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 post-mineralisation processes determine whether porphyry systems are exposed, buried, or eroded entirely. We present a novel, 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.
