First Break Detection in Seismic Reflection Data with Fuzzy ARTMAP Neural Networks

In this paper we investigate the use of a supervised, but self-organizing, Adaptive Resonance Theory type of neural network (Fuzzy-ARTMAP), for first break picking in seismic reflection data. First break picking is the accurate location of the leading energy pulse received by a geophone in response to a seismic shot. The performance of Fuzzy-ARTMAP is compared to our previous work with multi-layer perceptron and cascade-correlation neural nets[1]. Although the predictions of FuzzyARTMAP are less accurate by 2–8% for this problem, it has many features that make it a desirable candidate for a neural net implementation for first break detections: it learns quickly, efficiently and flexibly; it can be used in both on-line and off-line settings; it is easy to use, with few parameters; does not get trapped in local minima, and the fuzzy rules for mapping the input to the output can be extracted from the network.