Capturing, Preserving, and Digitizing Legacy Seismic Data from the Montserrat Volcano Observatory Analog Seismic Network, July 1995–December 2004

Thompson, Glenn; Power, John A.; Braunmiller, Jochen; Lockhart, Andrew B.; Lynch, Lloyd; McCausland, Wendy; Rowe, C. A.; Shea, Thomas M.; White, Randall A.; Breithaupt, C.

doi:10.1785/0220200012

Cited by 3 publications

(4 citation statements)

References 29 publications

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“…Data analyses from seismic and infrasound arrays have potential to underpin the development of alarm systems for Fuego, similar to present practice at other volcanoes such as Mt. Etna, Italy, (Ripepe et al, 2018;De Angelis et al, 2020), Alaskan volcanoes, USA, (Coombs et al, 2018;Power et al, 2020), and Soufrière Hills Volcano, Montserrat Island, (Thompson et al, 2020).…”

Section: Long-term Monitoringmentioning

confidence: 94%

Characterization of Acoustic Infrasound Signals at Volcán de Fuego, Guatemala: A Baseline for Volcano Monitoring

et al. 2020

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Section: Long-term Monitoringmentioning

confidence: 94%

Characterization of Acoustic Infrasound Signals at Volcán de Fuego, Guatemala: A Baseline for Volcano Monitoring

et al. 2020

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“…We choose the active learning user-selected batch size as 216 datapoints. Experienced volcano-seismic analysts can classify in the region of hundreds of volcano-seismic events in a day (e.g., Thompson et al, 2020). Therefore, we pick a figure for selecting batches in the lower region of this estimate as a reasonable quantity to be labelled at once before the models can be re-trained with the new data included.…”

Section: Active Learningmentioning

confidence: 99%

“…Seismic monitoring is one of the most common and earliest-employed tools to be deployed as part of a monitoring network (e.g., Tilling, 1989), and large seismic catalogues are created for most well-monitored volcanoes (Newhall et al, 2017). Digitisation and curation of legacy seismic data can yield a large quantity of previously-unlabelled or analysed data, or inconsistently labelled data due to changes in monitoring network, observatory staff and/or procedures (Thompson et al, 2020). An active learning approach could be used in these contexts to accelerate the process of event classification to provide retrospective insights on the characteristics of seismicity prior to eruptions.…”

Section: Applications For Active Learningmentioning

confidence: 99%

“…During times of volcanic crisis, new seismic stations may be installed within a seismic network, thus there may be no backlog of previous data to train an automatic classifier upon. Alternatively, digitisation of legacy data may also generate a large volume of unlabelled data for which there is a time-burden for digitising and verifying the associated labels (Thompson et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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A Deep Active Learning Approach to the Automatic Classification of Volcano-Seismic Events

et al. 2022

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Volcano-seismic event classification represents a fundamental component of volcanic monitoring. Recent advances in techniques for the automatic classification of volcano-seismic events using supervised deep learning models achieve high accuracy. However, these deep learning models require a large, labelled training dataset to successfully train a generalisable model. We develop an approach to volcano-seismic event classification making use of active learning, where a machine learning model actively selects the training data which it learns from. We apply a diversity-based active learning approach, which works by selecting new training points which are most dissimilar from points already in the model according to a distance-based calculation applied to the model features. We combine the active learning with an existing volcano-seismic event classifier and apply the model to data from two volcanoes: Nevado del Ruiz, Colombia and Llaima, Chile. We find that models with data selected using an active learning approach achieve better testing accuracy and AUC (Area Under the Receiver Operating Characteristic Curve) than models with data selected using random sampling. Additionally, active learning decreases the labelling burden for the Nevado del Ruiz dataset but offers no increase in performance for the Llaima dataset. To explain these results, we visualise the features from the two datasets and suggest that active learning can reduce the quantity of labelled data required for less separable data, such as the Nevado del Ruiz dataset. This study represents the first evaluation of an active learning approach in volcano-seismology.

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One hundred years of advances in volcano seismology and acoustics

Matoza

Roman

2022

Bull Volcanol

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Since the 1919 foundation of the International Association of Volcanology and Chemistry of the Earth’s Interior (IAVCEI), the fields of volcano seismology and acoustics have seen dramatic advances in instrumentation and techniques, and have undergone paradigm shifts in the understanding of volcanic seismo-acoustic source processes and internal volcanic structure. Some early twentieth-century volcanological studies gave equal emphasis to barograph (infrasound and acoustic-gravity wave) and seismograph observations, but volcano seismology rapidly outpaced volcano acoustics and became the standard geophysical volcano-monitoring tool. Permanent seismic networks were established on volcanoes (for example) in Japan, the Philippines, Russia, and Hawai‘i by the 1950s, and in Alaska by the 1970s. Large eruptions with societal consequences generally catalyzed the implementation of new seismic instrumentation and led to operationalization of research methodologies. Seismic data now form the backbone of most local ground-based volcano monitoring networks worldwide and play a critical role in understanding how volcanoes work. The computer revolution enabled increasingly sophisticated data processing and source modeling, and facilitated the transition to continuous digital waveform recording by about the 1990s. In the 1970s and 1980s, quantitative models emerged for long-period (LP) event and tremor sources in fluid-driven cracks and conduits. Beginning in the 1970s, early models for volcano-tectonic (VT) earthquake swarms invoking crack tip stresses expanded to involve stress transfer into the wall rocks of pressurized dikes. The first deployments of broadband seismic instrumentation and infrasound sensors on volcanoes in the 1990s led to discoveries of new signals and phenomena. Rapid advances in infrasound technology; signal processing, analysis, and inversion; and atmospheric propagation modeling have now established the role of regional (15–250 km) and remote (> 250 km) ground-based acoustic systems in volcano monitoring. Long-term records of volcano-seismic unrest through full eruptive cycles are providing insight into magma transport and eruption processes and increasingly sophisticated forecasts. Laboratory and numerical experiments are elucidating seismo-acoustic source processes in volcanic fluid systems, and are observationally constrained by increasingly dense geophysical field deployments taking advantage of low-power, compact broadband, and nodal technologies. In recent years, the fields of volcano geodesy, seismology, and acoustics (both atmospheric infrasound and ocean hydroacoustics) are increasingly merging. Despite vast progress over the past century, major questions remain regarding source processes, patterns of volcano-seismic unrest, internal volcanic structure, and the relationship between seismic unrest and volcanic processes.

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Capturing, Preserving, and Digitizing Legacy Seismic Data from the Montserrat Volcano Observatory Analog Seismic Network, July 1995–December 2004

Cited by 3 publications

References 29 publications

Characterization of Acoustic Infrasound Signals at Volcán de Fuego, Guatemala: A Baseline for Volcano Monitoring

Characterization of Acoustic Infrasound Signals at Volcán de Fuego, Guatemala: A Baseline for Volcano Monitoring

A Deep Active Learning Approach to the Automatic Classification of Volcano-Seismic Events

One hundred years of advances in volcano seismology and acoustics

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