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Integrating geological concepts, such as relative positions and proportions of the different lithofacies, is of highest importance in order to render realistic geological patterns. The truncated plurigaussian simulation method provides a way of using both local and conceptual geological information to infer the distributions of the facies and then those of hydraulic parameters. The method ( Le Loc’h and Galli 1994 ) is based on the idea of truncating at least two underlying multi-Gaussian simulations in order to create maps of categorical variable. In this article, we show how this technique can be used to assess contaminant migration in highly heterogeneous media. We illustrate its application on the biggest contaminated site of Switzerland. It consists of a contaminant plume located in the lower fresh water Molasse on the western Swiss Plateau. The highly heterogeneous character of this formation calls for efficient stochastic methods in order to characterize transport processes. [ABSTRACT FROM AUTHOR]

Because of the difficulty of describing the complex spatial and temporal patterns inherent to volcanism, the use of solely deterministic models is not sufficient for long-term estimation of volcanic hazards. In order to account for the intrinsic uncertainty of volcanism that occurs in space and time and with respect to event types and their intensity, the use of probabilistic models becomes quite natural for long-term hazard assessment. Here, we discuss a range of probabilistic approaches to forecast the future spatial distribution of volcanism, including kernel, adaptive kernel, and Cox process methods. An application to the volcanic arc of Tohoku illustrates the proposed methodology. [ABSTRACT FROM AUTHOR]

Abstract: Time series recorded at active volcanoes are often incomplete and can consist of small data sets. Due to the complexity of volcanic processes and inherent uncertainty, a probabilistic framework is needed for forecasting. A stochastic approach, named DEVIN, was developed to perform forecasts of volcanic activity. DEVIN is a multivariate approach based on geostatistical concepts which enables: (1) detection and quantification of time correlation using variograms, (2) identification of precursors by parameter monitoring and (3) forecasting of specific volcanic events by Monte Carlo methods. The DEVIN approach was applied using seismic data monitored from the Soufrière Hills Volcano (Montserrat). Forecasts were produced for the onset of dome growth with the help of potential precursors identified by monitoring of variogram parameters. Using stochastic simulations of plausible eruptive scenarios, these forecasts were expressed in terms of probability of occurrence. They constitute valuable input data as required by probabilistic risk assessments. [Copyright &y& Elsevier]

The assessment of potential volcanic eruptions is a critical aspect when evaluating the safety of populated areas. A stochastic approach has been developed for the analysis and simulation of data sampled at active volcanoes. This approach allows for the detection and quantification of time correlation, cross correlation and volcanic tremor decomposition in order to identify potential precursors of major eruptions. The stochastic approach has been applied to data gathered at Stromboli volcano. Multivariate kriging analysis of the tremor intensity and the number of events per day has enabled time component estimation which could furnish additional monitoring variables for the forecast of paroxysmal phases at Stromboli, i.e., phases of significantly increased energy output. This multivariate approach is sufficiently general to be applicable to the analysis of multiparametric datasets recorded at volcanoes with different types of eruptive behaviour. [Copyright &y& Elsevier]

Abstract: The origin of the continuous seismic noise recorded during the last few years in the volcanic island of Tenerife (Canary Islands, Spain) is under debate, due to the important implications in terms of hazard. The seismic noise is strongly affected by anthropogenic contamination but previous work has shown that nevertheless a significant fraction of this noise has a natural origin, an origin that appears to be related to the generation of local tectonic events within or close to the island. In this paper, the variogram tool is used to characterize the so called “persistence” of the seismic noise. This analysis can quantify how much memory the seismic noise has of its recent past. This has important implications in terms of how informative the recorded noise can be about its future evolution, and this in turn puts important constraints on how likely it is for the seismic noise to be the source of potential precursors highlighting variations in the volcanic activity that is supposed to generate (part of) this seismic noise. In particular, the memory characterization can help to distinguish the naturally generated seismic noise from the anthropogenic contamination. Although only a few clear examples have been observed until now, the possibility that this memory can be affected by the occurrence of local tectonic events is also discussed. [Copyright &y& Elsevier]

Abstract: In the framework of the EU-funded project “Multi-disciplinary monitoring, modelling and forecasting of volcanic hazard” (MULTIMO), multiparametric data have been recorded at the MULTIMO station in Montserrat. Moreover, several other long time series, recorded at Montserrat and at other volcanoes, have been acquired in order to test stochastic and deterministic methodologies under development. Creating a general framework to handle data efficiently is a considerable task even for homogeneous data. In the case of heterogeneous data, this becomes a major issue. A need for a consistent way of browsing such a heterogeneous dataset in a user-friendly way therefore arose. Additionally, a framework for applying the calculation of the developed dynamical parameters on the data series was also needed in order to easily keep these parameters under control, e.g. for monitoring, research or forecasting purposes. The solution which we present is completely based on Open Source software, including Linux operating system, MySql database management system, Apache web server, Zope application server, Scilab math engine, Plone content management framework, Unified Modelling Language. From the user point of view the main advantage is the possibility of browsing through datasets recorded on different volcanoes, with different instruments, with different sampling frequencies, stored in different formats, all via a consistent, user- friendly interface that transparently runs queries to the database, gets the data from the main storage units, generates the graphs and produces dynamically generated web pages to interact with the user. The involvement of third parties for continuing the development in the Open Source philosophy and/or extending the application fields is now sought. [Copyright &y& Elsevier]