242 ieee transactions on automation science …glaser.berkeley.edu/glaserdrupal/pdf/reily ieee...

242 IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 10, NO. 2, APRIL 2013

Mobile Phones as Seismologic Sensors: AutomatingData Extraction for the iShake System

Jack Reilly, Shideh Dashti, Mari Ervasti, Jonathan D. Bray, Steven D. Glaser, and Alexandre M. Bayen

Abstract—There are a variety of approaches to seismic sensing,which range from collecting sparse measurements with high-fi-delity seismic stations to non-quantitative, post-earthquakesurveys. The sparse nature of the high-fidelity stations and theinaccuracy of the surveys create the need for a high-density,semi-quantitative approach to seismic sensing. To fill this void,the UC Berkeley iShake project designed a mobile client-backendserver architecture that uses sensor-equipped mobile devices tomeasure earthquake ground shaking. iShake provides the generalpublic with a service to more easily contribute more quantita-tively significant data to earthquake research by automating thedata collection and reporting mechanisms via the iShake mobileapplication. The devices act as distributed sensors that enablemeasurements to be taken and transmitted with a cellular networkconnection. Shaking table testing was used to assess the qualityof the measurements obtained from the iPhones and iPods on abenchmark of 150 ground motions. Once triggered by a shakingevent, the devices transmit sensor data to a backend server forfurther processing. After a seismic event is verified by high-fidelitystations, filtering algorithms are used to detect falling phones,as well as device-specific responses to the event. A method wasdeveloped to determine the absolute orientation of a device toestimate the direction of first motion of a seismic event. A “virtualearthquake” pilot test was conducted on the UC Berkeley campusto verify the operation of the iShake system. By designing and fullyimplementing a system architecture, developing signal processingtechniques unique to mobile sensing, and conducting shaking tabletests to confirm the validity of the sensing platform, the iShakeproject serves as foundational work for further studies in seismicsensing on mobile devices.

Note to Practitioners—To try and recover ground motion mea-surements from the on- accelerometer in a smartphone, one shouldtake into account the additional variables in the environment inwhich the measurement was taken. Such variables include that thedevice may be moved by the user rather than the ground, that theaccelerometer may record internal phone vibrations rather thanexternal effects, and that the absolute orientation of the devicecannot be determined from the accelerometer readings alone. Thisreport gives practical procedures and algorithms to address these

Manuscript received September 09, 2012; accepted January 02, 2013. Dateof publication February 25, 2013; date of current version April 03, 2013. Thispaper was recommended for publication by Associate Editor R. Voyles and Ed-itor S. Sarma upon evaluation of the reviewers’ comments. This work was sup-ported by the U.S. Geological Survey (USGS), Department of Interior, underAward G10AP00006.J. Reilly, J. D. Bray, S. D. Glaser, and A. M. Bayen are with the Depart-

ment of Civil Engineering, University of California at Berkeley, Berkeley,CA 94720 USA (e-mail: [email protected]; [email protected];[email protected]; [email protected]).S. Dashti is with the Department of Civil, Environmental and Architectural

Engineering, University of Colorado Boulder, Boulder, CO 80302 USA (e-mail:[email protected]).M. Ervasti is with the VTT Technical Research Center, 02044 Oulu, Finland

(e-mail: [email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TASE.2013.2245121

newly introduced issues. We provide a three-step-loop procedureto only actively sense for significant ground motion when it isreasonably certain the phone is not in active use. We also give anumber of filtering techniques to apply to the raw accelerometersignal, such as isolating frequencies relative to seismic groundmotions, removing potential internal resonance of the device inthe output signal, and detecting whether a device has undergonesignificant disruption from forces not pertaining to groundmotion.Since the cardinal direction (north-south-east-west) and coordi-nate location are needed to determine fault movement properties,we give a procedure to recover absolute position and absoluteorientation from a combination of accelerometer, compass, andGPS information. By comparing the performance of the devices’accelerometer readings against high-fidelity accelerometers onshaking tables, we demonstrate that the resolution of the devices’measurements show potential for ground motion estimation.

Index Terms—Accelerometer, crowdsourcing, earthquake, mo-bile phones, participatory sensing, seismograph, signal processing.

I. INTRODUCTION

E MERGENCY responders must assess the effects of anearthquake exhaustively and rapidly so that they can re-

spond effectively to the damage it has produced. The iShakeproject created such a system by using a person’s mobile deviceto measure ground motion, intensity parameters and process thedata on a central server, rather than using a person as a mea-surement and reporting device. This research involves the useof the iPhone as a new ad-hoc sensor array based on participa-tory sensing, with mobile phones as the nodes of the sensingnetwork.Currently, much of post-earthquake damage assessment is

made based onModified Mercalli Intensity (MMI), a subjectivemeasure of earthquake shaking evaluated by individuals vol-unteering information [4]. iShake provides a quantitative mea-sure [12] of Arias intensity [20], which has been shown to bean excellent prediction of structural [28] and geotechnical [18]damage from shaking. One of the goals of this project is to feedthis additional intensity information back to those in damage as-sessment rapidly after an earthquake event.In particular, the iShake system was created to take advantage

of the new source of seismic sensing capabilities offered by theGPS, accelerometer and magnetometer on the smartphones. Aclient application for the iPhone and iPod products was devel-oped as the means of sensing ground motion activity. After apotential earthquake event is sensed, the data measured by theclient application are streamed rapidly to the backend servers,where the raw data can be properly processed and aggregatedwith other client data. Then, summary data from the event, inthe form of intensity maps [29], can be immediately provided

1545-5955/$31.00 © 2013 IEEE

REILLY et al.: MOBILE PHONES AS SEISMOLOGIC SENSORS: AUTOMATING DATA EXTRACTION FOR THE iShake SYSTEM 243

to emergency workers (as well to the general public) to aid inrescue missions.The envisioned use case for the iShake system is as follows.

Most smartphone users have been accustomed to charging theirphone nightly or while at their desk during a workday. Duringsuch times of inactivity, the mobile device will be leveraged as asensing and data-transmission unit. The on-board array of sen-sors (accelerometer, GPS, magnetometer) will then be enabledto capture ground-motion events and properly determine its ori-entation from any potential position, be it lying face down ona desk, or upright in a charger. While an unoptimized imple-mentation of the software may pose a problem to battery life,the ability of modern mobile operating systems to detect whena device is charging (such as Apple iOS and Android) circum-vents such issues. Additionally, the stationary nature of chargingphones also helps to target the ideal use case of sensing whilethe user is disengaged from the device. The aforementioned usecase motivates the design decisions taken while developing thesystem architecture for iShake.Contributions of this paper are enumerated as follows. The

iShake system is one of the first, fully functional systems [17] toenable users to report ground motion measurements from a mo-bile device to a centralized data-collection system. This is alsothe first known participatory sensing system to report groundmotion data within an orientation-aware context, giving both themagnitude and cardinal direction of shaking. Given the mobilenature of the computing platform, there are many uncertaintiesintroduced into the sensing environment. iShake makes contri-butions to the mobile computing field by developing algorithmsthat account for such uncertainties inherent to the field, and im-plementing techniques that reduce the noise associated with un-certainty. In Section III, a mobile client application architectureis developed to determine when sensor-data transmission shouldand should not occur, basing the decision on the mobile de-vice’s current environment. Further, Section IV details severalsignal processing algorithms that filter out environment-specificnoise, which is variable from device-to-device (such as smart-phones falling off a table while sensing for ground motion, ordevice-related resonance unrelated to the ground motion) andunavoidable in mobile computing and participatory sensing ap-plications. By implementing a circular buffer, the client soft-ware allows for very low-amplitude seismic waves to be cap-tured and transmitted to the central server, evenwhen the wave’sfirst-arrival amplitude was not enough to set off the application’strigger. In Section V, we present results from testing which sub-jected seven iPhone and iPod Touch devices to a suite of over150 shaking table tests [12]. This work is the first rigorous andquantitative testing of iPhone devices for the purpose of deter-mining the suitability of on-board accelerometers for seismicsensing application. In Section VI, we detail an actual field testof the iShake system conducted on the UCBerkeley campus andsurrounding areas, which demonstrated the feasibility of run-ning this system in real time.

II. RELATED WORK

In the last decade, significant progress was made in rapid,post-earthquake analysis, and visual representation of seismicdata [4], [11], [29]. The demand for immediate analysis ofearthquakes comes not only from the scientific community, but

also from public and private post-earthquake response groupsas well as preparedness exercises and disaster planning groups[12], [23].The USGS offers a service called ShakeMaps [29] that

provides shaking intensity and ground motion maps of earth-quakes minutes after an earthquake event happens. ShakeMapsleverages a network of regional seismic stations to create theirground motion estimates. Due the sparseness of the stations,ShakeMaps must interpolate over large areas to stabilizecontouring. The interpolated values from these plots haveno validation method due to the lack of seismic stations in agiven area. Given the growing number of smartphone users,particularly in urban areas, a denser mesh of sensor nodes canbe supplied by the iShake system to reduce the interpolationdistances involved in creating spatial intensity maps.The DYFI project is another program offered by the USGS,

which uses human observations that are voluntarily submittedthrough the Internet, hours to days after an earthquake, todevelop a Community Internet Intensity Map based largely onthe MMI scale [20]. In addition to untrained humans beingonly a rough qualitative indicator of earthquake effects, theresponse times of such data sources would be expected toincrease with the severity of the event. The iShake projectaims to create more quantitative sensing data than the DYFIapproach, while automating the response method via a mobileclient application. Reducing response time becomes importantas networks are known to experience downtime immediatelyfollowing natural disasters from network overload and infra-structure damage [21], [25].The idea for using mobile devices as an earthquake sensing

platform was inspired by the rising field of participatorysensing, introduced by Burke et al. [10], in which the idea ofthe “shareability” of individual’s sensor data is introduced. Ageneral model of crowdsourced computation has been proposedin [30]. Participatory sensing has become influential in civilengineering by introducing modern sensing techniques to tra-ditional civil engineering practices. In particular, Balakrishnan,Madden et al. have developed a mobile sensing platformattached to vehicles that enables monitoring of street surfaceconditions [14], [19], and new earthquake sensing techniqueshave emerged [11], [23], [26], which are detailed herein.In a first for mobile participatory sensing in seismic applica-

tions, Estrin et al. successfully captured an earthquake event inthe Los Angeles area using aNokia N95mobile phone [16]. Theacceleration signal showed a clear capturing of both the -waveand the harder to detect -wave of the earthquake event, givingpromise to the field of seismic mobile sensing.The Quake-Catcher Network (QCN) [11], a research project

led by Stanford University and UC Riverside, seeks to utilizelaptops and personal computers to record ground-motion data.When internal accelerometers are not available, inexpensive ex-ternal accelerometers can be used as well. TheQCN has had suc-cess in detecting and analyzing earthquakes with user devices,evidenced by accurately calculating the epicenter of an actualearthquake event.A number of months after the iShake system went live and

the mobile application was launched in the Apple App Store,the results of a mobile phone-based earthquake detection system


Fig. 1. Screen shots of the iShake client application. While the seismic sensingperforms silently in the background, information is fed back to the user in theform of a status center and aggregate virtual shake location map.

were published by the Community Seismic Network (CSN) team[17]. The architecture of CSN and iShake indeed share simi-larities, and CSN also conducted numerous simulated shakingand actual shaking table testing with Android mobile devicesto investigate the system’s ability to detect earthquakes. Thepresent paper differs from [17] by focusing on quantifying theability of iPhones to accurately recover important earthquake-related parameters such as Arias intensity and spectral ordi-nate, useful in earthquake-engineering studies, rather than early-warning detection applications. Additionally, a focus is given tocustomized signal processing techniques that reduce the effectof environmental variabilities, while [17] present a more genericstatistical approach based on Gaussian mixture models.

III. SYSTEM OVERVIEW

A client-server system architecture was designed to allowtransmission of data from the mobile devices to a central loca-tion. When an earthquake occurs, the devices will be triggeredto transmit their recordings to the server. The server will sub-sequently process the received signals by adjusting clock drift,determining the state/orientation of the signal, removing unre-lated signals, and filtering out noise and the captured responseof the device’s housing. Fig. 4 shows an overview of the iShakesystem. While data from the devices may be transmitted at anytime, only events registered by USGS (which publishes earth-quake events as soon as a few minutes after the event) are con-sidered actual earthquakes. All signals transmitted during thetime and within a certain radius of a USGS earthquake event arepulled to get a spatial description of the event from the iShakemeasurements. Finally, the compiled data is then fed back tothe devices for viewing. Fig. 1 shows an example of a shakemap that can be generated from the user-generated data. Theaggregation techniques for shake map generation is the subjectof future work.Due to the lack of orientation-aware sensors (such as a

magnetometer) in PC’s and USB-based accelerometers, ex-isting seismic participatory sensing systems do not provide anyinformation on the heading (the cardinal direction) of the signal[11], [17]. While this allows for computation of estimates forepicenter of earthquake and intensity values, there is no wayfor the measurements to capture direction of first arrival ordetermination of fault-plane [20]. The iPhone has the neces-sary sensors to satisfy the requirement that the client be ableto broadcast enough information to determine its coordinate

Fig. 2. An overview of the iShake client application’s background processes.

location (assisted GPS, “aGPS”), relative orientation (magne-tometer, accelerometer), and surface shaking (accelerometer).aGPS allows for accurate locating capabilities when combiningtraditional GPS sensing with cellular triangulation and wire-less-router location lookup-tables [3] in the absence of GPSavailability.Under ideal conditions, recovering orientation information

from a combination of the magnetometer and the accelerom-eter is done by comparing each sensors’ measurements toknown reference vectors the earth’s magnetic field and gravityrespectively via the Triad Algorithm [8]. Unfortunately, mag-netometer readings have been shown to be noisy, especiallyin variable environments where local magnetic effects can bepresent [6], [27]. Suggested approaches to overcoming thisdifficulty relevant to the iShake project include incorporatinggyroscope data [6], and using the phone’s camera to capturethe panorama as a stable reference point [27] (both sensorsavailable on recent iPhone models). Future work includesunderstanding how having many measurements for a singleevent can reduce the impact of individual heading measurementerrors in the estimation of direction of first arrival.

A. Client Application

The iShake client application is, functionally, a backgroundprocess that has two main components: sensing loop andsending loop. The purpose of the partition of sensing andsending tasks was to ensure no blocking of the sensing whilethe application attempts to make network connection for datatransmission. This allows the app to be in a perpetual state ofearthquake sensing.An overview of the architecture of the client application can

be seen in Fig. 2. While the sensing loop handles the interac-tion with the main application and the on-board sensors (and isdiscussed in depth in the proceeding sections), the sending loophandles local storage, transmission of recorded sensor events,and requeueing of events in the case of poor network connec-tion. The sending loop was designed to queue recently recordedevents immediately after recording to reduce the total latency ofthe iShake system. While a failed transmission of a recent eventmay not be useful for emergency response if not received after


Fig. 3. Different modes of sensing by the client application. When the “LeaveOn” condition is satisfied, the application will transition to the proceeding mode.After the sensor is determined to be steady, the application will enter buffermode. Once a trigger is set off, data will be streamed to the server, and theapplication cycle will repeat.

a day’s time, there still exists scientific in post-event analysis ofthe event. Thus, failed events are always stored locally and in-definitely requeued until an eventual successful transmission.For reasons such as data-usage rates and battery life, it is not

practical for the mobile devices to be continuously streamingdata to the servers. To handle this issue, a three-state model wascreated for the client application: Steady Mode, Trigger Mode,and StreamingMode. This model permits minimal transmissionof data to the server, while continuously recording and sensingfor probable earthquake events locally on the device. Fig. 3 de-picts the flow of the iShake client application.1) Steady Mode: To begin determination of earthquake

events, the mobile device must be stationary for a period oftime prior to recording. The reasons for this are twofold. First,to determine orientation of the device, the gravity vector mustbe determined, and this can only be accomplished if the deviceis not experiencing other forces. Second, the iShake projectis specifically analyzing recordings from stationary devices,thus devices carried on a moving person or experiencing asignificant amount of movement unrelated to seismic eventsshould not transmit their data to the server. Device movementis characterized by a change in the accelerometer reading.Using the previous accelerometer reading as the reference, themovement value of time step , is calculated by taking thenorm of the difference acceleration vector

(1)

where is the current time step, and is thecurrent acceleration vector. A moving average of the movementvalues are taken over a set period of time. A period of five sec-onds was chosen as a suitable interval. The sum represents thelack of stillness, or cumulative movement, of the device in the

recent history. For the device to be verified as still, the cumu-lative movement value must be under a certain threshold. If thecumulative movement is under the threshold, then the devicemoves onto the next state. Otherwise, the device will continueto record indefinitely its cumulative movement, keeping only ahistory of 5 s of previous movements.2) Trigger Mode: Earthquake waves are made up of several

different modes of shaking, each of which travels with a char-acteristic velocity. The first wave energy to be felt is from theprimary wave (p-wave), which has an amplitude significantlysmaller than that of the secondary wave (s-wave) [24]. Whilethe shaking caused by a p-wave is often not strong enoughto be quickly and unambiguously discerned from unrelatedbackground vibrations, capturing the p-wave is still of greatinterest to seismologists. The application is able to capturethe p-wave by always keeping in a circular memory buffer asegment of the signal recorded before the threshold triggeringof the system. The system is considered “triggered” when ashaking event above a predetermined threshold is recorded;this would most often be by the s-wave. Currently, the triggeris fired when an acceleration of 0.1 g is experienced by any ofthe three axes. A suitable buffer length was chosen to be 30 s,with a 15 s pretrigger.3) Streaming Mode: Soon after an actual earthquake event,

cellular coverage often becomes unreliable [21], [25]. Becauseof this, it is necessary to transmit data as soon as possible aftera shaking event is determined by the application. The applica-tion records “packs” of sensor readings (acceleration, heading,location) at 3 s intervals and immediately transmits the data tothe server. This is repeated for the 2 min following the shakingevent in order to capture the entire event. In case the device doesnot have service during the shaking event, a local copy of therecordings are stored locally, and placed into a queue to be sentonce service is available again.

B. Backend Hardware and Software

The iShake system has a unique load-handling specification,in that it must handle large and sudden spikes of requests anddata uploading during earthquake events, yet use only the min-imal amount of resources during the large periods of inactivity.The auto-scalability of the Google App Engine architecture fitthe needs of this application, and was chosen as the backend.Since the App Engine’s cloud-based architecture handles bothhosting and software solutions, the backend was able to be im-plemented with minimal developer time and a very low recur-ring cost for maintenance. Fig. 4 summarizes the backend archi-tecture implemented for the iShake system, while the followingsections focus on some of the larger backend components.1) Time Synchronization: Due to the high speed of seismic

waves, time synchronization on the order of milliseconds isrequired for estimation of earthquake epicenter. From empir-ical testing, clock drift on the iPhone was determined to beon the order of seconds per day. iPhones correct their clockdrift at a rate on the order of hours when connecting to cel-lular towers. The level of drift is unacceptable for calculationof an earthquake’s epicenter, which requires millisecond accu-racy and precise phasing information. To correct for clock drift,


Fig. 4. Integration of backend architecture and frontend architecture: high-level overview of the subprocesses of the iShake backend. The components aimto reduce noise in raw transmitted signals, and verify to correlation of those sig-nals with certified earthquake events.

iShake uses a system similar to QCN [11] that relies on Net-work Time Protocol (NTP) [2] communication to calculate thedifference in time measurement between the client and server.This value is then stored on the server and used as a correctionfactor to the data sent by the client. Open source projects suchas ios-ntp Eadie [13] serve as an implementation of NTP on iOSapplications.2) USGS Earthquake Verification: From high-fidelity

seismic stations [1], the USGS is able to detect an earthquakeevent and publish sensor recordings only minutes after theevent. Leveraging the high certainty of the USGS events,iShake is able to eliminate specific transmitted recordingsas possible earthquake events. If a device’s recording is nottransmitted within a certain range of time and space of a USGSearthquake event, then it is categorized as unverified. Similarly,device recordings can then be grouped based on their relativeproximity in time and space to a USGS earthquake event tobegin the process of data analysis for a specific earthquakeevent. Currently, the earthquake verification process is au-tomated on the iShake server for all submitted events in theCalifornia area. Although the scope is currently limited to thisregion, the process can easily be extended to other regions byadding more detected earthquake feeds to the iShake backend.

IV. EARTHQUAKE SIGNAL PROCESSING AND

SENSOR STATE DETERMINATION

The ubiquity of mobile devices enables sensing to take placein a larger variety of places and situations. The sacrifice forincreased coverage of sensing is an increase in the uncertaintyof the environment of sensing. Using mobile devices as seismicsensors introduces previously unconsidered factors to therecordings of the ground motion. One such uncertainty alreadydiscussed is orientation determination of the mobile device.This section discusses methods to detect scenarios that wouldaffect the signal produced by the mobile devices such as if thesignal is recording the actual ground motion, or also capturingsome of the response of the device housing, or if the device

Fig. 5. Signal processing of received phone acceleration signals.

experienced moving unrelated to the earthquake such as fallingoff a desk.Fig. 5 shows a high-level flow diagram of algorithm to which

incoming signals are subjected. Subprocesses of the processingalgorithm are described in the following subsections.

A. Bandpass Filter Over Seismic Region of Interest

Ground motions from earthquakes will typically have spec-tral values in the range of 0.3–0 Hz. Due to effects such asambient vibrations and sensor noise, the acceleration recordingfrom a device will often contain frequencies outside of thisrange. The contributions to the signal of frequencies outside therange of interest are treated as noise. To diminish the contribu-tion of ambient vibrations and device response, a butterworthbandpass filter is first applied to the acceleration signal [9].The filter is applied both in the forward and reverse directionto correct for phase distortion. Phase conservation becomesimportant when comparing time-series of the same earthquakeevent from different mobile devices. This subprocess is de-picted in Fig. 5 in the “Standard Bandpass Filter” subprocesswith low and high frequencies of and, ,respectively.

B. Phase Alignment for Same-Event Time Series

Some ground motion parameters that describe the charac-teristics of a shaking event require time-domain analysis (e.g.,Arias Intensity of the iPhone measurements compared to that ofthe reference accelerometer record [7]). To compare properlydevice signals in the time domain, the signals must be phase-aligned. While phase-alignment removes any information onearthquake travel-time, ground motion parameters such as AriasIntensity rely on the assumption of phase-alignment. For charac-teristics that account for signal propagation time, such as sourcelocalization, the phase-alignment procedure is not used. An al-gorithm based on cross-correlation was employed to properlyalign the phases of same-event signals. To find the phase mis-alignment between a discrete signal and with signallengths of time steps each, we seek a time delay

(2)

(3)

where denotes the time step. Time-shifting by secondswill allow for more accurate time-series comparisons betweensignals.


Fig. 6. Spectral response from 1978 Tabas, Iran Earthquake: Phone 6 showsunique peaks around 1 Hz and 15 Hz in the power spectrum of a simulatedshaking event. This is indicative of device-specific “resonance” which ampli-fies specific frequencies unique to a device. These errors can be decreased byattenuating the frequencies amplified by “resonance.”

C. Device “Resonance”

During shake-table testing, some devices experienced highlevels of resonance around frequencies particular to the specificphone. Such resonance appeared after repeated shake testingand may be the result of a loose connection to the contact sur-face or internal resonance with the housing of the device. Fig. 6shows an instance of resonance in the periodogram of Phone 6for a trial at ST-2. While the reference and other devices do nothave any dramatic spikes in the frequency spectrum (removingthe possibility of the spikes appearing because of ambient vi-brations), Phone 6 shows two pronounced spikes around 1 Hzand 15 Hz. By using a band-stop filter to attenuate the offendingfrequencies to an average level, the effect of resonance can beneutralized to a large degree.To include collected signals that may or may not include

the effects of resonance, an algorithm was developed for thebackend server that detects the presence of resonance, and thenapplies the band-stop filter, effectively removing some device-specific corruption from the received signal.The pseudocode and algorithmic details are given in Algo-

rithms 1 and 2. For an earthquake event , the inputs are themean spectral acceleration of the signals collected for thesame shaking event (as determined through spatial and tem-poral clustering of received signals) and a raw smartphone ac-celeration record that we wish to check for the pres-ence of resonance, where are all raw smartphone recordsattributed to an event .

Algorithm 1: FilterResonance

Input: mean spectrum signal for event ,phone accel signal to modify

Output: modified signal

do

do

if ResonanceDetected

then return FilterResonance( ,

BandStopFilter )

else return

The algorithm begins by selecting acandidate resonant frequency, (the current implementa-tion selects the frequency with the maximum spectral acceler-ation error value). From this peak value, the mean error value

is calculated for the sample points to the left and right,then the ratios of these values are calculated with respect tothe peak error, , where the ratios are and , respec-tively. If both ratios have a value lower than the “peak sharpnessthreshold,” , then the frequency error peak at is classi-fied as sharp enough to be considered a resonance peak. The

algorithm will subsequently reduce the ef-fect of noise added by the resonance with the offending fre-quency by applying a standard filter.

Algorithm 2: ResonanceDetected

Input: spectral accel. error signal

Output: logical result of resonance test

do (userprovided)

do (user provided)

do

do

do

if

then return True

else return False

In Fig. 5, the subprocess that detects and attenuates resonanceeffects is shown in the lower-left loop. Since the resonance sub-process is closed-loop, processing of resonance effects can suc-cessively applied until all effects are removed. We assume theleft and right bounding steps are checked to bewithin the boundsof error signal.

D. Falling Phone Detection

Since the devices measuring ground motion will be in morevolatile environments, they will be susceptible to outside forcesaffecting the signal. This includes devices falling, other objectsfalling on a device, or other similar events that would cause thedevice to measure non-earthquake-related effects. Signals pro-duced by devices experiencing sudden and conspicuously unre-lated forces will not accurately reflect the intensity of the under-lying ground motion, thus making useful the detection of suchevents. We analyze the Arias Intensity [20] of the signal in ourdetection algorithm. The Arias Intensity measures the accumu-lation of energy of an earthquake event and is defined by

(4)

where is the acceleration record at time , is the dura-tion period of the event, and is the acceleration of gravity. Thealgorithm employed detects large rates of change of the AriasIntensity that are sufficiently dissimilar to a signal verified tohave been produced by a given earthquake event. Phase-align-ment is necessary to properly compare the Arias Intensity plotsof the device and the reference signal. Fig. 7 shows an Arias In-tensity plot of a phone experiencing falling compared with thereference recording of the shaking event. The two signals match


Fig. 7. Arias Intensity for falling phone: the instance the falling phone felt asevere jolt can bewitnessed by the relative spike inArias intensity in comparisonto the reference acceleration.

well until the shaking event, after which the falling phone has alarge spike in intensity. When an unexplained spike in Arias In-tensity is detected, the signal will be discarded from earthquakeevent analysis. Fig. 5 depicts this falling phone algorithm de-ciding whether or not a received signal should be accepted. Thepseudocode for is given in Algorithm 3, whereis the Arias intensity, and the definition is given in (4).

Algorithm 3: FallDetected

Input: acceleration signal

Output: logical result of falling test

do (user provided)

do (using 4)

do

if

then return True

else return False

V. SHAKE TABLE TESTING RESULTS

A test procedure was devised to verify the quality of the ac-celerometer recordings in the context of earthquake sensing,of two types of mobile devices: four 3GS iPhones and threeiPod Touches (third generation). The devices were secured toa custom-built base platform that was then secured to a shakingtable. The base platform was designed to orient the devices atdifferent directions in order to test for biases among axes ofthe accelerometers. Also, attached to the base platform werehigh-quality accelerometers that were used as a reference for thedevice measurements. Dashti et al. [12] gives a detailed analysisof the shaking table testing procedure, which is discussed at ahigher level in this paper.A suite of 150 historical ground motion replays with a wide

range of amplitudes, durations, and frequency contents, as wellas sinusoidal motions were applied to the base of the shaketable to test the measured acceleration response of the devices.The devices and reference accelerometers captured the shakingevents in a series of trials. While testing site 1 (UC San Diego,

Fig. 8. Acceleration response spectra (5% damped) of the groundmotions usedat ST-1. The selected ground motions exhibit a wide range of spectral character-istics. This is intended to test the mobile device’s versatility in seismic sensing.The x-axis is the natural period of the SDOF system, and the y-axis is the spec-tral response for the given period.

ST-1) only had a uniaxial (1-D) shaker, testing site 2 (Rich-mond Field Station, ST-2) had three-dimensional (3-D) shakingcapabilities, and the reference accelerometers were oriented tocapture all axes of motion. Relatively high-quality referenceaccelerometers commonly used in earthquake engineeringresearch were mounted to serve as a comparative or “groundtruth” record of the shaking events with which to compare thelower-quality mobile phone sensors. Comparisons between themobile devices and the reference accelerometers were madefor each trial to validate the devices’ ground-motion capturingability.Fig. 8 shows the acceleration response spectra of the input

ground motions in tests ST-1 and ST-2. The acceleration re-sponse spectra is commonly used in earthquake engineeringto define the peak acceleration experienced by single-de-gree-of-freedom (SDOF) structures—with the same dampingratio (e.g., 5%) but different natural frequencies—to the baseacceleration time-history [22]. To subject the devices to shakingwith a wide range of amplitudes, the same earthquake eventpattern was sometimes applied multiple times at varying ampli-tudes. The varying amplitudes served to test the hypothesis thatthe iPhone’s recording accuracy would increase with higheramplitude shaking, as the signal-to-noise ratio decreased withhigher amplitudes [12].The rest of this section presents a summary of the main re-

sults of shake table testing, while further conclusions are dis-cussed in greater detail in [12], such as the increase in accuracyof measurements from the iPhones with respect to the increasein ground motion intensity.

A. Stationary Device Comparison

The accelerometer data was used to calculate commonground motion intensity parameters including peak groundacceleration (PGA), peak ground velocity (PGV), and peakground displacement (PGD) [20], Arias Intensity , (de-fined in (4)), and response spectra [22]. If accelerometers inmobile phones can accurately capture the acceleration responsespectra of most earthquake motions, then there is a compellingcase not only for earthquake researchers to benefit from thisdata, but practitioners as well. For example, when designing


Fig. 9. Stationary phones: the accelerometer records of the stationary phonecompare well to the reference accelerometers, even under integration to producevelocity and displacement records.

structures for earthquake loads, structural engineers mustdesign the natural frequency of the structure to minimize theeffects of earthquakes on the structure [22]. If mobile phonescould accurately reproduce the response spectra of a recordedearthquake, then a more localized description of the expectedseismic response of the structure may be obtained.The devices were shown to be capable of accurately capturing

the primary ground motion intensity parameters used by earth-quake engineers in design, such as PGA, PGV, and PGD. Fig. 9shows the velocity and displacement time-series recorded by thehigh-fidelity reference accelerometers, as well as the time-se-ries recorded by an iPhone device for the 1979 Imperial Valleyearthquake (6.4 M), recorded 40 miles from the epicenter. Therecords were calculated by successive integration of the orig-inal accelerometer signal after proper baseline correction andfiltering [9].The testing analysis done in this section focuses on the per-

formance of the mobile phone sensors when the device is rigidlyattached to the device board. This serves as a specific “best case”scenario for the application of smartphones as seismic sensors,as it requires the most constrained environment for sensing.It can be seen from Fig. 9 that the peaks from the two sources

are similar and occur at the same time. The PGA, PGV, andPGD values help in determining where the most severe shakingoccurred during an earthquake. A dense distribution of mobilephones running the iShake software would aid in the immediaterescue effort [23]. By providing emergency responders with in-formation on the hardest-hit areas after an earthquake, rescueefforts would be more focused and potentially more effective.Fig. 10 presents a comparison of the response spectra of the

reference andmobile devices. The mean responses of themobiledevices show promising results in frequency-domain analysis.They were able to capture the key periodic components that thereference signal indicates. The results show the devices can beused to aid engineers in the design of buildings and structures.With a more accurate characterization of local earthquakes, en-gineers will have better information when designing for earth-quake loads.

B. Device Bias

While a standalone accelerometer, such as one used by QCN,is designed as not to be biased by the housing of the sensor,

Fig. 10. Response spectrum with 5% damping: mean response of the devicescompared with the reference. The main trends from the reference signal arecaptured well by the mean response.

there is no such guarantee in the accelerometers provided bythe smartphones. The mounting of the accelerometer may haveits own resonance that would in turn affect the recordings of theaccelerometer. To investigate the consequences of resonance,the bias of the devices was calculated in the frequency domain.The bias value considered in this section is the tendency for a

particular device to overestimate or underestimate the spectralresponse of a certain frequency (as compared to the referenceaccelerometer’s response), for all frequencies in the domain ofimportance (0.3–30 Hz). Bias was calculated using the methodsof Augello et al. [5]. First, a residual error, , in the accelerationfrequency domain was calculated for each frequency and eachground motion

(5)

where is the spectral ordinate of the reference signal as afunction of frequency, ; is the spectral ordinate of thedevice signal; and is the ground motion index. For ST-2, thisprocess was repeated for each axis of motion, .The bias was obtained by calculating the mean residual error

for each frequency

(6)

where is the residual error for trial measured on axis ,and and are the number of ground motion trials and axesof motion, respectively.Fig. 11 shows the results of the bias calculations, after off-

setting of individual phones’ mean bias. The offset was addedin order to gain insight into systematic bias across all devices.After applying such offsets, the results reveal an apparent biasto overestimate the spectral response of midrange frequencies(1–10 Hz), and to underestimate higher frequencies ( 10 Hz).From observation of Fig. 11, one notices that the error bars aresmallest (i.e. the variance is smallest) in the midrange frequen-cies. With the smaller variance there is more confidence thatattenuating the midrange frequencies, by the amount estimated


Fig. 11. Bias of the devices for ST-1 testing. Across all phones, there is system-atic overestimation of midrange frequencies (1–10 Hz), and underestimation ofhigher frequencies ( 10 Hz).

by the bias parameters, will more faithfully estimate the trueground motions sensed by the devices.

VI. VIRTUAL EARTHQUAKE FIELD TEST

During the month of January 2011, a field test was conductedto evaluate the performance of the iShake system as well asproviding valuable feedback for a user study on participatorysensing applications on mobile devices [15]. Dozens of iShakeapplication users downloaded the free iShake application fromthe Apple App Store to participate in the field tests. The ma-jority of these users were from the Berkeley area.The field test consisted of two types of trials. The first type

alerted application users that a imaginary earthquake had oc-curred (via the Apple Push Notification Service), and then askedfor the users to give input on the emotional response of such in-formation being delivered through a mobile application.The second trial type, which is more related to the iShake

system and application target use case, asked for users to simu-late a “virtual earthquake” at a predetermined time (coordinatedthrough the same push notification process as the imaginaryearthquake). The virtual earthquake was simulated by havingall the participants activate the application, let the phone transi-tion into pretrigger mode, and then manually shake the phoneto set off the trigger and begin streaming individual shakingdata back to the server at the same time. Since the backend forapplication was intentionally chosen to be massively scalable(by using the Google App Engine infrastructure), the relativelymodest participant pool size was easily handled. While the suc-cessful handling of the modest number of concurrent requestswas expected, the virtual field test was useful as a proper veri-fication of the functionality of the state machine architecture ofthe client application, and enabled the creation of summary vi-sualizations. The summary visualizations were fed back to theparticipants after most participants were able to transmit theirrecordings to the server. Screen shots of the application and vi-sualizations used by the participants of the virtual earthquake(currently retired from the Apple App Store) can be seen inFig. 1.

VII. CONCLUSION

iShake is a system that allows anyone with an iPhone or iPodTouch device to participate in seismic sensing. This providesthe scientific community and emergency responders with adense array of ground motion data rapidly after an event, withassurance of quantitative accuracy previously unattainablefrom crowdsourced earthquake data. Through shake table tests,we have validated the accuracy of the device’s internal sensorsfor seismic sensing application. To account for environmentaluncertainties inherent to a mobile computing platform, novelsignal processing methods were developed to reduce the noiseintroduced from the variabilities. As evidenced by the largepool of “virtual earthquake” participants in our field study, andthe over 2600 users who have downloaded the iShake clientapplication (as of December 2012), people are intrigued byearthquakes and earthquake research. Ultimately, the iShakeproject is a system that turns this intrigue into positive societalimpact.Future directions of the project include investigation of laten-

cies involved in seismic sensing of earthquakes and possible ap-plications to earthquake early-warning systems, and battery-lifeoptimization techniques for scientific sensing on mobile phonesto increase the participation rate of crowdsourced sensing.

ACKNOWLEDGMENT

The authors thank Prof. T. Hutchinson and the staff at theUCSD South Powell Laboratory for their assistance and free useof their facilities. They are thankful for Deutsche Telekom andtheir interest in the project and ongoing support and for Prof. S.Mahin at UCBerkeley and PEER, who generously allowed us tomount the phones onto the multidirectional shaking table duringPEER structural engineering shaking campaigns. They grate-fully acknowledge Dr. D. Wald of USGS for his valuable rec-ommendations and advise throughout the project andwould alsolike to acknowledge that the original concept of using smart-phones as accelerometers came from Dr. R. Bonaparte, CEOof Geosyntec Consultants, Inc. Finally, we are indebted to D.Estrin for her pioneering work in this field and vision of partic-ipatory sensing.The views and conclusions contained in this document are

those of the authors and should not be interpreted as necessarilyrepresenting the official policies, either expressed or implied, ofthe U.S. Government.

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Jack Reilly, photograph and biography not available at the time of publication.

Shideh Dashti, photograph and biography not available at the time of publica-tion.

Mari Ervasti, photograph and biography not available at the time of publica-tion.

Jonathan D. Bray, photograph and biography not available at the time of pub-lication.

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