Volcanic Unrest and Pre-eruptive Processes: A Hazard and Risk Perspective

Volcanic unrest is complex and capable of producing multiple hazards that can be triggered by a number of different subsurface processes. Scientific interpretations of unrest data aim to better understand (i) the processes behind unrest and their associated surface signals, (ii) their future spatio-temporal evolution and (iii) their significance as precursors for future eruptive phenomena. In a societal context, additional preparatory or contingency actions might be needed because relationships between and among individuals and social groups will be perturbed and even changed in the presence of significant uncertainty. Here we analyse some key examples from three international and multidisciplinary projects (VUELCO, CASAVA and STREVA) where issues around the limits of volcanic knowledge impact on volcanic risk governance. We provide an overview of the regional and global context of volcanic unrest and highlight scientific and societal challenges with a geographical emphasis on the Caribbean and Latin America. We investigate why the forecasting of volcanic unrest evolution and the exploitability of unrest signals to forecast future eruptive behaviour and framing of response protocols is challenging, especially during protracted unrest. We explore limitations of current approaches to decision-making and provide suggestions for how future improvements can be made in the framework of holistic volcanic unrest risk governance. We investigate potential benefits arising from improved communication, and framing of warnings around decision-making timescales and hazard levels

Global observation of vertical-CLVD earthquakes at active volcanoes

Some of the largest and most anomalous volcanic earthquakes have non-double-couple focal mechanisms. Here, we investigate the link between volcanic unrest and the occurrence of non-double-couple earthquakes with dominant vertical tension or pressure axes, known as vertical compensated-linear-vector-dipole (vertical-CLVD) earthquakes. We determine focal mechanisms for 313 target earthquakes from the standard and surface wave catalogs of the Global Centroid Moment Tensor Project and identify 86 shallow 4.3 ≤ MW ≤ 5.8 vertical-CLVD earthquakes located near volcanoes that have erupted in the last ~100 years. The majority of vertical-CLVD earthquakes occur in subduction zones in association with basaltic-to-andesitic stratovolcanoes or submarine volcanoes, although vertical-CLVD earthquakes are also located in continental rifts and in regions of hot spot volcanism. Vertical-CLVD earthquakes are associated with many types of confirmed or suspected eruptive activity at nearby volcanoes, including volcanic earthquake swarms as well as effusive and explosive eruptions and caldera collapse. Approximately 70% of all vertical-CLVD earthquakes studied occur during episodes of documented volcanic unrest at a nearby volcano. Given that volcanic unrest is underreported, most shallow vertical-CLVD earthquakes near active volcanoes are likely related to magma migration or eruption processes. Vertical-CLVD earthquakes with dominant vertical pressure axes generally occur after volcanic eruptions, whereas vertical-CLVD earthquakes with dominant vertical tension axes generally occur before the start of volcanic unrest. The occurrence of these events may be useful for identifying volcanoes that have recently erupted and those that are likely to erupt in the future

Modeling volcanic unrest by data assimilation

Volcanic activity may lead to potential volcanic eruptions, but it also provides critical information for understanding the physical processes within a volcanic system. Combining multiple observations and advanced physical models allows us to explore the response of the surrounding host rock to changes in the physical condition in a magmatic system. This work focuses on developing and applying a robust data-model fusion framework to investigate the mechanisms involved in volcanic unrest, such as deformation, failure, and pore fluid migration. First, using a series of tests based on the synthetic data, I optimize a data assimilation technique, Ensemble Kalman Filter (EnKF), to improve its performance in forecasting volcanic unrests with multiple geodetic observations. Then, the robustness of the EnKF is confirmed in application to the unrest and 2009 eruption of Kerinci volcano, Indonesia. To understand the effects of uncertain rheology on our model results, I conduct a systematic sensitivity study to determine the impact of rheology on the host-rock failure prediction. With a better understanding of the uncertainties in my models, I establish numerical models by integrating multiple observations to investigate the magma reservoir dynamics, crustal stress, failure-related seismicity, and hydrological interactions of two different magmatic systems, Laguna del Maule in the Andes, Chile, and Atka in the Aleutian, USA. In both systems, the pre-existing structures and pore fluids play critical roles in catalyzing seismicity, redistributing masses, and delaying/trigger eruptions

Automatic Detection of Volcanic Unrest Using Blind Source Separation with a Minimum Spanning Tree Based Stability Analysis

Repeated synthetic aperture radar (SAR) acquisitions can be utilized to produce measurements of ground deformations and associated geohazards, such as it can be used to detect signs of volcanic unrest. Existing time series algorithms like permanent scatterer analysis and small baseline subset are computationally demanding and cannot be applied in near real time to detect subtle, transient, and precursory deformations. To overcome this problem, we have adapted a minimum spanning tree based spatial independent component analysis method to automatically detect sources related to volcanic unrest from a time series of differential interferograms. For a synthetic dataset, we first utilize the algorithm's capability to isolate signals of geophysical interest from atmospheric artifacts, topography, and other noise signals, before monitoring the evolution of these signals through time in order to detect the onset of a period of volcanic unrest, in near real time. In this article, we first demonstrate our approach on synthetic datasets having different signal strengths and temporal complexities. Second, we demonstrate our approach on a couple of real datasets, one acquired in 2017-2019 over the Colima volcano, Mexico, showing the occurrence of previously unrecognized short-term deformation events and the other over Mt. Thorbjorn in Iceland acquired over 2020. This shows the strength of the deep learning application to differential interferometric SAR measurements, and highlights that deformation events occurring without eruptions, which may have previously been undetected

Using Machine Learning to Automatically Detect Volcanic Unrest in a Time Series of Interferograms

The latest generation of synthetic aperture radar satellites produce measurements of ground deformation at the majority of the world's subaerial active volcanoes and can be used to detect signs of volcanic unrest. We present an automatic detection algorithm that uses these data to automatically warn when deformation at a volcano departs from the background. We demonstrate our approach on synthetic data sets and the unrest leading to the 2018 eruption of Sierra Negra (Galapagos). Our algorithm encompasses spatial independent component analysis and uses a significantly improved version of the ICASO algorithm, which we term ICASAR, to robustly perform spatial independent component analysis. We use ICASAR to isolate signals of geophysical interest from atmospheric signals, before monitoring the evolution of these signals through time in order to detect the onset of a period of volcanic unrest

Data acquisition system for volcano monitoring with real-time transmission, low cost and low power consumption

Postprint (published version

Keeping watch over Colombia’s slumbering volcanoes

The Volcanological and Seismological Observatories of Manizales, Pasto and Popayan (Colombian Geological Survey) monitor and study the active volcanoes of Colombia using seismological, geodetic, geochemical and other techniques. Since 2009, permanent GNSS stations have been installed to complement classical geodetic measurements (e.g., tilt, EDM). At the moment, there are a total of 20 GNSS stations installed at Nevado del Ruiz, Cerro Machín, Puracé and Galeras volcanoes. Nevado del Ruiz has remained the most dynamic of the active Colombian volcanoes since its tragic eruption of 13 November 1985. The most significant deformation occurred between 2007 and 2012, when inflation, associated with magma migration and several small to moderate explosive eruptions in 2012 (VEI less or equal to 3), was observed. Galeras has experienced more than 25 moderate Vulcanian eruptions (VEI less or equal to 3) since 1989. In particular, the deformation network detected significant signals associated with magma migration and the extrusion of lava domes in 1991, 2005, 2008 and 2012. Puracé volcano has been the site of more than 10 minor eruptive episodes (VEI=2) in the past century, most recently in 1977. Monitoring of this volcano started in 1994. Unrest at Puracé since that time has been characterized by significant increases in seismic activity but with little or no deformation. We employ GAMIT/GLOBK to process GPS data from the monitoring network with support from the Volcano Disaster Assistance Program (U.S. Geological Survey). Additionally, differential processing is carried out using the commercial package Trimble 4D Control. Preliminary results for 2012 show no significant deformation at Puracé and Galeras volcanoes. On the other hand, the time series from Nevado del Ruiz shows a minor inflation (2-4 cm/yr) associated with the eruptive activity of 2012

Source and dynamics of a volcanic caldera unrest : Campi Flegrei, 1983–84

Acknowledgements We thank Tiziana Vanorio, Antonella Amoruso, Luca Crescentini, Nicholas Rawlinson, Yasuko Takei, and David Cornwell for the valuable suggestions regarding the methodology and interpretation. Reviews from Tim Greenfield and two anonymous reviewers helped improving both clarity of the manuscript and interpretation. The Royal Society of Edinburgh - Accademia dei Lincei Bilateral Agreement, the Santander Mobility Award of the College of Physical Sciences, University of Aberdeen, and the TIDES EU COST action granted L.D.S. travel grants for the realisation of this study. E.D.P. has been supported by the EPHESTO and KNOWAVES projects, funded by the Spanish Ministry of Education and Science.Peer reviewedPublisher PD