors also considered different initialization parameters of the optimization process from the point of the acceleration of the convergence. Finally, the authors carried out successful approbation of the algorithm A 0 on synthetic and real data. Further practical development of the algorithm A 0 will be aimed at increasing the robustness of its operation, as well as in application in more complex models of real seismic data. The practical value of the research is to increase the resolving power of the wave field by reducing the contribution of interference, which gives new information for seismic-geological modeling. View Full-Text
Keywords: differential evolution; discrete loss function; limiting accuracy; the Ricker wavelet; geological medium factors differential evolution; discrete loss function; limiting accuracy; the Ricker wavelet; geological medium factors

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Krasnov, F.; Butorin, A. High-Resolution Seismic Data Deconvolution by A0 Algorithm. Geosciences 2018, 8, 497.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Geosciences EISSN 2076-3263 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top