Nature of Project(s): Experimental and numerical
Essential Background: EMSC8024 (Foundations of Analytical Techniques and Data Science), EMSC 8023 (Advanced Data Science) or EMSC 8022 (Advanced Analytical Techniques).
Palaeomagnetism has made fundamental contributions to Earth Science by underpinning the global plate tectonic paradigm and by providing the geomagnetic polarity timescale for calibrating geological time. Palaeomagnetic analysis also provides an understanding of Earth’s magnetic field and the deep-Earth dynamo processes that generate the geomagnetic field and its variations, including providing knowledge of geomagnetic polarity reversals, excursions, and secular variations. A variety of rock types carry stable palaeomagnetic signals that are carried by nanoparticulate magnetic rock-forming minerals (e.g., magnetite, hematite, maghemite, pyrrhotite, greigite, goethite). These minerals commonly occur in nature within the ideal single domain (SD) grain size range. The Nobel laureate, Louis Néel, demonstrated that SD materials (in which magnetic particles have homogeneous magnetisation) can retain stable magnetisations for periods exceeding the age of the Earth. The long-term stability of these magnetisations provides the basis for the widespread usefulness of palaeomagnetism in Earth Science.
Beyond reconstruction of Earth’s magnetic field history, environmental magnetism employs physics-based techniques to characterize and quantify magnetic minerals in natural materials. These minerals are characteristic of certain processes, such as climate change, sediment transport, biological activity, and pollution and, thus, provide insights into many environmental questions. For example, magnetic properties of minerals are used as proxies for environmental change in palaeoclimate, paleoceanography, studies of the provenance of sediments, pollution, and archaeology. Magnetic minerals are almost ubiquitous in nature, which means magnetic techniques can be used to address a wide range of environmental problems.
Possible Future Research Avenues:
- Exploring first-order reversal curves. Magnetic rock-forming minerals can record a wide range of climatic and environmental processes. Minerals such as magnetite and hematite contain either or both of Fe3+ and Fe2+, which are readily oxidized or reduced so that mineral transformations occur in response to changing environmental conditions (e.g., monsoon-related humid/arid climate cycles). The international discipline of environmental magnetism is based on detecting such environmental signals through analysing magnetic property variations of natural materials. The most effective method for understanding such complex magnetic property variations are first-order reversal curve (FORC) diagrams, which allow assessment of the magnetic response of all particles in a material. FORC diagrams are now used widely in paleomagnetism and environmental magnetism, and are also used to an equal extent in solid state physics. Much progress has been made in understanding the information carried by FORC diagrams since their development in the late 1990s, and we are in a particularly fertile phase of FORC knowledge exploitation at present. Our research group has a strong interest in working to develop new FORC measurement, data processing, and numerical modelling protocols. This work is at the cutting-edge of FORC applications in both Earth science and solid state physics with wide ranging applications in both environmental reconstruction and materials science, including in the magnetic recording industry.
- Quantification of Australian mineral dusts in the Earth System. An estimated 2 billion tonnes of mineral dust are transported annually by the Earth’s wind systems. These huge quantities of sediment affect the Earth system directly, via scattering and absorption of solar and terrestrial radiation, and indirectly by acting as cloud formation nuclei. These processes influence Earth’s radiative balance and hydrological cycle. Wind-blown dust that reaches the oceans provides biologically available iron, which is a key micronutrient for photosynthesis, promoting ocean uptake of atmospheric CO2 by marine organisms. Southern Ocean reconstructions show that as much as half of the global CO2 drawdown during glacial-interglacial cycles results from fertilization of the ocean by iron-rich dust. The global dust cycle is changing on a variety of temporal and spatial scales and remains one of the most poorly understood elements of the Earth system. Modern observations of regional dust activity provide a limited description of a global system with a long dynamic history. Therefore, it is essential to reconstruct the role of dust in the Earth system on geological time scales. Environmental magnetic measurements are capable of quantifying trace amounts of iron minerals in dusts and providing novel insights in the evolution of the dust cycle. New magnetism-based quantification techniques must be developed to provide a powerful means for estimating Australian dust fluxes through geological time and reconstructing their role in the global dust cycle.
- Applications of probabilistic programming in environmental magnetism. Environmental magnetism aims to identify and quantify natural processes based on the composition of magnetic mineral assemblages. Thus, separation of environmental magnetic signals into meaningful parts is an essential task. This has led geophysicists to develop and adapt experimental and data processing techniques to “unmix” complex magnetic mineral assemblages into environmentally informative parts. Many tools are available to perform decomposition of mixed signals to obtain information pertinent to environmental investigations, but the results of these techniques are often ambiguous and lack crucial information, such as model uncertainties and reliability. Probabilistic programming is transforming the practice of data science and revolutionizing the way that scientists draw inferences from data. Such approaches offer a new rigorous approach to unmix environmental magnetic information, with a focus on automated inference based on a user-defined model. Will you be the person to revolutionize the analysis of magnetic data and solve key environmental problems using probabilistic programming?