Neural Architecture Search for Inversion
Cheng Zhan, Licheng Zhang, Xin Zhao, Chang-Chun Lee, Shujiao Huang

TL;DR
This paper explores neural architecture search and loss function design to improve deep learning models for inversion problems, aiming to find optimal architectures and cost functions for better performance.
Contribution
It introduces a method to optimize neural architectures and loss functions specifically for inversion tasks, extending prior work with automated architecture search.
Findings
Identified more effective loss functions for inversion
Demonstrated neural architecture search improves inversion accuracy
Compared different neural network architectures for inversion
Abstract
Over the year, people have been using deep learning to tackle inversion problems, and we see the framework has been applied to build relationship between recording wavefield and velocity (Yang et al., 2016). Here we will extend the work from 2 perspectives, one is deriving a more appropriate loss function, as we now, pixel-2-pixel comparison might not be the best choice to characterize image structure, and we will elaborate on how to construct cost function to capture high level feature to enhance the model performance. Another dimension is searching for the more appropriate neural architecture, which is a subset of an even bigger picture, the automatic machine learning, or AutoML. There are several famous networks, U-net, ResNet (He et al., 2016) and DenseNet (Huang et al., 2017), and they achieve phenomenal results for certain problems, yet it's hard to argue they are the best for…
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Taxonomy
TopicsSeismic Imaging and Inversion Techniques · Seismology and Earthquake Studies · Reservoir Engineering and Simulation Methods
MethodsResidual Connection · Softmax · Concatenated Skip Connection · *Communicated@Fast*How Do I Communicate to Expedia? · Dropout · Convolution · Global Average Pooling · Average Pooling · Dense Block · Batch Normalization
