Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears Project

Cortés, Guillermo Ruperto Martín; Carniel, Roberto; Lesage, Philippe; Mendoza, M. Ángeles; Lucia, Ivo Della

doi:10.3389/feart.2020.616676

Cited by 19 publications

(12 citation statements)

References 52 publications

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“…This is a common situation at many volcanoes that are poorly monitored or lack recent volcanic activity. Specifically for these cases, an innovative, multi-volcano approach was developed by the recent EU funded VULCAN.ears project (Cortés et al, 2021). A Volcano-Independent VSR (VI.VSR) system was proposed, in which universal recognition models are trained with data of several volcanoes to become portable and robust.…”

Section: Seismicity Evolution From Supervised Recognition Systemsmentioning

confidence: 99%

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The Seismicity of Lipari, Aeolian Islands (Italy) From One-Month Recording of the LIPARI Array

et al. 2021

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Seismic activity in volcanic settings could be the signature of processes that include magma dynamics, hydrothermal activity and geodynamics. The main goal of this study is to analyze the seismicity of Lipari Island (Southern Tyrrhenian Sea) to characterize the dynamic processes such as the interaction between pre-existing structures and hydrothermal processes affecting the Aeolian Islands. We deployed a dense seismic array of 48 autonomous 3-component nodes. For the first time, Lipari and its hydrothermal field are investigated by a seismic array recording continuously for about a month in late 2018 with a 0.1–1.5 km station spacing. We investigate the distribution and evolution of the seismicity over the full time of the experiment using self-organized maps and automatic algorithms. We show that the sea wave motion strongly influences the background seismic noise. Using an automatic template matching approach, we detect and locate a seismic swarm offshore the western coast of Lipari. This swarm, made of transient-like signals also recognized by array and polarization analyses in the time and frequency domains, is possibly associated with the activation of a NE-SW fault. We also found the occurrence of hybrid events close to the onshore Lipari hydrothermal system. These events suggest the involvement of hot hydrothermal fluids moving along pre-existing fractures. Seismological analyses of one month of data detect signals related to the regional tectonics, hydrothermal system and sea dynamics in Lipari Island.

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Section: Seismicity Evolution From Supervised Recognition Systemsmentioning

confidence: 99%

“…The VI.VSR approach aims to automatically search typical seismic events in continuous data streams recorded at any volcano (Cortés et al, 2021). The VI.VSR algorithm requires previously modeling of each different type of seismic events or classes prior to recognizing them.…”

Section: Seismicity Evolution From Supervised Recognition Systemsmentioning

confidence: 99%

The Seismicity of Lipari, Aeolian Islands (Italy) From One-Month Recording of the LIPARI Array

et al. 2021

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“…Figure 4 illustrates the active learning and randomly-selected learning curves for models trained on the Nevado del Ruiz dataset. classification accuracies achieved on noisy seismic data (e.g., Cortés et al, 2021) but lower than those achieved for the less noisy Llaima dataset.…”

Section: Nevado Del Ruiz Datasetmentioning

confidence: 82%

“…Seismic waveforms can be modified depending on external effects which may include path effects, such as the soil/ bedrock characteristics, or the station geometries. Indeed, Cortés et al (2021) suggest that the agreement between seismic analysts is approximately 80%. Incorrect classifications are a source of noise, and Linville et al (2019) show that for a tectonic seismic catalogue 70% of the machine learning classifier error may be attributed to analyst error during event labelling.…”

Section: Limitationsmentioning

confidence: 99%

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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“…However, the handling of such big databases to extract the information about the events from the seismic data is a challenge, because it heavily relies on manual event picking. [5] Several automatic methods to extract volcanic explosion events from seismic data have been described previously. The short-term-average/long-term-average (STA/LTA) algorithm [6] is a classical trigger algorithm to detect sudden changes in the data.…”

Section: Introductionmentioning

confidence: 99%

AWESAM: A Python Module for Automated Volcanic Event Detection Applied to Stromboli

Fenner¹,

Rümpker²,

Li³

et al. 2021

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Many active volcanoes in the world exhibit Strombolian activity, which is typically characterized by relatively frequent mild events and also by rare and much more destructive major explosions and paroxysms. Detailed analyses of past major and minor events can help to understand the eruptive behavior of the volcano and the underlying physical and chemical processes. Catalogs of volcanic eruptions may be established using continuous seismic recordings at stations in the proximity of volcanoes. However, in many cases, the analysis of the recordings relies heavily on the manual picking of events by human experts. Recently developed Machine Learning-based approaches require large training data sets which may not be available a priori. Here, we propose an alternative automated approach: the Adaptive-Window Volcanic Event Selection Analysis Module (AWESAM). This process of creating event catalogs consists of three main steps: (i) identification of potential volcanic events based on squared ground-velocity amplitudes, an adaptive MaxFilter, and a prominence threshold. (ii) catalog consolidation by comparing and verification of the initial detections based on recordings from two different seismic stations. (iii) identification and exclusion of signals from regional tectonic earthquakes. The software package is applied to publicly accessible continuous seismic recordings from two almost equidistant stations at Stromboli volcano in Italy. We tested AWESAM by comparison with a hand-picked catalog and found that around 95 % of the eruptions with a signal-to-noise ratio above three are detected. In a first application, we derive a new amplitude-frequency relationship from over 290.000 volcanic events at Stromboli during 2019-2020. The module allows for a straightforward generalization and application to other volcanoes worldwide.

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Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears Project

Cited by 19 publications

References 52 publications

The Seismicity of Lipari, Aeolian Islands (Italy) From One-Month Recording of the LIPARI Array

The Seismicity of Lipari, Aeolian Islands (Italy) From One-Month Recording of the LIPARI Array

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

AWESAM: A Python Module for Automated Volcanic Event Detection Applied to Stromboli

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