Leveraging automated deep learning (AutoDL) in geosciences

Abstract

Machine Learning (ML) and Deep Learning (DL) have demonstrated notable success in diverse geoscience domains. However, the traditional ML/DL algorithms often rely on laborious trial-and-error processes to identify suitable hyperparameters and neural architectures, necessitating advanced ML expertise. The emerging paradigm of Automated Machine Learning (AutoML) and Automated Deep Learning (AutoDL) aims to generate high-quality ML/DL models with minimum human intervention. A typical AutoDL pipeline encompasses data preparation, feature engineering, model generation, and model evaluation. In this study, we explore the potential of the AutoDL plug-in for hyperparameter and architecture optimizations, to address three geoscience challenges: classification task of lithology prediction and regression tasks such as shear wave velocity (Vs) and total organic carbon (TOC) predictions. Our results demonstrate promising outcomes for these tasks, potentially offering greater reliability and reproducibility than the ML algorithms. Furthermore, AutoDL presents two practical advantages: (1) Artificial Intelligence or AI democratization, which aspires to provide individuals with limited ML/DL expertise access to this approach's benefits, and (2) reduced carbon footprint by circumventing energy-intensive trial-and-error tasks associated with traditional ML/DL, thus promoting a more sustainable future for humanity.