1. Reduction of Train Noise from Telluric Current Data by Neural Networks Kazuki Joe (System Designer) Toshiyasu Nagao (VAN Method Advisor) Mika Koganeyama (Neural Network Implementor) Moyo Sugita (Visualization Implementor) Nara Women’s University Tokai University

2. Earthquakes in Japan （ 1990-1994 ）

4. What is short-term earthquake prediction?

5. Time Earthquake Electro-Magnetic Phenomena Aftershock Definition of Earthquake

6. Earthquake Dec. 1994 Feb. 1995 3 months 90 days

7. VAN method _ Designed by Greek physicists _ enable to observe SESs 50m 1km N Short dipoles （ 30 ～ 200m ） Long dipoles （ several km ） electrode

8. Telluric Current Data(TCD) _ Feeble current that flows in the earth surface –potential difference between 2 points by burying electrodes in the earth _ observation points –42 points (Tokai and Hokuriku area) –8 channels or 16 channels for each observation point –observe every 10 seconds (8640 data on one day) _ seismic electric signals(SESs)

9. Seismic electric signals(SESs) _ Current changes before earthquake –earthquake is a kind of events of destroying rocks –current flows before rocks are destroyed _ 20 ～ 30 minutes one-way amplitude _ find the signals by specialists 300 frames （ 50 minutes ） about 160 frames （ 27 minutes ）

10. Case Study _ Big earthquake in Greece, Pirgos city （ in March, 1993 ） –Seismic electric signal was detected before the earthquake. –By the prediction, some part of resident are evacuated. half of buildings (about 4000 ridge) were destroyed completely or partially no casualties effectiveness of TCD Investigate TCD in Japan

11. Problem of the use of VAN method in Japan _ TCD components in Japan TCD train noise （ about 90% ） other noise SES

12. Characteristics of Train Noise TCD Timetable (Nagano railway Matsushiro station) 6:10 6:46 7:268:06 Regularity of the appearance Similarity of the shape can be learned & recognized by Neural Networks Up-train Down-train

13. Train noise reduction filter - Basic Idea - train noise reduction filter Train noise + SES constructed by neural network SES

14. Problem of Constructing the Filter by Neural Networks _ NNs require training and supervising samples –the TCD with train noise and SESs are very rare (only several ten cases) –no TCD with the same SESs without the train noise Generate training and supervising samples artificially

15. Artificial Generation of Training & Supervising Samples _ Pre-processed TCD (LF components are cut) 300 frames 120 ～ 250frames （ about 20 ～ 40minutes ） Train noiseNatural noise

16. Artificial Generation of Training & Supervising Samples _ Training data _ Supervising data ＋ ＋ Train noiseNatural noise SES ＋ Natural noise SES 300 frames

17. Artificial Generation of Training & Supervising Samples _ More shift-tolerant neural network to time series data –train noise and SES are shifted right for several points as shown below Train noise Supervising data Training data

18. Experiment Result _ After the learning, only train noise from unknown TCD data could be removed. –unknown TCD is generated artificially by train noise and an SES

19. Demonstration _ Artificial generation of TCD with train noise and an SES arbitrarily _ Train noise reduction of TCD with SESs _ Train noise reduction of unknown TCD

20. Conclusion and Future Work _ It turned out that just train noise can be removed from TCD by neural networks _ Can be a big progress toward automatic short-term earthquake prediction _ More learning with other observation points _ Design and implementation of SES detector _ Other method...