Classification of Seismic Windows Using Artificial Neural Networks

Abstract

We examine the plausibility of using an Artificial Neural Network (ANN) and an Importance-Aided Neural Network (IANN) for the refinement of the structural model used to create full-wave tomography images. Specifically, we apply the machine learning techniques to classifying segments of observed data wave seismograms and synthetic data wave seismograms as either usable for iteratively refining the structural model or not usable for refinement. Segments of observed and synthetic seismograms are considered usable if they are not too di_erent, a heuristic observation made by a human expert, which is considered a match. The use of the ANN and the IANN for classification of the data wave segments removes the human computational cost of the classification process and removes the need for an expert to oversee all such classifications. Our experiments on the seismic data for Southern California have shown this technique to be promising for both classification accuracy and the reduction of the time required to compute the classification of observed data wave segment and synthetic data wave segment matches.