Intellectual Merit
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This research directly assists SCEC's mission toward probabilistic seismic hazard analysis (item 3 in SCEC5 Thematic Areas), as the methods here are directly useful for assessing, comparing and improving probabilistic models for earthquake occurrences, especially ETAS and STEP.
In particular, our implementation of Voronoi deviance residuals and super-thinned residuals and their application to the assessment of ETAS and STEP as implemented in CSEP has suggested important areas for improving these state-of-the-art models for earthquake forecasting. As a statistician rather than a seismologist, I have been been able to give a fair assessment of these types of models without bias toward one model or the other. I have been working closely with Maximilian Werner, Bill Savran, Danijel Schorlemmer, David Jackson and others involved with CSEP and WGCEP, and will continue to do so in order to explain how the residual methods explored here may readily be used to assess models in these earthquake forecasting projects. The improved assessment techniques and more accurate forecasts of seismicity resulting from this research will also further our understanding of seismicity and the mechanisms for its generation.
In addition, addressing SCEC5's research priorities P5.c, P5.d, and P5.a, the tools described in this research, especially Voronoi and superthinned residuals, can be used to help calibrate and detect departures from fit for earthquake simulation models such as Stanford's QCN model and data and to suggest areas where such models can be improved, and in particular the R code produced in this research can be used to assist other SCEC researchers in performing this analysis. |
Broader Impacts
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The research in this project augments methods for assessing seismicity forecasts and also contributes to our understanding of which seismicity models currently perform best. These improvements will lead to improved forecasts of seismicity, which may save lives, prevent catastrophic financial losses, and aid in safe urban planning, and in addition will increase our understanding of earthquakes and their causes and clustering features. I have been working closely with members of CSEP and will continue to do so in order to help incorporate the residual methods explored here for their earthquake forecasting projects. In addition, this project aided the research toward the Masters degrees in Statistics for 2 UCLA graduate students (Joshua Ward and Zhe Zhang). |