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Construction of a Neural Network Autoencoder to map the Fourier spectral amplitudes of ground motion triggered by earthquakes to a low dimensional manifold

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Ground_Motion_Autoencoder

Dimensionality of ground motion data

Construction of a Neural Network Autoencoder to map the Fourier spectral amplitudes of ground motion triggered by earthquakes to a low dimensional manifold and sampling from the low dimensional manifold to generate ground motion data.

Data

The synthetic data can be generated by using synthetic generation code in the src folder. The real data can be downloaded from the ESM website.

Paper:

Reza Dokht Dolatabadi Esfahani, Kristin Vogel, Fabrice Cotton, Matthias Ohrnberger, Frank Scherbaum, Marius Kriegerowski; Exploring the Dimensionality of Ground‐Motion Data by Applying Autoencoder Techniques. Bulletin of the Seismological Society of America 2021;; 111 (3): 1563–1576. doi: https://doi.org/10.1785/0120200285

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Construction of a Neural Network Autoencoder to map the Fourier spectral amplitudes of ground motion triggered by earthquakes to a low dimensional manifold

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