Abstract
A neural network module has been implemented in the Prototype International Data Centre (PIDC) for automated identification of the initial phase type of seismic detections. Initial training of the neural networks for stations of the International Monitoring System (IMS) requires considerable effort. While there are many seismic phases in the analyst-reviewed database that can be assumed as the ground-truth resource of the initial phase type of Teleseism (T), Regional P (P), and Regional S (S), no ground-truth database of noise (N) is available. To reduce analyst effort required in building a ground-truth database, an “Adaptive Training Approach” is proposed in this paper. This approach automatically selects training patterns to take advantage of the learning ability of neural networks and information on the accumulated observation database. Using this approach, neural networks were trained on the data provided by station STKA, Australia. The performance of automated phase identification has been improved significantly by the retrained neural networks. This approach is also validated by comparison with the performance using the ground-truth noise database.
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References
Anant, K. S. and Dowla, F. U. (1997), Waveform Transform Methods for Phase Identification in Three-omponent Seismograms, Bull. Seismol. Soc. Am. 87, 1598–1612.
Bache, T. C., Matt, S. R., Wang, J., Fung, R. M., Kobryn, C., and Given, J. W. (1990), The Intelligent Monitoring System, Bull. Seismol. Soc. Am. 80, 1833–1851.
Cichowicz, A. (1993)An Automatic S-phase Picker, Bull. Seismol. Soc. Am. 81, 180–189.
Duda, R. O. and Hart, P. E. Pattern Recognition and Classification (John Wiley, New York, 1973) 482 pp.
Jurkevics, A. (1988), Polarization Analysis of Three-component Array Data, Bull. Seismol. Soc. Am. 78, 1725–1743.
Roberts, R. G., Christoffersson, A., and Cassidy, F. (1989), Real-time Event Detection, Phase Identification and Source Location Estimation Using Single-station Three-component Seismic Data, Geophys. J. 97, 471–480.
Rumelhart, D. E., Mcclelland, J. L., and the PDP RESEARCH GROUP (1986), Learning Representations by Back-propagating Errors, Nature 332, 533–536.
Sereno, T. and Patnaik, G. (1993), Initial Wave-type Identification with Neural Networks and its Contribution to Automated Processing in IMS Version 3.0, Technical Report, SAIC-93/1219.
Suteau-Henson, A, (1991), Three-component Analysis of Regional Phases at NORESS and ARCESS: Polarization and Phase Identification, Bull. Seismol. Soc. Am. 81, 2419–2440.
Wang, J. and Teng, T. L. (1995), Artificial Neural Network-based Seismic Detector, Bull. Seismol. Soc. Am. 85, 308–319.
Wang, J. and Teng, T. L. (1997), Identification and Picking of S Phase using an Artificial Neural Network, Bull. Seismol. Soc. Am. 87, 1140–1149.
Zurada, J. M. Introduction to Artificial Neural Systems (West Publishing Company, St. Paul, 1992) pp. 163–250.
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© 2002 Springer Basel AG
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Wang, J. (2002). Adaptive Training of Neural Networks for Automatic Seismic Phase Identification. In: Der, Z.A., Shumway, R.H., Herrin, E.T. (eds) Monitoring the Comprehensive Nuclear-Test-Ban Treaty: Data Processing and Infrasound. Pageoph Topical Volumes. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-8144-9_7
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DOI: https://doi.org/10.1007/978-3-0348-8144-9_7
Publisher Name: Birkhäuser, Basel
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