Nowadays,
modern disaster mitigation strategies are strongly supported by wider
accessibility to solid and efficient computational resources, as well
as to vast labeled databases. Earthquake engineering and seismology
are among the disciplines that thrives on this auspicious tide.
In
the past, the analysis of a very limited amount of
back-then-available seismic data steered the comprehension of the
earthquake phenomenon. Supported by solid geophysical and
mathematical models, observational geophysics steered the earthquake
prediction science, fostered by an increasing number of high-quality
seismic traces, recorded worldwide. From the latter, empirical and
statistical methods and correlations sprouted, providing engineers
with reliable estimations of earthquake intensity measures and
realistic synthetic waveforms, alongside the associated
uncertainty.
However, whenever High-Performance-Computing
started making ground in seismology, they assumed a prominent role in
constructing 3-D numerical deterministic physics-based earthquake
scenarios and in rendering realistic ground motion on urban areas.
However, numerical simulations did not take down data analysis, due
to (1) the unresolved trade-off between computational burden and
desired wave-motion resolution; and to (2) the difficulty in
performing several realizations of the same earthquake scenario,
spanning the complex and multi-variate uncertainty space, so to
attach consistent statistics to the synthetic prediction.
Therefore,
a newly adopted strategy is emerging, which overtakes the traditional
dichotomy between data analysis and numerical simulations, providing
an data-integrated computational tool for earthquake prediction and
uncertainty quantification. This has been made possible by the
prominent role of machine learning in modern engineering.
This
talk clarifies some aspects of this strategy, providing possible
exploitation of numerical simulations in conjunction with data
analysis.
First
of all, I present some achievements we made in constructing
high-fidelity 3-D broad-band (0-7Hz) Source-to-Site (BBS2S)
earthquake scenarios, as the result of the synergistic efforts of the
three French research teams at CentraleSupélec, Institut de
Physique du Globe de Paris (IPGP) and the Commissariat à l'Énergie
Atomique et aux Énergies Alternatives (CEA), within the SINAPS@
project framework.
The innovative holistic philosophy to
investigate a ground shaking event is portrayed. With this
all-embracing approach in mind, the community research outcome aims
at providing a High-Performance (HP) and portable multi-tool
computational platform, capable of dealing with the manifold nature
of an earthquake phenomenon itself, i.e. Spanning among the
simulation of the source mechanism, the reproduction of the
heterogeneous rheology of the geomaterials embodying the Earth's
crust structure, the presence of surface/buried topography, of the
bathymetry and of the ocean. All the mentioned features feed a
high-performance 3-D wave propagation numerical solver, capable of
virtually reproduce the multi-scale/-dimension earthquake phenomenon,
with ever decreasing numerical dispersion.
In
a second phase, I present the ANN2BB kernel, i.e. A tool to produce
broad-band (0-30Hz) synthetic ground motion wave-forms, exploiting
BBS2S numerical simulations and Artificial Neural Networks (ANN).
ANN2BB was crafted during our collaboration with Politecnico di
Milano (Italy) with the intent of facing the present need to
transition from engineering seismology studies, limited at 10 Hz at
most, towards structural dynamics analyses, that need to be fed with
realistic input motion up to 30 Hz. The proposed approach makes use
ANNs trained on a set of strong-motion records, to predict the
response spectral ordinates at short periods. The essence of the
procedure is, first, to use the trained ANN to estimate the
short-period response spectral ordinates, using as input the
long-period ones obtained by the BBS2S, and, then, to enrich the
BBS2S time histories at short periods by scaling iteratively their
Fourier spectrum, with no phase change, until their response spectrum
matches the ANN target spectrum. The proposed approach reproduces in
a realistic way the engineering features of earthquake ground motion,
including the peak values and their spatial correlation structure.
Finally,
the talk will be concluded by showing the outcomes of BBS2S and
ANN2BB applied to the study of three test cases: (1) the 2007 Niigata
Chuetsu Oki earthquake and the seismic behaviour of the Unit 7 of the
Kashiwazaki-Kariwa Nuclear Power Plant.