Deep Artificial Neural Networks as a Tool for the Analysis of Seismic Data

Author:

Kislov K. V.,Gravirov V. V.

Publisher

Allerton Press

Reference22 articles.

1. Böse, M., Wenzel, F., and Erdik, M., PreSEIS: A Neural Network-Based Approach to Earthquake Early Warning for Finite Faults, Bull. Seismol. Soc. Am., 2008, vol. 98, no. 1, pp. 366–382.

2. Cybenko, G., Approximation by superpositions of a sigmoidal function, Math. Control Signals Syst., 1989, no. 2, pp. 303–314.

3. Friedman, J.H., An overview of predictive learning and function approximation, in From Statistics to Neural Networks, vol. 136 of NATO ASI Series, Cherkassky, V., Friedman, J.H., and Wechsler, H., Eds., Berlin: Springer, 1994, pp. 1–61.

4. Gravirov, V.V. and Kislov, K.V., The seismic data preparation program for training of an artificial neural network for ultrashort earthquake warning system, in Book of Abstracts, 10th International Conference “Problems of Geocosmos”, St. Petersburg, 2014, pp. 86–87. http://geo.phys.spbu.ru/ geocosmos/book_of_abstracts.pdf. Accessed December 31, 2014.

5. Gravirov, V.V. and Kislov, K.V., DataCollector: Software for Compiling the Output Data Volumes to Learn Neural Networks in the Matlab System, State Software Registration Certificate no. 2015611797, 2015.

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