Modern physical volcanology is situated between two different research approaches: multi-disciplinary data acquisition in field and laboratory settings, and analytical and computer-based multi-parameter modelling. Ideally, any data should be interpreted with reference to a physical model; on the other hand, modelling attempts should be constrained by data. Unfortunately, this has not always been the case and some of the reasons for the difficulties encountered willbe analysed in this study.
Problems in data analysis can often be traced back to the widespread misunderstanding that a time series (or spatial data profile) of acquired data represents the natural phenomenon that a research team set out to study. Magma movement in the volcanic plumbing system, for example, may lead to a pressure increase in a certain region of a magmatic conduit. The conduit walls may be deformed in an elastic or plastic manner depending on the magnitude of the pressure change as well as the material properties involved. This deformation will propagate, but not necessarily instantaneously,all the way to the flank of the volcanic edifice, where an instrument such as a tilt meter, a broadband seismometer ora strainmeter will detect a corresponding signal. This signal, however, is superimposed on the response of the nearby topography to the internal deformation, as well as all other signals generally referred to as noise. Depending on the topography, an inflation at shallow depth for example, can lead, counter-intuitively, to a subsidence of a part of thevolcanic flank. Furthermore, any instrument will only pick up that particular part of the ground displacement that theinstrument was designed to detect. A tiltmeter will measure the (rigid) rotation of the flank and record the corresponding angle; the broadband seismometer will record the time-derivative (velocity) of the displacement in a certain frequency band corresponding to its frequency characteristics (very much like human ears), typically between 50 Hz and0.008 Hz, or 120 s period; a strainmeter will directly record the displacement, but only the horizontal components, and hear only the long period part. Finally, this signal is digitized in certain time intervals, and appears in a recording medium as a time series. This ‘bunch of numbers’ has little to do with the original, localized deformation of the conduit wall caused by the pressurization of the volcanic system. Intensive data processing is necessary to retrieve the original ground displacement, at the instrument location, from the recorded time series. Intensive modelling is then necessary to link the ground displacement at the surface of the volcanic edifice to the physical processesat the depth that caused it.
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Statistics in Volcanology
Statistics in Volcanology is a comprehensive guide to modern statistical methods applied in volcanology written by today's leading authorities. The volume aims to show how the statistical analysis of complex volcanological data sets, including time series, and numerical models of volcanic processes can improve our ability to forecast volcanic eruptions. Specific topics include the use of expert elicitation and Bayesian methods in eruption forecasting, statistical models of temporal and spatial patterns of volcanic activity, analysis of time series in volcano seismology, probabilistic hazard assessment, and assessment of numerical models using robust statistical methods. Also provided are comprehensive overviews of volcanic phenomena, and a full glossary of both volcanological and statistical terms.
Statistics in Volcanology is essential reading for advanced undergraduates, graduate students, and research scientists interested in this multidisciplinary field.