Using Statistical and Computer Models to Quantify Volcanic Hazards

Bayarri, M. J.; Berger, James O.; Calder, Eliza S.; Dalbey, Keith; Lunagómez, Simon; Patra, Abani; Pitman, E. Bruce; Spiller, Elaine; Wolpert, Robert L.

doi:10.1198/tech.2009.08018

Cited by 109 publications

(119 citation statements)

References 26 publications

Supporting

Mentioning

118

Contrasting

Order By: Relevance

“…The particular model we are using has been well described by Jones et al [20]. It has been successfully employed in a variety of computer experiments such as an ocean circulation model [15], a hazard-effect model for volcano eruption prediction [3], and an arctic sea ice simulation [11]. However, the typical approach by statisticians is to fit the Gaussian process model and then evaluate the results of a variance decomposition.…”

Section: Statisticsmentioning

confidence: 99%

Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration

Torsney-Weir

Saad

Möller

et al. 2011

IEEE Trans. Visual. Comput. Graphics

105

View full text Add to dashboard Cite

Abstract-In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage technique: We start with a sparse sampling of the parameter space and apply a statistical model to estimate the response of the segmentation algorithm. The statistical model incorporates a model of uncertainty of the estimation which we use in conjunction with the actual estimate in (visually) guiding the user towards areas that need refinement by placing additional sample points. In the second stage the user navigates through the parameter space in order to determine areas where the response value (goodness of segmentation) is high. In our exploration we rely on existing ground-truth images in order to evaluate the "goodness" of an image segmentation technique. We evaluate its usefulness by demonstrating this technique on two image segmentation algorithms: a three parameter model to detect microtubules in electron tomograms and an eight parameter model to identify functional regions in dynamic Positron Emission Tomography scans.Index Terms-Parameter exploration, Image segmentation, Gaussian Process Model. MOTIVATIONFor visual analysis image data often need to be segmented. Segmentation refers to the process of partitioning the image into multiple segments, i.e. sets of pixels or voxels, that form contiguous and semantically meaningful regions. If each of these regions is marked by a unique identifier, image segmentation simply means labelling of pixels or voxels. In biomedical imaging, where images are acquired using some kind of tomography or microscopy, segmented regions might correspond to anatomical structures in the case of nonfunctional imaging, and to regions with specific physiological activity in the case of functional imaging.In recent years a variety of semi-and fully automatic techniques have been developed to address the segmentation problem [32]. However, even the current state-of-the-art approaches fall short of providing a "silver bullet" for image segmentation. This has several reasons. One reason is that given some image, the segmentation problem is not well defined; in fact it depends on the application which regions are semantically meaningful. Another reason is that due to different image degradation factors such as low signal-to-noise ratio, imaging artifacts, partial volume effects and shape variability, different kinds of a priori knowledge need to be included. Additionally, the majority of the existing segmentation methods rely on and are sensitive to setting a number of parameters. For example, most of the algorithms contain weighting parameters between multiple competing image-driven or prior-driven cost terms in an attempt to mimic the cognitive capabilities of expert users (e.g. radiologists for medical images).A good parameter setting is usually found by a manual trial and error procedure. Th...

show abstract

Section: Statisticsmentioning

confidence: 99%

Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration

Torsney-Weir

Saad

Möller

et al. 2011

IEEE Trans. Visual. Comput. Graphics

105

View full text Add to dashboard Cite

show abstract

“…However, some examples exist: Cox processes have been applied to the assessment of long-term volcanic hazards (e.g., Jaquet et al, 2000Jaquet et al, , 2008Jaquet and Carniel, 2006); self-exciting processes have been introduced for modeling the temporal eruptive pattern of volcanic fields (e.g., Bebbington and Cronin, 2011); a complete long-term PDC hazard assessment procedure has been developed at the Montserrat volcano, combining probability estimates of time, size and direction of the flows (Bayarri et al, 2009(Bayarri et al, , 2015. A detailed review also including other approaches has been described in Bebbington (2013).…”

Section: Temporal Model For Explosive Eruptionsmentioning

confidence: 99%

The Effects of Vent Location, Event Scale, and Time Forecasts on Pyroclastic Density Current Hazard Maps at Campi Flegrei Caldera (Italy)

et al. 2017

View full text Add to dashboard Cite

This study presents a new method for producing long-term hazard maps for pyroclastic density currents (PDC) originating at Campi Flegrei caldera. Such method is based on a doubly stochastic approach and is able to combine the uncertainty assessments on the spatial location of the volcanic vent, the size of the flow and the expected time of such an event. The results are obtained by using a Monte Carlo approach and adopting a simplified invasion model based on the box model integral approximation. Temporal assessments are modeled through a Cox-type process including self-excitement effects, based on the eruptive record of the last 15 kyr. Mean and percentile maps of PDC invasion probability are produced, exploring their sensitivity to some sources of uncertainty and to the effects of the dependence between PDC scales and the caldera sector where they originated. Conditional maps representative of PDC originating inside limited zones of the caldera, or of PDC with a limited range of scales are also produced. Finally, the effect of assuming different time windows for the hazard estimates is explored, also including the potential occurrence of a sequence of multiple events. Assuming that the last eruption of Monte Nuovo (A.D. 1538) marked the beginning of a new epoch of activity similar to the previous ones, results of the statistical analysis indicate a mean probability of PDC invasion above 5% in the next 50 years on almost the entire caldera (with a probability peak of ∼25% in the central part of the caldera). In contrast, probability values reduce by a factor of about 3 if the entire eruptive record is considered over the last 15 kyr, i.e., including both eruptive epochs and quiescent periods.

show abstract

“…There are indeed many methods-kriging, metamodels, support vector machines-by which such surrogates may be constructed and there exists a body of literature on the topic (Simpson et al 2001;Clarke et al 2005). One often used emulator is the GAuSsian Process (GASP) emulator, which assumes the regression has the form of a trend plus a Gaussian (Kennedy & O'Hagan 2001;O'Hagan 2006;Bayarri et al 2009;Conti & O'Hagan 2010). Rougier (2008) in his construction of a multi-variate emulator called the outer product emulator mapped the field output directly by including parametric regression terms on the output index.…”

Section: (F ) Bayes Linear Methodsmentioning

confidence: 99%

Digital elevation model uncertainty and hazard analysis using a geophysical flow model

et al. 2012

View full text Add to dashboard Cite

This paper describes a new methodology to quantify the variation in the output of a computational fluid dynamics model for block and ash flows, when the digital elevation model (DEM) of the terrain and other inputs are given as a range of possible values with a prescribed uncertainty. Integrating these variations in the possible flows as a function of input uncertainties provides well-defined hazard probabilities at specific locations, i.e. a hazard map. Earlier work provided a methodology for assessing hazards based on variations in flow initiation and friction parameters. This paper extends this approach to include the effect of terrain error and uncertainty. The results are based on potential flows at Mammoth Mountain, CA, and Galeras Volcano, Colombia. The analysis establishes the soundness of the approach and the effect of including the uncertainty in DEMs in the construction of probabilistic hazard maps.

show abstract

Using Statistical and Computer Models to Quantify Volcanic Hazards

Cited by 109 publications

References 26 publications

Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration

Tuner: Principled Parameter Finding for Image Segmentation Algorithms Using Visual Response Surface Exploration

The Effects of Vent Location, Event Scale, and Time Forecasts on Pyroclastic Density Current Hazard Maps at Campi Flegrei Caldera (Italy)

Digital elevation model uncertainty and hazard analysis using a geophysical flow model

Contact Info

Product

Resources

About