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2. Automatic identification and quantification of volcanic hotspots in Alaska using HotLINK: the hotspot learning and identification network.

Author

Saunders-Shultz, Pablo, Lopez, Taryn, Dietterich, Hannah, Girona, Tarsilo, Massimetti, Francesco, Titos, Manuel, and Ramsey, Michael

Subjects
MODIS (Spectroradiometer), AUTOMATIC identification, THRESHOLDING algorithms, CONVOLUTIONAL neural networks, COMPUTER vision, COMPUTER engineering
Abstract

An increase in volcanic thermal emissions can indicate subsurface and surface processes that precede, or coincide with, volcanic eruptions. Space-borne infrared sensors can detect hotspots -- defined here as localized volcanic thermal emissions -- in near-real-time. However, automatic hotspot detection systems are needed to efficiently analyze the large quantities of data produced. While hotspots have been automatically detected for over 20 years with simple thresholding algorithms, new computer vision technologies, such as convolutional neural networks (CNNs), can enable improved detection capabilities. Here we introduce HotLINK: the Hotspot Learning and Identification Network, a CNN trained to detect hotspots with a dataset of -3,800 satellite-based, Visible Infrared Imaging Radiometer Suite (VIIRS) images from Mount Veniaminof and Mount Cleveland volcanoes, Alaska. We find that our model achieves an accuracy of 96% (Fl-score 0.92) when evaluated on -1,700 unseen images from the same volcanoes, and 95% (Fl-score 0.67) when evaluated on -3,000 images from six additional Alaska volcanoes (Augustine Volcano, Bogoslof Island, Okmok Caldera, Pavlof Volcano, Redoubt Volcano, Shishaldin Volcano). In comparison with an existing threshold-based hotspot detection algorithm, MIROVA (Coppola et al., Geological Society, London, Special Publications, 2016, 426, 181-205), our model detects 22% more hotspots and produces l2% fewer false positives. Additional testing on -700 labeled Moderate Resolution Imaging Spectroradiometer (MODIS) images from Mount Veniaminof demonstrates that our model is applicable to this sensor's data as well, achieving an accuracy of 98% (F1-score 0.95). We apply HotLINK to 10 years of VIIRS data and 22 years of MODIS data for the eight aforementioned Alaska volcanoes and calculate the radiative power of detected hotspots. From these time series we find that HotLINK accurately characterizes background and eruptive periods, similar to MIROVA, but also detects more subtle warming signals, potentially related to volcanic unrest. We identify three advantages to our model over its predecessors: 1) the ability to detect more subtle volcanic hotspots and produce fewer false positives, especially in daytime images; 2) probabilistic predictions provide a measure of detection confidence; and 3) its transferability, i.e., the successful application to multiple sensors and multiple volcanoes without the need for threshold tuning, suggesting the potential for global application. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures

Author

Lecocq, Thomas, Hicks, Stephen P., Van Noten, Koen, van Wijk, Kasper, Koelemeijer, Paula, De Plaen, Raphael S. M., Massin, Frederick, Hillers, Gregor, Anthony, Robert E., Apoloner, Maria-Theresia, Arroyo-Solorzano, Mario, Assink, Jelle D., Bueyuekakpinar, Pinar, Cannata, Andrea, Cannavo, Flavio, Carrasco, Sebastian, Caudron, Corentin, Chaves, Esteban J., Cornwell, David G., Craig, David, den Ouden, Olivier F. C., Diaz, Jordi, Donner, Stefanie, Evangelidis, Christos P., Evers, Laslo, Fauville, Benoit, Fernandez, Gonzalo A., Giannopoulos, Dimitrios, Gibbons, Steven J., Girona, Tarsilo, Grecu, Bogdan, Grunberg, Marc, Hetenyi, Gyorgy, Horleston, Anna, Inza, Adolfo, Irving, Jessica C. E., Jamalreyhani, Mohammadreza, Kafka, Alan, Koymans, Mathijs R., Labedz, Celeste R., Larose, Eric, Lindsey, Nathaniel J., McKinnon, Mika, Megies, Tobias, Miller, Meghan S., Minarik, William, Moresi, Louis, Marquez-Ramirez, Victor H., Mollhoff, Martin, Nesbitt, Ian M., Niyogi, Shankho, Ojeda, Javier, Oth, Adrien, Proud, Simon, Pulli, Jay, Retailleau, Lise, Rintamaki, Annukka E., Satriano, Claudio, Savage, Martha K., Shani-Kadmiel, Shahar, Sleeman, Reinoud, Sokos, Efthimios, Stammler, Klaus, Stott, Alexander E., Subedi, Shiba, Sorensen, Mathilde B., Taira, Taka'aki, Tapia, Mar, Turhan, Fatih, van der Pluijm, Ben, Vanstone, Mark, Vergne, Jerome, Vuorinen, Tommi A. T., Warren, Tristram, Wassermann, Joachim, Xiao, Han, Lecocq, Thomas, Hicks, Stephen P., Van Noten, Koen, van Wijk, Kasper, Koelemeijer, Paula, De Plaen, Raphael S. M., Massin, Frederick, Hillers, Gregor, Anthony, Robert E., Apoloner, Maria-Theresia, Arroyo-Solorzano, Mario, Assink, Jelle D., Bueyuekakpinar, Pinar, Cannata, Andrea, Cannavo, Flavio, Carrasco, Sebastian, Caudron, Corentin, Chaves, Esteban J., Cornwell, David G., Craig, David, den Ouden, Olivier F. C., Diaz, Jordi, Donner, Stefanie, Evangelidis, Christos P., Evers, Laslo, Fauville, Benoit, Fernandez, Gonzalo A., Giannopoulos, Dimitrios, Gibbons, Steven J., Girona, Tarsilo, Grecu, Bogdan, Grunberg, Marc, Hetenyi, Gyorgy, Horleston, Anna, Inza, Adolfo, Irving, Jessica C. E., Jamalreyhani, Mohammadreza, Kafka, Alan, Koymans, Mathijs R., Labedz, Celeste R., Larose, Eric, Lindsey, Nathaniel J., McKinnon, Mika, Megies, Tobias, Miller, Meghan S., Minarik, William, Moresi, Louis, Marquez-Ramirez, Victor H., Mollhoff, Martin, Nesbitt, Ian M., Niyogi, Shankho, Ojeda, Javier, Oth, Adrien, Proud, Simon, Pulli, Jay, Retailleau, Lise, Rintamaki, Annukka E., Satriano, Claudio, Savage, Martha K., Shani-Kadmiel, Shahar, Sleeman, Reinoud, Sokos, Efthimios, Stammler, Klaus, Stott, Alexander E., Subedi, Shiba, Sorensen, Mathilde B., Taira, Taka'aki, Tapia, Mar, Turhan, Fatih, van der Pluijm, Ben, Vanstone, Mark, Vergne, Jerome, Vuorinen, Tommi A. T., Warren, Tristram, Wassermann, Joachim, and Xiao, Han

Abstract

Human activity causes vibrations that propagate into the ground as high-frequency seismic waves. Measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic caused widespread changes in human activity, leading to a months-long reduction in seismic noise of up to 50%. The 2020 seismic noise quiet period is the longest and most prominent global anthropogenic seismic noise reduction on record. Although the reduction is strongest at surface seismometers in populated areas, this seismic quiescence extends for many kilometers radially and hundreds of meters in depth. This quiet period provides an opportunity to detect subtle signals from subsurface seismic sources that would have been concealed in noisier times and to benchmark sources of anthropogenic noise. A strong correlation between seismic noise and independent measurements of human mobility suggests that seismology provides an absolute, real-time estimate of human activities.

Published
2020

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