Quantitative Assessment of Volcanic Thermal Activity from Space Using an Isolation Forest Machine Learning Algorithm
Abstract
:1. Introduction
2. Materials
Volcanoe Selection
- Mt. Etna is a large basaltic composite volcano located on the east coast of Sicily (Italy). Although Etna is a prevalently effusive volcano, since 1986, there has been a clear increase in explosive eruptions of medium intensity from the summit craters, better known as paroxysmal events. These events are characterized by the development of several km high ash plumes and lava overflows from the crater rim [43].
- Klyuchevskoy is an active and relatively young volcano located in the northern part of the Central Kamchatka Depression (Russia). Its recent activity is characterized by the effusion of voluminous basaltic andesite lava flows, commonly associated with moderate to violent explosive activity [44].
- Lascar volcano is a composite active stratovolcano located in the Antofagasta Region of Chile. The activity of the Lascar volcano is characterized by the persistent fumarolic activity, occasional small steam explosions with phreatic characteristics, the formation of lava flows, and explosive events [45,46]. Dome building and collapse have also been observed [47].
- Popocatépetl is an andesitic stratovolcano that lies ∼60 km SE of Mexico City (Mexico). The ongoing eruptive activity of Popocatépetl volcano has been characterized by vulcanian explosions, ash-poor “exhalations” of volcanic gas, and periodic lava dome growth, subsidence, and explosive destruction [48].
- Volcán de Fuego (or Fuego) is the most active stratovolcano in Guatemala. Since 1999, Fuego has been in a new period of eruptive activity characterized by persistent activity consisting of slow lava effusion, discrete strombolian and ash-rich explosions, and occasional higher-intensity explosive paroxysmal eruptions [49,50].
- Stromboli is the north-easternmost island of the Aeolian archipelago (Italy). The activity consists of intermittent mild explosions [51]. The ordinary activity is periodically broken by the emission of lava flow and by two types of violent explosions known as “major explosions” and “paroxysms” [52].
3. Data
4. Methods
- Fit LST Time Series under “Normal” Conditions
- Use the Non-linear Least Squares (NLS) Regression model to fit the LST time series under normal conditions.
- Identify anomalies as portions of the signal that the model fails to reconstruct accurately.
- Detect Anomalies Using the Isolation Forest Model
- Adopt the Isolation Forest model to detect anomalies from the reconstruction error time series.
- Utilize the Isolation Forest algorithm to efficiently identify outliers or anomalies in the data.
- Categorize Thermal Activity Level Based on Anomaly Scores
- Use the scores of the detected anomalies to categorize the thermal activity level.
- Assign higher scores to anomalies indicating more significant deviations from the normal pattern, thereby prioritizing areas of heightened thermal activity.
4.1. Fitting the “Normal” Thermal Trend: The NLS Algorithm
4.2. Anomaly Detection: Isolation Forest
4.3. Classifying Volcanic Activity: Thermal Activity Levels
5. Results
6. Discussion
6.1. Thermal Anomaly Detection
6.2. Thermal Activity Levels
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Corradino, C.; Malaguti, A.B.; Ramsey, M.S.; Del Negro, C. Quantitative Assessment of Volcanic Thermal Activity from Space Using an Isolation Forest Machine Learning Algorithm. Remote Sens. 2024, 16, 2001. https://doi.org/10.3390/rs16112001
Corradino C, Malaguti AB, Ramsey MS, Del Negro C. Quantitative Assessment of Volcanic Thermal Activity from Space Using an Isolation Forest Machine Learning Algorithm. Remote Sensing. 2024; 16(11):2001. https://doi.org/10.3390/rs16112001
Chicago/Turabian StyleCorradino, Claudia, Arianna Beatrice Malaguti, Micheal S. Ramsey, and Ciro Del Negro. 2024. "Quantitative Assessment of Volcanic Thermal Activity from Space Using an Isolation Forest Machine Learning Algorithm" Remote Sensing 16, no. 11: 2001. https://doi.org/10.3390/rs16112001