The dynamics of large silicic systems from satellite remote sensing observations: the intriguing case of Domuyo volcano, Argentina

Large-scale demonstration of machine learning for the detection of volcanic deformation in Sentinel-1 satellite imagery

Radar (SAR) satellites systematically acquire imagery that can be used for volcano monitoring, characterising magmatic systems and potentially forecasting eruptions on a global scale. However, exploiting the large dataset is limited by the need for manual inspection, meaning timely dissemination of information is challenging. Here we automatically process ~ 600,000 images of > 1000 volcanoes acquired by the Sentinel-1 satellite in a 5-year period (2015-2020) and use the dataset to demonstrate the applicability and limitations of machine learning for detecting deformation signals. Of the 16 volcanoes flagged most often, 5 experienced eruptions, 6 showed slow deformation, 2 had non-volcanic deformation and 3 had atmospheric artefacts. The detection threshold for the whole dataset is 5.9 cm, equivalent to a rate of 1.2 cm/year over the 5-year study period. We then use the large testing dataset to explore the effects of atmospheric conditions, land cover and signal characteristics on detectability and find that the performance of the machine learning algorithm is primarily limited by the quality of the available data, with poor coherence and slow signals being particularly challenging. The expanding dataset of systematically acquired, processed and flagged images will enable the quantitative analysis of volcanic monitoring signals on an unprecedented scale, but tailored processing will be needed for routine monitoring applications.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00445-022-01608-x.

Landslide Sensitivity and Response to Precipitation Changes in Wet and Dry Climates

Slow-moving landslides are hydrologically driven. Yet, landslide sensitivity to precipitation, and in particular, precipitation extremes, is difficult to constrain because landslides occur under diverse hydroclimatological conditions. Here we use standardized open-access satellite radar interferometry data to quantify the sensitivity of 38 landslides to both a record drought and extreme rainfall that occurred in California between 2015 and 2020. These landslides are hosted in similar rock types, but span more than ∼2 m/yr in mean annual rainfall. Despite the large differences in hydroclimate, we found these landslides exhibited surprisingly similar behaviors and hydrologic sensitivity, which was characterized by faster (slower) than average velocities during wetter (drier) than average years, once the impact of the drought diminished. Our findings may be representative of future landslide behaviors in California where precipitation extremes are predicted to become more frequent with climate change.

Integration of DInSAR Time Series and GNSS Data for Continuous Volcanic Deformation Monitoring and Eruption Early Warning Applications

With approximately 800 million people globally living within 100 km of a volcano, it is essential that we build a reliable observation system capable of delivering early warnings to potentially impacted nearby populations. Global Navigation Satellite System (GNSS) and satellite Synthetic Aperture Radar (SAR) document comprehensive ground motions or ruptures near, and at, the Earth’s surface and may be used to detect and analyze natural hazard phenomena. These datasets may also be combined to improve the accuracy of deformation results. Here, we prepare a differential interferometric SAR (DInSAR) time series and integrate it with GNSS data to create a fused dataset with enhanced accuracy of 3D ground motions over Hawaii island from November 2015 to April 2021. We present a comparison of the raw datasets against the fused time series and give a detailed account of observed ground deformation leading to the May 2018 and December 2020 volcanic eruptions. Our results provide important new estimates of the spatial and temporal dynamics of the 2018 Kilauea volcanic eruption. The methodology presented here can be easily repeated over any region of interest where an SAR scene overlaps with GNSS data. The results will contribute to diverse geophysical studies, including but not limited to the classification of precursory movements leading to major eruptions and the advancement of early warning systems.

The 2018 reawakening and eruption dynamics of Steamboat Geyser, the world's tallest active geyser

Steamboat Geyser in Yellowstone National Park's Norris Geyser Basin began a prolific sequence of eruptions in March 2018 after 34 y of sporadic activity. We analyze a wide range of datasets to explore triggering mechanisms for Steamboat's reactivation and controls on eruption intervals and height. Prior to Steamboat's renewed activity, Norris Geyser Basin experienced uplift, a slight increase in radiant temperature, and increased regional seismicity, which may indicate that magmatic processes promoted reactivation. However, because the geothermal reservoir temperature did not change, no other dormant geysers became active, and previous periods with greater seismic moment release did not reawaken Steamboat, the reason for reactivation remains ambiguous. Eruption intervals since 2018 (3.16 to 35.45 d) modulate seasonally, with shorter intervals in the summer. Abnormally long intervals coincide with weakening of a shallow seismic source in the geyser basin's hydrothermal system. We find no relation between interval and erupted volume, implying unsteady heat and mass discharge. Finally, using data from geysers worldwide, we find a correlation between eruption height and inferred depth to the shallow reservoir supplying water to eruptions. Steamboat is taller because water is stored deeper there than at other geysers, and, hence, more energy is available to power the eruptions.