V019
Volcano Hazard Monitoring using Statistical Methods and Artificial Intelligence
Session ID#: 157741
Session Description:
Monitoring of volcanic hazards presents extraordinarily challenging problems, from detecting and quantifying hazardous phenomena during eruptive events to forecasting their impact to assess risks to people and property. Helping address these problems, however, is an abundance of satellite and ground-based data-sets with ever‐improving temporal, spatial, and spectral resolutions that are mostly open and publicly available. This exceptional combination of pressing challenges and abundant data is leading to the growing use of statistical analysis and methods of artificial intelligence (AI) to solve problems of volcanic hazards. Machine learning, a type of AI in which computers learn from data, 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 session welcomes contributions that cross-fertilize efforts in traditional ground-based volcano monitoring systems with technological innovation from satellite remote sensing and advanced statistical methods and artificial intelligence for developing a better understanding of volcanic hazards.
Co-Organized:
Co-Sponsor(s):
Index Terms:
0555 Neural networks, fuzzy logic, machine learning [COMPUTATIONAL GEOPHYSICS]
4314 Mathematical and computer modeling [NATURAL HAZARDS]
8485 Remote sensing of volcanoes [VOLCANOLOGY]
8488 Volcanic hazards and risks [VOLCANOLOGY]
Primary Convener: Ciro Del Negro, INGV - National Institute of Geophysics and Volcanology, Etna Volcano Observatory, Catania, Italy
Conveners: Eleonora Amato, University of Palermo, Department of Mathematics and Computer Science, Palermo, Italy, Claudia Corradino, INGV - National Institute of Geophysics and Volcanology, Etna Volcano Observatory, Catania, Italy and Federica Torrisi, University of Catania, Department of Electrical, Electronics and Computer Engineering, Catania, Italy
Automatic first arrival picking of P and S-wave using machine learning from temporary observation records on a volcanic island, Hachijojima (1100863)
Hiroyuki Azuma1, Hikaru Kunimasa1, Ryohei Tottori1, Yoshiya Oda1, Toshiki Watanabe2 and Toshifumi Matsuoka3, (1)Tokyo Metropolitan Univ, Hachioji, Japan, (2)Nagoya University, Nagoya, Aichi, Japan, (3)Fukada Geological Institute, Tokyo, Japan
Dynamic Strain Anomalies Detection at Stromboli before 2019 Vulcanian Explosions Using Machine Learning (1115752)
Pierdomenico Romano1, Bellina Di Lieto1, Agata Sangianantoni1, Silvia Scarpetta2 and Roberto Scarpa2, (1)Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Vesuviano, Napoli, Italy, (2)Università degli Studi di Salerno, Dipartimento di Fisica "E. R. Caianiello", Salerno, Italy
Artificial Intelligence Techniques applied to Remote Sensing data for Volcano-Tectonic Features Extraction (1117477)
Anna Maria Gargiulo1, Josè Francisco Guerrero Tello2, Ilaria Moriero3, Mauro Coltelli2, Maria Marsella3 and Antonio Genova1, (1)Sapienza University of Rome, Department of Mechanical and Aerospace Engineering, Rome, Italy, (2)INGV National Institute of Geophysics and Volcanology, Osservatorio Etneo, Catania, Italy, (3)Sapienza University of Rome, Department of Civil, Constructional and Environmental Engineering, Rome, Italy
Volcano Hazard Monitoring at Mount Etna using Multispectral Satellite Imagery and Artificial Intelligence (1130544)
Ciro Del Negro1, Eleonora Amato1,2, Claudia Corradino1 and Federica Torrisi1,3, (1)INGV - National Institute of Geophysics and Volcanology, Etna Volcano Observatory, Catania, Italy, (2)University of Palermo, Department of Mathematics and Computer Science, Palermo, Italy, (3)University of Catania, Department of Electrical, Electronics and Computer Engineering, Catania, Italy
Combined satellite and ground-based measurements of volcanic CO2 fertilization of a pine forest in northeastern Yellowstone caldera, USA (1177749)
Robert Bogue, McGill University, Earth & Planetary Sciences, Montreal, QC, Canada, Peter Munroe Douglas, California Institute of Technology, Pasadena, United States, John Stix, McGill University, Montreal, Canada and Joshua B. Fisher, Chapman University, Orange, United States
High-resolution DEM Generation from Ground-based and Satellite Remotely Sensed Datasets – A Case Study from Pacaya Volcano, Guatemala (1179185)
Taha Sadeghi Chorsi1, Mitchell Scott Hastings2, Judit Maria Maria Gonzalez Santana3, Edgar Roberto Mérida Boogher4, Mel Rodgers2, Rocco Malservisi2, Robert Van Alphen III2, Christelle Wauthier3 and Timothy H Dixon2, (1)University of South Florida, School of Geosciences, Tampa, United States, (2)University of South Florida, School of Geosciences, Tampa, FL, United States, (3)The Pennsylvania State University, Department of Geosciences, University Park, PA, United States, (4)Insivumeh, Guatemala City, Guatemala
Short term probabilistic eruption forecasting at Whakaari volcano, New Zealand (1180617)
David Dempsey1, Alberto Ardid1, Andreas W Kempa-Liehr2 and Shane J Cronin3, (1)University of Canterbury, Christchurch, New Zealand, (2)University of Auckland, Auckland, New Zealand, (3)University of Auckland, School of Environment, Auckland, New Zealand
