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Bayesian survey design to optimize resolution in waveform inversion

Hugues A. Djikpesse, Mohamed R. Khodja, Michael D. Prange, Sebastien Duchenne and Henry Menkiti
Bayesian survey design to optimize resolution in waveform inversion
Geophysics (April 2012) 77 (2): R81-R93

Abstract

We describe a Bayesian methodology for designing seismic experiments that optimally maximize model-parameter resolution for imaging purposes. The proposed optimal experiment design algorithm finds the measurements that are likely to optimally reduce the expected uncertainty on the model parameters. This Bayesian D-optimality-based algorithm minimizes the volume of the expected confidence ellipsoid and leads to the maximization of the expected resolution of the model parameters. Computational efficiency is achieved by a greedy algorithm in which the design is sequentially improved. In contrast to minimizing the uncertainty volume over the entire subsurface simultaneously, a refinement of the algorithm minimizes the marginal uncertainties in a region of interest. Minimizing marginal uncertainties simultaneously accounts for quantitative prior model uncertainties while honoring a qualitative focus on particular regions of interest. The benefits of the proposed method over traditional non-Bayesian ones are demonstrated with several geophysical examples. These include reducing large seismic data volumes for real-time imaging and solving the problem of designing seismic surveys that account for source bandwidth, signal-to-noise ratio, and attenuation.


ISSN: 0016-8033
EISSN: 1942-2156
Coden: GPYSA7
Serial Title: Geophysics
Serial Volume: 77
Serial Issue: 2
Title: Bayesian survey design to optimize resolution in waveform inversion
Affiliation: Schlumberger-Doll Research, Department of Mathematics and Modeling, Cambridge, MA, United States
Pages: R81-R93
Published: 201204
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 38
Accession Number: 2012-059464
Categories: Applied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus.
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2018, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 201231
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