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Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification

Geoscientists mainly identify subsurface geologic features using exploration-derived seismic data. Classification or segmentation of 2D/3D seismic images commonly relies on conventional deep learning methods for image recognition. However, complex reflections of seismic waves tend to form high-dimen...

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Detalles Bibliográficos
Autores principales: Geng, Zhi, Wang, Yanfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7335201/
https://www.ncbi.nlm.nih.gov/pubmed/32620867
http://dx.doi.org/10.1038/s41467-020-17123-6
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author Geng, Zhi
Wang, Yanfei
author_facet Geng, Zhi
Wang, Yanfei
author_sort Geng, Zhi
collection PubMed
description Geoscientists mainly identify subsurface geologic features using exploration-derived seismic data. Classification or segmentation of 2D/3D seismic images commonly relies on conventional deep learning methods for image recognition. However, complex reflections of seismic waves tend to form high-dimensional and multi-scale signals, making traditional convolutional neural networks (CNNs) computationally costly. Here we propose a highly efficient and resource-saving CNN architecture (SeismicPatchNet) with topological modules and multi-scale-feature fusion units for classifying seismic data, which was discovered by an automated data-driven search strategy. The storage volume of the architecture parameters (0.73 M) is only ~2.7 MB, ~0.5% of the well-known VGG-16 architecture. SeismicPatchNet predicts nearly 18 times faster than ResNet-50 and shows an overwhelming advantage in identifying Bottom Simulating Reflection (BSR), an indicator of marine gas-hydrate resources. Saliency mapping demonstrated that our architecture captured key features well. These results suggest the prospect of end-to-end interpretation of multiple seismic datasets at extremely low computational cost.
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spelling pubmed-73352012020-07-09 Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification Geng, Zhi Wang, Yanfei Nat Commun Article Geoscientists mainly identify subsurface geologic features using exploration-derived seismic data. Classification or segmentation of 2D/3D seismic images commonly relies on conventional deep learning methods for image recognition. However, complex reflections of seismic waves tend to form high-dimensional and multi-scale signals, making traditional convolutional neural networks (CNNs) computationally costly. Here we propose a highly efficient and resource-saving CNN architecture (SeismicPatchNet) with topological modules and multi-scale-feature fusion units for classifying seismic data, which was discovered by an automated data-driven search strategy. The storage volume of the architecture parameters (0.73 M) is only ~2.7 MB, ~0.5% of the well-known VGG-16 architecture. SeismicPatchNet predicts nearly 18 times faster than ResNet-50 and shows an overwhelming advantage in identifying Bottom Simulating Reflection (BSR), an indicator of marine gas-hydrate resources. Saliency mapping demonstrated that our architecture captured key features well. These results suggest the prospect of end-to-end interpretation of multiple seismic datasets at extremely low computational cost. Nature Publishing Group UK 2020-07-03 /pmc/articles/PMC7335201/ /pubmed/32620867 http://dx.doi.org/10.1038/s41467-020-17123-6 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Geng, Zhi
Wang, Yanfei
Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification
title Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification
title_full Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification
title_fullStr Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification
title_full_unstemmed Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification
title_short Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification
title_sort automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7335201/
https://www.ncbi.nlm.nih.gov/pubmed/32620867
http://dx.doi.org/10.1038/s41467-020-17123-6
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