Skip to main content
Log in

Estimation of Mining Tremor Occurrence by Using Neural Networks

  • Published:
pure and applied geophysics Aims and scope Submit manuscript

Abstract

—Changes of the primary strain-stress state (caused by interaction between natural conditions and mining activity) can result, under special circumstances, to the origin of seismic induced events. The question of induced seismic activity prediction was treated as a problem of time series extrapolation of maximum cumulative amplitudes and numbers of seismic events recorded per day. The treatment was carried out by means of Multilayered Perceptron Neural Networks (MLP NN). The application to mining tremor prediction has been tested and methodological conditions have been obtained. It was proved that the prediction of the number of mining tremors per day is more precise than the prediction of future energy (maximum amplitudes). Further advance, based on the processing of seismo-acoustic activity series, is introduced.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Author information

Authors and Affiliations

Authors

Additional information

Received January 31, 1998, accepted July 8, 1998.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rudajev, V., Číž, R. Estimation of Mining Tremor Occurrence by Using Neural Networks. Pure appl. geophys. 154, 57–72 (1999). https://doi.org/10.1007/s000240050221

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s000240050221

Navigation