On the Nature of Logic Trees in Probabilistic Seismic Hazard Assessment

2012 ◽  
Vol 28 (3) ◽  
pp. 1291-1296 ◽  
Author(s):  
Roger Musson

An objection sometimes made against treating the weights of logic tree branches as probabilities relates to the Kolmogorov axioms, but these are only an obstacle if one believes that logic tree branches represent a seismic source model or ground motion model as being “true.” Models are never true, but some models are better than others. It is argued here that a logic tree weight represents the probability that the model in question is better than the others considered. Only one branch can be the best one, and one branch must be the best one. It is also argued that there are situations in PSHA where uncertainty exists but the analyst lacks the means to express it. Therefore it is not necessarily the case that more information increases uncertainty; it may be that more information increases the possibility of expressing uncertainty that was previously unmanageable.

2021 ◽  
Author(s):  
Athanasia Kerkenou ◽  
Constantinos Papazachos ◽  
Basil Margaris ◽  
Christos Papaioannou

<p>The broader Aegean area is one of the highest seismicity regions in Europe, with almost half of the European seismicity released in this region, often with damaging mainshocks, such as the recent <strong>M</strong>7.0 Samos event. While several Probabilistic Seismic Hazard Assessment (PSHA) studies have been performed for this area, an attempt to quantify the main factors controlling PSHA has not been performed. To study the effect that each input factor (seismic source model, GMPE, seismicity parameters, etc.) has on the seismic hazard calculations, an <strong>OFAT</strong> (One Factor at A Time) analysis has been conducted. For this analysis we considered two standard peak ground motion parameters, PGA and PGV, for a typical PSHA scenario, namely 10% probability of exceedance for a mean return period of 50 years (equivalent to a 476 yr return period). For the analysis the following factors were considered: a) Four (4) seismicity area-type source models for the broader Aegean area (Papazachos, 1990; Papaioannou and Papazachos, 2000; Woessner et al., 2015; Vamvakaris et al., 2016), as well as various uncertainties for the associated G-R seismicity parameters and active fault geometries of each seismic source, b) ten (10) Ground Motion Prediction Equations (GMPEs), which contain four NGA-West2 (Abrahamson et al., 2014; Boore et al., 2014; Campbell and Bozorgnia, 2014; Chiou and Youngs, 2014), two European (Bindi et al., 2011; Cauzzi and Faccioli, 2008) and four “Greek” (Theodulidis and Papazachos, 1992; Skarlatoudis et al., 2003; Danciu and Tselentis, 2007; Chousianitis et al., 2018) equations, as well as a variable number of sigma for each equation and, c) the minimum (Mmin) and maximum (Mmax) source magnitude of each seismic source. Tornado diagrams (Howard, 1988) were generated for 42 selected sites of seismological interest that span the study area, allowing to explore the extent of each factor’s effect on the PSHA results. The sensitivity analysis results suggest that the GMPE selection, as well as uncertainties in the G-R parameters <strong>a</strong> and <strong>b</strong> are the most critical factors, significantly affecting the PGA/PGV levels for all sites. They also reveal a strong correlation of PSHA sensitivity with other seismicity parameters. For example, the employed source model and Mmax play a more critical role for regions of low seismicity, while the least important factor is the selected Mmin. The spatial distribution of the PSHA sensitivity on the various factors considered was also examined through the generation of several maps, exposing regions of high and of low PSHA uncertainty. The results can be efficiently employed by scientists and engineers in order to focus research and application efforts for a targeted uncertainty minimization of the most critical factors (which may not be the same for all sub-regions of the examined Aegean area), as well as to evaluate the reliability and uncertainty of the current PSHA estimates that are employed in seismic design.</p>


Author(s):  
Monique Terrier-Sedan ◽  
Didier Bertil

AbstractDesigning a seismic source model based on the most complete description of potentially active faults and on the kinematics of their latest movements is an essential requirement in seismic hazard studies, at regional and local scales. A study to characterize active faults in the Hispaniola island (today’s Haiti and Dominican Republic) has been conducted in the framework of the probabilistic seismic hazard assessment for Santo Domingo (capital of the Dominican Republic). In this work, we present a seismotectonic map of Hispaniola and its surroundings, based on a compilation and synthesis of geological, geophysical, geodetic and seismological data. Based on these data, distinct seismic zone sources are proposed and classified as either intercrustal domains, major active faults or subduction zones. Each seismic source is described according to several parameters, including its mechanism and current rate of deformation, the associated seismicity and its estimated maximal magnitude. These results constitute an essential database for a homogeneous evaluation of the seismic hazards of Hispaniola.


2000 ◽  
Vol 43 (1) ◽  
Author(s):  
R. M. W. Musson

The input required for a seismic hazard study using conventional Probabilistic Seismic Hazard assessment (PSHA) methods can also be used for probabilistic analysis of hazard using Monte Carlo simulation methods. This technique is very flexible, and seems to be under-represented in the literature. It is very easy to modify the form of the seismicity model used, for example, to introduce non-Poissonian behaviour, without extensive reprogramming. Uncertainty in input parameters can also be modelled very flexibly - for example, by the use of a standard deviation rather than by the discrete branches of a logic tree. In addition (and this advantage is perhaps not as trivial as it may sound) the simplicity of the method means that its principles can be grasped by the layman, which is useful when results have to be explained to people outside the seismological/engineering communities, such as planners and politicians. In this paper, some examples of the Monte Carlo method in action are shown in the context of a low to moderate seismicity area: the United Kingdom.


2005 ◽  
Vol 43 (2) ◽  
pp. 248-256 ◽  
Author(s):  
S.A. Ketcham ◽  
M.L. Moran ◽  
J. Lacombe ◽  
R.J. Greenfield ◽  
T.S. Anderson

2018 ◽  
Vol 11 (15) ◽  
Author(s):  
I. El-Hussain ◽  
Y. Al-Shijbi ◽  
A. Deif ◽  
A. M. E. Mohamed ◽  
M. Ezzelarab

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