Remote Sensing of Volcanoes and Volcanic Processes: Integrating Observation and Modelling
Volcanoes have played a profound role in shaping our planet, and volcanic activity is a major hazard locally, regionally and globally. Many volcanoes are, however, poorly accessible and sparsely monitored. Consequently, remote sensing is playing an increasingly important role in tracking volcano behaviour, while synoptic remote sensing techniques have begun to make major contributions to volcanological science. Volcanology is driven in part by the operational concerns of volcano monitoring and hazard management, but the goal of volcanological science is to understand the processes that underlie volcanic activity. This volume shows how we may reach a deeper understanding by integrating remote sensing measurements with modelling approaches and, if available, ground-based observations. It includes reviews and papers that report technical advances and document key case studies. They span a range of remote sensing applications to volcanoes, from volcano deformation, thermal anomalies and gas fluxes, to the tracking of eruptive ash and gas plumes. The result is a state-of-the-art overview of the ever-growing importance of remote sensing to volcanology.
Volcano deformation and eruption forecasting
Published:January 01, 2013
Paul Segall, 2013. "Volcano deformation and eruption forecasting", Remote Sensing of Volcanoes and Volcanic Processes: Integrating Observation and Modelling, D. M. Pyle, T. A. Mather, J. Biggs
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Recent advances in Global Positioning System (GPS), tilt and Interferometric Synthetic Aperture Radar (InSAR) have greatly increased the availability of volcano deformation data. These measurements, combined with appropriate source models, can be used to estimate magma chamber depth, and to provide information on chamber shape and volume change. However, kinematic models cannot constrain magma chamber volume, and provide no predictive capability.
Volcanic eruptions are commonly preceded by periods of inflation. Under appropriate conditions, eruptions are ‘inflation predictable’; that is, subsequent eruptions occur when inflation recovers the deflation during the preceding event. Notable successes in forecasting eruptions have come largely through the ability to discern repeatable patterns in seismic activity, ground deformation and gas emission, combined with historical and geological evidence of past eruptive behaviour. To move beyond empirical pattern recognition to forecasting based on deterministic physical–chemical models of the underlying dynamics, will require integration of different data types and models. I suggest two areas poised for progress: quantitative integration of deformation and seismicity; and model-based forecasts conditioned on estimates of material parameters and initial conditions from inversion of available datasets.
Deformation and seismicity are the principal geophysical methods for volcano monitoring, and in some cases have signalled dyke propagation minutes to hours prior to eruptions. Quantitative models relating these processes, however, have been lacking. Modern theories of seismicity rate variations under changing stress conditions can be used to integrate deformation and (volcano–tectonic) seismicity into self-consistent inversions for the spatio-temporal evolution of dyke geometry and excess magma pressure. This approach should lead to improved resolution over existing methods and, perhaps, to improved real-time forecasts.
The past few decades have also witnessed a marked increase in the sophistication of physical–chemical models of volcanic eruptions. I review conduit models that can be combined with GPS and extrusion rate data through Markov Chain Monte Carlo (MCMC) inversion to estimate the absolute volume of the crustal magma chamber, initial chamber overpressure, initial volatile concentrations and other parameters of interest. The MCMC estimation procedure can be extended to deterministic forecasting by using the distribution of initial conditions and material parameters consistent with available data to initiate predictive forward models. Such physics-based MCMC forecasts would be based on all knowledge of the system, including data up to the current date. The underlying model is completely deterministic; however, because the method samples initial conditions and physical parameters consistent with the given data, it yields probabilistic forecasts including uncertainties in the underlying parameters. Because there are almost certain to be effects not factored into the forward models, there is likely to be a substantial learning curve as models evolve to become more realistic.