Abstract
This paper presents an investigation of the long short-term memory (LSTM) neural networks for seismogram time series prediction. LSTM has been widely used for time series prediction problems and achieved excellent results therefore it is interesting to utilize this technique for seismology. A seismogram from Albuquerque, New Mexico (Anmo), USA provided by the IRIS website is used for the experiment. The seismogram is recorded to observe passive seismic activities like Earthquake. The data is recorded with a 16 kHz sampling frequency by a station in Albuquerque. The network is with 200 hidden units and trained with 250 maximum iterations. The LSTM is able to achieve 0.00306 of root mean squared error (RMSE).
Original language | English |
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Title of host publication | Advances in Science, Technology and Innovation |
Publisher | Springer Nature |
Pages | 207-209 |
Number of pages | 3 |
DOIs | |
State | Published - 2022 |
Publication series
Name | Advances in Science, Technology and Innovation |
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ISSN (Print) | 2522-8714 |
ISSN (Electronic) | 2522-8722 |
Bibliographical note
Publisher Copyright:© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords
- Long short-term memory
- Seismogram
- Time series forecasting
ASJC Scopus subject areas
- Architecture
- Environmental Chemistry
- Renewable Energy, Sustainability and the Environment