论文标题
钻孔电阻率测量的自动化机器学习
Automated machine learning for borehole resistivity measurements
论文作者
论文摘要
深神经网络（DNNS）为反转井眼电阻率测量的反转提供了实时解决方案，以近似向前和逆操作员。可以使用极大的DNN来近似运营商，但需要大量的培训时间。此外，在训练后评估网络还需要大量的内存和处理能力。此外，我们可能会过度拟合模型。在这项工作中，我们提出了一个评分函数，该评分函数与参考DNN相比，可以说明DNN的准确性和大小，该参考DNN为操作员提供了良好的近似值。使用此评分函数，我们使用DNN体系结构搜索算法获得小于参考网络的准最佳DNN；因此，在培训和评估过程中，需要更少的计算工作。准最佳的DNN提供了与原始大型DNN的可比精度。
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.
