2019
DOI: 10.3390/geosciences9100422
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Use of Macroseismic Intensity Data to Validate a Regionally Adjustable Ground Motion Prediction Model

Abstract: In low-to-moderate seismicity (intraplate) regions where locally recorded strong motion data are too scare for conventional regression analysis, stochastic simulations based on seismological modelling have often been used to predict ground motions of future earthquakes. This modelling methodology has been practised in Central and Eastern North America (CENA) for decades. It is cautioned that ground motion prediction equations (GMPE) that have been developed for use in CENA might not always be suited for use in… Show more

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Cited by 8 publications
(1 citation statement)
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References 57 publications
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“…The paper "Use of Macroseismic Intensity Data to Validate a Regionally Adjustable Ground Motion Prediction Model" by Y. Tang et al [16] presents a regionally adjustable ground motion prediction equation (GMPE), known as the component attenuation model (CAM), by which a diversity of crustal conditions can be covered in one model. In addressing the challenge of validating a GMPE for use in an area where instrumental data are scarce, Modified Mercalli intensity (MMI) data inferred from peak ground velocity values predicted by CAM are compared with the records of MMI of past earthquake events, as reported in historical archives.…”
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confidence: 99%
“…The paper "Use of Macroseismic Intensity Data to Validate a Regionally Adjustable Ground Motion Prediction Model" by Y. Tang et al [16] presents a regionally adjustable ground motion prediction equation (GMPE), known as the component attenuation model (CAM), by which a diversity of crustal conditions can be covered in one model. In addressing the challenge of validating a GMPE for use in an area where instrumental data are scarce, Modified Mercalli intensity (MMI) data inferred from peak ground velocity values predicted by CAM are compared with the records of MMI of past earthquake events, as reported in historical archives.…”
mentioning
confidence: 99%