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Deep Recurrent Architectures for Seismic TomographyNormal access

Authors: A. Adler, M. Araya-Polo and T. Poggio
Event name: 81st EAGE Conference and Exhibition 2019
Session: AI/Digitalization for Interpretation - Geological and Model Building Interpretation
Publication date: 03 June 2019
DOI: 10.3997/2214-4609.201901512
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 569Kb )
Price: € 20

This paper introduces novel deep recurrent neural network architectures for Velocity Model Building (VMB), which is beyond what Araya-Polo et al 2018 pioneered with the Machine Learning-based seismic tomography built with convolutional non-recurrent neural network. Our investigation includes the utilization of basic recurrent neural network (RNN) cells, as well as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells. Performance evaluation reveals that salt bodies are consistently predicted more accurately by GRU and LSTM-based architectures, as compared to non-recurrent architectures. The results take us a step closer to the final goal of a reliable fully Machine Learning-based tomography from pre-stack data, which when achieved will reduce the VMB turnaround from weeks to days.

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