Recognition of Stratum Lithology of Seismic Facies Based on Deep Belief Network

DOI

10.2991/aiie-16.2016.81How to use a DOI?

Keywords

Restricted Boltzmann Machine (RBM); Deep Belief Network (DBN); seismic facies; lithologic recognition

Abstract

The Deep Belief Network (DBN) is one of the major algorithms of deep learning. It simulates human brain to extract the features efficiently, so that the model has much strong learning ability. Because it is difficult to extract features from a variety of seismic data effectively, multiple sampling points of seismic data are used as inputs. Then we use DBN to extract the features from seismic data, which can be stacked by RBMs layer-by-layer. The model of lithological recognition can be constructed from previous step, further to recognize stratum lithology. By experiments and practical application, it is proved that partial strata information can be utilized effectively when multiple sampling points of seismic data are used as inputs. In this way, we can effectively recognize the stratum lithology based on DBN.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Guohe Li
AU  - Yang Zheng
AU  - Ying Li
AU  - Weijiang Wu
AU  - Yunfeng Hong
AU  - Xiaoming Zhou
PY  - 2016/11
DA  - 2016/11
TI  - Recognition of Stratum Lithology of Seismic Facies Based on Deep Belief Network
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 354
EP  - 357
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.81
DO  - 10.2991/aiie-16.2016.81
ID  - Li2016/11
ER  -