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Jordan Edward Lubbers

Publications and source records attributed to Jordan Edward Lubbers.

9 recordsLinked to original sources
USGS2026

Constraining sources of mid-Pleistocene to present explosive volcanism in the Gulf of Alaska using machine learning and compositional data analysis

The long-term eruptive record of a region helps better elucidate magmatic processes at depth as well as volcanic hazards at the surface. Typically, reconstructing such records is done using proximal tephrostratigraphy and linking individual tephras to source volcanoes. These records, however, can only be accurately constructed if they are both well preserved and correctly linked to source volcanoes—not a trivial task, especially in areas such as Alaska that have experienced numerous glaciation events since the Pleistocene. This ultimately necessitates another way of assessing the long-term volcanic record such that these histories may be better discerned. Here we present data from 70 marine core tephras from the Gulf of Alaska, which have a virtually uninterrupted depositional record going back through the mid-Pleistocene. We utilize compositional data analysis techniques to quantify 37 eruptions over the span of eight cores, machine learning classification and conformal prediction algorithms to assign the most probable volcanic source(s) to each eruption, and multivariate distance-based metrics when machine learning classification algorithms are inappropriate. We find that the Mount Katmai magmatic system is the most probable volcanic source for analyzed tephras and that large volcanic centers such as Mount Katmai, Fisher Caldera, and Emmons Lake volcanic center have produced nearly invariant incompatible trace element ratio magmas, allowing for them to be confidently identified in long-term tephra records. This highlights the utility of trace elements for tephra studies, especially when paired with compositional data analysis, multivariate statistical tests, and petrologically informed discriminants.

Alaska↗
USGS2026

Examples of eruption response teams from the Alaska Volcano Observatory

During times of eruption response, volcano observatories need to organize themselves differently than during normal operations. The number of formal operational roles grows to ensure that critical responsibilities are covered, including management of all activities at the observatory as well as increased staffing to ensure proper surveillance of data and issuance of timely notices and warnings. The scope and approach differ for each observatory and, in fact, for different eruptions. The Alaska Volcano Observatory (AVO) maintains an extensive monitoring program and issues forecasts and warnings about volcanic eruptions and unrest in Alaska. Since 2000, AVO has used formal roles to perform these duties and has implemented a variety of team approaches to respond to larger eruptions. For the Augustine (2006) and Redoubt (2009) eruptions, management scaled from 3 up to ~ 8 people in a command team to cover all aspects of the response. During these and other eruptions (e.g., Okmok, 2008), an operations room was staffed continuously to cover real-time responsibilities pertaining to monitoring and issuing alerts while the command team focused on overall management. More recently, such as for Bogoslof in 2016–2017 and Shishaldin in 2019 and 2023, AVO used a virtual real-time response team to handle warning tasks and variations of an Observatory Volcanic Event Response Team (OVERT; Moran et al. 2024) to manage overall observatory response activities. In 2025, AVO employed a formal OVERT for the first time to oversee its response to unrest at Mount Spurr. Frequent implementation, nimble scaling, constant evaluation, flexibility, and good communication make the team approach effective. We present examples of several response teams used over the last 25 years, and lessons learned from them, in the hope that these will be helpful to other observatories facing crisis responses. These examples may also allow stakeholders and the public to better understand how observatories work.

Alaska↗
USGS2025

Fast or slow: An evaluation of Ti-in-quartz diffusion coefficients through comparisons of quartz and plagioclase diffusion times

Diffusion geochronometry using Ti-in-quartz has become a valuable method in understanding the evolution of silicic magmas. However, four different options for Ti diffusivity ( D Ti ) currently exist, spanning three orders of magnitude, resulting in substantially different estimated times and interpretations. We present Ti-in-quartz diffusion times for the Cerro Galán Ignimbrite using the Cherniak et al. [2007] ( 10.1016/j.chemgeo.2006.09.001 ), Audétat et al. [2021] ( 10.1130/g48785.1 ), Audétat et al. [2023] ( 10.1038/s41467-023-39912-5 ), and Jollands et al. [2020] ( 10.1130/g47238.1 ) D Ti value and (1) compare these against plagioclase diffusion times derived from the same samples, (2) consider evidence for Ti diffusion in quartz under relevant magmatic timescales, and (3) compute derived quartz growth rates for crystals from the Cerro Galán Ignimbrite. On all accounts, we find that the Cherniak et al. [2007] diffusion coefficient yields diffusion times that agree much better with independent evidence than those derived using slower D Ti values [Jollands et al. 2020; Audétat et al. 2021; 2023].

Volcanica↗
USGS2025

lasertram: A Python library for time resolved analysis of laser ablation inductively coupled plasma mass spectrometry data

Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) data has a wide variety of uses in the geosciences for in-situ chemical analysis of complex natural materials. Improvements to instrument capabilities and operating software have drastically reduced the time required to generate large volumes of data relative to previous methodologies. Raw data from LA-ICP-MS, however, is in counts per unit time (typically counts per second), not elemental concentrations and converting these count ratesto concentrations requires additional processing. For complex materials where the ablated volume may contain a range of material compositions, a moderate amount of user input is also required if appropriate concentrations are to be accurately calculated. In geologic materials such as glasses and minerals that potentially have numerous heterogeneities (e.g., microlites or other inclusions) within them, this is typically determiningwhether the total ablation signal should be filtered to remove these heterogeneities. This necessitates that the LA-ICP-MS data processing pipeline is one that is not automated, but is also designed to enable rapid and efficient processing of large volumes of data. Here we introduce , a Python library for the time resolved analysis of LA-ICP-MS data. We outline its mathematical theory, code structure, and provide an example of how it can be used to provide the time resolved analysis necessitated by LA-ICP-MS data of complex geologic materials. Throughout the pipeline we show how metadata and data are incrementally added to the objects created such that virtually any aspect of an experiment may be interrogated and its quality assessed. We also show, that when combined with other Python libraries for building graphical user interfaces, it can be utilized outside of a pure scripting environment. can be found at https://doi.org/10.5066/P1DZUR3Z

Applied Computing and Geosciences↗
USGS2025

Glass laser ablation-inductively coupled plasma-mass spectrometry analysis methods, precision, and accuracy data for tephra studies in Alaska

This publication reports the analytical conditions, standard reference material (SRM) results, and preferred post-processing methodologies for laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) measurements supporting tephra studies in Alaska between 2018 and 2024. We evaluate the long-term accuracy and precision of our methodologies by comparing our calculated SRM concentrations to the Geological and Environmental Reference Materials database (GeoReM) preferred concentration values for the following SRMs: BCR-2G, BHVO-2G, ATHO-G, NIST-612, GSD-1G, and GSE-1G. We show that our LA-ICP-MS methodologies produce accurate and consistent measurements across numerous analytical sessions, even when instrumentation changed. Overall, these results indicate that Alaska tephra matrix glass measurements, like SRM measurements, are accurate, precise, and comparable between analytical sessions. This work allows us to better correlate tephra units from Alaska volcanoes throughout the Alaska-Aleutian arc, ultimately enhancing our understanding of spatiotemporal patterns of volcanism in the region. This enhanced understanding will aid in refining volcanic hazard classification and response strategies. Future versions of this dataset will provide updates to SRM results or analytical routines for sessions that have transpired since the publishing of this version.

Alaska↗
USGS2024

Constraining magma storage conditions of the Toba magmatic system: A plagioclase and amphibole perspective

Silicic magma reservoirs are responsible for producing the largest explosive eruptions in the geologic record. Petrologic and geochronological data provide evidence for these systems spending substantial periods of time (10 4 –10 5 yrs) within the upper crust prior to eruption; however, the long-term thermochemical evolution of these systems is not fully understood, as existing petrologic data make it challenging to quantify the time interval a magmatic system has spent at certain temperatures, or its “thermal history”. Here, we investigate the 74 ka Youngest Toba Tuff (YTT), one of the largest explosive eruptions in the geologic record, to better constrain the long-term thermal evolution of its magmatic system. We combine forward models of Sr diffusion in plagioclase and hornblende, mineral thermometry, and pre-existing trace-element evolution models to quantify the thermochemical evolution of the YTT magmatic system. We find that plagioclase crystals record decades to centuries of storage at temperatures 750 C, while hornblende records up to 6200 years at the same temperatures. Hornblende crystallizes at temperatures around 820 C and adjusting our diffusion modeling to this temperature results in no more than 900 years at initial crystallization conditions. Combined with previous trace-element modeling work, these results indicate that although there was chemical diversity for long durations in the YTT magma system sufficient to produce unique composition eruptive products, the entire system was experiencing a relatively similar thermal history that did not allow for large bodies of eruptible magma to be present for long periods ( 10 2 –10 3 years). Rather, we suggest that magmas within the YTT magmatic system were stored for long durations at thermal conditions where they were uneruptible and only remobilized within a few centuries prior to eruption.

Sumatra↗
USGS2023

Probabilistic source classification of large tephra producing eruptions using supervised machine learning: An example from the Alaska-Aleutian arc

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.

Alaska↗