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Ambient Noise Tomography (ANT) as a Scalable Data Platform for Machine-learning Driven Mineral Discovery

Description: 

The transition from a carbon-driven economy to an economy driven by full electrification requires substantial additional inputs of critical minerals, in particular copper. However, currently forecast production is insufficient to meet projected demand. To meet this demand, we require a robust pipeline of future mining projects, beginning at the exploration phase. Future efforts in exploration furthermore require methodologies that can search for mineralization under substantial cover, as most accessible and economic near-surface deposits are thought to have been discovered. Ambient Noise Tomography (ANT) has, in the last few years, become increasingly utilized by the exploration geology community as a geophysical sensing methodology that is particularly useful for mineral exploration under cover due to its ability to illuminate the full mineral system at varying scales. This presentation outlines the integration of ANT into geophysical machine learning (ML) exploration tools, including local mineral prospectivity, hypothesis falsification of geological models and optimal experimental design.

Session: New Directions in Environmental, Seismic Hazard and Mineral Resource Exploration Studies - I

Type: Oral

Date: 4/17/2025

Presentation Time: 02:15 PM (local time)

Presenting Author: Jack

Student Presenter: No

Invited Presentation: Yes

Poster Number:

Authors