Clustering of geotechnical properties of marine sediments through self-organizing maps: An example from the Zakynthos canyon-valley system, Greece

Abstract

A methodology is proposed in order to investigate clustering tendency of data referring to geotechnical properties that describe the recent sedimentary cover at the head of Zakynthos canyon/valley system in western Greece. Furthermore, the technology of unsupervised artificial neural networks (ANNs) is applied to the particular data sets coming from a submarine environment. Self-organizing maps (SOMs) are used due to visualization and clustering capabilities for analyzing high dimensional data. SOMs implement an orderly mapping of a high-dimensional distribution onto a regular low-dimensional grid. The detected clusters correspond to different sediment types (thus, they have a clear "physical meaning") recognized from sedimentological analysis in each of the examined data sets. The algorithm is also designed for classification in terms of supervised learning and was applied in order to predict the appropriate sediment type in new data incorporating geologists' knowledge. A coupled model of SOMs using interaction matrix theory was finally applied in order to rate the examined geotechnical properties in an objective and quantified approach. The results were reasonable and illustrate that the most dominant parameters in the studied area are undrained shear strength, water content and silt percentage. © Springer Science + Business Media B.V. 2010.