Slowness vector correction for teleseismic events with artificial neural networks

Abstract

The slowness anomalies cause serious location errors. The objective of this study is to create a mapping from observed slowness values to corrected values, which will provide more accurate locations. Artificial neural networks (ANNs) are efficient tools for mapping one multidimensional space to another. ANNs have been applied to compute slowness vector corrections for teleseismic events. Separate databases were used for training, testing and validating the networks. The training data set consisted of 2218 events in the period 1988–1992. An independent test database consisted of 1091 events from the year 1993 and the first half of 1994. The observed slowness vectors were computed using a three-station array of short period stations, KEF, SUF and KAF, in central Finland. The type of neural network was multi-layer perceptron. To improve the learning capability of the networks, a set of region-dependent extra inputs, resembling bias inputs, were added to the input layer. Several nets of different sizes were tested. The smallest net with only two hidden nodes gave best results. The median of error of the validation database dropped from 523 to 138 km. The median of error after correction is smaller than achieved with the method previously used with these stations. Due to the good interpolation capability of the neural net, the corrections decreased the location error even on areas which had no previous events in the training database. The method can be applied to slowness vector correction at any type of station or array, which produces slowness and azimuth values, if the mapping from the observed slowness values to calculated values is unambiguous.