Automated machine learning for borehole resistivity measurements
This work addresses computational efficiency for geophysical data analysis, but it is incremental as it builds on existing DNN and architecture search methods.
The paper tackled the problem of high computational cost and overfitting in deep neural networks used for borehole resistivity inversion by proposing a scoring function that balances accuracy and model size, enabling architecture search to find a smaller network with comparable accuracy.
Deep neural networks (DNNs) offer a real-time solution for the inversion of borehole resistivity measurements to approximate forward and inverse operators. It is possible to use extremely large DNNs to approximate the operators, but it demands a considerable training time. Moreover, evaluating the network after training also requires a significant amount of memory and processing power. In addition, we may overfit the model. In this work, we propose a scoring function that accounts for the accuracy and size of the DNNs compared to a reference DNN that provides a good approximation for the operators. Using this scoring function, we use DNN architecture search algorithms to obtain a quasi-optimal DNN smaller than the reference network; hence, it requires less computational effort during training and evaluation. The quasi-optimal DNN delivers comparable accuracy to the original large DNN.