On the use of Kohonen Neural Networks for site effects assessment by means of H/V weak-motion spectral ratio: Application in Rio-Antirrio (Greece)

摘要

The investigated area, located around the Rio-Antirrio Strait, Central Greece, has been the target of a seismic microzonation campaign. Seventy seismic stations have been deployed for a period of 4 months, recording in continuous mode. Despite the high level of urban noise, we compiled a data set of 95 earthquakes recorded at most of the 70 sites. By employing the attributes of self-organizing maps (SOMs), a quality-control and signal-improving method is proposed. A SOM (Kohonen, 1997) is a type of unsupervised neural network. The main property of SOMs utilized is that while the competitive learning algorithm on whom this method is based maps the input data on an n-dimensional grid of neurons, the topological relations (proximity of patterns in input data) are preserved in the output space. SOM is applied to the horizontal-to-vertical spectral ratios (HVSR) of every weak event analyzed for each station separately and allows a better evaluation of the stability of the HVSR.