Comparing Probabilistic Seismic Hazard Maps with ShakeMap Footprints for Indonesia

Pothon, Adrien; Guéguen, Philippe; Buisine, Sylvain; Bard, Pierre Yves

doi:10.1785/0220190171

Cited by 5 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assuming that the number of seismic events in a given time obey the Poisson distribution [47] , the probability of k occurrences of a seismic event can be expressed as :…”

Section: Resultsmentioning

confidence: 99%

Probabilistic seismic hazard analysis of BPNN predicting PGA

Guo

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

Probabilistic seismic hazard analysis (PSHA) is recognized as a reasonable method for quantifying seismic threats. Traditionally, the method ignores the effect of focal depth and in which the ground motion prediction equations (GMPEs) are applied to estimate the probability distribution associated with the possible motion levels induced by the site earthquakes, but it is limited by the unclear geological conditions, which makes it difficult to give a uniform equation, and the equation cannot express the nonlinear relationship in geological conditions. Hence, this paper proposes a method to consider the seismic focal depth for the PSHA with the example of California, and use a back propagation neural network (BPNN) to predict peak ground acceleration (PGA) instead of the GMPEs. Firstly, the measured PGA and unknown PGA seismic data applicable to this method are collected separately. Secondly, the unknown PGA data are supplemented by applying the BPNN based on the measured PGA data. Lastly, based on the full-probability equation PSHA considering the focal depth is completed and compared with the current California seismic zoning results. The results show that using the BPNN in the PSHA can ensure computational accuracy and universality, making it more suitable for regions with the unclear geological structures and providing the possibility of adding other parameters to be considered for the influence of PSHA.

show abstract

“…Assuming that the number of seismic events in a given time obey the Poisson distribution [47] , the probability of k occurrences of a seismic event can be expressed as :…”

Section: Resultsmentioning

confidence: 99%

Probabilistic seismic hazard analysis of BPNN predicting PGA

Guo

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Meanwhile, in order to determine the advancement in the BPNN prediction results, this paper plotted the sample B database in Figure 10 and compared it with the results in the specification. Assuming that the number of seismic events in a given time obeys the Poisson distribution [47], the probability of k occurrences of a seismic event can be expressed as: Assuming that the number of seismic events in a given time obeys the Poisson distribution [47], the probability of k occurrences of a seismic event can be expressed as:…”

Section: Resultsmentioning

confidence: 99%

Probabilistic Seismic Hazard Analysis of a Back Propagation Neural Network Predicting the Peak Ground Acceleration

Guo,

Li,

Zhang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Probabilistic seismic hazard analysis (PSHA) has been recognized as a reasonable method for quantifying seismic threats. Traditionally, this method ignores the effect of the focal depth, in which the ground motion prediction equations (GMPEs) are applied to estimate the probability distribution associated with the possible motion levels induced by the site earthquakes, but it is limited by the unclear geological conditions, which makes it difficult to provide a uniform equation, and these equations cannot express the non-linear relationship under geological conditions. Hence, this paper proposed a method to consider the seismic focal depth for the PSHA with the example of California and used a back propagation neural network (BPNN) to predict the peak ground acceleration (PGA) instead of the GMPEs. Firstly, the measured PGA and unknown PGA seismic data applicable to this method were collected separately. Secondly, the unknown PGA data were supplemented by applying the BPNN based on the measured PGA data. Lastly, based on the full-probability equation, PSHA considering the focal depth was completed and compared with the current California seismic zoning results. The results showed that using the BPNN in the PSHA can ensure computational accuracy and universality, making it more suitable for regions with unclear geological structures and providing the possibility of adding other parameters to be considered for the influence of the PSHA.

show abstract

“…information, and slope- v S 30-based site conditions. In recent studies requiring spatially distributed information as input ground motion, ShakeMap intensities have been successfully used to compensate for insufficient instrumental data (Del Gaudio et al, 2020; Jaiswal and Wald, 2010; Mak and Schorlemmer, 2016; Pothon et al, 2020; Silva and Horspool, 2019). The USGS ShakeMap provides the MSI value in modified Mercalli intensity scale (Figure 3a).…”

Section: Building-damage Prediction Databasementioning

confidence: 99%

Testing machine learning models for seismic damage prediction at a regional scale using building-damage dataset compiled after the 2015 Gorkha Nepal earthquake

et al. 2022

View full text Add to dashboard Cite

Assessing post-seismic damage on an urban/regional scale remains relatively difficult owing to the significant amount of time and resources required to acquire information and conduct a building-by-building seismic damage assessment. However, the application of new methods based on artificial intelligence, combined with the increasingly systematic availability of field surveys of post-seismic damage, has provided new perspectives for urban/regional seismic damage assessment. This study analyzes the effectiveness and relevance of a number of machine learning techniques for analyzing spatially distributed seismic damage after an earthquake at the regional scale. The basic structural parameters of a portfolio of buildings and the post-earthquake damage surveyed after the Nepal 2015 earthquake are analyzed and combined with macro-seismic intensity values provided by the United States Geological Survey ShakeMap tool. Among the methods considered, the random forest regression model provides the best damage predictions for specified ground motion intensity values and structural parameters. For traffic-light-based damage classification (three classes: green-, amber-, and red-tagged buildings based on post-earthquake damage grade), a mean accuracy of 0.68 is obtained. This study shows that restricting learning to basic features of buildings (i.e. number of stories, height, plinth area, and age), which could be readily available from authoritative databases (e.g. national census) or field-surveyed databases, yields a reliable prediction of building damage (4 features/3 damage grade accuracy: 0.64).

show abstract

Comparing Probabilistic Seismic Hazard Maps with ShakeMap Footprints for Indonesia

Cited by 5 publications

References 21 publications

Probabilistic seismic hazard analysis of BPNN predicting PGA

Probabilistic seismic hazard analysis of BPNN predicting PGA

Probabilistic Seismic Hazard Analysis of a Back Propagation Neural Network Predicting the Peak Ground Acceleration

Testing machine learning models for seismic damage prediction at a regional scale using building-damage dataset compiled after the 2015 Gorkha Nepal earthquake

Contact Info

Product

Resources

About