scholarly journals Field Insights and Analysis of the 2018 Mw 7.5 Palu, Indonesia Earthquake, Tsunami and Landslides

2021 ◽  
Author(s):  
Marcella G. Cilia ◽  
Walter D. Mooney ◽  
Cahyo Nugroho
Keyword(s):  
Ground Motion ◽  
Field Studies ◽  
Soil Liquefaction ◽  
Ground Motion Prediction Equations ◽  
Ground Motion Prediction ◽  
Severe Damage ◽  
Surface Rupture ◽  
Field Surveys ◽  
Tsunami Runup ◽  
Maximum Value

AbstractA devastating Mw 7.5 earthquake and tsunami struck northwestern Sulawesi, Indonesia on 28 September 2018, causing over 4000 fatalities and severe damage to several areas in and around Palu City. Severe earthquake-induced soil liquefaction and landslides claimed hundreds of lives in three villages within Palu. The mainshock occurred at 18:03 local time at a depth of 10 km on a left-lateral strike-slip fault. The hypocenter was located 70 km north of Palu City and the rupture propagated south, under Palu Bay, passing on land on the west side of Palu City. The surface rupture of the earthquake has been mapped onshore along a 30 km stretch of the Palu-Koro fault. We present results of field surveys on the effects of the earthquake, tsunami and liquefaction conducted between 1–3 and 12–19 of October 2018. Seismic intensities on the Modified Mercalli Intensity (MMI) scale are reported for 375 sites and reach a maximum value of 10. We consolidate published tsunami runup heights from several field studies and discuss three possible interrelated tsunami sources to explain the variation in observed tsunami runup heights. Due to limited instrumentation, PGA and PGV values were recorded at only one of our field sites. To compensate, we use our seismic intensities and Ground Motion to Intensity Conversion Equations (GMICEs) and Ground Motion Prediction Equations (GMPEs) developed for similar tectonic regions. Our results indicate that the maximum predicted PGAs for Palu range from 1.1 g for GMICEs to 0.6 g for GMPEs.

scholarly journals Ground Motion Prediction Equations for Malaysia Due to Subduction Zone Earthquakes in Sumatran Region

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 23920-23937
Author(s):  
M. S. Liew ◽  
Kamaluddeen Usman Danyaro ◽  
Mazlina Mohamad ◽  
Lim Eu Shawn ◽  
Aziz Aulov
Keyword(s):  
Subduction Zone ◽  
Ground Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Subduction Zone Earthquakes

scholarly journals Ground motions in urban Los Angeles from the 2019 Ridgecrest earthquake sequence

2021 ◽  
pp. 875529302110039
Author(s):  
Filippos Filippitzis ◽  
Monica D Kohler ◽  
Thomas H Heaton ◽  
Robert W Graves ◽  
Robert W Clayton ◽  
...  
Keyword(s):  
Los Angeles ◽  
Ground Motion ◽  
Ground Motions ◽  
Earthquake Sequence ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Velocity Models ◽  
Spectral Accelerations

We study ground-motion response in urban Los Angeles during the two largest events (M7.1 and M6.4) of the 2019 Ridgecrest earthquake sequence using recordings from multiple regional seismic networks as well as a subset of 350 stations from the much denser Community Seismic Network. In the first part of our study, we examine the observed response spectral (pseudo) accelerations for a selection of periods of engineering significance (1, 3, 6, and 8 s). Significant ground-motion amplification is present and reproducible between the two events. For the longer periods, coherent spectral acceleration patterns are visible throughout the Los Angeles Basin, while for the shorter periods, the motions are less spatially coherent. However, coherence is still observable at smaller length scales due to the high spatial density of the measurements. Examining possible correlations of the computed response spectral accelerations with basement depth and Vs30, we find the correlations to be stronger for the longer periods. In the second part of the study, we test the performance of two state-of-the-art methods for estimating ground motions for the largest event of the Ridgecrest earthquake sequence, namely three-dimensional (3D) finite-difference simulations and ground motion prediction equations. For the simulations, we are interested in the performance of the two Southern California Earthquake Center 3D community velocity models (CVM-S and CVM-H). For the ground motion prediction equations, we consider four of the 2014 Next Generation Attenuation-West2 Project equations. For some cases, the methods match the observations reasonably well; however, neither approach is able to reproduce the specific locations of the maximum response spectral accelerations or match the details of the observed amplification patterns.

scholarly journals Selection of Ground Motion Prediction Equations for the Global Earthquake Model

2015 ◽  
Vol 31 (1) ◽  
pp. 19-45 ◽  
Author(s):  
Jonathan P. Stewart ◽  
John Douglas ◽  
Mohammad Javanbarg ◽  
Yousef Bozorgnia ◽  
Norman A. Abrahamson ◽  
...  
Keyword(s):  
Ground Motion ◽  
Selection Process ◽  
Selection Procedure ◽  
Empirical Models ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Intensity Measures ◽  
Earthquake Model

Ground motion prediction equations (GMPEs) relate ground motion intensity measures to variables describing earthquake source, path, and site effects. From many available GMPEs, we select those models recommended for use in seismic hazard assessments in the Global Earthquake Model. We present a GMPE selection procedure that evaluates multidimensional ground motion trends (e.g., with respect to magnitude, distance, and structural period), examines functional forms, and evaluates published quantitative tests of GMPE performance against independent data. Our recommendations include: four models, based principally on simulations, for stable continental regions; three empirical models for interface and in-slab subduction zone events; and three empirical models for active shallow crustal regions. To approximately incorporate epistemic uncertainties, the selection process accounts for alternate representations of key GMPE attributes, such as the rate of distance attenuation, which are defensible from available data. Recommended models for each domain will change over time as additional GMPEs are developed.

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