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

Area of Science:

• Earth Science
• Geology
• Economic Geology

Background:

• Porphyry copper discoveries are decreasing, impacting net-zero targets.
• Understanding post-mineralization erosion and uplift is crucial for deposit preservation.
• Existing models often lack dynamic landscape evolution and time-dependent factors.

Purpose of the Study:

• To develop and validate a physically based landscape evolution model for predicting porphyry copper deposit preservation.
• To integrate geological and climate data for a more comprehensive exploration tool.
• To identify new exploration targets and assess preservation likelihood in complex tectonic settings.

Main Methods:

• Developed a physically based landscape evolution model incorporating variable erodibility, uplift, climate, and sea level changes.
• Applied the model to New Guinea's complex mountain ranges.
• Integrated the landscape model with machine learning-derived ore formation probabilities.

Main Results:

• The model successfully quantifies preservation potential by considering geological timescales and dynamic processes.
• Application in New Guinea validated the model's ability to predict known endowment and identify new targets.
• The integrated approach constrains the likelihood of deposit preservation.

Conclusions:

• The developed open-source model offers a flexible and affordable tool for mineral exploration.
• This physically based approach advances beyond conceptual and time-static models for deposit preservation assessment.
• The method is particularly valuable in dynamic tectonic settings with complex geological histories.