Journal
SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
Volume 29, Issue 6, Pages 1097-1101Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.soildyn.2008.11.008
Keywords
Seismic site effects; H/V spectral ratio; Self-organizing maps
Funding
- PRIN [2007PTRC4C002]
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Data interpretation is one of the most important and thorny tasks in geosciences. Difficulties occur especially in non-invasive geophysical techniques and/or when the data that have to be analyzed are multidimensional, non-linear and highly noisy. Another important task is to ensure an efficient automatic data analysis, in order to allow a data interpretation as independent as possible from any a priori knowledge. This paper describes the post-processing application of a kind of neural network (self-organizing map, SOM) to the identification of the fundamental HVSR frequency of a given site. SOM results can be represented as two-dimensional maps, with a non-parametric mapping that projects the high dimensional original dataset in a fashion that provides both an unsupervised clustering and a highly visual representation of the data relationships. This innovative application of the SOM algorithm is presented with a case study related to the characterization of a mineral deposit. (C) 2008 Elsevier Ltd. All rights reserved.
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