2021 — 2023 |
Trail, Dustin Nakajima, Miki |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Csedi: Searching For Hadean Impacts: Clues From the Sudbury Impact Basin and Machine Learning Approaches @ University of Rochester
This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Mechanisms of crustal formation provide crucial information on the planetary environment. For example, if Earth’s crust formed during the Hadean eon (4.5-4.0 billion years ago) in the same way as the modern Earth, this might indicate that early Earth had plate tectonics. This has significant implications for the early Earth’s environment, given that plate tectonics promote volatile cycling and chemical weathering, which in turn provide key ingredients for life. However, it is also possible that a significant fraction of early Earth’s crust formed by other mechanisms, such as crystallization from magmas that formed by meteorite impacts. This hypothesis does not require early Earth to have had plate tectonics. Information about either mechanism is challenging to obtain because of our extremely limited access to preserved terrestrial materials older than 4 billion years. Most of our direct information about the Hadean crust has been obtained from the extremely resistant mineral called zircons, with ages that approach 4.4 billion years. Zircons contain trace impurities, such as titanium and rare earth elements. Their concentrations can be affected by the formation mechanisms of the magmas from which zircons crystallize, e.g., related to plate tectonics v. meteorite impacts. Here, we will investigate early Earth’s crust formation mechanisms though a multi-pronged approach that includes detailed analysis of trace element chemistry in Hadean zircon. These will then be compared with simulations that predict the trace element chemistry in zircon due to meteorite impact and melting of the early Earth. With these results, we will be able to estimate possible Hadean crust composition and hence its genesis due to impact-related processes. This project will also support students training in modern computational methods, machine learning, and modern methods of mineral analysis. Two of the students will write their senior theses on this project, providing opportunities for the students to participate in this cutting-edge research project. The outcome will be presented at the Rochester Museum and Science Center (RMSC) to enhance local community engagement.
This will be the first time machine learning methodologies have been explored to characterize the origin of zircons based on their trace element abundances. We will train our machine learning model with more recently formed zircons whose origins are well known, and subsequently apply the model to the Hadean zircons to identify their origins. We propose to conduct the following tasks; (1) we will characterize trace impurities in zircons formed from impact-induced magma by analyzing samples provided by the Smithsonian Institute, (2) We will collect rock samples from the Sudbury impact basin, which formed 1.85 billion years ago and has the largest preserved crust crystallized from impact-induced magma on Earth, and (3) we will conduct impact simulations and melt evolution calculations, which will be compared with whole rock chemistry and zircon trace elements from the Sudbury basin samples. Once chemical, physical and machine learning models are developed, we will apply it to Hadean zircon data to identify whether some of the zircons formed by impact. Finally, based on our model and trace element abundances in Hadean zircons, we will explore the Hadean crust composition and its potential relation to impact-related processes. Our unique and comprehensive approach will provide new insights to understand the poorly constrained environment of the early Earth. By understanding the crust formation process during the Hadean, we can better constrain the early Earth's environments, including crust production rate and volatile cycling. Moreover, with machine learning being a key component of this project, data preservation and archiving will be a top priority. We plan to archive our analyzed sample data in a similar format as the extant GeoRoc database, which will be publicly accessible. We will dedicate one class for this project and student participation will be a key aspect of this proposed work. Through the class activity, the students will learn impact processes, Sudbury geology, and the geochemical techniques involved in rock (x-ray fluorescence) and mineral (laser ablation inductively coupled plasma mass spectrometry) analysis.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
|
0.909 |