Predicting the preservation of buried ore deposits using deep-time landscape evolution modeling

Abstract

Porphyry copper discoveries are declining despite rising demand to meet net-zero targets, highlighting the need for innovative exploration strategies. While many advances have focused on ore formation at depth, a major challenge remains in understanding how erosion and uplift over millions of years affect deposit preservation. These postmineralization processes determine whether porphyry systems are exposed, buried, or eroded entirely. We present a physically based landscape evolution model that incorporates spatially variable erodibility, dynamic uplift histories, climate and sea level change, and evolving topography over geological timescales. This richer input data, combined with tighter calibration, enables quantification of preservation potential and marks a step beyond prior conceptual and time-static models. We apply the model to New Guinea’s geologically complex mountains and integrate it with machine learning–derived ore formation probabilities. The combined model predicts known porphyry endowment, identifies new targets, and constrains preservation likelihood, validating this open-source method as a flexible and affordable exploration tool in dynamic tectonic settings.