Abstract
Neural networks are powerful and elegant computational tools that can be used in the analysis of geophysical signals. At Lawrence Livermore National Laboratory, we have developed neural networks to solve problems in seismic discrimination, event classification, and seismic and hydrodynamic yield estimation. Other researchers have used neural networks for seismic phase identification. We are currently developing neural networks to estimate depths of seismic events using regional seismograms. In this paper different types of network architecture and representation techniques are discussed. We address the important problem of designing neural networks with good generalization capabilities. Examples of neural networks for treaty verification applications are also described.
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© 1996 Springer Science+Business Media Dordrecht
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Dowla, F.U. (1996). Neural Networks in Seismic Discrimination. In: Husebye, E.S., Dainty, A.M. (eds) Monitoring a Comprehensive Test Ban Treaty. NATO ASI Series, vol 303. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0419-7_41
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DOI: https://doi.org/10.1007/978-94-011-0419-7_41
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-4187-4
Online ISBN: 978-94-011-0419-7
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