PREDICTIVE MODELING OF CRITICAL METAL ABUNDANCE IN SEAFLOOR MASSIVE SULFIDE DEPOSITS: A MACHINE LEARNING APPROACH

Seafloor massive sulfide (SMS) deposits exhibit complex geochemical variability, requiring a deeper understanding of the factors influencing their metal content. This study utilizes a global geochemical dataset of SMS deposits to develop robust classification and prediction models using a machine learning (ML) approach.

We present a novel multi-stage ensemble learning framework to predict critical metal concentrations within SMS systems. We employ K-Means clustering to partition the dataset, incorporating both trace metal concentrations and geological features (spreading rate, depth), and create a new classification scheme. A Random Forest classification model then leverages this classification scheme to assign the appropriate class label for new vent field data lacking trace metal content. Finally, an XGBoost regression model predicts critical metal compositions for SMS associated hydrothermal vent systems.

Our results offer an informative framework for estimating critical metal abundance in SMS deposits while revealing important insights into the influence of spreading rate, depth, hydrothermal fluid temperature, and sedimentation. Notably, Co content reveals a strong correlation with depth, hydrothermal fluid temperature, and potential association with pyrite formation. These findings demonstrate the potential of ML for unraveling the complex geochemical processes governing the formation and enrichment of critical metals within SMS deposits. This framework provides valuable insights and paves the way for a more comprehensive understanding of these critical marine resources.