4:15 PM - 4:30 PM
[SSS04-04] Insights into data-driven tectonic regionalisation in seismic hazard analysis
Keywords:seismic hazard, regionalisation, fuzzy logic
These drawbacks can be overcome with the use of more objective and replicable data-driven methodologies for defining tectonic regions using global seismotectonic information. The regionalisation process can be implemented in an automatic computational scheme which is reproducible, comprehensible from a geophysical rationale, and capable of revision or refinement when new data is introduced database. However, tectonic regionalisation in seismic hazard, as in many other problems in earth system science, is a complicated problem, owing to the variety of parameters and uncertainty as well as to the vague definition of the ‘tectonic homogeneity’.
In this work we test a classification-scheme based on fuzzy logic that allows dealing concepts that are approximate rather than precise;. Since it is able to quantify and manipulate uncertainty with mathematical rigour, it represents a suitable, feasible and effective tool to deal with tectonic regionalisation issues. Moreover, it supports the incorporation of the expert judgement into the classification process (e.g., with higher seismic moment and lower quality factor, it suggests a higher degree of belief to be a tectonic active region). The proposed regionalisation methodology accounts for uncertainty by assigning to each point within the classified area a membership degree to the tectonic regions considered; this result can be incorporated into logic-tree models, a widely used tool for quantification of epistemic uncertainties in probabilistic seismic hazard assessment.
We describe a global tectonic regionalisation model for use in seismic hazard applications using a data-driven fuzzy regionalization methodology that largely relies on global seismotectonic databases and models (e.g., seismic moment rate, quality factor, shear-wave velocity), and its potential application.