Clara Yoon, Karianne Bergen, Ossian O’Reilly, Fantine Huot ... · CopyLeaks – detect plagiarism...

The FAST Method for Earthquake Detection: Application to Seismicity During the Initial Stages of the Guy-Greenbrier, Arkansas, Earthquake Sequence

Clara Yoon, Karianne Bergen, Ossian O’Reilly, Fantine Huot, Bill Ellsworth, Greg Beroza (Stanford University)

Yihe Huang (University of Michigan)

Earthquakes: Nucleation, Triggering, Rupture, and Relationships to Aseismic Processes October 3, 2017, Cargese

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Earthquake Monitoring Across Scales(meters)

Global(107)

Local(104-5)

Reservoir(103-4)

Regional(105-6)

Lab(<100)

DecreasingArrayAperture

IncreasingSensorDensity

IncreasingFrequencyCommonGoal:

-detect-locate-characterize

earthquakesascompletelyandasaccuratelyaspossible.

Mines(101-3)

LongDuraOon(Large-T)Yearsofcon+nuouswaveforms

BigNetworks(Large-N)1000sofsensors

WeneedscalablealgorithmstoextractusefulinformaOonfromthesemassivedatavolumes

Seismologyhaslotsofdata…

Standard Approach to Detection/Location

(1)DetecOon(STA/LTA)(2)AssociaOon(3)LocaOon(4)CharacterizaOon

EarleandShearer[1994]

Standard approach works well when…

Eventsarerecordedat>3staOonsEventsareimpulsiveEventsdon’toverlap

…weakeventswithemergentarrivals(likeLFEs)

Standard approach works less well for …

KatsumataandKamaya[2003]

…smalleventswithtoofewarrivalstolocate


…OverlappingEventsduringintenseacOvity


Detection SensitivityCo

mpu

tatio

nal E

ffici

ency

STA/LTA

General Applicability

HIGH

LOWInsensiOvetoweakand/ornon-impulsivesignals,needmulOplestaOons

HIGH

Slip occurring at different times in the same place, generates identical seismograms.

We can look for a repeating signal from a repeating source, but most sources don’t exactly repeat.

38 Repeats of Earthquake on the Calaveras Fault

Adjacent Earthquakes on the Calaveras FaultEarthstructureisessenOallyconstantAdjacentearthquakeshavesimilarwaveforms.Candetectearthquakesbysearchingforsimilarwaveforms

Shellyetal.[2007]

Template Matching

Template-based detection is powerful (few Type II errors)

LFEsplantedinrealdataatsnrof0.134/36aredetected

Shellyetal.[2007]

Detection Sensitivity

Com

puta

tiona

l Effi

cien

cy

STA/LTAA


STemplate Matching

LOWNeedtemplatewaveformapriori

HIGH

HIGH

Informed Similarity Search

Exhaustive Search for Similar Waveforms“Autocorrela+on”-Uninformedsearchforsimilarsignal–detecteventsbycrosscorrela+ngallwindowpairs.

Brownetal.[2008]

Autocorrelation for LFE Detection

Aguiaretal.[2017]

Aguiaretal.[2017]

Detection SensitivityCo

mpu

tatio

nal E

ffici

encyTemplate mmpm

Matchingat g STA/LTAA


S

AutocorrelationHIGHHIGH

LOWNaïve Uninformed Similarity Search

Detection SensitivityC

omp

utat

iona

l Effi

cien

cyGeneral Applicability

New approach: FAST

mplate mplate TempmmpmmpmpmmmmmmmmmtchingMaatt g A/LTASTAAS AAS

AutocorrelationutocorrelatAutocorrrelatatocorutoAutocorrrelatirrelatir on

HIGHHIGH

HIGH

Shazam–idenOfysongsfromasampleofrecording.

Soundhound–idenOfysongsfromsinging(seriously).

TinEye–Searchthewebforthesourceofaknownimage.

YouTube–detectcopyrightinfringement

CopyLeaks–detectplagiarism

Altavista–removeduplicatewebpagesfromsearchresults

Some Big Data Technologies for Similarity Search

FAST (Fingerprinting And Similarity Thresholding)

Data

Preprocessing

FeatureExtrac+on

DatabaseGenera+on&Search

Post-processing

Detec+onResults AB

1.  DataRepresenta+on

2.  Fastapproximatesimilaritysearch

Fingerprinting

•  “Fingerprint” waveform with sparse, diagnostic description•  Store fingerprints in database and search it efficiently

WaveformDataCompression

BinaryFingerprint

DataCompression

ClaraYoon Fingerprint

Step 1: Time Series to Spectrogram

Step 2: Spectrogram to Spectral ImagesTofindshortdura+onevents,dividespectrogramintooverlappingspectralimages

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Step 3: Spectral Image to its Wavelet Transform

Goal:compressnonsta+onaryseismicsignal–  Compute2Ddiscretewavelettransform(Haarbasis)ofspectralimagetogetwaveletcoefficients

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Step 4: Wavelet Transform to Top Coefficients

Keydiscrimina+vefeaturesareconcentratedinafewwaveletcoefficientswithhighestdevia+on

–  Keeponlysign(+or-)ofthesecoefficients,setrestto0

Datacompression,robusttonoise


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Step 5: Top Coefficients into a Binary Fingerprint

FingerprintmustbecompactandsparsetostoreindatabaseConverttopcoefficientstoabinarysequenceof0’s,1’s

•  Nega+ve:01,Zero:00,Posi+ve:10


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Jaccard SimilarityHowsimilarare2binaryfingerprints?

Jaccardsimilarity:“resemblance”

J A,B( ) =A∩BA∪B

A B

J A,B( ) =A∩BA∪B

A B

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Similar Waveforms èSimilar Fingerprints

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Fingerprints Should be Discriminative

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Names are a compact “Fingerprint”

Names are compact, but not discriminative.

How to select “discriminative” coefficients?

Red represents intersection of fingerprints for samples 1 and 2.

0.266 0.117 0.040

True Detections vs. False Positives

Don’t choose largest coefficients, choose those on the tails of a distribution.

Suppresses false detections of persistent noise, but maintains high accuracy for relatively rare earthquake signals.

Trade-offCurves

FAST Workflow

Locality Sensitive Hashing groups similar fingerprints drawn from a high-dimensional space with high probability

Waveformsearchquery Database

sMatch!

Yoonetal.(2015)

Min-Hash uses multiple random hash functions to map a binary fingerprint to a single integer.

The probability of two fingerprints A and B mapping to the same integer is equal to their Jaccard similarity.

Min-Hash reduces dimensionality while preserving the similarity between A and B in a probabilistic manner.

Why is FAST Fast for Large T?

Ignores>1011pairs

Detects<103pairs

Outputs<105pairs

A

B

1 week 2 weeks 1 month 3 months 6 months

3 days 1 week 2 weeks 1 month 3 months 6 months1 day

1 day

1 week

1 hour

1 year

20 years

Yoonetal.(2015)

Why is FAST Fast for Large T?

Ignores>1011pairs

Detects<103pairs

Outputs<105pairs

Yoonetal.(2015)

80km

Guy-Greenbrier Sequence in Arkansas

HydraulicS+mula+on(Fracking)wellsuseastagedinjec+onof

fluidtoincreasepermeabilityandaccesshydrocarbons.

Deepdisposalwellsinjectproducedwater,or

frackingflowbackwater,togetridofit.

Guy-Greenbrier, Arkansas Sequence

92.5˚W 92.4˚W 92.3˚W 92.2˚W 92.1˚W35.2˚N

35.3˚N

35.4˚N

92.5˚W 92.4˚W 92.3˚W 92.2˚W 92.1˚W35.2˚N

35.3˚N

35.4˚N

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Earth

quak

e D

epth

km

WHAR

ARK1

ARK2

M=4.7

Well 1

Well 2

Well 3

Well 4

Well 5

Well 6

Well 8

Guy

Greenbrier0 10

km

N

-Startedinjec+ng2010-07-07

-Startedinjec+ng2010-08-16

Injec3ondata:Horton(2012),SRL

Swarmdura+on:July2010–June2011

2011-02-27

Catalogevents(Seismik):2010-06-01to2010-09-01

Wastewaterinjec+onwell

Town

Seismicsta+on

Catalogevents(ANSS):2010-09-01to2011-10-31

3 Months Guy-Greenbrier Induced Seismicity

3 Quarry Blasts

43/24

Quarry Image, 2009-07-23

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Quarry Image, 2010-09-15

Mostlikelyloca+onofquarryblastson2010-06-24,2010-07-02,2010-08-10:(35.2928,-92.3973,0kmdepth)

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Dept

h (k

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1D Velocity Model

Vp, Ogwari 2016Vs, Ogwari 2016Vp, New Model from QuarryVs, New Model from Quarry

ImprovedPandS-wavevelocitystructurebasedonknownquarryblastsusingVelest.Sparsenetwork–(3)3-componentstaOonshypo-DDusingPandS700maposterioriadjustment.

92.5˚W 92.4˚W 92.3˚W 92.2˚W 92.1˚W35.2˚N

35.3˚N

35.4˚N

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35.3˚N

35.4˚N

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Depth

km

WHAR

ARK1

ARK2

Quarry

Well 1

Well 2

Well 3

Well 4

Well 5

Well 6

Well 8

0 10

km

N

Group Earthquakes into Clusters �(-1.1 < M < 1.8)

46Earthquakes2010-06-01to2010-09-01

Wastewaterinjec+onwellProduc+onwells+mulated2010-06-01to2010-09-01

92.5˚W 92.4˚W 92.3˚W 92.2˚W 92.1˚W35.2˚N

35.3˚N

35.4˚N

92.5˚W 92.4˚W 92.3˚W 92.2˚W 92.1˚W35.2˚N

35.3˚N

35.4˚N

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Depth

km

WHAR

ARK1

ARK2

Quarry

Well 1

Well 2

Well 3

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N

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C#1

C#2

C#3

C#4

C#5C#6

C#7

C#8

C#9

C#10

C#11C#12

C#13

C#14C#15

C#16Earthquakes2010-06-01to2010-09-01

Wastewaterinjec+onwellProduc+onwells+mulated2010-06-01to2010-09-01

C#18

Group Earthquakes into Clusters �(-1.1 < M < 1.8)

S+mula+onatnearbywell

Cluster #1: 3143 events �(667 located + 2525 assigned)

Further Evidence for Earthquakes �Induced by Hydraulic Stimulation

92.3˚W92.3˚W

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Dep

th

kmARK2

Well 1

0 1 2

km

Composite Focal MechanismN60°EShmax(HurdandZoback,2012]

If double-couple, plane of seismicity is right-lateral – contrary to stress. More likely that first motions reflect combined tensile & shear failure.

ConsistentwithhydraulicdiffusivityofD≈1m2/s

Conclusions I

BothwastewaterinjecOonandhydraulicsOmulaOonappeartotriggerearthquakes–probablysomenaturalearthquakesaswell.Itischallengingtodisentangledifferentinfluences–requiresgooddata(bothseismicandinjecOon).PrecisionseismologyisapowerfultooltoprovideaclearerpictureofinducedseismicityandthenucleaOonprocesstotheextentitisexpressedinseismicity.

Conclusions II

Seismologyhas:•  LongduraOondata(Large-T)•  Bignetworks(Large-N)

èFASTalgorithmenablesdata-drivendiscovery

Moredata

Morememory

MorecompuOngpower

Needbemeralgorithms

Now Future

Scale of Effort

Yoon et al. (2015) (2017a) (2017b)

Progress on FAST for Large-T Problems

•  140xFASTerthanoriginal.

•  Reducedmemoryrequirements.

•  ReducedfalsedetecOonrate.

•  Improvedpost-processing.

•  Workingtowardspublicrelease.

FAST for a Decade of Continuous Data

Yoonetal.(2017b)

FAST over a Network

BergenandBeroza(2017)

Network FAST for Iquique

~580newdetecOonsin17daysbeforethemainshock.Canbeusedastemplatestoincreasethatnumber.

Machine Learning for Earthquake Detection

Huotetal.(2017)

Labeleddataasinputtoneuralnetwork(mostofwhatwerecordisnoise)

Huotetal.(2017)

99.5%accuracywhentrained,validatedandtestedononestaOon.Accuracydropsto98.2%,withmulOplestaOonsbutwithonlyalimiteddataset.

ML for Earthquake Detection

Scale of Seismic Observations

Nakata (2017)

Clara Yoon, Karianne Bergen, Ossian O’Reilly, Fantine Huot ... · CopyLeaks – detect plagiarism...

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