Creating the Logic Tree

Author(s):  
David Allison ◽  
Harold Peters
Keyword(s):  
2020 ◽  
Vol 18 (14) ◽  
pp. 6119-6148
Author(s):  
Graeme Weatherill ◽  
Fabrice Cotton

Abstract Regions of low seismicity present a particular challenge for probabilistic seismic hazard analysis when identifying suitable ground motion models (GMMs) and quantifying their epistemic uncertainty. The 2020 European Seismic Hazard Model adopts a scaled backbone approach to characterise this uncertainty for shallow seismicity in Europe, incorporating region-to-region source and attenuation variability based on European strong motion data. This approach, however, may not be suited to stable cratonic region of northeastern Europe (encompassing Finland, Sweden and the Baltic countries), where exploration of various global geophysical datasets reveals that its crustal properties are distinctly different from the rest of Europe, and are instead more closely represented by those of the Central and Eastern United States. Building upon the suite of models developed by the recent NGA East project, we construct a new scaled backbone ground motion model and calibrate its corresponding epistemic uncertainties. The resulting logic tree is shown to provide comparable hazard outcomes to the epistemic uncertainty modelling strategy adopted for the Eastern United States, despite the different approaches taken. Comparison with previous GMM selections for northeastern Europe, however, highlights key differences in short period accelerations resulting from new assumptions regarding the characteristics of the reference rock and its influence on site amplification.


2007 ◽  
Vol 164 (2-3) ◽  
pp. 577-592 ◽  
Author(s):  
Tadashi Annaka ◽  
Kenji Satake ◽  
Tsutomu Sakakiyama ◽  
Ken Yanagisawa ◽  
Nobuo Shuto

Bauingenieur ◽  
2017 ◽  
Vol 92 (10) ◽  
pp. S 14-S 20
Author(s):  
Mathias Raschke

Die Quantifizierung der Schätzunsicherheit einer probabilistischen Erdbebengefährdungsanalyse mittels des Logic-Tree-Ansatzes muss aus statistischer Sicht verworfen werden. Denn zum einen wird in der Logic-Tree Analyse die Fehlerfortpflanzung nicht konsequent berücksichtigt, zum anderen können die verwendeten Expertenmeinungen nicht wissenschaftlich validiert oder falsifiziert werden. Der Logic-Tree-Ansatz kann nur als eine Art Sensitivitätsstudie und/oder Model-Averaging, verstanden werden, wobei der Mehrwert des Model-Averaging statistisch zweifelhaft ist. Die Darstellung der Ergebnisse einer Logic-Tree Analyse als Quantile, Vertrauens- oder Fehlerintervalle ist irreführend. Im Gegensatz zum Logic-Tree kann die hier vorgestellte Variante der Fehlerfortpflanzung nach der Delta-Methode wissenschaftlich überprüft werden. In dem Beispiel funktioniert die Verknüpfung von Delta-Methode und Monte-Carlo-Simulation gut. Die geschätzten Standardfehler der Gefährdungskurve stimmen mit den wirklichen Standardfehlern überein. Auch können Vertrauensbereiche mit der Annahme der Normalverteilung approximiert werden. Nur die Berücksichtigung der relevanten Schätzfehler aller Teilmodelle einer Gefährdungsanalyse ermöglicht eine realistische und prüfbare Quantifizierung der Unsicherheiten der Gefährdungsschätzung. Unabhängig davon können unbekannte, signifikante systematische Fehler (Bias) und Überparametrisierung (Overfit) von Teilmodellen ein Problem sein. Letzteres treibt den kumulierten Schätzfehler in die Höhe.


2017 ◽  
Vol 89 (18) ◽  
pp. 9734-9741 ◽  
Author(s):  
Jiao Yang Lu ◽  
Xin Xing Zhang ◽  
Wei Tao Huang ◽  
Qiu Yan Zhu ◽  
Xue Zhi Ding ◽  
...  

2019 ◽  
Vol 19 (10) ◽  
pp. 2097-2115 ◽  
Author(s):  
Panjamani Anbazhagan ◽  
Ketan Bajaj ◽  
Karanpreet Matharu ◽  
Sayed S. R. Moustafa ◽  
Nassir S. N. Al-Arifi

Abstract. Peak ground acceleration (PGA) and study area (SA) distribution for the Patna district are presented considering both the classical and zoneless approaches through a logic tree framework to capture the epistemic uncertainty. Seismicity parameters are calculated by considering completed and mixed earthquake data. Maximum magnitude is calculated using three methods, namely the incremental method, Kijko method, and regional rupture characteristics approach. The best suitable ground motion prediction equations (GMPEs) are selected by carrying out an “efficacy test” using log likelihood. Uniform hazard response spectra have been compared with Indian standard BIS 1893. PGA varies from 0.38 to 0.30 g from the southern to northern periphery considering 2 % probability of exceedance in 50 years.


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.


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.


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