ishake: mobile phones as seismic sensors shideh dashti, jack reilly, jonathan bray, alexandre bayen,...

iShake Team Meeting

iShake: Mobile Phones as Seismic Sensors

Shideh Dashti, Jack Reilly, Jonathan Bray, Alexandre Bayen, Steven Glaser, Ervasti Mari

U.C. Berkeley

Funded by the US Geological Survey under NEHRP Award G10AP00006

Acknowledgements:

UC San Diego and UC Berkeley Shaking Table facilities

Special thanks to Professor Mahin at UC Berkeley and Professor Hutchinson at UC San Diego

Good morning, its a pleasure to be here.

Im going to talk about the iShake project on the use of mobile phones as seismic sensors.

Before I start, I would like to thank the individuals who made this research possible. First Jack Reilly, the graduate student researcher on this project, and the principal investigators were Professors Bray, Bayen, and Glaser at UC Berkeley, and Mari Ervasti a visiting scholar from Finland working on the user interface side of the project.

This project was funded by the USGS and I would like to acknowledge the staff at the UCSD and UCB Shaking Table Facilities and particularly professors Mahin at Berkeley and Hutchinson at UCSD for their assistance.

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ShakeMap

M=7.2 Baja California EQ from USGS (2010)

Post-Earthquake Information

USGS

UC Berkeley Shideh Dashti

Monday, August 06, 2012

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Emergency responders must see the effects of an earthquake clearly and rapidly so that they can respond effectively to the damage it has produced. Great strides have been made recently in developing methodologies that deliver rapid and accurate post-earthquake information. Examples of successful existing products are the USGS high quality shake intensity map (that David discussed in his very interesting presentation) and

you see that you have good definition where there are stations and poor definition where there are not stations. While it does contain algorithms for estimating ground motions in areas of sparse station coverage through interpolation and use of rapid finite-fault analyses, which include generalized site amplification, its reliability is hindered directly by the limited number of high quality instruments available.

Community Internet Intensity Map (CIIM)


USGS




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Emergency responders must see the effects of an earthquake clearly and rapidly so that they can respond effectively to the damage it has produced. Great strides have been made recently in developing methodologies that deliver rapid and accurate post-earthquake information. Examples of successful existing products are the USGS high quality shake intensity map (that David discussed in his very interesting presentation)

and the Did You Feel It? community based project, which depends on people to observe and respond and provides a sense of the intensity of shaking, as a first approximation. A single MMI is assigned to a zip code and zip codes that have no response are shown as gray here.



Did You Feel It? or Community Internet Intensity Map (CIIM)

ShakeMap



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Emergency responders must see the effects of an earthquake clearly and rapidly so that they can respond effectively to the damage it has produced. Great strides have been made recently in developing methodologies that deliver rapid and accurate post-earthquake information. Examples of successful existing products are the USGS shake intensity map, which is mostly based on high-quality instrument measurements.

and the Did You Feel It? community based project, which depends on people to observe and respond based on sense of the intensity of shaking, which David just discussed in his great presentation.

Note the differences in the CIIM and ShakeMap portrayals of the 2010 Baja California Earthquake.

The resolution of ShakeMap depends on the number of stations in a given area and the availability of low quality but useful data in DYFI-map depends on what zip codes are represented.

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accuracy



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However, shortcomings still exist.

There is a gap between the high quality, but sparse, ground motion instrument data that are used to help develop ShakeMap and the low quality, but sometimes larger quantity, human observational data collected to construct a Did You Feel It? (DYFI)-based map.

Lets have a show of hands for people who have smartphones? Ok

Our goal is to use your smartphones to bridge this gap.

Rather than relying on individuals feedback as our measurement devices sometime after the earthquake, in the iShake project we use their cell phones to measure ground motion intensity parameters and automatically deliver the data to our servers immediately after the event for processing and dissemination.

In this participatory sensing paradigm, semi-quantitative shaking data from numerous cellular phones might enable the USGS to produce shaking intensity maps more rapidly and accurately than presently possible.

While the proportion of smartphones remains a minority of the phones in use in the world today, the technology onboard these phones is permeating the fleet of more affordable phones available to the public. Thanks to mass production, the cost of advanced sensors has dropped significantly; for example, the cost of a standard GPS module for a phone presently costs the manufacturer between $1 and $10. Cameras have also become a standard feature of all phones, and it is expected that the incorporation of accelerometers will follow a similar path in the near future.

Sensor Connection Evaluation

Acceleration (g)

Time (s)

Predicted Loose Probability

Actual Loose State

Training Set Model Prediction

Moreover, even for phones that are connected to the ground or wall, the connection may be loose. Additional experiments were performed and a probabilistic approach was adopted to determine the phone connection type, using the measured signal.

System Architecture



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A client application and backend server was developed, the performance of which needed to be evaluated through experiments and field tests.

iShake

Sensor Quality Evaluation

Shaking Table Tests

Testing of Phone Connections

Systems

Client Application

Backend Server

Field Tests

Systems

User Studies

The scope of the initial phase of the iShake project was to:

1) evaluate the quality of the phone as a sensor

2) Develop the systems architecture, consisting of a client application and backend server, the performance of which needed to be evaluated through experiments and field tests.

iShake

Sensor Quality Evaluation

Shaking Table Tests

Testing of Phone Connections

Systems

Client Application

Backend Server

Field Tests

Systems

User Studies

In this presentation, I will mostly discuss the shaking table tests that were performed to evaluate the performance of a class of cell phones, in this case iPhones.

Shaking Table Tests

The phone sensor is an imperfect device with performance variations among phones of a given model as well as between models. The sensor in this case is the entire phone, not just the micro-machined transducer inside. Therefore, the quality and reliability of these phones as seismic sensors first needed to be understood.

Even if we can obtain good quality data from these phones, what about the response of phones that are not rigidly connected to the ground and may be on a table during an earthquake? Can we gain valuable information from those phones? How about a phone that falls? How can we detect that in the recorded signal?

To answer these questions, a series of 1-D and 3-D shaking table tests were performed at UCSD and UC Berkeley, respectively.

This is the shaking table at UCSD and here is the phone setup: In each test seven iPhones and iPod Touch devices that were mounted at different orientations were subjected to earthquake ground motions. 3 high-quality seismic accelerometers were mounted next to each phone and on the base platform to provide reference accelerations in orthogonal directions

Input Ground Motions

Period (s)

Spectral Acceleration (g)

5% Damping

Time (s)

Arias Intensity(m/s)

A suite of 140, 1-D and 3-D realistic ground motions were applied to the base platform during these experiments to study the mobile sensors response for a wide range of ground motions.

...

The selected motions for the 1-D shakes shown here, had a combination of near fault events with the forward directivity effect and less intense, strike slip events recorded at a large distance from faults, in order to better study the mobile sensors response for a wide range of ground motions.

These earthquake ground motions were primarily selected based on probabilistic seismic hazard analyses for a site in downtown Los Angeles and for the University of California, Berkeley campus, because the methodologies are being developed and tested in California first. The seismic hazard at these two sites was mostly dominated by

Application Software

For the purposes of recording acceleration data from all the phones at the same time during a shaking table test, an independent pilot application was developed. This client application is able to send and receive commands to and from a server, while the server stores the recorded shakes and displays them to the user on request.

Results: Stationary Phones

Acceleration (g)

Reference

Time (s)

iPhone

Fourier Accel. (cm/s)

Frequency (Hz)

Initially, all phones were rigidly mounted to their holders.

Following these experiments, the ground motions recorded by seven phone sensors were compared with the reference to carefully document the differences.

The comparisons show promise in the quality of phone recordings, particularly in terms of estimating PGA, PGV, and PGD for most ground motions.

Time (s)

Results: Stationary Phones

Velocity (cm/s)

Reference

Time (s)

iPhone

Displacement (cm)

Response Spectra


5% Damping

Frequency (Hz)

The 5% damped acceleration response spectra recorded by the phones compared reasonably well with the reference.

5% Damping

Response Spectra

Frequency (Hz)


Mean phone

Reference

Particularly, when comparing the averaged spectrum of the seven phones.

These observations are helpful in evaluating the response of the entire phone local array as a seismic sensor.

Goodness of Fit

Bias (log(g))

Period (s)

The goodness-of-fit between the acceleration response spectra obtained from the phones and the reference is shown in terms of phones bias and uncertainty here.

These plots combine the bias of each individual phone during all the input ground motions in a given experiment as a function of frequency or period. They indicate that the bias is sufficiently low during the period range of interest for most engineering applications (about 0.1 to 1 sec) in both experiments.

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Arias Intensity

Arias Intensity (m/s)

Time (s)

In general, however, the phone sensors showed a tendency for over-estimating the ground motion energy and hence, Arias Intensity (Ia).

Quantifying Phone Error

Normalized Prediction Error

Peak Ground Accel. (g)

Peak Ground Displacement (cm)

NPE=MSE/PGA2

MSE

Now, stepping back a little bit, to measure the accuracy and consistency of the acceleration-time histories recorded by individual phones lets look at the normalized mean squared error term, which is an overall measure of the error in the entire time history. The errors appeared to reducd sharply for stronger ground motions.

With the available data, these errors can be statistically evaluated based on key input ground motion properties. A parametric study of these errors will lead to better estimates of key ground motion parameters from a cluster of lower-quality phone measurements.

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Phones Allowed to Move Freely

Following the experiments with phones rigidly connected to their holders, additional tests were performed on phones that were allowed to move freely on the shaking table. In a few cases, frictional or rubber covers were used on the phones to minimize sliding or their independent movements.

Shaking Table Tests

This is a video of the two phones with frictional covers left freely on the shake table during a strong 3-D shake.

As we can see here, one of the phones that had a less sticky cover moved slightly but for the most part, these covers were successful in minimizing phones sliding.

Phones Allowed to Move with Cover

Fourier Accel.

Acceleration (g)

Time (s)

Frequency (Hz)


Frequency (Hz)

As shown here, the acceleration response of these two phones compared reasonably well with that of the reference. The acceleration amplitudes recorded by this phone, however, were slightly under-estimated due to its minor tendency to slide.

Also overall, the acceleration response spectra compared relatively well.

Phone Allowed to Fall

X-Direction

Y-Direction

Z-Direction


Time (s)

Reference

Falling Phone

Acceleration (g)

Reference

Falling Phone

Now what if the phone falls? We need to identify and remove its signal from the dataset. So, we tested this by allowing a phone to actually fall during one of the events.

Looking at its response, as expected we see a large spike in the acceleration record. Which was also evident as a sudden increase in the corresponding Arias Intensity-time histories at the time of falling.

This may be used as an initial screening tool to detect the falling instruments and remove them.

Phone Allowed to Fall

Z-direction

Acceleration (g)

Time (s)


Reference

Falling Phone

Which was also evident as a sudden increase in the corresponding Arias Intensity-time histories at the time of falling.

This may be used as an initial screening tool to detect the falling instruments and remove them.

Additional research is underway to better understand the response of falling and moving phones as well as other types of smart phones such as androids.

accuracy



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In conclusion,

When communicating the intensity of shaking with the public and emergency responders after an earthquake, on one side of the spectrum we have the high quality shakemap, the accuracy of which depends on the availability of strong motion stations, and on the other side of the spectrum we have the low quality but potentially high quantity DYFI based maps based on human observations.

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iShake occupies a third space, as it can provide immediate post-EQ information with a potentially large number of sensors and relatively good quality.

Phones were generally successful in capturing key intensity parameters during shaking table tests.

iShake Application

http://ishakeberkeley.appspot.com

If you are interested to be a part of iShake and contribute to this research, please talk to me or Jack after this session for downloading our app on your iPhone or participating in our upcoming field tests in January. In fact the iShake app is available for free download on the app store as of two days ago.

quality

quan

tity

USGS Shake Intensity Maps

Did YouFeel It?

Figure 5: Plot of data used to train the model. Most of the data points areclustered at the tails, with a few outliers

The figure shows a very good prediction dichotomy of the sample points,with only one overlapping point (a false positive). To test whether or not themodel was fitting intricacies of the specific data-set, the model is run against atest data set, that was not used for training the model.

4.2 Test Data Set and Classification Results

The results from Figure 6 show the same true confirmation properties of thetraining set, as 95% of the loose phones were reported as loose. What differs isthe number of false positives reported by the model, as there was only one falsepositive from the testing set, but half of the rigid testing set were also reportedas loose (assuming an acceptance level of 75%).

There could be a number of reasons for the reporting of false positives. Themost obvious reason for these results would be improper restraining of the rigidphones to the table. Also, due to the proximity of the phones and response of

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Figure5:Plotofdatausedtotrainthemodel.Mostofthedatapointsare

clusteredatthetails,withafewoutliers

Thegureshowsaverygoodpredictiondichotomyofthesamplepoints,

withonlyoneoverlappingpoint(afalsepositive).Totestwhetherornotthe

modelwasttingintricaciesofthespecicdata-set,themodelisrunagainsta

testdataset,thatwasnotusedfortrainingthemodel.

4.2TestDataSetandClassicationResults

TheresultsfromFigure6showthesametrueconrmationpropertiesofthe

trainingset,as95%oftheloosephoneswerereportedasloose.Whatdiffersis

thenumberoffalsepositivesreportedbythemodel,astherewasonlyonefalse

positivefromthetestingset,buthalfoftherigidtestingsetwerealsoreported

asloose(assuminganacceptancelevelof75%).

Therecouldbeanumberofreasonsforthereportingoffalsepositives.The

mostobviousreasonfortheseresultswouldbeimproperrestrainingoftherigid

phonestothetable.Also,duetotheproximityofthephonesandresponseof

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Figure 4: Closer view of testing, where the application interface can be seen.

3.2 Testing Configuration

The concept behind the testing was to establish one rigidly-attached phoneper trial as a reference accelerometer, and serve as the base-measurement tocalculate error values. Then, a different assortment of rigidly-attached andloosely-attached iPhones were also included in the trial. A trial will begin,thephones will begin recording, and then a shaking event is imposed on the surfaceof testing. Finally, the trial will be transmitted to the server, with the properlabellings of the states of the phone for the given trial (loose or rigid).

This process was repeated 20 times for the training set and 10 more times forthe testing set. To remove such factors as specific-phone bias and phone-locationon table, the phones were rearranged, and reference accelerometer duties alter-nated between all the phones. The hopes of these measures was to eliminatethe possibility that the tests were isolating hidden variables, such as site-specificresponses on the testing table to shakes.

The end result of the testing was a set of loose phone errors and rigid phoneerrors for a number of phones over a number of trials. The training set gives theneeded information for determining the parameters for the multivariate distribu-tions over the axis-specific input error parameters, and the logistic distributionparameters over the axial likelihoods.

4 Results and Analysis

4.1 Parameter Estimation

The trials ran from the testing are split into two sets: training data and testingdata. The training data was run to calculate model parameters as described inSection 2.2. Then, the training data was rerun to get classification predictions.The results are shown in Figure 5.

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iShake Clients

Earthquake Feed

iShakeserver anddatabase

iShake Earthquake Noti!cationand Visualization

iShake Clients

Earthquake Feed

iShake

server and

database

iShake Earthquake Notication

and Visualization

0

0.5

1

1.5

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2.5

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3.5

4

00.511.522.533.54

T (sec)

Sa (g)

5% Damping

0

2

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051015202530

Time (sec)

Ia (m/s)

MSE(accelerationphone ) =[aphone (ti) areference (ti)]

2

i=1

N

N

MSE(acceleration

phone

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[a

phone

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reference

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quality

quan

tity

USGS Shake Intensity Maps

Did YouFeel It?

iShake

ishake: mobile phones as seismic sensors shideh dashti, jack reilly, jonathan bray, alexandre bayen,...

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