Probabilistic seismic hazard map of Papua New Guinea  

Authors / CoAuthors

Ghasemi, H. | Leonard, M. | Spiliopoulos, S. | Cummins, P.

Abstract

Probabilistic seismic hazard map of Papua New Guinea, in terms of Peak Ground Acceleration, is developed for return period of 475 years. The calculations were performed for bedrock site conditions (Vs30=760 m/s). Logic-tree framework is applied to include epistemic uncertainty in seismic source as well as ground-motion modelling processes. In this regard two source models, using area source zones and smoothed seismicity, are developed. Based on available geological and seismological data, defined seismic sources are classified into 4 different tectonic environments. For each of the tectonic regimes three Ground Motion Prediction Equations are selected and used to estimate the ground motions at a grid of sites with spacing of 0.1 degree in latitude and longitude. Results show high level of hazard in the coastal areas of Huon Peninsula and New Britain/ Bougainville regions and relatively low level of hazard in the southern part of the New Guinea highlands block. In Huon Peninsula, as shown by seismic hazard disaggregation results, high level of hazard is caused by modelled frequent moderate to large earthquakes occurring at Ramu-Markham Fault zone. On the other hand in New Britain/Bougainville region, the geometry and distance to the subduction zone along New Britain Trench mainly controls the calculated level of hazard. It is also shown that estimated level of PGAs is very sensitive to the selection of GMPEs and overall the results are closer to the results from studies using more recent ground-motion models.

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document

eCat Id

82662

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Keywords

Australian and New Zealand Standard Research Classification (ANZSRC)

• Natural Hazards

( {1} )

• External Publication

( {1} )

• Abstract

( {1} )

• Pacific Nations

( {1} )

• Hazard Response

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• Natural Hazards

( {1} )

• earthquakes

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• Papua New Guinea - PNG

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• Published_External

Publication Date

2015-01-01T00:00:00

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geoscientificInformation

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