In this study, we develop a Diffusion Transformer (referred as to DiT1D) for
synthesizing realistic earthquake time histories. The DiT1D generates realistic
broadband accelerograms (0-30 Hz resolution), constrained at low frequency by
3-dimensional (3D) elastodynamics numerical simulations, ensuring the
fulfillment of the minimum observable physics. The DiT1D architecture,
successfully adopted in super-resolution image generation, is trained on
recorded single-station 3-components (3C) accelerograms. Thanks to Multi-Head
Cross-Attention (MHCA) layers, we guide the DiT1D inference by enforcing the
low-frequency part of the accelerogram spectrum into it. The DiT1D learns the
low-to-high frequency map from the recorded accelerograms, duly normalized, and
successfully transfer it to synthetic time histories. The latter are
low-frequency by nature, because of the lack of knowledge on the underground
structure of the Earth, demanded to fully calibrate the numerical model. We
developed a CNN-LSTM lightweight network in conjunction with the DiT1D, so to
predict the peak amplitude of the broadband signal from its low-pass-filtered
counterpart, and rescale the normalized accelerograms rendered by the DiT1D.
Despite the DiT1D being agnostic to any earthquake event peculiarities
(magnitude, site conditions, etc.), it showcases remarkable zero-shot
prediction realism when applied to the output of validated earthquake
simulations. The generated time histories are viable input accelerograms for
earthquake-resistant structural design and the pre-trained DiT1D holds a huge
potential to integrate full-scale fault-to-structure digital twins of
earthquake-prone regions.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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