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Application of Machine Learning in Volcano Monitoring

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Dear Colleagues,

Volcanic eruptions represent significant natural hazards to communities living at the edge of active volcanoes. Detecting and tracking hazardous phenomena during eruptive events and forecasting the severity of their potential impact is fundamental to reducing the risks to people and property. Along with ground-based networks of seismometers, GNSS sensors, webcams, and gas sampling, volcano monitoring increasingly relies on satellite remote sensing methods to provide information on volcanic hazards. These large data sets can provide relevant hazard information throughout the entire hazard period, beginning with the detection of eruption precursors and ending with the estimation of a volcanic source model associated with an analyzed eruption. Under this perspective, machine learning (ML) approaches represent valuable means to efficiently learn complex and hidden patterns from big amounts of heterogeneous data. The use of machine learning is gaining importance in volcanology, not only for monitoring purposes (i.e., in real time) but also for later hazards analysis (e.g., modelling tools).

This Special Issue welcomes papers that cross-fertilize efforts in traditional volcano monitoring with new technological innovations from satellite remote sensing and machine learning techniques for increasing the capability to forecast, detect and track hazardous volcanic activity worldwide.

Dr. Claudia Corradino
Dr. Ciro Del Negro
Guest Editors

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Keywords

volcano monitoring
volcanic hazard
satellite remote sensing
ground-based networks
machine learning
classification
change detection
artificial neural networks (ANNs)

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19 pages, 15056 KiB

Open AccessArticle

Classification of Video Observation Data for Volcanic Activity Monitoring Using Computer Vision and Modern Neural NetWorks (on Klyuchevskoy Volcano Example)

by Sergey Korolev, Aleksei Sorokin, Igor Urmanov, Aleksandr Kamaev and Olga Girina

Remote Sens. 2021, 13(23), 4747; https://doi.org/10.3390/rs13234747 - 23 Nov 2021

Cited by 2 | Viewed by 2383

Abstract

Currently, video observation systems are actively used for volcano activity monitoring. Video cameras allow us to remotely assess the state of a dangerous natural object and to detect thermal anomalies if technical capabilities are available. However, continuous use of visible band cameras instead of special tools (for example, thermal cameras), produces large number of images, that require the application of special algorithms both for preliminary filtering out the images with area of interest hidden due to weather or illumination conditions, and for volcano activity detection. Existing algorithms use preselected regions of interest in the frame for analysis. This region could be changed occasionally to observe events in a specific area of the volcano. It is a problem to set it in advance and keep it up to date, especially for an observation network with multiple cameras. The accumulated perennial archives of images with documented eruptions allow us to use modern deep learning technologies for whole frame analysis to solve the specified task. The article presents the development of algorithms to classify volcano images produced by video observation systems. The focus is on developing the algorithms to create a labelled dataset from an unstructured archive using existing and authors proposed techniques. The developed solution was tested using the archive of the video observation system for the volcanoes of Kamchatka, in particular the observation data for the Klyuchevskoy volcano. The tests show the high efficiency of the use of convolutional neural networks in volcano image classification, and the accuracy of classification achieved 91%. The resulting dataset consisting of 15,000 images and labelled in three classes of scenes is the first dataset of this kind of Kamchatka volcanoes. It can be used to develop systems for monitoring other stratovolcanoes that occupy most of the video frame. Full article

(This article belongs to the Special Issue Application of Machine Learning in Volcano Monitoring)

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21 pages, 3211 KiB

Open AccessArticle

Classifying Major Explosions and Paroxysms at Stromboli Volcano (Italy) from Space

by Claudia Corradino, Eleonora Amato, Federica Torrisi, Sonia Calvari and Ciro Del Negro

Remote Sens. 2021, 13(20), 4080; https://doi.org/10.3390/rs13204080 - 13 Oct 2021

Cited by 13 | Viewed by 2452

Abstract

Stromboli volcano has a persistent activity that is almost exclusively explosive. Predominated by low intensity events, this activity is occasionally interspersed with more powerful episodes, known as major explosions and paroxysms, which represent the main hazards for the inhabitants of the island. Here, we propose a machine learning approach to distinguish between paroxysms and major explosions by using satellite-derived measurements. We investigated the high energy explosive events occurring in the period January 2018–April 2021. Three distinguishing features are taken into account, namely (i) the temporal variations of surface temperature over the summit area, (ii) the magnitude of the explosive volcanic deposits emplaced during each explosion, and (iii) the height of the volcanic ash plume produced by the explosive events. We use optical satellite imagery to compute the land surface temperature (LST) and the ash plume height (PH). The magnitude of the explosive volcanic deposits (EVD) is estimated by using multi-temporal Synthetic Aperture Radar (SAR) intensity images. Once the input feature vectors were identified, we designed a k-means unsupervised classifier to group the explosive events at Stromboli volcano based on their similarities in two clusters: (1) paroxysms and (2) major explosions. The major explosions are identified by low/medium thermal content, i.e., LSTI around 1.4 °C, low plume height, i.e., PH around 420 m, and low production of explosive deposits, i.e., EVD around 2.5. The paroxysms are extreme events mainly characterized by medium/high thermal content, i.e., LSTI around 2.3 °C, medium/high plume height, i.e., PH around 3330 m, and high production of explosive deposits, i.e., EVD around 10.17. The centroids with coordinates (PH, EVD, LSTI) are: C_p (3330, 10.7, 2.3) for the paroxysms, and C_me (420, 2.5, 1.4) for the major explosions. Full article

(This article belongs to the Special Issue Application of Machine Learning in Volcano Monitoring)

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