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The role of data science and machine learning to achieve sustainable mineral exploration

Lorenz, S.

Europes’ journey towards a sustainable and digitized future relies fundamentally on the secured supply with critical raw materials. Lithium, Rare Earths, Indium and Tungsten, to name a few, are fundamental requisites for green and smart technologies, e-mobility and the energy transition. Securing the access to such materials is one of the fundamental questions for Europes ambition to deliver the Green Deal. Fast, versatile and accurate but at the same time socially acceptable and less invasive mineral exploration technologies are required to meet this goal. Innovative sensors and acquisition platforms for spectral imaging allow to gain insights on the composition of materials without destroying or even touching them. The complexity, diversity and sheer amount of respective data produced in a typical exploration scenario require a joint understanding of geoscience, image processing and machine learning to retrieve geologically meaningful results in a reasonable time. The presentation will give an overview on our current research highlighting important challenges in modern mineral exploration in regards to image processing, multi-sensor data fusion, data classification and real-time processing.

  • Open Access Logo Invited lecture (Conferences) (Online presentation)
    Women in Machine Learning & Data Science (WiMLDS) Meetup, 17.03.2021, online, online

Permalink: https://www.hzdr.de/publications/Publ-33169
Publ.-Id: 33169