Automated machine learning for borehole resistivity measurements

• URL: http://arxiv.org/abs/2207.09849v1
• Date: Wed, 20 Jul 2022 12:27:22 GMT
• Title: Automated machine learning for borehole resistivity measurements
• Authors: M. Shahriari, D. Pardo, S. Kargaran, T. Teijeiro
• Abstract summary: Deep neural networks (DNNs) offer a real-time solution for the inversion of borehole resistivity measurements. It is possible to use extremely large DNNs to approximate the operators, but it demands a considerable training time. In this work, we propose a scoring function that accounts for the accuracy and size of the DNNs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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