Multimodal machine learning for 3-dimensional characterization of hidden groundwater and geothermal resources

Abstract: We present a multimodal machine learning (MML) workflow to assimilate and simultaneously predict the 3d distribution of numeric and categorical features along a groundwater-geothermal continuum. Success of the MML workflow relies on a transductive learning algorithm that projects field modalities onto a single embedding space (hypersurface). Multimodalities can include any combination of measured (point field) and derived (multiphysics based numerical model inversions, data driven machine learning, and multiphysics informed machine learning) features. The proposed MML workflow is applied to assimilate randomly shuffled subsets of Hawaii Play Fairway modalities and predict subsurface geophysical, geologic, and hydrogeologic features at the Islands of Lanai and Hawaii. Despite challenging field data characteristics (disparate, scale dependent, spatially limited, sparse, and uncertain), the MML workflow yields a single 3d transdisciplinary model that generalizes well to independent data presented to the trained model. The predicted features are used to identify hidden groundwater and geothermal resources at Lanai, and geothermal resources at Hawaii. Other interpreted subsurface features at Lanai include basalt, batholith, dike swarm, pluton, sill, mantle, Moho, and 3d geothermal stratigraphic units; whereas interpreted subsurface features at Hawaii include 3d velocity layering, 3d earthquake-fault associations, 3d fault systems; basalt, oceanic crust, magmatic underplating, lithospheric flexure, mantle, and Moho. This study provides new capabilities for characterizing continuous subsurface groundwater and geothermal features for sustainable living in the Hawaiian Islands and other geothermal sites worldwide. Keywords: Machine learning, 3d hidden groundwater resources, hidden geothermal resources fault system, oceanic crust, magmatic underplating, lithospheric flexure, Moho, Hawaii