Q C N e d S i t s a r e n v f o i R r d C U - - PowerPoint PPT Presentation

E L I Z A B E T H S . C O C H R A N 2 2 M A R C H 2 0 1 1 I S G C 2 0 1 1 a c h k e C e r N u a e t w Q o e r h k T Q C N e d S i t s a r e n v f o i R r d C U - - B r i n s g l o i n o h g c S S e & i s m e s o l o m g y t o H o

Co-PI: Software Architect: Co-PI: Jesse F. Lawrence Carl Christensen Elizabeth S. Cochran Stanford University Stanford University UC Riverside Educational Coordinator: PhD Student PhD Student Jennifer Saltzman Corrie Neighbors Angela Chung Stanford University UC Riverside Stanford University

Introduction to QCN Low cost seismic network that utilizes: 1. MEMS Sensors We use triaxial MEMS accelerometers internal to laptops or connected to desktops via USB Benefits: Very low cost sensing $0 – laptops $30-150 – desktops USB-connected triaxial accelerometer

Introduction to QCN Low-cost seismic network that utilizes: 1. MEMS Sensors Microelectromechanical systems (MEMS) accelerometers utilize interdigitized fingerlike structures that measure a change in capacitance due to an applied acceleration Widely used in cars for airbag deployment, phones for screen orientation, and laptops for harddrive protection From O’Reilly et al., 2009

Introduction to QCN Low cost seismic network that utilizes: 2. Volunteer Computing Volunteers donate CPU time to monitor sensors attached to their computer. We use the Berkeley Open Infrastructure for Network Computing (BOINC) open-source distributed computing platform Advantages: 1) Reduced infrastructure costs (existing networked computers process data and send information to us 2) Easy to modify software and push changes to participants

Current Network Participants Earthquake Locations — 1400+ Stations globally in 67 countries — Recorded earthquakes between M2.6 and M8.8

Data Collection MEMS Sensor Specifications Previous Generation JW24F8 – 10 bit sensor (4 mg) MotionNode – 12 bit sensor (1 mg) Current MotionNode JoyWarrior JW24F14: 14 bit sensor (.24 mg; $50) Next Generation (2011-2012) ON-16: 16 bit sensor (6 µ g; $50) 16 bit Sensor ON-24: 24 bit sensor (0.24 µ g; $130)

M6.7 Northridge scaled to 0.5 g Shake Table Tests • Single harmonic • Frequencies range 0.2 – 10 Hz • Acceleration range 0.03g – 2g • Earthquake ground motion • Scaled Northridge (0.5g and 1g)

Data Collection Location Initial location based on IP address More accurate location from participant input into a Google Map interface

Data Collection Timing Network Time Protocol (NTP): Since 1985 Multi-tier system grounded to GPS Clocks Atomic Clocks Radio Clocks Peer-to-peer method often provides better than 0.1 second accuracy, often +/- 20 msec. http://en.wikipedia.org/wiki/Network_Time_Protocol Frassetto et al. (SRL, 2003)

— Initially transfer minimal data: 3e4 California ¡ Time ¡ Amplitude on each components 2e4 Number ¡ Significance ¡ Station information (location, sensor type) 1e4 — Overall small trigger latency: ¡ 3.62 seconds within California 0 0 2 4 6 8 10 Trigger Latency (sec) ¡ 4.29 seconds globally 8e4 1 World 0.8 6e4 Cumulative Distribuion Number 0.6 4e4 0.4 2e4 0.2 California World 0 0 0 2 4 6 8 10 0 2 4 6 8 10 Trigger Latency (sec) Trigger Latency (sec)

Example: Recent Aftershock Deployments Goal: Rapidly deploy dense sensor networks in urban areas after significant earthquakes The best way to densely record a moderate earthquake is instrument the region around a recent large earthquake. Approach: Foreshock with subsequent mainshock and aftershock sequence • Maintain a pool of sensors (200+) Collaborate with local universities and research centers • Recruit volunteers through social media Source: USGS

Installed ~180 sensors in New Zealand in the week following the 3 Sept 2010 M7.2 earthquake Collaboration between GNS and QCN

Darfield earthquake continues to have a vigorous aftershock sequence and is being recorded by the QCN array. Source: USGS 2011

Real-Time Event Detection 1. Trigger message sent from client station 2. Server correlates triggers within: • 100 seconds • 200 km radius 3. Check moveout • Is wave traveling at seismic velocities? ! T ij " ! D ij / V min + ! 4. Issue a detection if the # of triggers > regional threshold

Real-Time Detection After a detection is issued we estimate: Location: 1. ¡ Triggers may be P or S arrivals ¡ Starting location is set to the location of the first trigger ¡ Grid search of possible locations ¡ Iterate to find best location 2. Magnitude: • Vector sum of PGA: PGA • Updated amplitude every 1, 2, and 4 seconds • Use empirical distance-magnitude relationship (e.g. Campbell, 1981; 1989; Wu et al., 2003; Cua and Heaton, 2007): PGA = 1 b exp 1 ( ) ( ) ! d a M L ! c ln R

Improving Event Detection Final event characterization: Initial event characterization: 257 seconds after the origin time 5 seconds after the origin time 194 total triggers from 104 stations 11 triggers

Real-time Detections to date: • Detection running since mid-September • All detections in New Zealand – no other location currently has either: • Dense enough network of stations • Earthquakes • First detections occur within ~9-10 seconds from the earthquake origin time Event locations and magnitudes are revised using updated amplitude data from 1-4 seconds after the event.

What Can We Learn From Dense Networks? 1. Extremely dense ground motion records for seismic hazard analysis and emergency response 2. Large number of records to invert for source characteristics including rupture velocity and slip distribution 3. Rapid and real-time building response analysis

— Low-cost MEMS and distributed sensing techniques could provide valuable acceleration data for: ¡ Real-time event detection and characterization ¡ Dense observations for earth structure and seismic hazard ¡ High resolution source imaging ¡ Large-scale building response studies — Current sensor are 14 bit and will be integrating 16 bit and 24 bit soon — Partnering with many international groups to expand the network

Thank you to all of the QCN participants, especially K-12 teachers and classrooms QCN is funded by: Project website: qcn.stanford.edu Any Questions?