EMRP3.5

Paleomagnetic measurements of natural remanent magnetization were – until recently -- performed on large, millimeter to centimeter-sized rock samples using classical rock magnetometers. Developments of magnetic imaging (i.e., quantum diamond microscope (QDM) and scanning SQUID microscope) allow for the measurement of individual, weakly magnetic (10^-16 Am²) particle clusters within a sample. However, this increased spatial resolution adds to the complexity of the magnetic signal and requires novel approaches in analyzing the data.

Commonly, the magnetic moment of sources within a sample are analyzed by dipole inversion, meaning fitting a dipole signal to a cropped part of a magnetic map. While this gives accurate results for sources that are well separated and dipole-like in character, recovering net magnetic moments from complex maps proved difficult. 

Machine learning (ML) has been used to study a vast number of problems and has become a part of our daily lives. Here, we present the first ML model to predict net magnetic moments from magnetic maps. Our ML models consist of three simple convolutional neural networks for source declination, inclination, moment magnitude, each of which has been trained on a large (500k) synthetic map dataset. The models are tested against several datasets, varying in size, source distribution and resolution. Our models can quickly and accurately recover the magnetic moment, even for very complex (i.e., low dipolarity) maps. The accuracy of the models is < 8˚ for inclination and declination and < 10 % for moment. Surprisingly, we find little correlation in the prediction accuracy to the dipolarity of the map. These results show that ML is a promising alternative to dipole inversions, especially for maps with low dipolarity parameters, where traditional dipole inversions can fail.

How to cite: Volk, M., Trubko, R., Fu, R., and Meade, B.: Machine Learning models for Moment Magnetometry applied to High-Resolution Magnetic Maps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8871, https://doi.org/10.5194/egusphere-egu22-8871, 2022.

The stability and acquisition of remanent magnetization in Earth and planetary rocks is controlled by the magnetization state of magnetic particles. Magnetic properties are often characterized by magnetic grain size. Besides grain size, also stress, magnetostatic interactions, and grain shape influence and modify magnetic stability. Moreover, internal stress in magnetite may substantially affect remanence acquisition and might be responsible for enhanced remanence for which the processes are often still enigmatic.

To better understand the impact of internal stress on the efficiency of remanence acquisition, their contribution needs to be separated from the magnetostatic and demagnetizing energy contributions. Direct observations of the influences of magnetocrystalline and stress anisotropy on the magnetic behavior of magnetite are obscured by the large demagnetizing energy. A new temperature-dependent hysteresis measurement and evaluation procedure was developed by Béguin & Fabian, (2021), which allows the determination and separation of demagnetizing energy and internal stress. The validity of the method was demonstrated for a suite of synthetic magnetite samples under different stress conditions.

Here we present the first results of applying the new scaled reversible work (SRW) method to a set of natural magnetite bearing samples. We used samples with homogeneous and heterogeneous microstructures and end-member stress-free magnetite samples. The magnetic behavior as function of temperature is less ideal for the natural samples than for the previously studied synthetic magnetite samples, for example, the Curie temperatures are less distinct. Here we present how these challenges can be overcome, and present additional evaluation steps to ease the interpretation of the temperature-dependent hysteresis data.

How to cite: Béguin, A., Fabian, K., Church, N., and McEnroe, S.: Determining Demagnetization Energy and Internal Stress in Natural Magnetite Bearing Samples, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12541, https://doi.org/10.5194/egusphere-egu22-12541, 2022.

The last few years have been marked by a number of motivating novel ideas and methodological advancements in paleomagnetic analysis (e.g. trans-hierarchical uncertainty propagation), observational and theoretical geodynamics, and paleogeographical modeling (e.g. optimisation and Bayesian approaches). Many of these developments offer new insights on, and/or approaches to estimating, the past motions of tectonic plates—but so far these developments have largely unfolded in isolation of one another. In November 2021 an international group of 15 young scientists with highly complementary backgrounds (spanning the aforementioned fields) gathered to explore and discuss these exciting new developments and to brainstorm strategies that may enable their integration. We anticipate that the integration of these diverse new ideas and methods will open new frontiers in plate tectonic research, and notably lead to much better-constrained paleogeographic models. In this presentation, we will share some of the insights and strategies that emerged from the workshop, including the advantages of conducting paleomagnetic analysis at the site-level, the application of emerging paleomagnetic Euler pole analysis frameworks, and the use of insights extracted from Earth-like geodynamic models (which self-generate plate tectonic behavior) to further constrain the results of these paleomagnetic methods. We also present some preliminary results of early experiments putting these strategies into practice on a paleomagnetic dataset from North America.  

How to cite: Domeier, M., Arnould, M., Eyster, A., Gallo, L. C., Gürer, D., Király, Á., Robert, B., Rolf, T., Sapienza, F., Shephard, G. E., Swanson-Hysell, N., Vaes, B., van der Boon, A., Wu, L., and Zhang, Y.: Frontiers in quantitative paleogeography and paleomagnetism, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6653, https://doi.org/10.5194/egusphere-egu22-6653, 2022.

Ferromanganese concretions are abundant in many parts of the coastal Baltic Sea and hence provide an important yet underused archive to investigate these shallow sea areas in detail. Coastal sea areas can be best described as filters, and they have a focal role in the biogeochemistry of riverine nutrients and suspended particles during their transport to open sea. Colloidal iron and manganese oxyhydroxides, originating from the surrounding catchment area and/or from adjacent anoxic sediments, accumulate and ultimately lead to the authigenic formation of ferromanganese concretions at an unusually fast growth rate, generally in low-carbon environments with limited sedimentation. The porous concretions come in various sizes and shapes, and host diverse microbial communities with reductive and oxidative metabolisms that can affect both the concretion growth and dissolution. Despite that, the specific formation mechanisms and (bio)mineralization processes of different concretion morphotypes are poorly constrained.

We have investigated the magnetic properties of Baltic Sea ferromanganese concretions of different morphotypes, depositional environments, water depths and geographical locations. While the magnetic mineral concentrations change according to salinity and water depth, the concentrations are also seemingly controlled by the concretion morphotype. Preliminary results of laboratory remanences also indicate the magnetic mineral assemblages are dominated by SD-sized ferrimagnetic particles with magnetic properties similar to those of magnetosomes. The possible presence of magnetotactic bacteria within the concretions provides novel insights into the biogeochemistry of low sediment accumulation and low-carbon coastal environments of the Baltic Sea.

This research is part of the Fermaid project, funded by the Academy of Finland grant 332249.

How to cite: Wasiljeff, J., Salminen, J., and Virtasalo, J.: Magnetic properties of Baltic Sea ferromanganese concretions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7425, https://doi.org/10.5194/egusphere-egu22-7425, 2022.