T59 Open Science, Open Data: Geoinformatics and Why it Should be on Everyone’s Radar
Sunday, 22 September 2024
1:30 PM - 5:30 PM
206B
MINERAL INFORMATICS: LEVERAGING DATA SCIENCE FOR GROUNDBREAKING DISCOVERIES IN EARTH AND PLANETARY SYSTEMS
Paper number: T59-3
Program: Technical Sessions
Time: 1:55 PM – 2:15 PM
Authors
-
Anirudh PrabhuCarnegie Institution for Science, Washington, DC
-
Robert M. HazenCarnegie Institution for Science, Washington, DC
-
Ahmed EleishRensselaer Polytechnic Institute, Troy, NY
-
Michael L. WongCarnegie Institution for Science, Washington, DC
-
Cadence BoucherCarnegie Institution for Science, Washington, DC
Abstract
Mineral informatics and data-driven discovery in Earth and planetary science is facilitating rapid advancement of our understanding of the mineralogy of our planet and neighboring bodies, how they have evolved through deep time and what geologic processes characterize each stage of planetary evolution, and how, on Earth, life is dynamically intertwined with the geosphere. Data-driven investigations utilizing innovative methods (e.g., mineral network analysis, mineral association analysis) to characterize the multidimensional relationships amongst the myriad attributes of these systems. Mineral informatics methods have led to the discovery of new mineral localities,1 the prediction of mineral inventories at Mars analog sites,1 mineralogical biosignatures from the planetary scale to the sample scale,2 complex mineral compositions on Mars via X-ray diffraction data alone,3 and quantification of the role of water, life, and rare elements in Earth’s mineralogical makeup.4
Through integrated, multidisciplinary exploration, we are poised to embark on a transformative journey, crossing new frontiers in mineralogy and advancing our understanding of complex, evolving Earth and planetary systems. This synergistic approach promises groundbreaking discoveries, uncovering the fundamental interconnections that underpin our planet's complexly intertwined geologic and biologic history.
[1] Morrison et al (2023) Machine learning approaches for predictive mineralogy in Earth and planetary science: A study in mineral association analysis, PNAS Nexus, 2(5)
[2] Morrison et al (2020) Exploring carbon mineral systems: Recent advances in C mineral evolution, mineral ecology, and network analysis, Frontiers, 8, 208
[3] Morrison et al (2018) Predicting Multi-Component Mineral Compositions in Gale crater, Mars with Label Distribution Learning, AGU, P21I-3438
[4] Hazen & Morrison (2022) On the paragenetic modes of minerals: A mineral evolution perspective, Am Min, 107, 1262–1287
Geological Society of America Abstracts with Programs. Vol. 56, No. 5
doi: 10.1130/abs/2024AM-404525
© Copyright 2024 The Geological Society of America (GSA), all rights reserved. Permission is hereby granted to the author(s) of this abstract to reproduce and distribute it freely, for noncommercial purposes. Permission is hereby granted to any individual scientist to download a single copy of this electronic file and reproduce up to 20 paper copies for noncommercial purposes advancing science and education, including classroom use, providing all reproductions include the complete content shown here, including the author information. All other forms of reproduction and/or transmittal are prohibited without written permission from GSA Copyright Permissions.