Leveraging automated deep learning (AutoDL) in geosciences

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 (V_s) 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.