Predicting ground shaking intensities using DYFI data and estimating event terms to identify induced earthquakes

Abstract

There has been a significant increase in earthquakes in Central and Eastern US (CEUS) in recent years. This increase in seismicity has been associated with human activities like wastewater injection, and is referred to as induced seismicity. One of the components for hazard and risk calculation from induced seismicity is the level of ground shaking expected from an earthquake at a site of interest. In CEUS, because of historically low seismicity, there is limited information to predict these shaking intensities. Even with the recent increase in seismicity, the sparsity of seismic networks limits the available information. US Geological Survey (USGS) collects and maintains a Did you feel it? (DYFI) database where users report online when they feel an earthquake. DYFI data is much more widely available than other ground motion data and is used here to generate a ground motion intensity prediction model. Additionally, we assess the hypothesis that intensities generated from induced earthquakes tend to be different than those from natural earthquakes. A mixed-effects regression model is used as our primary prediction model since it allows estimation of random effects associated with earthquakes and regions. We show results from various prediction functions used in our model and conclude that the intensity predictions could not be differentiated for induced events.

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Cite this paper

@inproceedings{Gupta2014PredictingGS, title={Predicting ground shaking intensities using DYFI data and estimating event terms to identify induced earthquakes}, author={Abhineet Gupta}, year={2014} }