Lee Seung-Jae, Yoon Hyung-Koo
Department of Construction and Disaster Prevention Engineering, Daejeon University, Daejeon 34520, Korea.
Sensors (Basel). 2021 Feb 18;21(4):1412. doi: 10.3390/s21041412.
Electrical resistivity is used to obtain various types of information for soil strata. Hence, the prediction of electrical resistivity is helpful to predict the future behavior of soil. The objective of this study is to apply deep learning algorithms, including deep neural network (DNN), long-short term memory (LSTM), and gated recurrent unit (GRU), to determine the reliability of electrical resistivity predictions to find the discontinuity of porosity and hydraulic conductivity. New DNN-based algorithms, i.e., LSTM-DNN and GRU-DNN, are also applied in this study. The electrical resistivity values are obtained using 101 electrodes installed at 2 m intervals on a mountaintop, and a Wenner array is selected to simplify the electrode installation and measurement. A total of 1650 electrical resistivity values are obtained for one measurement considering the electrode spacing, and accumulated data measured for 15 months are used in the deep learning analysis. A constant ratio of 6:2:2 among the training, validation, and test data, respectively, is used for the measured electrical resistivity, and the hyperparameters in each algorithm are moderated to improve the reliability. Based on the deep learning model results, the distributions of porosity and hydraulic conductivity are deduced, and an average depth of 25 m is estimated for the discontinuity depth. This paper shows that the deep learning technique is well used to predict electrical resistivity, porosity, hydraulic conductivity, and discontinuity depth.
电阻率用于获取土壤地层的各种信息。因此，电阻率预测有助于预测土壤的未来行为。本研究的目的是应用深度学习算法，包括深度神经网络（DNN）、长短期记忆（LSTM）和门控循环单元（GRU），来确定电阻率预测的可靠性，以找出孔隙率和水力传导率的不连续性。本研究还应用了基于DNN的新算法，即LSTM-DNN和GRU-DNN。电阻率值是通过在山顶以2米间隔安装的101个电极获得的，并且选择了温纳阵列以简化电极安装和测量。考虑到电极间距，一次测量总共获得1650个电阻率值，并且将15个月测量的累积数据用于深度学习分析。测量的电阻率在训练、验证和测试数据之间分别采用6:2:2的固定比例，并且对每种算法中的超参数进行调整以提高可靠性。基于深度学习模型结果，推导了孔隙率和水力传导率的分布，并且估计不连续深度的平均深度为25米。本文表明深度学习技术很好地用于预测电阻率、孔隙率、水力传导率和不连续深度。
