Probabilistic Source Classification of Large Tephra Producing Eruptions Using Supervised Machine Learning: An Example From the Alaska‐Aleutian Arc

Abstract

Alaska contains over 130 volcanoes and volcanic fields that have been active within the last 2 million years. Of these, roughly 90 have erupted during the Holocene, with many characterized by at least one large explosive eruption. These large tephra‐producing eruptions (LTPEs) generate orders of magnitude more erupted material than a “typical” arc explosive eruption and distribute ash thousands of kilometers from their source. Because LTPEs occur infrequently, and the proximal explosive deposit record in Alaska is generally limited to the Holocene, we require a method that links distal deposits to a source volcano where the correlative proximal deposits from that eruption are no longer preserved. We present a model that accurately and confidently identifies LTPE volcanic sources in the Alaska‐Aleutian arc using only in situ geochemistry. The model is a voting ensemble classifier comprised of six conceptually different machine learning algorithms trained on proximal tephra deposits that have had their source positively identified. We show that incompatible trace element ratios (e.g., Nb/U, Th/La, Rb/Sm) help produce a feature space that contains significantly more variance than one produced by major element concentrations, ultimately creating a model that can achieve high accuracy, precision, and recall on predicted volcanic sources, regardless of the perceived 2D data distribution (i.e., bimodal, uniform, normal) or composition (i.e., andesite, trachyte, rhyolite) of that source. Finally, we apply our model to unidentified distal marine tephra deposits in the region to better understand explosive volcanism in the Alaska‐Aleutian arc, specifically its pre‐Holocene spatiotemporal distribution.

Geochemistry π: Automated Machine Learning Python Framework for Tabular Data

“…Furthermore, thanks to community databases, humans and machines can access large amounts of data effectively through the Findable, Accessible, Interoperable, and Reusable principle (Chamberlain et al, 2021;Farrell et al, 2021;Goldstein et al, 2014;Klöcking et al, 2023;Lehnert et al, 2000). Under these circumstances, geochemical data mining has become a practical approach for elucidating the processes occurring on Earth's surface and within its interior, at present and throughout geological history, such as tephrachronology and tephra studies (Bolton et al, 2020;Lubbers et al, 2023), hydrological studies (Shaughnessy et al, 2021;Wen et al, 2021), magmatic processes (Boschetty et al, 2022;Cortés et al, 2007;Costa et al, 2023;Keller et al, 2015), thermobarometry (Higgins et al, 2022;Jorgenson et al, 2022;Petrelli et al, 2020), tectonic distrimiantion (Doucet et al, 2022;Petrelli & Perugini, 2016;Ueki et al, 2018), sedimentary composition reflecting crust composition over time (Lipp et al, 2021;Ptáček et al, 2020), and long-term secular cooling of the mantle (Keller & Schoene, 2012).…”

Cryptotephra studies applied to a deep marine sedimentary record in the northern Hikurangi Trough, Aotearoa New Zealand

“…for at least the past 30 years (Lowe et al, 2017), however, in comparison, the use of coding is a relatively new and still uncommon method applied to cryptotephra studies (e.g., Lubbers, 2023). The use of statistics and coding is important as it can appropriately handle large quantities of data, and case studies have shown that PCA in particular can be used to differentiate between separate groups within sets of data (Figure 1-5; Peti et al, 2019).…”