Abstract
The growing installation of industrial facilities for subsurface explorations
worldwide, particularly in proximity of urbanized areas, requires
refinements in understanding both the mechanisms for triggering the
induced seismicity and their effects in terms of hazard. In fact,
particularly in proximity of densely populated areas, induced low-to-moderate,
high-frequency seismicity can be clearly felt by population and,
in some cases, can produce damages to non-structural elements of
buildings or even structural damages when rural buildings are involved.
As a consequence, it is nowadays definitely important to be able
to estimate time-dependent seismic hazard for providing a guide during
the field operations and for monitoring their direct effects in the
surrounding areas. In the present work a time-dependent probabilistic
seismic hazard analysis is presented. The technique is aimed at integrating
the models of earthquakes occurrence which best correlate with field
operations and ground-motion prediction techniques in a Bayesian
framework to estimate in a time-evolving approach the probability
of exceedance of selected ground motion parameters, that are of engineering
interest. Using data from different geothermal areas, e.g. The Geysers
in Northern California, seismic hazard analysis is performed through:
1. Identification of the earthquake occurrence model which, on the
basis of statistical tests, best correlates with the observed seismicity;
2. Time and space analysis of the recurrence relationship, mainly
the b-value of the Gutenberg-Richter relationship; 3. Estimation
of the maximum expected magnitude earthquake; 4. Selection of the
best ground-motion parameters and predictive equation; 5. The selection
of the most appropriate exposure times that cannot be classic ones
for example 475 years. Finally, the availability of high-quality
catalogues covering long periods offers a unique opportunity for
testing the proposed technique. In this respect, using different
portions of the available catalogues a first setting of the technique
is performed, and the prediction capability is evaluated through
a statistical analysis
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