TY  - JOUR
A1  - Esfahani, Reza Dokht Dolatabadi
A1  - Vogel, Kristin
A1  - Cotton, Fabrice Pierre
A1  - Ohrnberger, Matthias
A1  - Scherbaum, Frank
A1  - Kriegerowski, Marius
T1  - Exploring the dimensionality of ground-motion data by applying autoencoder techniques
JF  - Bulletin of the Seismological Society of America : BSSA
N2  - In this article, we address the question of how observed ground-motion data can most effectively be modeled for engineering seismological purposes. Toward this goal, we use a data-driven method, based on a deep-learning autoencoder with a variable number of nodes in the bottleneck layer, to determine how many parameters are needed to reconstruct synthetic and observed ground-motion data in terms of their median values and scatter. The reconstruction error as a function of the number of nodes in the bottleneck is used as an indicator of the underlying dimensionality of ground-motion data, that is, the minimum number of predictor variables needed in a ground-motion model. Two synthetic and one observed datasets are studied to prove the performance of the proposed method. We find that mapping ground-motion data to a 2D manifold primarily captures magnitude and distance information and is suited for an approximate data reconstruction. The data reconstruction improves with an increasing number of bottleneck nodes of up to three and four, but it saturates if more nodes are added to the bottleneck.
Y1  - 2021
U6  - https://doi.org/10.1785/0120200285
SN  - 0037-1106
SN  - 1943-3573
VL  - 111
IS  - 3
SP  - 1563
EP  - 1576
PB  - Seismological Society of America
CY  - El Cerito, Calif.
ER  -