Netherlands Dataset: A New Public Dataset for Machine Learning in Seismic Interpretation
Machine learning and, more specifically, deep learning algorithms have seen remarkable growth in their popularity and usefulness in the last years. This is arguably due to three main factors: powerful computers, new techniques to train deeper networks and larger datasets. Although the first two are...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
26.03.2019
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Subjects | |
Online Access | Get full text |
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Summary: | Machine learning and, more specifically, deep learning algorithms have seen
remarkable growth in their popularity and usefulness in the last years. This is
arguably due to three main factors: powerful computers, new techniques to train
deeper networks and larger datasets. Although the first two are readily
available in modern computers and ML libraries, the last one remains a
challenge for many domains. It is a fact that big data is a reality in almost
all fields nowadays, and geosciences are not an exception. However, to achieve
the success of general-purpose applications such as ImageNet - for which there
are +14 million labeled images for 1000 target classes - we not only need more
data, we need more high-quality labeled data. When it comes to the Oil&Gas
industry, confidentiality issues hamper even more the sharing of datasets. In
this work, we present the Netherlands interpretation dataset, a contribution to
the development of machine learning in seismic interpretation. The Netherlands
F3 dataset acquisition was carried out in the North Sea, Netherlands offshore.
The data is publicly available and contains pos-stack data, 8 horizons and well
logs of 4 wells. For the purposes of our machine learning tasks, the original
dataset was reinterpreted, generating 9 horizons separating different seismic
facies intervals. The interpreted horizons were used to generate approximatelly
190,000 labeled images for inlines and crosslines. Finally, we present two deep
learning applications in which the proposed dataset was employed and produced
compelling results. |
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DOI: | 10.48550/arxiv.1904.00770 |