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A Boltzmann machine for high-resolution prestack seismic inversion

Son Dang Thai Phan and Mrinal K. Sen
A Boltzmann machine for high-resolution prestack seismic inversion
Interpretation (Tulsa) (August 2019) 7 (3): SE215-SE224

Abstract

Seismic inversion is one popular approach that aims at predicting some indicative properties to support the geologic interpretation process. Existing inversion techniques indicate weaknesses when dealing with complex geologic area, where the uncertainties arise from the guiding model, which are provided by the interpreters. We have developed a prestack seismic inversion algorithm using a machine-learning algorithm called the Boltzmann machine. Unlike common inversion approaches, this stochastic neural network does not require a starting model at the beginning of the process to guide the solution; however, low-frequency models are required to convert the inversion-derived reflectivity terms to the absolute elastic P- and S-impedance as well as density. Our algorithm incorporates a single-layer Hopfield neural network whose neurons can be treated as the desired reflectivity terms. The optimization process seeks the global minimum solution by combining the network with a stochastic model update from the mean-field annealing algorithm. Also, we use a Z-shaped sample sorting scheme and the first-order Tikhonov regularization to improve the lateral continuity of the results and to stabilize the inversion process. The algorithm is applied to a field 2D data set to invert for high-resolution indicative P- and S-impedance sections to better capture some features away from the reservoir zone. The resulting models are strongly supported by the well results and reveal some realistic features that are not clearly displayed in the model-based deterministic inversion result. In combination with well-log analyses, the new features appear to be a good prospect for hydrocarbon.


ISSN: 2324-8858
EISSN: 2324-8866
Serial Title: Interpretation (Tulsa)
Serial Volume: 7
Serial Issue: 3
Title: A Boltzmann machine for high-resolution prestack seismic inversion
Affiliation: University of Texas at Austin, Department of Geological Sciences, Austin, TX, United States
Pages: SE215-SE224
Published: 201908
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 35
Accession Number: 2020-011116
Categories: Economic geology, geology of energy sourcesApplied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Annotation: Part of a special section on Machine learning in seismic data analysis, edited by Zeng, H.
Illustration Description: illus.
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2020, American Geosciences Institute.
Update Code: 202008
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