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Abstract

Summary

Recently, many deep-learning approaches have been applied to geophysical problems, such as seismic processing and interpretation, to aid in the exploration of hydrocarbon reservoirs. Convolutional neural networks (CNNs) are a popular new method to identify salt bodies in seismic data, by analyzing image segmentation and feature extraction. In this study, four ensemble classifiers were trained to analyze the importance of various seismic attributes with respect to the predictability of a salt body. By choosing seismic attributes with the highest importance as input data to a multi-channel CNN architecture, we successfully improved the accuracy of salt prediction. Both binary and multi-label salt classifications are shown, as well as comparisons of salt classification probability maps generated from models trained by seismic-only data vs models trained using seismic-plus-attributes data. The results demonstrated that using seismic-plus-attributes models significantly improved the continuity of salt boundaries and reduced unwanted artifacts, whilst also converging faster during training.

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/content/papers/10.3997/2214-4609.202032049
2020-11-30
2024-04-29

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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202032049
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Analysis of Seismic Attributes to Assist in the Classification of Salt by Multi-channel Convolutional Neural Networks
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202032049
/content/papers/10.3997/2214-4609.202032049
/content/papers/10.3997/2214-4609.202032049
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