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Community Seismic Network. Daniel Obenshain along with K. Mani Chandy, Robert Clayton, Andreas Krause, Michael Olson, Matthew Faulkner, Leif Strand, Rishi Chandy, Daniel Rosenberg, Annie Tang, and others California Institute of Technology. XKCD.

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community seismic network

Community Seismic Network

Daniel Obenshain

along with

K. Mani Chandy, Robert Clayton, Andreas Krause,

Michael Olson, Matthew Faulkner, Leif Strand,

Rishi Chandy, Daniel Rosenberg, Annie Tang,

and others

California Institute of Technology

slide2
XKCD

Image by Randall Munroe of xkcd.com, Creative Commons Attribution-Noncommercial 2.5 License

internet and earthquakes
Internet and Earthquakes
  • In the comic:
    • The tweets travel faster than the earthquake
    • Other users get quake information before it hits
    • They are too slow to do anything about it.
background
Background
  • Earthquakes are dangerous threats
    • USGS estimates 2000 deaths and $200 billion damages from 7.8 magnitude quake
background1
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
background2
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
background3
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
sensor network is too sparse
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.

sensor network is too sparse1
Sensor Network is too Sparse

Ten thousand sensors!

Both a 3 second wave and a 1 second wave.

early warning can help1
Early Warning Can Help

Slow trains

Stop elevators

early warning can help2
Early Warning Can Help

Slow trains

Stop elevators

Open fire station doors

early warning can help3
Early Warning Can Help
  • The information can also help the electrical grid.

Southern California Edison Territory

early warning can help4
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

early warning can help5
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

benefits
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
expand the network
Expand the Network
  • We want to add more data.
expand the network1
Expand the Network
  • We want to add more data.
  • Why not get data from as many sources as possible?
expand the network2
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.
expand the network3
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.
the client
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

example client cell phone
Example Client – Cell Phone
  • Measures 3-D acceleration
  • Program runs in background
  • Especially good while charging
example client cell phone1
Example Client – Cell Phone
  • Martin Lukac (of UCLA) recorded a minor seismic event on a Nokia phone, with different software.
picking algorithm
Picking Algorithm
  • How often should the client send data to the server?
picking algorithm1
Picking Algorithm
  • How often should the client send data to the server?
  • Only when significant shaking is occurring.
picking algorithm2
Picking Algorithm
  • How often should the client send data to the server?
  • Only when significant shaking is occurring.
  • How does the client know?
picking algorithm3
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.
picking algorithm4
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.”
picking algorithm5
Picking Algorithm

STA/LTA > trigger

picking algorithm6
Picking Algorithm
  • STA – Short Term Average : the average acceleration over the past several data points

STA/LTA > trigger

picking algorithm7
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

picking algorithm8
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

picking algorithm9
Picking Algorithm

Short Term Average

Long Term Average

Accelerometer

picking algorithm10
Picking Algorithm

Short Term Average

Long Term Average

New Data

Accelerometer

picking algorithm11
Picking Algorithm

Short Term Average

Long Term Average

Accelerometer

picking algorithm12
Picking Algorithm
  • If STA/LTA > trigger is true, then we have “picked.”
picking algorithm13
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.
picking algorithm14
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.
picking algorithm15
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
picking algorithm16
Picking Algorithm
  • After sending a message to the server, the client will wait a while before picking again.
picking algorithm17
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.
picking algorithm18
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
picking algorithm19
Picking Algorithm
  • Five tunable parameters.
picking algorithm20
Picking Algorithm
  • Five tunable parameters.
    • Length of STA
picking algorithm21
Picking Algorithm
  • Five tunable parameters.
    • Length of STA
    • Length of LTA
picking algorithm22
Picking Algorithm
  • Five tunable parameters.
    • Length of STA
    • Length of LTA
    • Value of trigger
picking algorithm23
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
picking algorithm24
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
picking algorithm25
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.
slide53
GUI
  • Acceleration data is displayed in real time on the user’s screen.
slide54
GUI
  • Acceleration data is displayed in real time on the user’s screen.
  • Promotes use of the software.
slide55
GUI
  • Acceleration data is displayed in real time on the user’s screen.
  • Promotes use of the software.
  • Can be used in science classrooms to explain project.
slide56
GUI
  • Acceleration data is displayed in real time on the user’s screen.
  • Promotes use of the software.
  • Can be used in science classrooms to explain project.
  • Each message to the server marked by a red line.
slide57
GUI
  • 3 Axes
slide58
GUI
  • 3 Axes
  • Data streams from the right
slide59
GUI
  • 3 Axes
  • Data streams from the right
  • The red line represents a message to the server
client server messages
Client-server messages
  • Registration
  • Picks
  • Heartbeat
client server messages1
Client-server messages
  • Registration
    • Location (or an approximation)
    • Given a client id
  • Picks
  • Heartbeat
client server messages2
Client-server messages
  • Registration
  • Picks
    • Short UDP message
    • Location, time, and acceleration experienced
    • Can be a playback message!
  • Heartbeat
client server messages3
Client-server messages
  • Registration
  • Picks
  • Heartbeat
    • Check in with server
    • Get any updates, playback info
    • For security, server will never push updates. Client must call in to get updates.
client server messages4
Client-server messages
  • Registration
  • Picks
  • Heartbeat
  • All are in XML format.
    • Human readable
    • Easily extended
    • Can easily interface with other, similar projects
sensor validation
Sensor Validation
  • Tested our sensor with artificial event.
sensor validation1
Sensor Validation
  • Tested our sensor with artificial event.
  • Compared our sensor to the SCSN (Southern California Seismic Network) sensor in the basement of Millikan Library.
sensor validation2
Sensor Validation
  • Tested our sensor with artificial event.
  • Compared our sensor to the SCSN (Southern California Seismic Network) sensor in the basement of Millikan Library.
  • Caused seismic activity with a sledgehammer.
sensor validation4
Sensor Validation
  • We have since switched to better noise filtering and a better sensor
  • Still, the correlation is visible
server
Server

Four main tasks

Handle new user registration

server1
Server
  • Four main tasks
    • Handle new user registration
    • Listen for pick messages
server2
Server
  • Four main tasks
    • Handle new user registration
    • Listen for pick messages
    • Handle heartbeat messages
server3
Server
  • Four main tasks
    • Handle new user registration
    • Listen for pick messages
    • Handle heartbeat messages
    • Analyze data
server4
Server

Registration Handler

Pick Handler

Database

Associator

Heartbeat Handler

udp vs tcp
UDP vs TCP

We send messages using two different protocols.

  • TCP (Transmission Control Protocol)
    • Handshake delay
    • Error correction
udp vs tcp1
UDP vs TCP

We send messages using two different protocols.

  • TCP (Transmission Control Protocol)
    • Handshake delay
    • Error correction
  • UDP (User Datagram Protocol)
    • Fast
    • Unreliable
pick message handler
Pick Message Handler

Pick messages are sent using UDP packets.

  • Reasons:
    • Unsure of condition of network
    • Speed is important
indiana jones effect
Indiana Jones Effect

From Indiana Jones and the Raiders of the Lost Ark.

pick message handler1
Pick Message Handler

Listen for incoming picks

pick message handler2
Pick Message Handler
  • Listen for incoming picks
    • Parse message
pick message handler3
Pick Message Handler
  • Listen for incoming picks
    • Parse message
    • Check for playback flag
      • If the flag is not present, the pick is stored in the database.
      • If the message is flagged as playback, it is written to a separate table in the database.
server operation
Server Operation

Clients

Clients

Clients

Clients

Clients

Clients

Clients

Clients

Clients

Equipment, devices,

notification systems

Registration

Listener

Heartbeat

Listener

Alert Listener

Calculation

Handler

Warning

Handler

Core processing

Controller

server side analysis
Server-side Analysis
  • Bayesian decision-making
server side analysis1
Server-side Analysis
  • Bayesian decision-making
  • Once posterior is sufficient, we send Early Warning
tested on fake data
Tested on Fake Data
  • Priors from Gutenberg-Richter law (mag vs. num)
  • Fake data stream with errors in it
  • Located epicenter to within 25 km
  • Located epicenter in 10 seconds
display data
Display data
  • Heat map
  • Hotter colors = higher magnitude picks
pause for example3
Pause for Example

http://map.communityseismicnetwork.org/

google app engine
Google App Engine
  • Server implemented on Google App Engine
  • Our data is sent to and stored there
  • Robust, scalable
machine learning
Machine Learning
  • Feedback loop
  • Five parameters (or more!) per client
  • If a client picks more often than normal, tune it down
  • If a client picks less often than normal, tune it up
machine learning1
Machine Learning
  • A client next to construction
  • Not useful for small seismic events
  • Still useful for big ones!
  • Set the threshold higher.
thank you

Thank You

Q&A Session