MI2023 - 18 Liao Chang: A machine-learning approach to multiscale environmental magnetism
Channel: MagNetZ
Date: 2023-01-17

Magnetic Interactions 2023 conference at the University of Cambridge
Session 4: Environmental Magnetism

Title: A machine-learning approach to multiscale environmental magnetism: framework and applications to heterogeneous environmental samples
Author: Liao Chang from Peking University, Beijing, China

Quantitative, accurate and efficient decoding of magnetic signals in environmental magnetism can be challenging, due to complexities of highly heterogeneous natural magnetic mineral assemblages, coupled to the fact that the underlying mechanisms linking environmental variables to bulk magnetic properties are often poorly understood. This limits the potential of magnetic proxies to reveal quantitative information about environmental conditions. To tackle this problem, we are developing new multiscale magnetic mineral characterisation frameworks for magnetic mineral quantifications with unprecedented accuracy and efficiency. The new framework involves mapping out precisely the microstructural and chemical composition of magnetic mineral assemblages within natural samples directly at the micrometre and nanometre scale, and developing machine-learning based analytical toolboxes for data analyses and realistic micromagnetic model constructions. We have incorporated rapidly developing machine learning algorithms into the analytical framework, such as magnetic mineral phase quantification through pattern recognition. We applied the framework in case studies to analyse targeted sediment, igneous and biological samples to use magnetic proxies, for example, for determining magnetic biosignatures, and tracing the response of ocean oxygenation to climate shifts. By establishing the nanoscale mineralogical processes and calculating the resulting magnetic properties micromagnetically, the new machine-learning assisted framework is applicable to a wide range of environmental problems for quantitative, accurate and efficient decoding of environmental magnetic signals.

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00:00 - Intro
00:19 - Presentation
16:05 - Questions