Coseismic surface rupturing caused by a large earthquake has a remarkable potential for damaging the infrastructure located on or near active faults. Surface faulting hazard can be addressed by eluding the known active fault zones, but complete avoidance is not always ideal or even possible, and in these cases, probabilistic estimates of fault displacement hazard prove useful. Probabilistic analysis enables estimating the site-specific hazard that can be considered in design and cost optimization of any type of infrastructure. Here in this work, this approach is applied to a point-like structure, that can be for instance a wind farm or a platform, situated near a major fault. Probabilistic Fault Displacement Hazard Analysis (PFDHA) allows quantification of surface rupturing hazard at possible locations of a site under development near an active fault. In this work PFDHA is applied to a site near a major fault. The scope is to see if the site-specific fault displacement analysis is aligned with the common fault zoning approaches, or if the calculations result acceptable displacement levels at minor distances to the mapped fault than the fault zoning would suggest. We apply the earthquake approach of PFDHA for estimating the fault displacement hazard at the site considering the literature-based data of the surface faulting and displacement parameters. Different fault zoning strategies give a minimum distance that should be kept clear around active faults, but they are meant to be applied to structures for human occupancy. The results of the analysis presented here give an insight of the advantages that a probabilistic approach can provide, especially in areas of a high number of mapped surface faults or areas of major fault complexities. Considering the surface displacement hazard is essential even when the developments are not directly situated on an active fault but in their vicinity, and the probabilistic approach enables more precise accounting for fault displacement hazard than simplified safety distances. This, in some cases, may result in more efficient land use and planning.
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Nurminen et al. (2024) studied this question.
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