Geology topics

Research about Bogoslof Volcano

Source-linked reports with geographic coverage including Bogoslof Volcano.

6 recordsLinked to original sources

USGS2025

Case study of deep learning image segmentation for the purposes of rapid 2D petrographic analysis in volcanic rocks

Automation using deep learning methods is a useful alternative to manual methods of petrographic segmentation, but often requires user familiarity with coding and/or algorithms. We examine the Dragonfly TM program's deep learning tools for application by users with a variety of skill levels as a method for petrographic image segmentation. An image processing methodology, bimodal image stacking, was created for low-input-data, high-efficacy training of models which can then be applied to varied samples. Using backscatter electron images we show that the resulting model segmentations agree with manual segmentation total and modal crystallinity values within 5%, and calculated plagioclase crystal size distribution (CSD) values within 2σ, despite limitations in discriminating mafic phases. Model creation and training takes <24 hours, 1–3 hours of which are supervised, and the resultant model can then be applied to new uncharacterized samples in <15 minutes per image. This allows for non-experts to create and utilize deep learning models to segment images of variable brightness and texture, at low user-time cost and resulting in size and shape data which are within uncertainty of manual segmentation. While some limitations are noted (for example, sieve-textured phases may need manual correction, and different minerals with similar BSE intensity may not be resolved as separate phases), this methodology can be utilized for general application of models to wide ranges of volcanic crystalline and bubble textures, and to create a library of models for rapid petrological analysis during volcanic eruptions.

Alaska↗

USGS2023

Dating individual zones in phenocrysts from the 2016–2017 eruption of Bogoslof volcano provides constraints on timescales of magmatic processes

We investigate the rates of magmatic processes using clinopyroxene diffusion chronometry on volcanic products erupted in August 2017 at the end of the 9-month eruption of Bogoslof volcano. The eruptive products contain plagioclase, clinopyroxene, and amphibole, all of which exhibit sharp chemical boundaries and are occasionally observed in multi-phase crystal clots with shared zoning boundaries across different mineral phases. At the shared boundaries in crystal clots, clinopyroxene and plagioclase continued to grow but abruptly changed composition from Mg# 81.7 ± 5.8 to 72.9 ± 3.0 and An 82.5±1.4 to An 61.3±5.7 , respectively. Additionally, the sharp boundary marks where amphibole became unstable and began forming a reaction rim. Synthesizing these observations, we were able to determine that the shared boundaries formed as a result of rapid decompression during magma ascent, followed by storage in a shallow cryptodome, where magma accumulated prior to erupting. In order to determine the timescales of magma ascent and subsequent crystal residence times, we applied diffusion chronometry on zoned clinopyroxene phenocrysts using Mg# concentrations at 1056 °C determined from Fe Ti oxide pairs. Our diffusion modeling results show that diffusion began at the stepwise boundaries in clinopyroxenes no more than180 days before the final explosive event. These results were then used to calculate crystal growth rates for shared plagioclase and amphibole rims, as shared zones in crystal clots indicate that the boundaries in all three phases formed contemporaneously. We calculate growth rates of plagioclase crystals (1.7 ± 0.99 × 10 −6 um/s) and amphibole reaction rims (2.8 ± 0.47 × 10 −6 um/s). The calculated natural growth rate of plagioclase was then used to constrain additional magmatic timescales from growth rate chronometry, results of which support our diffusion timescales. Our results indicate that the distinct boundaries in all three mineral phases formed due to ascent-driven decompression followed by shallow emplacement of mafic magma that occurred continually throughout the course of the eruption. By subtracting diffusion timescales from the date that the samples were erupted, the oldest crystal boundaries correspond to March 2017, seemingly correlating with increases in both seismicity and SO 2 emissions. These observations may suggest that our petrochronometric results are supported by interdisciplinary observations.

Alaska↗

USGS2020

Evolving infrasound detections from Bogoslof volcano, Alaska: Insights from atmospheric propagation modeling

Bogoslof volcano, a back-arc volcano in Alaska’s Aleutian arc, began an eruptive sequence in mid-December 2016 that ended in late August 2017, with 70 individual eruptive episodes. Because there were no local seismic or infrasound stations on the island, the Alaska Volcano Observatory (AVO) relied on distant geophysical networks and remote sensing techniques to assess activity during the eruption. AVO maintains six infrasound arrays to monitor activity along the Aleutian arc: Adak, the Island of Four Mountains, Okmok, Akutan, Sand Point, and Dillingham. Eruption detection at infrasound arrays is subject to local as well as mesoscale meteorological conditions that vary greatly over both short and long timescales. Infrasound detections from the array nearest to Bogoslof (Okmok), with a latency of about 3 min, played a crucial role in monitoring activity during the eruption. Despite the relative proximity of the Okmok array to Bogoslof (60 km), infrasound detections were not uniformly observed with only about two-thirds of the events successfully detected. The farthest array at Dillingham (816 km) detected approximately half of the explosive events, with all other arrays detecting less than half of the events. We compare observations with infrasound propagation model predictions, using both normal mode and parabolic equation forward models, to interpret the variation in detections of the 70 explosive events across the AVO infrasound network. The forward models utilize the newly created, publicly available AVO-G2S atmospheric reconstruction using numerical weather predictions data for the lower atmosphere, coupled with upper atmosphere empirical models of wind speeds and temperature. We find that long-range detections (> 100 km) of Bogoslof events are largely aligned with seasonal variability in favorable propagation conditions, while regional detections (< 100 km) are less consistent with propagation modeling. Understanding the output of numerical models in comparison to past observations will facilitate their use in future operational settings for AVO and other observatories.

Alaska↗

USGS2020

Infrasound generated by the 2016-2017 shallow submarine eruption of Bogoslof volcano, Alaska

The 2016–2017 shallow submarine eruption of Bogoslof volcano produced numerous infrasound signals over 9 months that were recorded on six Alaska Volcano Observatory (AVO) arrays at ranges of 59 to over 800 km from the volcano. The lack of geophysical monitoring near Bogoslof and the repeated production of volcanic clouds to flight levels made monitoring by remote infrasound critical during the eruption; for the first time, AVO relied extensively on automated infrasound detections from regional arrays to dispatch timely notifications of the ongoing activity. Most of the 70 eruptive events were detected on at least one array, but no array detected all of the events mainly because atmospheric conditions were highly variable during the eruption. Acoustic propagation modeling helps explain some of the variation in array detections but also highlights limitations in regional propagation models. To our knowledge, this is the first example of well-recorded infrasound from an explosive eruption occurring in shallow seawater, providing extensive insights into eruption dynamics in this unique environment. The dominance of low-frequency infrasound (0.1–1 Hz) is attributed to eruptions occurring beneath tens of meters of seawater. Higher-frequency infrasound signals were mostly limited to eruptions where the vent was isolated from major interaction with seawater or in several cases where a lava dome grew above sea level.

Alaska↗

USGS2018

Historical eruptions and hazards at Bogoslof volcano, Alaska

Bogoslof volcano is a submarine volcano in the southern Bering Sea (53.9272°N, 168.0344°W), located 100 kilometers (km) west of Dutch Harbor/Unalaska, and 40 km north of Umnak Island. The volcano has a relatively long history of scientific investigation and several of its historical eruptions have been documented during brief visits to the area since the late 1700s. The purpose of this report is to provide a modern volcanological perspective on past eruptions of Bogoslof and to readdress some of the eruptive phenomena described in historical documents and reports. We also present for the first time a brief analysis of the hazards posed by Bogoslof eruptions. While this report was being prepared, Bogoslof volcano was in an ongoing state of eruptive activity that began in mid-December 2016. Detectable eruptive activity ended in late August 2017 and the volcano has remained quiet since then. Because we have not yet visited Bogoslof Island and have only a few distal tephra samples from two eruptive events, we will not discuss in detail the 2016–17 eruptive sequence, but will provide some information for comparative purposes. When more detailed data has been collected, a more extensive review of the 2016–17 Bogoslof eruption should be the subject of future reports.

Alaska↗

USGS2018

Using earthquakes, T waves, and infrasound to investigate the eruption of Bogoslof Volcano, Alaska

The 2016‐2017 eruption of Bogoslof volcano, a submarine stratovolcano in the Bering Sea, produced 70 discrete explosive eruptions over 8 months. With no local monitoring data, activity was seismically recorded on nearby islands 50‐100 km away, limiting the detection and resolution of seismic observations. We construct a matched filter catalog of 3199 events from 49 earthquake families, many of which occurred with hydroacoustic T waves of varying strength. We then use a 2D finite difference model to show that hydroacoustic amplitudes should decrease with increased source depth beneath the edifice and leverage each family's seismically recorded T wave amplitude as a proxy for source depth, which we compare to regional infrasound data. This unique combination of using P and S waves to detect events, T waves as a proxy for depth, and infrasound for precise timing of emissions allows us to interpret the dynamics and evolution of the Bogoslof eruption.

Alaska↗