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Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification
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Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification
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Journal Article
Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification
2020
Overview
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.
The authors present an automated design approach to propose a new neural network architecture for seismic data analysis. The new architecture classifies multiple seismic reflection datasets at extremely low computational cost compared with conventional architectures for image classification.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
