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Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics.

Michelioudakis, D. G. and Hobbs, R. W. and Caiado, C. C. S. (2018) 'Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics.', Geophysical journal international., 213 (3). pp. 2161-2176.

Abstract

Estimating the depths of target horizons from seismic reflection data is an important task in exploration geophysics. To constrain these depths we need a reliable and accurate velocity model. Here, we build an optimum 2D seismic reflection data processing flow focused on pre – stack deghosting filters and velocity model building and apply Bayesian methods, including Gaussian process emulation and Bayesian History Matching (BHM), to estimate the uncertainties of the depths of key horizons near the borehole DSDP-258 located in the Mentelle Basin, south west of Australia, and compare the results with the drilled core from that well. Following this strategy, the tie between the modelled and observed depths from DSDP-258 core was in accordance with the ± 2σ posterior credibility intervals and predictions for depths to key horizons were made for the two new drill sites, adjacent the existing borehole of the area. The probabilistic analysis allowed us to generate multiple realizations of pre–stack depth migrated images, these can be directly used to better constrain interpretation and identify potential risk at drill sites. The method will be applied to constrain the drilling targets for the upcoming International Ocean Discovery Program (IODP), leg 369.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1093/gji/ggy093
Publisher statement:This article has been accepted for publication in Geophysical Journal International ©: 2018 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
Date accepted:08 March 2018
Date deposited:09 March 2018
Date of first online publication:09 March 2018
Date first made open access:No date available

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