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http://dx.doi.org/10.7582/GGE.2022.25.2.059

Improvements in Patch-Based Machine Learning for Analyzing Three-Dimensional Seismic Sequence Data  

Lee, Donguk (Marine Active Fault Research Unit, Korea Institute of Ocean Science and Technology)
Moon, Hye-Jin (Marine Active Fault Research Unit, Korea Institute of Ocean Science and Technology)
Kim, Chung-Ho (Marine Active Fault Research Unit, Korea Institute of Ocean Science and Technology)
Moon, Seonghoon (Marine Active Fault Research Unit, Korea Institute of Ocean Science and Technology)
Lee, Su Hwan (Marine Active Fault Research Unit, Korea Institute of Ocean Science and Technology)
Jou, Hyeong-Tae (Marine Active Fault Research Unit, Korea Institute of Ocean Science and Technology)
Publication Information
Geophysics and Geophysical Exploration / v.25, no.2, 2022 , pp. 59-70 More about this Journal
Abstract
Recent studies demonstrate that machine learning has expanded in the field of seismic interpretation. Many convolutional neural networks have been developed for seismic sequence identification, which is important for seismic interpretation. However, expense and time limitations indicate that there is insufficient data available to provide a sufficient dataset to train supervised machine learning programs to identify seismic sequences. In this study, patch division and data augmentation are applied to mitigate this lack of data. Furthermore, to obtain spatial information that could be lost during patch division, an artificial channel is added to the original data to indicate depth. Seismic sequence identification is performed using a U-Net network and the Netherlands F3 block dataset from the dGB Open Seismic Repository, which offers datasets for machine learning, and the predicted results are evaluated. The results show that patch-based U-Net seismic sequence identification is improved by data augmentation and the addition of an artificial channel.
Keywords
machine learning; seismic sequence identification; U-Net; Netherlands F3 block;
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