Community Seismic Network for Early Warning

1. Community Seismic Network for Early Warning Rishi Chandy Rita A. and Øisten Skjellum SURF Fellow Daniel Obenshain Kiyo and Eiko Tomiyasu SURF Scholar Daniel Rosenberg Kiyo and Eiko Tomiyasu SURF Scholar Annie Tang Mentors: K. Mani Chandy, Robert Clayton, Andreas Krause California Institute of Technology

2. Who are we? Daniel Obenshain Rishi Chandy Daniel Rosenberg Annie Tang

3. Our Mentors Dr. K. Mani Chandy Professor of Computer Science Dr. Rob Clayton Professor of Geophysics Dr. Andreas Krause Assistant Professor of Computer Science Michael Olson Grad Student Computer Science

4. Background • Earthquakes are dangerous threats • USGS estimates 2000 deaths and $200 billion damages from 7.8 magnitude quake

5. Background • Earthquakes are dangerous threats • USGS estimates 2000 deaths and $200 billion damages from 7.8 magnitude quake • Early warning could minimize suffering • Activate safeguards in critical operations

6. Background • Earthquakes are dangerous threats • USGS estimates 2000 deaths and $200 billion damages from 7.8 magnitude quake • Early warning could minimize suffering • Activate safeguards in critical operations • Providing early warning is an interesting problem • Bayesian decision theory, geology, distributed computing

7. Background • Earthquakes are dangerous threats • USGS estimates 2000 deaths and $200 billion damages from 7.8 magnitude quake • Early warning could minimize suffering • Activate safeguards in critical operations • Providing early warning is an interesting problem • Bayesian decision theory, geology, distributed computing • Current seismic network is too sparse • Can’t provide enough early warning

8. Sensor Network is too Sparse A sensor network of one thousand sensors. A sensor network of one hundred sensors. SCSN (Southern California Seismic Network) has ~350 sensors right now.

9. Sensor Network is too Sparse Ten thousand sensors! Both a 3 second wave and a 1 second wave.

10. Early Warning Can Help Slow trains

11. Early Warning Can Help Slow trains Stop elevators

12. Early Warning Can Help Slow trains Stop elevators Open fire station doors

13. Early Warning Can Help • The information can also help the electrical grid. Southern California Edison Territory

14. Early Warning Can Help • The information can also help the electrical grid. • The grid can be shut down and made safe prior to severe shaking. Southern California Edison Territory

15. Early Warning Can Help • The information can also help the electrical grid. • The grid can be shut down and made safe prior to severe shaking. • Power back in a day, not weeks after earthquake. Southern California Edison Territory

16. Benefits • Provide Early Warning • Easy deployment in areas without existing seismic networks • Peru and Indonesia • Cell phones are prevalent • Identify hard-hit areas quickly • Direct first responders

17. That’s why we’re doing it.What about how we’re doing it?

18. Expand the Network • We want to add more data.

19. Expand the Network • We want to add more data. • Why not get data from as many sources as possible?

20. Expand the Network • We want to add more data. • Why not get data from as many sources as possible? • Add in acceleration devices of different types, cell phones, laptops, etc.

21. Expand the Network • We want to add more data. • Why not get data from as many sources as possible? • Add in acceleration devices of different types, cell phones, laptops, etc. • The User installs some client software and his or her acceleration data becomes part of the network.

22. The Client Server Registration Handler Server Alert Listener Error, No Update, or Handlers Registration Handler Sensor Handler Calculation Handler Alert Handler Core processing Handlers and Queues managed Controller Returns Proceed, Error, or New Handlers Registration handler invoked on first run

23. Picking Algorithm • How often should the client send data to the server?

24. Picking Algorithm • How often should the client send data to the server? • Only when significant shaking is occurring.

25. Picking Algorithm • How often should the client send data to the server? • Only when significant shaking is occurring. • How does the client know?

26. Picking Algorithm • How often should the client send data to the server? • Only when significant shaking is occurring. • How does the client know? • It performs a simple calculation on the incoming data stream.

27. Picking Algorithm • How often should the client send data to the server? • Only when significant shaking is occurring. • How does the client know? • It performs a simple calculation on the incoming data stream. • We call this the “Picking Algorithm.”

28. Picking Algorithm STA/LTA > trigger

29. Picking Algorithm • STA – Short Term Average : the average acceleration over the past several data points STA/LTA > trigger

30. Picking Algorithm • STA – Short Term Average : the average acceleration over the past several data points • LTA – Long Term Average : the average acceleration over more data points STA/LTA > trigger

31. Picking Algorithm • STA – Short Term Average : the average acceleration over the past several data points • LTA – Long Term Average : the average acceleration over more data points • trigger – a threshold STA/LTA > trigger

32. Picking Algorithm Short Term Average Long Term Average Accelerometer

33. Picking Algorithm Short Term Average Long Term Average New Data Accelerometer

34. Picking Algorithm Short Term Average Long Term Average Accelerometer

35. Picking Algorithm • If STA/LTA > trigger is true, then we have “picked.”

36. Picking Algorithm • If STA/LTA > trigger is true, then we have “picked.” • The algorithm then waits a little bit before sending a message to the server.

37. Picking Algorithm • If STA/LTA > trigger is true, then we have “picked.” • The algorithm then waits a little bit before sending a message to the server. • This is to make sure it sends data from the peak of the wave.

38. Picking Algorithm 1 2 3 Pause for this length of time before sending a message to the server. • Detected significant shaking • Maximum shaking • Sent message to server

39. Picking Algorithm • After sending a message to the server, the client will wait a while before picking again.

40. Picking Algorithm • After sending a message to the server, the client will wait a while before picking again. • This is to stop the client from picking multiple times for the same shaking.

41. Picking Algorithm 1 2 Delay for this length of time before picking again. • Last message sent to server • The coda of the earthquake, where we don’t want to pick

42. Picking Algorithm • Five tunable parameters.

43. Picking Algorithm • Five tunable parameters. • Length of STA

44. Picking Algorithm • Five tunable parameters. • Length of STA • Length of LTA

45. Picking Algorithm • Five tunable parameters. • Length of STA • Length of LTA • Value of trigger

46. Picking Algorithm • Five tunable parameters. • Length of STA • Length of LTA • Value of trigger • How long to wait after picking before sending a message to the server

47. Picking Algorithm • Five tunable parameters. • Length of STA • Length of LTA • Value of trigger • How long to wait after picking before sending a message to the server • How long to wait between messages

48. Picking Algorithm • Five tunable parameters. • Length of STA • Length of LTA • Value of trigger • How long to wait after picking before sending a message to the server • How long to wait between messages • They can all be tuned by the server, on a client-by-client basis.

49. GUI • Acceleration data is displayed in real time on the user’s screen.

50. GUI • Acceleration data is displayed in real time on the user’s screen. • Promotes use of the software.