Characterizing buried sedimentary structures through the use of seismic data is part of many geoscientific projects. The evolution of seismic acquisition and processing capabilities has made it possible to acquire ever-growing amounts of data, increasing the image resolution so that sedimentary objects (geobodies) can be imaged with greater precision within sedimentary layers. However, exploring and interpreting them in large datasets can be tedious work. Recent practice has shown the potential of automated methods to assist interpreters in this task. In this paper, a new semi-supervised methodology is presented for identifying multi-facies geobodies in three-dimensional seismic data, while preserving their internal facies variability and keeping track of the input uncertainty. The approach couples a nonlinear data-driven method with a novel supervised learning method. It requires a prior delineation of the geobodies on a few seismic images, along with a priori confidence in that delineation. The methodology relies on a learning of an appropriate data representation, and propagates the prior confidence to posterior probabilities attached to the final delineation. The proposed methodology was applied to three-dimensional real data, showing consistently effective retrieval of the targeted multi-facies geobodies mass-transport deposits in the present case.
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The authors are grateful to the CGG Houston office for the provision of and permission to publish data, and to Karine Labat for proofreading the article.
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