Access to this full-text is provided by De Gruyter.

Content available from

**Open Geosciences**This content is subject to copyright. Terms and conditions apply.

Cent. Eur. J. Geosci. • 5(2) • 2013 • 236-253

DOI: 10.2478/s13533-012-0127-6

Central European Journal of Geosciences

Evaluation of seismogenesis behavior in Himalayan

belt using data mining tools for forecasting

Research Article

Pushan Kumar Dutta1,2∗, O. P. Mishra3†, Mrinal Kanti Naskar1,2‡

1 Advanced Digital Embedded System Lab, Jadavpur University, Kolkata, India

2 Electronics and Communication Dept., Jadavpur University, Kolkata, India

3 SAARC Disaster Management Centre [SDMC],New Delhi, India

Received 13 March 2013; accepted 15 May 2013

Abstract: In the proposed study, non-linear behavioral patterns in the seismic regime for earthquakes in the Himalayan basin

have been studied using a complete, veriﬁed EQ catalogue comprised of all major events and their aftershock

sequences in the Himalayan basin for the past 110 years [1900-2010]. The dataset has been analyzed to give

better decision making criteria for impending earthquakes. A series of statistical tests based on multi-dimensional

rigorous statistical studies, inter-event distance analyses, and statistical time analyses have been used to obtain

correlation dimensions. The time intervals of earthquakes within a seismic regime have been used to train the

neural network to analyze the nature of earthquake patterns in the diﬀerent clusters. The results obtained from

descriptive statistics show high correlation with previously conducted gravity studies and radon anomaly variation.

A study of the time of recurrence of the numerical properties of the regime for 60 years from 1950 to 2010 for

the Himalayan belt for analysis of signiﬁcant EQ failure events has been done to ﬁnd the best ﬁt for an empirical

data probability distribution. The distribution of waiting time of swarm events occurring in the Himalayan basin

follows a power-law model, while independent events do not ﬁt the power-law distribution. This suggests that

probability of the occurrence of swarm events [M 66.0] with frequent shaking may be more frequent than that of

the occurrence of independent events of magnitude [M >6.0] in the Himalayan belt. We propose a three- layer

feed forward neural network model to identify factors, with the actual occurrence of the maximum earthquake

level M as input and target vectors in Himalayan basin area. We infer through a series of statistical results and

evaluations that probabilistic forecasting of earthquakes can be achieved by ﬁnding the meta-stable cluster zones

of the Himalayan clusters for the spatio-temporal distribution of earthquakes in the area.

Keywords: Himalayan belt • seismic cycle • seismic precursor • power law model • meta-stable cluster zone • Neural Network

Model • trends • variation of seismicity

©Versita sp. z o.o.

∗E-mail:ascendent1@gmail.com

†E-mail:opmishra2010.saarc@gmail.com

‡E-mail:mrinalnaskar@yahoo.co.in

1. Introduction

ThestudyofEQgenerationmechanismshasbeenapuz-

zlefortheentiregeo-scientiﬁccommunitydespiteaseries

ofstudiesintheﬁeldofseismologyindiﬀerentpartsof

theglobe.EQtremorshavebeenmonitoredinSouthAsia

236

P. K. Dutta, O. P. Mishra, M. K. Naskar

Figure 1. Geological map of North East India and Indo Burma Plane

after Mishra [2011].

andrecordedEQsFig.1forvariousspatio-temporalmap-

pinghavebeenfoundsubjecttoregionaltectonicsettings

reportedin[55].Aconcentratedeﬀorttostudyspatialand

temporalvariationsinseismicactivityandusethemtorec-

ognizepatternsthatprecedelargeearthquakeshasbeen

madebyscientiststoidentifyprecursorypatterns[50,52],

seismicquiescence[30,36],swarmsinseismicactivityan-

alyzedby[31,32,83]andseismicityﬂuctuations[35,37].

Ongoingresearchinto earthquake processcanonlybe

improvedby thestudyof measurements ofpastearth-

quakesandsurfacedeformation. Ahighlysophisticated

statistical interpretation of instrumental, historical and

paleo-earthquakecatalogsaretheonlysystematicrecords

whichanalyzetheearthquakegenerationprocessatdepth

throughimagingtectonicenvironments. Statisticalanal-

ysisisonemethodofpredictingearthquakes. Thehis-

toryofearthquakesinagivenregionrevealsthepropen-

sityforrecurrent,orcyclical,patternsoftheearthquakes.

Ifearthquakes in agivenregionhave arecurrentpat-

tern,thenalong-termpredictioncanbemadebasedon

therecurrentpattern. Importantquestionsthatneedto

beansweredinordertounderstandtheearthquakepat-

ternsinHimalayasinvolvethestudyofformationandin-

teractionofearthquakeclusterssuchasforeshocks, af-

tershocksandearthquakeswarms[25],theroleofﬂuids

intheearthquakegenerationprocess[59], the study of

gravityrecordsinthestudyofrocklayerdepthswhere

earthquakesoriginate [43] andlocalizedseismicquies-

cence[85].Althoughevaluationofthelinksbetweenpre-

cursiveeventsandearthquakeshasprogressed,theex-

tremecomplexityofthesystemhassofarnotenabledthe

developmentofactualearthquakeforecastingtechniques.

Thesolutiontothisproblemdependsprimarilyonwhether

wecanobtainreliableinformationonseismicprecursory

events[72,74]andtheirbehaviorinconnectionwithphys-

icalmechanisms. Thedetectionofaprecursoryseismic

signal[73]conformstocommonphysicalmodelsforde-

tectionofanomalieswithintheseismogeniccrustwhere

earthquakeprecursoryactivity is likelytooccur. Time

delaysbetweensuccessiveearthquakeshaveacharacter-

isticdistributionforintereventdistributionforearthquake

analyzeddata[18,58]inadenselymonitoredearthquake-

proneregion.Earthquakepatternstendtofolloweithera

mainshock,aftershocksequenceorformswarmtypeearth-

quakeclusters[46]. Inordertoinvestigatethepotential

occurrenceofprecursorsandseismiccyclesinthe,the

natureofthegenerationmechanismassociatedwiththe

statisticaldistributionofearthquakeoccurrencehastobe

formulated. Awiderangeofstatisticaldistributionscan

beusedwithessentiallynophysicaljustiﬁcationunless

anaturalmodeforpower-lawdistributionsis made for

earthquakehazardassessments[29].Thepowerlawsand

fractalpropertiesreﬂectthenotionofscaleinvariance[33]

thatreferstothespontaneousbreakdownofafracturefor

discretetimeperiodsforEQoccurrence.Theseproperties

areimportantandinterestingbecausetheycharacterize

systemswithmanyrelevantscalesandlong-rangeinter-

actions[70,71]ofthefaultsastheypotentiallyexistinthe

crustbeforetheoccurrenceofEQ.Intheproposedwork,

weintegrateallsuchstudiestounderstandthenatureof

seismicityintheHimalayanbelt. InSection2,weana-

lyzethecatalogtostudythespatio-temporalclusters

oftheHimalayanBasintolocateclusterswhereanoma-

lousclusteringofseismicactivityincreaseoccursinthe

region. InSection3, aprobabilitydistributionanalysis

involvingrecurrencetimeofEQfortemporalclusterse-

quenceshasbeenmadetostudythemaximumlikelihood

estimatorsforﬁttingthepower-lawdistributiontotimeto

studytherecurrenceintervaloftheEQdataalongwith

thegoodness-of-ﬁtbasedapproachinthepower-lawﬁt

withintheclustersforindependentmainshocksandswarm

activity. InSection4,wecomparethestatisticalresults

withprecursorydatastudiesforthedescriptivestatistics

involvedinEQcataloganalysispreviouslyconducted. In

Section5,ananalysisofourresultshasbeenpresented

forfuturestudies.

2. Seismological Data and Statisti-

cal Analyses

TheHimalayanbeltisoneofthemostseismicallyactive

zonesinAsia,whichwitnessedaseriesofdeadlyearth-

quakes[EQ]inthepastcentury.Inthepresentstudy,we

analyzedacompleteEQcatalogueconsistingofallmajor

eventsandtheiraftershocksequencesintheHimalayan

basinforthepast110years[1900-2010]. Thecatalogue

forourcomprehensiveearthquakeanalysesistakenfrom

therecentlyproducedcatalogueofNathetal.[2010]that 237

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

Figure 2. Frequency of earthquake occurrence in Himalaya Belt.

containedseismicallymonitoredearthquakedatarecorded

duringtheyears1900-2010byvariousagenciesofthe

entireSouthAsia,whicharestatisticallyanalyzedinthis

studytounderstandthenatureandextentofseismoge-

nesisintheHimalayanbasin. Wehavecorrelatedour

resultswithotheravailablepublishedinferencestounder-

standthenatureofearthquakeswarmsandclusters[47].

InFigure2,weattemptedtoshow thecompletenessof

theearthquakedatawhichhasbeenusedinthisstudy.

Yearwiseearthquakecompletenessanalysesshowedthat

earthquakecatalogueduringtheyear1900–1950iscom-

pleteforthemagnitude[5.0>M>2.0]&[2.0>M<2.5]

during the year1950-2010. However, completenessof

earthquakedatafromoverallcataloguesuggeststhatit

is complete forthe eventsof magnitudes, varyingfrom

2.0to6.0[Figure3]. Figure4clearlydemonstratesthat

theoccurrenceofearthquakes[M>2.0]isdrasticallyin-

creasedduringtheyear2000–2010incomparisonto

pastdecades[1950–1999]. Thisobservationsuggests

thattheseismicactivityintherecentdecadeshasbeen

enhancedbecauseoffrequentreleaseof strainbeneath

theHimalayanbasincausingfrequentshakingduetomi-

crotomoderateearthquakes[M66.0]intherecentyears

fortheHimalayanregion.TheEQCatalogofNathetal,

2010hasbeentestedfordetaileddataﬁtandcanbeused

toconductapatternanalysisofseismiccycle.

ThenatureofseismogenesisintheHimalayanbasinas

hasbeen doneinother zones [7,24,41]. Inorderto

conductaseriesofstatistical analyses, ahomogeneous

dataﬁtcatalogueisnecessarytoevaluatetheseismo-

tectonicenvironmentintheHimalayanregion. Thedata

setof4125eventsofthepast60yearscomprisesofmain-

shock,aftershockandforeshockeventsoccurringacross

variousspatial and temporalzones. Adescriptivehis-

Figure 3. Frequency Magnitude relationship of earthquakes(Y axis

number of earthquakes and X axis mag >2.0).

Figure 4. Histogram plot showing completeness of catalog and

earthquake mainshocks >5.0 in 1950 – 2010 for the hi-

malayan basin(Y axis as magnitude and x axis as Year).

togramplot[Fig.2]ofallmagnitude[>5.0]showsthatthe

frequencyofoccurrenceofEQsintheHimalayanbelthas

increasedoverrecentyears[51]. Thecumulativemagni-

tudeoftheEQshasbeenparticularlylowduringthepast

coupleofyears. ThenumberofEQswasonadecline

during 1960-70s after which the histogram plot shows

thatthenumberofEQshasbeen constantly rising al-

thoughtheyarescatteredinnature.Thisissigniﬁcantto

thepreviouslyconductedstudythatprovesthattheHi-

malayanearthquakeshavebeenmoderateinnature[4].

InordertostudyEQgenesisrelevanttoacertainregion,

238

P. K. Dutta, O. P. Mishra, M. K. Naskar

westudytheoccurrenceofEQsinthediﬀerentspatio-

temporalclustersoftheHimalayanbelt. Itisnecessary

toevaluatethespatio-temporalclustersofseismicityin

theHimalayanbasinbylookingatthe pastoccurrence

ofthesigniﬁcantEQsfromtheEQcatalog[Fig.2]that

hadasevereimpactonthetectonicsettings.Inthisstudy

weupdatethenumberofEQsasspeciﬁedinthespatio-

temporalclustersandconductaseriesofstatisticaland

analyticaltestsontheseclusters. Analyzingthepattern

ofearthquakeswithineachcluster,asigniﬁcantobserva-

tionestablishes[53,63]thattheclustersareconstituted

ofindependenteventsaswellasswarmsequenceandin

studyingtheircombinations we canﬁndaboutthena-

tureofseismiccyclesandthepatternof seismogenesis

intheHimalayas. AsthesetofreliablequantitativeEQ

catalogueshavingcompletecatalogueinformationforall

historicaleventsareverylimited,weneedtoanalyzeto

identifythebestdatasets[Fig.3] avalaible. Wehave

maderigorousstatisticalinvestigationsusingtheSPSS

toolforunderstandingthefrequencydistributionsofEQs

from1950to2010tosearchforEQhavingmagnitude>

5.0fromnEQcataloguetounderstandthenatureofEQ

patterns.Recurrenceintervalsareameansofexpressing

thelikelihoodthatagivenmagnitudeofearthquakewill

beexceededinaspeciﬁednumberofyearsandareanim-

portantfactorinanalyzingthehumanrehabilitationand

generalseismicresponsemechanismasin[38]. Diﬀer-

entseismogenicfeaturesaﬀectthekindofseismicrelease

fortheEQdepending ontheclusteredandbackground

seismicity[2]andcanbeusedtodescribetheseismicity

ofanareainspace,timeandmagnitudedomainsandto

studythefeaturesofindependenteventsandstronglycor-

relatedones,separately.Thetwodiﬀerentkindsofevents

givediﬀerent informationonthe seismicity ofanarea.

Fortheshort-term[orreal-term]predictionofseismicity

andtoestimateparametersofphenomenologicallawswe

needagooddeﬁnitionoftheEQclusters. Metastable

clustersorblockscascade[56]orcoalescetogeneratea

largeeventinwhichlargenumberofmetastableblocks

arelost[26,57,64,65]whichexhibitsapower-lawcoa-

lescenceofmetastableclusterswhosenumber-sizedistri-

butionofclustersispowerlaw. Recurrenceintervalsare

meansofexpressingthelikelihoodthatagivenmagnitude

EQwillbeexceededinaspeciﬁednumberofyears[40].

Thusouraimistostudythespatialclustersandthena-

tureofoccurrenceofforeshockandaftershocktremorsto

identifythenatureofmainshockoccurrencesinorderto

locatefutureEQevents. Identifyinglikelysourcezones

ofsuchearthquakeswouldlargelycontributeinseismic

hazardassessmentindiﬀerentspatio-temporalclusters

inHimalayanbasin. Frequencymagnituderelationships

for4125eventsofMw>3.0werecalculated.Itwasfound

Figure 5. Logarithmic plot of the magnitude of earthquakes for n

number of events of earthquake.

thatthecatalogiscompleteabovemagnitude2[Fig.4]

whichwasthecutoﬀmagnitudetakenforfurtheranaly-

sis. Theestimationoftheparametersofseismicregime

wascarriedoutfromthis“cleaned”catalogue. Thestudy

hasbeendonebystudyingoccurrencesoffrequentEQs

aboveacertainthresholdiftherearemultipleeventshav-

ingspatial,preferreddistributionwithinaspeciﬁedpe-

riodoftime[45]. A study ofthecomplexseismicityof

theHimalayanregionbythestatisticalanalysisofhis-

toricalEQdataisineﬀectivewithoutthepriorcarecon-

cerningthecompletenessofhistoricalcataloguesusedin

themodeling[39]. AstudyoffrequencyoftheEQmag-

nitudes[Fig.2] over the entirepopulationrevealshigh

homogeneityofdatasets.Thecataloguehasahighrate

ofhomogeneityandfollowsthegenericMscaleframe-

worknecessarytocalculateintermagnitudeandtimeof

recurrencerelationships[10].

It has been accepted that earthquakes generally obey

Gutenberg–Richterscaling[19]. Apower-lawdecayfor

thecorrelations as afunctionoftimetranslates into a

power-lawdecayofthespectrumasafunctionfrequency.

Intheseperiodsoftime,thefunctionN[>φ]followsthe

powerlaw. N(>)∝-bN[>φ](1)

The slope of log-linear portions in daily distributions,

whichdeterminesthepowerexponentinequation[1]drops

duringfaulting[15]anditgrowsagainaftertheevent.The

parameterdependson

therelativeamountoflowandhigh-energyevents[the

lower,the larger the contribution ofpowerfulevents]

anditsdecreasesignalstheinvolvementofmoreimpor-

tanteventsinthefractureprocess.Thisisexplainedusing

thelogarithmicplotformagnitudeMforNevents. The

natureofthepowerlawdecayplotiscorrelatedwiththe

empiricaldataoftimerecurrencedistributionforwhich

distributiondecaysexponentially[34]Thismeans,forin-

stance,thatthereoccurlotsofsmallearthquakes,buta 239

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

fewbigones. Ifweconsiderearthquakesofaparticu-

larmagnitude,thereare4timesasmanywith1/2that

magnitude,anda1/4asmanywithtwicethemagnitude.

Earthquakesthataretimesaslargeintermsofenergy

comparedtoearthquakeswhosemagnitudeissmallare

alsorarerbyafactorof1/2wasshownbytheseismol-

ogistsGutenbergandRichterassimulatedinFig.5. The

exponentvariesfromonephenomenontoanother,butin

allcasesthepowerlawmeanstheeventshavenotypical

size,anditsuggeststhatallevents,largeandsmall,have

thesamecause. Thiskindofscalingappearsinnatural

systemsthatarepoisedontheedgeofchangeorinacrit-

icalstate[13]andsuchcriticalstatesseemtoarisenatu-

rallyinmanycomplexsystems.Thecumulativenumberof

earthquakesinaregionwithmagnitudesgreaterthanor

equaltoM,isrelatedtoM[28]. Usingthisobservation

analternativeapproachforthestudyofmainshockevents

hasbeenobservedforexaminingthecumulativeproba-

bilitydistributionofEQrecurrencetimefor seismically

activeEQs[Fig.5]. Westudytheoverallcharacteristics

ofthetemporalclustersinthemodelandinter-occurrence

timeintervalsbetweenevents[26]. Itisfoundthatdistri-

butionofearthquakesizefulﬁlsapowerlaw.Increaseof

foreshockanddecayofaftershockactivityvarieswithtime

distributionbutforanyeventrupturesizeishighlydepen-

dentonthemainshockmagnitudewhosedissipationrates

aredependentonthesizeoftherupturezone. Thiswill

shedlightonthenatureofseismicclustersfortheregion.

Onepossibleconfoundingeﬀectintheforegoinganaly-

sisistheunevendistributionofeventsizesinthedataset.

Thisdistributionisatypicalofearthquakecatalogs,inthat

thenumberofeventsislimitedbothonthehighenddue

tothesmallnumberofeventsatlargemagnitudes,and

atthelowendduetothedearthofsmalleventswhich

arewell-recordedenoughtobemodeled. Thephysical

signiﬁcanceofthespatio-temporalstatisticalanalysesof

theseismicityintheHimalayanbasinprovidesabetter

insightintothenatureandextentofearthquakedistribu-

tionanditsbearingonthesub-surfacestructuralhetero-

geneitiesinrelationtodiﬀerentialstrainaccumulationin

thesub-surfaceseismogenichostrocks.

Wecheckforthecumulativenumberofeventsassociated

withineachclustertoassessthefrequencyofshockoc-

currenceineveryclustersandhowvariationsarebound

toexistsigniﬁcanttoeachclusterblock.

We then analyze the cumulative distribution of earth-

quakesforeachclustershowingthetotalnumberofEQsin

theperiod1950–2010againstclusters[Fig.7].Thecluster

patternintheeightzonesrevealstemporal–spatialasso-

ciationofearthquakes[Table2].Between1964and2006,

Kashmircluster[A]experiencedthemaximumnumberof

earthquakes[250]havinganaveragemainshockperiodof

Figure 6. Spatio-temporal clusters outlined in black [A, B, C, D,

E, F, G and H] in Lesser Himalaya. The name of the

clusters are AKashmir, BKangra, CGarhwal, D

Kumaun–West Nepal, EEast Nepal, FSikkim, G

Bomdila and HEastern Syntaxis. Note the inter-

action between Himalayan Thrust planes [MFT-MBT-

MCT] and Peninsular crosscutting faults [RFRopar

fault, MDFMahendragarh–Dehradun Fault, GBFGreat

Boundary Fault, WPFWest Patna Fault, EPFEast

Patna Fault, MSRMFMunger Saharsha Ridge Marginal

Fault, MKFMalda-Kishanganj Fault and BFBomdila

Fault]. MFTmain frontal thrust, MBTmain bound-

ary thrust, MCTmain central thrust, ITSIndus-Tsangpo

Suture, JamJammu, SiSimla, LeLeh, DdDehra

Dun, NdNew Delhi, JaiJaipur, AllAllahabad,Sh

Shillong.Cluster Analysis AKashmir, BKangra, C

Garhwal, DKumaun–West Nepal, EEast Nepal, F

Sikkim, GBomdila and HEastern Syntaxis from

[Mukhopadhay et al, 2010 Fig. 1].

Table 1. Depth [km] distribution of the EQ epicenter in eight cluster

zones in Lesser Himalaya.

Cluster Numberof

mainshock

EQ NumberofEQ

[45kmdepth] Recurrence

time

tillnextEQfor

largestshock

[indays]

A

B

C

D

E

F

G

H

250

74

46

94

46

54

82

12

12

13

08

14

3

12

18

03

295

836

76

283

173

233

171

474

295daysbetweenlargesteventsofMag>4.5followedby

Kumaun–WestNepal[clusterD,74]andBomdila[cluster

G,82]suﬀeringmilderevents. WhileEasternSyntaxis

[clusterH]containsonly12events,theotherfourclus-

ters,Kangra[B,74],Garhwal[C,47],EastNepal[E,28]

and Sikkim [F,54], have amoderatenumber ofevents.

ClusterChoweveristhemostreactiveregionamongall

thefactionshavingshownageneraltendencytoproduce

eventsinthespanof76days. Analyzingthepatternof

earthquakeswithineachcluster,it isapparentthatthe

240

P. K. Dutta, O. P. Mishra, M. K. Naskar

(a) (b)

(c) (d)

(e) (f)

(g)

Figure 7. Distribution of earthquake mainshock occurrence in the various clusters[A,B,C,D,E,F,G].

clustersareconstitutedofindependenteventsaswellas

foreshock-mainshock-aftershock[FMA]sequenceandits

combinations.

Aftermakingasetofrigoroustesttoobservecharacter-

isticsoftemporalsequencesandindependenteventsin

theclusterzones,thedatashowsimportantvariationsto

revealthenatureofseismiceventoccurrenceandassoci-

atedpatternsuniquetotheclusterintheHimalayanbelt.

Havinganalyzedthepatternofearthquakeswithineach

cluster,theclustersareconstitutedofindependentevents

241

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

Figure 8. Analysis of events in the various cluster zones

[A,B,C,D,E,F,G,H] as from Fig. 5.

aswellasFMAsequenceanditscombinationsandhigh

swarmactivity[Table1].Intheclusters,earthquakesthat

formcompleteFMAsequenceoranycombinationofFMA

areplacedintemporalsequence,whereastheeventsthat

donotregistertoanyforeshock[FS]and/oraftershock

[AS]are treated asindependenteventswhile anevent

whichcomprisesoflocalized tremors of uniformmagni-

tudeshow swarm activityandwillbedemonstrated as

suchinFig.8. Thecharacteristicsoftemporalsequence

andindependenteventsintheclusterzonesshowinter-

estingvariations. Itisnoteworthythatinalltheclus-

ters,therearemoreindependenteventsthantheearth-

quakesclassiﬁedundertemporal [FMA] sequences [Ta-

ble4aand4b].Theonlyexceptionisthe‘clusterA’that

hasmoreeventsinFMAsequenceduetoKashmirearth-

quakeof08.10.2005[Mw7.6]with1FSand211AS.The

magnitudesof‘individualearthquakes’varyfrom4.5to5.8

inalltheclusters,apartfromahighermagnitudeevent

ofMw6.1[01.06.2005]inclusterH.Manyofthefolds

weretightenedwereoverturnedwhiletoppling.Thecom-

pressivestrainandthebuckledupHimalayancrustbroke

alongtheMCT.TheMCTzoneofbrittleductiledeforma-

tionshowsapureshearcomponentofdeformation.

Manyexperimentaldataaimtocharacterizediﬀerentas-

pectsofpre-seismicbehaviorforprecursorystrainchanges

andpreearthquakedeformationrates.Aseriesofstatisti-

caltestsonthenatureofearthquakegeneratingprocesses

associatedwithseismiccycleofEQoccurrencesinthe

Himalayanbasinareconductedusingmultidimensional

rigorousstatisticalstudies,inter-eventdistanceanalyses,

andstatisticaltimeanalysesforobtainingcorrelationdi-

mensions for seismic, radon andfocal depthpattern of

eventstobetterunderstandthenatureandextentofseis-

mogenesisintheHimalayanbelt.

3. Power-lawEQ frequency distribu-

tion

Seismicoccurrenceinclustersisaprecursoryseismicity

patternwhichhasbeenalsoobservedbeforemanystrong

EQs by several authors [60,78,79,84].Understanding

theoccurrenceofperiodsofacceleratedaswellasde-

pressedseismicity[seismicquiescence]andlocalizedclus-

teredactivity[swarmsandaftershocksequences]isimpor-

tantforunderstandingdistributionsofinter-eventtimes

betweensuccessiveearthquakesinahierarchyofspatial

domainsizesandmagnitudes. Theproposedstudypro-

videsevidenceofaninter-clustercorrelationmechanism

betweenthe diﬀerent earthquakesbystudying acata-

logofseismiceventsofref.[1]. Earthquakeshavestrong

temporalcorrelationsbetweeneventsasrelatedinthis

study[8,16,76].Inthisstudy,analternativeapproachof

extrememainshockeventsthathasbeenobservedforex-

aminingthecumulativeprobabilitydistributionofEQre-

currencetime[17]andbvalueforseismicallyactiveEQs.

Thedistributionsofdistancesandwaitingtimesbetween

aquakeanditsrecurrenteventsbothfollowapower-law

decay.Ontheotherhand,[62]haveadvancedtheregion-

time-length[RTL]method, which investigates seismicity

patternchanges prior tolargeEQslooking for power-

lawdecayoftheinterevent-timedistribution. Indepen-

dentlargeeventsoverdiﬀerent timelines do notfollow

thepowerlawasthepvalueshows.Usingthisstudywe

analyzethepowerlawdistributionmodeltoﬁndtheset

ofparametersandstudyglobaldynamicsofruptureprop-

agationbasedontheinteractionsbetweenevents. Time

seriesofreliablemeasuressuchaswaitingtimedistri-

butionforEQandbvalueforEQscangivesigniﬁcant

accountsoftheunderlyingdynamicalbehaviorofthese

systemswithrespecttoseismicprecursors[11]especially

whentheresultingprobabilitydistributionspresentre-

markablefeaturessuchasanalgebraictail,usuallycon-

sideredthefootprintofself-organizationandtheexistence

ofcriticalpoints. Thisisadirectconsequenceofacas-

cadingfailureinvolvingseveralofitsfaultsundertheef-

fectofamainshockEQwhichisduetomagnitudecor-

relationsbetweenintereventearthquakes. Thedegree

ofdistributionplaysadeﬁnitiveroleinnonequillibrium

criticalsystems. Apower-lawdistributionrequiresscale

invariance[self-similarity]overtherangeconsidered,and

is the only statistical distribution that does not intro-

duceacharacteristiclength/time scale. A majorques-

tioniswhetherearthquakesalsoobeypower-law[fractal]

frequency-sizestatistics.Fromstudiesitisdiﬃculttoin-

terpretthenatureofthestress-generatingpatternofa

region-whetherthesystemfollowsagreaternumberof

spatialmainshock-aftershockpatternsorfollowsaninde-

242

P. K. Dutta, O. P. Mishra, M. K. Naskar

pendentpatternofproducingalmostperiodicaloccurrence

ofearthquakeswarmsthatmightbehelpfulinestablish-

ingthenatureoftheclustersintheHimalayanbasinin

thepostseismicresponsestate. Thepowerexponentde-

creasesasthe‘catastrophe’approaches,andexhibitsa

trendtorestoreitsinitialvalueafterthelarge-scaleper-

turbation. Onthebasisofdamagemechanicstheory,[9]

proposedapowerlawforthetimevariationofthecu-

mulativeBenioﬀstrain,S[squarerootofseismicenergy],

releasedbyacceleratingpre-shocksintheregionwhere

thesepre-shocksoccurinthecriticalregion.Interevent-

timedistributionisuniversal[20]afteraccountingforthe

overall activity level it shows the existence of cluster-

ingbeyondthedurationofaftershockbursts. Recurrence

timesshouldbeconsideredforbroadareas,ratherthan

forindividualfaults,andcouldprovideimportantinsights

inthephysicalmechanismsof earthquakesoccurringin

aswarmorindependently. Inter-event-timedistribution

canbeused for anon-parametricreconstructionofthe

mainshockmagnitudefrequencydistribution.Earthquake

swarmsarestronglyclusteredinspaceandtimeandcan-

notbedescribedbydominantlawofsequences[42].Using

powerlawanalysisinempiricaldatadistributionwecan

reproducebothtypesofseismicitydependentonthepa-

rameterregionandidentifywhichoftheclusteringmech-

anismsofswarmactivityortemporalsequenceofmain-

shockscanbecharacterizedbytheintereventtimedis-

tributionwithapowerlawscale.Ananalysisofdatafrom

thedenselymonitoredearthquakeswarmregionwhichis

famousforepisodicburstsoflargenumbersofspatiallylo-

calized,smalltointermediatesizedearthquakes,showed

apower-lawdecay of theinterevent-timedistribution.

Analysisofthemathematicalmodelofpowerlawscales

withseismicprecursoryevents. Theincreaseinnumber

offoreshockclustersshowsthatthemagnitudeoftheEQ

doesnotincreasebeyondacertainthreshold.Ithasbeen

putto study by[80]that asperitiesmaybecharacter-

izedbybvaluesandhighstressregimesmarkingplaces

susceptibletofuturelargeEQsby estimatingthelocal

recurrencetimeforactiveclustersandearthquakeswarm

activity.Theresultsareinaccordancewiththestudythat

thetemporalclusteringofindependentlargeeventshav-

ingagreatertimedistributionisabsentintheHimalayas

asthepowerlawofoccurrenceisfollowedbytheearth-

quakeswarms.Thispaperanalysesfortheﬁrsttime,and

asfarasweknow,thestatisticsofmajorEQeventsin

aseismicfaultnetworkintheHimalayanbeltfromthis

aforementionedcomplexsystemsapproachandthelikeli-

hoodofcorrelationdimensions.TheKolmogorov-Smirnov

[KS]statisticisusedinthissense,whichisdeﬁnedasthe

maximumdistanceDbetweenthecumulativedistribution

functionsofthedataS[x]andtheﬁttedmodel.Theafore-

Figure 9. Cumulative distribution of earthquake swarms in Hi-

malayan basin.

Figure 10. Cumulative distribution of mainshocks of mag >5.0 in

earthquake.

mentionedKSstatisticisusedagainasagoodness-of-ﬁt

testbetweenrealdataandsyntheticallygeneratedpower-

lawdistributeddatahavinganeﬀectasseismicprecursor

intheHimalayanEQdistribution. Followingthestatis-

ticalanalysis, we estimate thebasicparametersofthe

power-lawmodel,thencalculatethegoodness-of-ﬁtbe-

tweenthedataandthepowerlawandﬁnallywecompare

thepowerlawwithalternativehypothesesviaalikelihood

ratiotest. Ataildistributionalsohasbeenmadetoun-

derstandtailpropertiesoftheexceedancefortheregion

withinterarrivalscanbeestimated,thenlimitingextreme

valuedistributionsgoverningthemaximumobservationor

exceedingvaluecanbeusedtostudyrecurrenceintervals

forextremeeventswithpower-lawinterarrivals.

Forthemeasureofwaiting time distributionsformain-

shock distribution and earthquake swarm, we give the

numberofoccurrences n, meanx,standarddeviation r,

maximumobservedoccurrencex, lower bound to the

power-lawbehaviorxmin,scalingparametervalueα,oc-

currencesinthepowerlawtailntailandp-value. The

lastcolumnindicatesthesupportforwhethertheobserved

dataiswell approximated by a power-lawdistribution.

Estimated uncertainties for xmin, αand ntail are also

shown.Measuressuchasntailandxminarefundamental

toestimatethespanofthepower-lawbehaviorandtode- 243

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

Table 2. Cumulative distribution functions P[x] and their maximum likelihood power law ﬁts [discontinuous straight line] for the EQ catalog for

associated EQ event measure like earthquake swarm measures and waiting time between mainshocks of mag >5.0.

DatasetforPL n <x> σxmax xmin αntail p Support

Swarm 450537.23 456 600 201.04[160.07]16.8901±19.1167 47.5±25 0.12 Likely

RTime 131 127.79650.091000 126.6±722.02 ±0.2036 76.1±0.95 0 Noeﬀect

Figure 11. Aftershock distributions in the past 100 years.

velopfurtherquantitativemodelsandvalidationofseismic

precursorymodelslikeseismicquiescence.

4. Correlation possibility between

EQoccurrence and seismic precursor

variation

Thecorrelationbetweenprecursoryanomaliesbeforethe

eventcanbeofhelpinlocalizingEQevents. Theyare

assumedtoindicateachangeofthesystemstate[6]when

thesystemisgoingtoundergoaruptureorisgoingtoun-

dergoselforganization. Ashort-termpowerlawincrease

ofseismicactivityoccursimmediately priortothemain

shocksonaverage,e.g. groundwaterchangesorelec-

tromagneticemissions[12,66–69]orforeshockclusters.

EQsoccurinclusters.Intheseclusters,thetemporaldis-

tributionofeventsseemstoberelatedtoEQmagnitude.

Twowidelyknownexamplesarethemagnitude–frequency

distribution[i.e. theGutenberg–Richterlaw]andtheaf-

tershockdecayrate[i.e.theOmorilaw].Ithasbeenfound

thatmoreisthenumberofforeshockclustersinthesource

zoneoftheearthquake,thegreaterarethechancesthat

themagnitudeoftheearthquakedoesnotexceedacertain

limitwithintheactiveregion. Westudiedthehomoge-

neousEQdatabase[2]forthedistributionofaftershocks

inspace,abundance,magnitudeandtime[Fig.10].Inves-

tigationstodateallshowthataftershocksfollowsimple

statisticalbehavior. Weconductedastudyofaftershock

distributionofmagnitude>3.0afteraseismicmainshock

occurrenceandfoundthattheaftershock distributionis

highlycorrelatedtothemainshockriseoverthetimepe-

riod.

Table 3. The total number of sequences with at least one aftershock

for diﬀerent mainshock magnitude intervals.

Mainshockmagnitudeinterval 5-5.4 5.5-6.4 6.5-7.4 7.5-9.0

Numberofsequences 278 103 30 3

Thelargerthemainshock is, thefewereventsoccurin

thistimeinterval,oneobservesatransitionintheseismic

activityfromahighertoalowerlevel. Theonsetofthis

relativeseismicquiescencedependsonthesizeofthefol-

lowingmainshock,namelythelongerthedurationofthe

seismicquiescenceis,thelargerisonaveragethesubse-

quentmainshock.Theaccumulatedenergyincreaseswith

thedurationofseismicquiescence[81]thatis,theproba-

bilityforalargeeventalsoincreases.Similarstudiesto

correlategravitystudiesandgeochemicaldatawithsta-

tisticalinferencestudieshavebeendoneto understand

howanomalousvariationsofprecursorsaﬀectthenormal

distributionofearthquakes.

4.1. Surface plot and Gravity Studies

Usingmatlabwegeneratedasurfaceplotofthelocalized

mainshock[Fig.11]eventsofmagnitudev>6.0.Wehave

appliedasetofstatisticaltoolstoevaluatethenatureof

mainshockandaftershocktremorsthathaveoccurredin

thepast60yearsintheHimalayanBasininthelatitude

rangeof24–34°Nandthelongitudeof74–98°E.The

spatialplotrevealsthatmostoftheEQmainshockevents

occurredataspatialdepthof0-20kmneartothesurface

[Fig.12].Ahistogramplotforthefocal

depths shows that the spatial distribution of the focal

depthhasaneﬀectiveskewingcoeﬃcienttowardstheEQs

havingsmallerfocaldepth.Therigoroussetofanalytical

investigationsmatchestheseismicprobingconductedfor

244

P. K. Dutta, O. P. Mishra, M. K. Naskar

Table 4. Earthquake swarm events in the diﬀerent clusters A-G for the Himalayas.

Swarm nature corresponding to metastable Clusters[n*p]

wherenisthenumberofclustersandpisthenumberof

eventscorrespondingtocluster. ThresholdaveragemagnitudeofEQincluster

A[3*3] 5.1

B[4*2] 5.2

C[3*3] 4.9

D[3*5] 4.8

E[3*2] 5.5

F[3*1] 5.1

Figure 12. A 3D spatial plot of spatial projection depth for events in

km.

EQfocal-depthdistributionintheHimalayanareasug-

gestingthatmost of theseismicactivitiesareconﬁned

withintheupper50km.Skewnessofthedistributionacts

asaneﬀectiveindicatorthatthebrittlecrustresultsina

faultrupturenearthesurface. Studiesinfocaldepthalso

leadsustotheevaluationofthespatialdepthoftheMoho

undertheHimalayas.Understandingthenatureandspa-

tialoccurrenceofEQcanhelpusinfuturetorelateevents

tovaryingmohodepth. Theductilebrittletransition[21]

regiongivesustheabilitytoanalyzethestructuralcom-

plexitiesofthesourceregion for rupture andthe seis-

mogenesisofthemohorovicdiscontinuity,formedatthe

timeofHimalayanorogenygivingthegranitegenesisof

theregion.Seismicprobingstudiescarriedoutacrossthe

highpeakregionoftheHimalayasshows75kmasthe

largestdepthoftheMohoundertheHimalayas[77].The

crustaldepthof75kmEQs[Fig.13]recordedinthisstudy

occurredbetweentheMBTandtheMCT.EQoccurrence

cangiveusgoodinsightastothecrustandmantlestruc-

turesexistingbelow surfacefeatures. Suchstudiesas

donebytheIndepthteam[82]havecontributedtoknowl-

edgeabouttheHimalayanlithosphere,includingcrustal

thicknesses.

Figure 13. Seismic probing studies with gravity to ﬁnd the focal

depth of earthquakes in the himalayan front arc.

Figure 14. Statistical study of focal depth patterns from catalogue

events for the Himalayan front-arc.

Thestatisticalstudyevaluationsuggestthatthedistribu-

tionofEQarelimitedintheuppercrustofrockthatmight

betakenupforfutureanalysis. 245

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

Figure 15. Distribution of earthquakes across diﬀerent months.

4.2. Correlation between geochemical data

and month wise earthquake distributions

Anumberofcomplexmathematicalmethodshavebeende-

velopedtoinferstressvariationsmeasuredthroughanal-

ysisofvariationsofradoncausedbytheearthquake[23].

Weconductedaseasonaltrendforthedatasetusinga

studentsamplettest. Ouranalysisofthedatasetre-

vealsthatthelikelihoodofoccurrenceofanEQhaving

magnitude6followsagreaterseasonaloccurrenceusing

theprogressivewindowapproachcomparedtomagnitude

5EQs. ThedatarevealsthatEQmainshocksarepre-

dominantlydistributedthroughouttheyears[Fig.14]but

themonthsofMarchAprilandOctoberNovemberexpe-

riencesahighseismicactivitycomparedtotherestofthe

year. Wealsoconductedastudytoidentifytherelation

betweenradondeclinelevelsandseismicity

ﬂuctuationsthroughouttheyear.Apositivetrendbetween

EQmainshocksfrequencyformonthswiththenatureof

radonanomalychangeshowsthatseasonalstrainvaria-

tioniscorrelatedwithvariationsof geodeticstrainand

seismicity[7]. Investigationscarriedoutthroughoutthe

worldoverthepast20yearshavesubstantiatedsignif-

icantvariations of radonconcentrationswhich may oc-

curwithraregeophysicaleventssuchasEQandvolca-

noes[1,14,75]. [61]hadcarriedoutastudyinAmritsar

tostudythecorrelationofradonsoilgaswithEQs. Re-

sultsoftheanalysisofshort-termvariationsindiﬀerent

parameterspriortothe

consideredEQconﬁrmtheexistingideasoninhomogene-

ityofspatialmanifestationofEQsprecursors. Theprob-

abilityofdetectingshorttermprecursorsincreasesinthe

epi-centralareaofimpendingEQs.61and27hadcarried

outextensiveradonmonitoringinnontectonic environ-

ments[Fig.16]closetotheHimalayanseismicallyactive

basintodetectincreaseinradonexhalationbeforeanEQ

occursduetostrainbuildupinthearea[54].

5. Neural Network Analysis

The relationship between two parameters d_value and

b_valuewithoccurrenceofbigearthquakesisdetected

byneuralnetwork. AnANNusuallyhasaninputlayer,

oneormoreintermediateorhiddenlayersandoneoutput

layerwhichproducestheoutputresponseofthenetwork.

Whenanetworkiscycled,theactivationsoftheinputunits

arepropagatedforwardtotheoutputlayerthroughthe

connectingweights. Inputscouldbeconnectedtomany

nodeswithvariousweights,resultinginaseriesofout-

puts,onepernode. Theconnectionscorrespondroughly

totheaxonsandsynapsesinabiologicalsystem,andthey

provideasignaltransmissionpathwaybetweenthenodes.

Statisticalestimatorsarehighlyunstableowinginestab-

lishingalinearmechanismowingtotheshorthistoryof

theforecastzones. Anonlinearmechanismoffeedback

analysisbasedonerrorestimatewillbehighlyfruitfulas

itwillbeabletograspthediﬀerence between conven-

tionalforecastbasedontimeseriesanalysisandforecast

oflevelcrossingtimeofprecursorybehavioranalysis.The

trendoftheseismicactivitybycombiningneuralnetwork

modelandseismicfactorshasbeenstudied.Inthispaper,

avariationofseismicitywasintroducedtoreﬂectthecor-

respondingvariationofthefrequencyofearthquake. The

learningprocessforBPnetworkhasthefollowingfour

components.

Inputmodedesigninvolvesuseofattribute(9nodes)fully

connectedtothenodesoftheadjacentmiddlelayers.Dur-

ingthetrainingphaserecordsareselectedrandomlyfrom

thetrainingphasesetandonepassoftheﬁleiscompleted

usingoutputerrorbackpropogationpassedthroughthe

hiddennodestotheinputlayersasfeedback.

Thisrepetioncycleinvolvesmemorytrainingthroughfor-

wardmodepropagatinganderrorbackpropagationofthe

calculationdoneforeveryalternatecycle.Afterfullneu-

ralnetworktraining;arecalltesttodeterminewhether

globalerroristendingtoaminimumvalueisdone. The

predictedvalueiscomparedwiththesuitablemagnitude

valuefortheﬁnalresults. Asthe stability of the sys-

temisdeterminedbasedonerroroftheminimumvalue

higherminimumvalueisusedtoclassifytheseverityof

thequake.

246

P. K. Dutta, O. P. Mishra, M. K. Naskar

5.1. The three-layer BP network design is-

sues to be considered

MakingtheBPnetworkdesignshouldbeconsideredwith

thenumberoflayers,thenumberofneuronsineachlayer

ofthenetwork,theinitialvalueandthelearningrateas-

pects.

1.thenumberoflayersofthenetwork

Ithasbeenprovedthatathree-layerBPnetwork

canachievemulti-dimensionalunitcubeRmtoRn

mappingthatcanapproximateanyrationalfunc-

tion.Thisactuallygivesadesignofthebasicprin-

ciplesoftheBPnetwork.Increasingthenumberof

layerscanfurtherreduceerrorsandimproveaccu-

racy,butalsoenablesnetworkcomplexity,thereby

increasingthetrainingtimeofthenetworkweights.

Andreductionoferrortoimprovetheaccuracycan

actuallybeobtainedbyincreasingthenumberof

neurons of thehidden layer, the resultsof their

trainingismoreeasilyobservedandadjustedrather

thanincreasingthe number oflayers. Sounder

normalcircumstances,priorityshouldbegivento

increasethenumberofthehiddenlayerneurons.

2.Thenumberofhiddenlayerneurons

Networktrainingtoimprovetheaccuracycanbe

increasedbyusingahiddenlayerandoutputlayer

withlinearactivationfunction. Theevaluationofa

networkdesignquality,theﬁrstisitsaccuracy,the

longerthetrainingtimedependsonthenumberof

recurrencecyclesandeverycyclespenttime.

3.theselectionoftheinitialvalueoftherightvalue

Asthesystemis non linearandthenitialvalue

oftheselectedlearningprocessisaconstraintto

ascertainthelocalminimum,thebackpropogation

hasbeencustomized. Thebackpropogationnet-

workisaverypowerfultoolforconstructingnon-

lineartransferfunctionsbetweenseveralcontinu-

ousvaluedinputsandtheoneormorecontinuous

valuedoutputs. Itisfoundthatifinitialvalueis

toolargeorsmallitaﬀectslearningspeed,initial

valueof theweightsshouldpreferably uniformly

distributedfractionalexperiencevalue,valueofini-

tialweightsaresetsinthe[-1,1]domainthereare

selectarandomnumberbetween[-2.4/F,2.4/F],

whereFisthenumberofinputfeature. Toavoid

thedirectionofadjustmentoftheweightsofeach

stepinsamedirectionthatincreasestheprocessing

time,theinitialvalueissetrandomnumbers.

4.learningrate

Thelearningratedetermineseachtimetheamount

ofchangeinweightvaluethatisgeneratedinthe

loop. Afastlearningratemayleadtosystemin-

stability.However,theslowrateoflearningleads

tolongertrainingtimeandmayproduceveryfull

convergence,butitisimportanttoensurethatthe

errorvalueofthenetworkoutofthetroughofthe

errorsurfaceeventuallytendstothesmallesterror

value. So,ingeneral,wetendtochooseaslower

rateoflearninginordertoguaranteethestability

ofthesystem. Thechoiceofthelearningrateis

between0.01–0.8.

Asuitablelearningrateforeachspeciﬁcnetworkis

present,butformorecomplexnetworks,thediﬀer-

entpartsoftheerrorsurfacemayneedadiﬀerent

learningrate. Inordertoreducetrainingtimesto

ﬁndthelearningrateandtrainingtime,themore

appropriatemethodistheuseofadaptivelearn-

ingratechanges,whereadiﬀerentlearningrateis

automaticallysettothetrainingofthenetworkat

diﬀerentstages.Generallyspeaking,thefasterthe

learningrate,thefastertheconvergence,butwith

oscillation;whiletheslowerthelearningrate,the

slowertheconvergence.

5.theexpectederrorselect

Inthetrainingprocessofthenetwork,theexpected

errorvalueisobtainedbycomparingtheminima

with“right”,thehiddenlayernodes. Smallerex-

pectationstimesfortheinputnodesadjacenttothe

hiddenlayerandthetrainingtimetogettheerror

torely ongivesusgood results. Undernormal

circumstances,asacomparison, while two diﬀer-

entexpectederrorofthenetworkistrained,hidden

layeroftheneuralnetworktoachieveanycontin-

uousfunctionapproximation,butsomeparameters

selecttheappropriatetrainingprocesscanspeed

upthetrainingoftheneuralnetwork,shortenthe

trainingtimeandachievedsatisfactoryresultsof

thetrainingoftheneuralnetwork.

Thisisdoeninthehiddenlayerinthe following

manner

(a)adjusttheamountproportionaltotheerror,

thatis,thegreatertheerror,thegreaterthe

magnitudeofadjustment.

(b)toadjusttheamountoftheinputvalueispro-

portional to thesize, the greatertheinput

value,inthislearningprocessbecomesmore

active,andtheirassociatedweightsadjust-

mentshouldbethegreater. 247

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

(c)adjustthe amount oflearningcoeﬃcientis

proportionalto. Usuallylearningcoeﬃcient

is0.01–0.8,inordertomakethewholepro-

cessoflearningtoacceleratewithoutcausing

oscillationinvariablelearningrate.

Thiswillbeusedtotakealargerlearningearlyin

thelearningcoeﬃcientwillbegraduallyreduced

asthelearningprocessproceedsitsvalue.

5.2. Proposed Method

Inthispaperwedescribeamethodwhichhastwostages,

asapreliminarystagewehavetotakeseismicnetwork

signals,electricpre-seismicsignalsandtheaveragemag-

nitudeofpreviousearthquakes,ifanyarerecordedinthe

pastdataandintheﬁnalstage,thosedatacollectedinthe

preliminarystagearegiventotheANNasinputs.ANN

hastobetrainedwithearthquakeknowledgerepresenta-

tionandemploysanonlinearandbackpropagationalgo-

rithmtoproducethepreciseprediction.Wehavetotrain

theANNhiddenlayerwithearthquakeknowledgerepre-

sentation,backpropagationandautoassociativeneural

networkalgorithms. Theabovetrainingisnecessaryfor

thedesignbecausethenatureofinputdataisnonlinear.

Theanalysisoftheaftershocksaimedatonehand the

spatiotemporalcharacterization[22]byreviewingandre-

ﬁningthehypocentrallocations,thedeterminationofthe

seismicgenerationmechanismsandidentiﬁcationofthe

mainactivestructures. Amethodologytopredictmag-

nitudeoftheearthquake[49]usingneuralnetworkhas

beendescribed. Theyhave takenseismicityindicators

inputsfortheANNmodel.[5]proposedamethodforthe

estimationofpeakgroundaccelerationusingANNbytak-

inginputslikemagnitude,hypocentraldistanceandav-

erageshearwavevelocity. [3]proposedamethodusing

ANNtoforecastearthquakesinnorthernRedSeaarea.

Theypresenteddiﬀerentstatisticalmethodsanddataﬁt-

tingsuchaslinear,quadraticandcubicregression. The

studyconsideredenergeticpropertiesofseismicitywith-

outconsideringtheearthquakesdistributioninspaceand

time.Basedontheseseismicdata,theyextracted11fore-

castfactors and theactualoccurrenceof the maximum

earthquakelevelMasinputandtargetvectors.Thefore-

castfactorswere:

1.themaximumnumberofseismiclevel

2.bvalues

3.Averageearthquake’smagnitude

4.theaveragelatitude

5.Theaveragelatitudedeviation

6.AverageLongitude

7.themeanlongitudedeviation

8.MLgreaterthan115thenumberofearthquake

9.thenumberofadjacenttwo-yearearthquakeevents

istakentoﬁndtheabsolutevalueofmorethanan

orderofmagnitudeaveragemeandividedby10and

thenroundedforabsolutevalue

10.subsequentDvalue

11.themaximumearthquakemagnitude.

TheyhavetakenseismicityindicatorsinputsfortheANN

model.Theydidnottakerecurrencetimeforoccurrenceof

earthquakeeventtothemodel.Howeveralotofin-depth

analysisisnecessaryforanalysisofprecursorybehavior

withseismicindicatoranalysis. Themainsigniﬁcanceof

neuralnetworksliesingenerationandadaptabilityand

alsoinvolvesstrengthforincorporatingnon-linearitiesto

themodelscenario.Inordertoevaluatetheperformance

oftheneuralnetworkapproach,theadjustedweightsare

thenusedtoprocessadatasamplewhosetargetvalues

arealsoknown,similartotrainingdata. Seismichazard

analysishasbeen carried outusingamulti-layerfeed

forwardnetworktrainedwithabackpropagationlearn-

ingalgorithm.Thearchitectureofthenetworkconsistsof

aninputlayerwith4neurons,oneandtwohiddenlay-

erswithappropriatehiddenneurons.Inordertoanalyze

thenatureof the earthquakeweanalyzedthe state of

earthquakebasedonthenumberofaftershocks against

therecurrencetimeofearthquakes. Theinputneurons

areﬁxedbythenumberofinputvariables[e.g.,thechar-

acteristicvaluesforvariousstagesofseismicitycyclefor

atimeseries].Theoutputneuronsareﬁxedbythenum-

berofoutputsdesired[e.g.,thepredictedcharacteristic

valuesfornumberofstagesofseismicitycyclesofinter-

est]. Iftheperformanceofthenetworkontestingdata

setsisfoundtobesatisfactory,thenetworkissupposed

tohave generalization capabilityoveranyotherset of

similardata. Thestudyoftheseismicclustersandfrac-

talpropertieswithrecurrencetimeofearthquakesinthe

zoneformthebasisforunderstandingtheself-similarityof

theseismicprocess.Oncetestedsuccessfully,thetrained

networkmaybeusedtoprocessunknowndatainorderto

predictthecharacteristicvaluesforthosedatasets. By

discoveringtherelevancebetweentwoseismicparameters

[d_valueandb_value]withoccurrenceofbigearthquakes

duringatime,wherecomplicatedandindirectrelation-

shipsaretobeidentiﬁedandprocessedthroughanalysis

ofthestrongestearthquakefromeachwindow.Basedon

theanalysiswehad32targetsfortrainingandtestingthe

neuralnetwork.Fromthese32pairsofinputs/targets,we

248

P. K. Dutta, O. P. Mishra, M. K. Naskar

Figure 16. Categorization of earthquakes.

Figure 17. Adaptive competing networks to predict the seismic rat-

ing.

select16pairsfortrainingnetwork,and16pairsfortest-

ingit.Resultoftrainingandtestingfortheneuralnetwork

indicatedinearthquakeswasfoundthroughsimulation.A

relationshipbetweentwoparametersd_valueandb_value

withtheoccurrenceofbigearthquakesisdetectedbythe

neuralnetwork. Howeversomeexitoftherealvariation

canbeobservableintheoutputofneuralnetwork,because

ofthecomplexitynatureofthisphenomenon.

These results may have important implications for the

study of dynamical behavior of earthquake generating

mechanismintheHimalayas. TheHimalayantimese-

riesofearthquakesofsizewithmagnitude4andhigheris

modeledbyhighnumberofvariablesi.e.,bettermodeled

bythestochasticorahigh-dimensionalprocess.

Basedontheactualdata,wecandividetheearthquake

levels. Inthisarticle,theﬁrstcategorycorrespondsto

the“generallevel", the second category correspondsto

"medium-level"thirdcategorycorrespondstothe“sever-

itylevel”withaclassiﬁcationleveldiﬀerenttoregression

analysis.

6. Conclusion

Our interpretation of geophysical, geological and geo-

chemicaldataforEQgenesispatternsintheHimalayan

basinsuggestthatinter-seismicuppercrustaldeformation

maybelocalizedalongshallowthrustfaultswithintense

groundmotionwithlargedisplacementandhighaccel-

erationalongthebrittlefracturezonesintheHimalayas.

Theabovesetofstatisticalanddescriptiveanalysisforex-

tractingthenatureofseismicpatternsintheHimalayan

beltrevealsthatthefrequencyofEQshasincreasedwith

theyears.ThenumberofEQsmainshocksof5.0ormore

hasbeen high inthepast few years. Theinterevent

timelydistribution plays averyeﬀectiverolein earth-

quakemainshockdistributionintheclusterearthquakes.

Utilizingneuralnetworksasatoolintheanalysisofb

value,dvalueandrecurrencedistributionsformainshock

andaftershockeventsfortheseismicregimetoforecast

earthquakesgivesinsighttotheearthquakesizevariation

inthevariousclustersofHimalayas.Thepatternofmain

shockswithmag>5.0hasapositivecorrelationwiththe

aftershockpatterndistribution. Thedistributionoffocal

depthoftherecordedEQshadanegativeskewshowing

thattheEQsweremorelikelytoemergenearthesurface

ofthebrittlecrust.TheseasonaltrendofEQmainshocks

respondfavorablywiththeradonanomalyobserved. The

numberofEQsofmagnitudes greater than 5aremore

inthe month ofSeptemberOctober when radonpeaks

aremore. Thisshowsthatsecularinter-seismicstrainis

modulatedbystrongseasonalvariationsthatdependson

localsurfaceloadvariations.ThefrequencytableforEQs

ofmag>5.0andthatofmag>6.0wasinvestigatedus-

ingtheindependenttsampletest.Theresultsshowthat

theEQmainshocksofmag>6.0tendstofollowasignif-

icantlypredictabletimewindowcomparedtomag>5.0

populations. Theaftershockpatternswereclustered in

the2000–2010decadeproducingthehighestnumberof

aftershocksaround2008forallpopulationofmainshock

pattern.Therestillexistmanycomplexitiesnotexplained

inthissystem.Theoverallstudyusingseismicprecursors

hasgivenussuﬃcientinsightovertheHimalayanearth-

quakesoccurringinclusterbelts.Theindependentearth-

quakesarebasicallyrandomoccurrenceintheHimalayan

regionandfollownostrictdistribution. Onthecontrary

earthquakeswarmsaremoreﬁnelydistributedandself

organizemore.Ongoingresearchwillbefocusedinana-

lyzingthestructureandtopologyoffaultnetworksatthe

stateofseismicquiescenceandhowinter-eventdistribu-

tionaﬀectsthestressdynamicsofaftershockdistributions

andwhetherastatisticalinferencecanbedrawntoﬁnd

arelationshipgeneratedbytheaftershockofaprevious

earthquakeintheclusterandthecomingevent. 249

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

Table 5. Seismic Annual value of seismic activity indicators

YearsHighestnumber

of The magni-

tude BvalueAverage

MagnitudeLatitude

Deviation Longitude

Deviation Average

Latitude Average

Longitude Number

oftimes Number

oftimes

Poor Dvalue Maximum

Magnitude

1997 0.3125 0.45 0.4902 0.7639 0.93 0.4643 0.1765 0.0473 0.5 0.1 3.9

1998 0.3125 0.49 0.3333 0.8611 0.57 0 0 0.8581 12. 2.4 6.3

1999 0 0.65 0.7647 1.0000 0.96 0.1786 0.0588 0.2462 0.03 0.28 3.5

2001 0 0.60 0.0196 0.8889 0.94 0.3214 0.1765 0.1081 0.2 0.4 3.9

2002 0.1875 0.50 0.3137 0.5972 0.80 0.1786 0.3529 0.1419 1.0 1.1 5.0

2003 0 0.62 0 0.8194 0.96 1.0000 0.2353 0 0.2 0.15 3.5

2004 1.0000 0.36 1.0000 0 0.53 0.1786 1.0000 1.0000 15.0 2.8 6.3

2005 0.5000 0.43 0.5686 0.1528 0.7 0.1429 0.9412 1.0000 0 0.22 4.1

2006 0.1875 0.42 0.6471 0.7917 1.12 0.2857 0.5882 0.0405 0.02 1.0 5.1

2007 0.5000 0.43 0.6078 0.6528 0.89 0.3214 0.6471 0.0405 0 0.3 5.4

2008 0.3125 0.43 0.6078 0.8333 1.05 0.4286 0.5882 0.0878 1.00 0.14 4.0

2009 0.3161 0.45 0.5000 0.7853 1.00 0.4424 0.1825 0.0501 0.4 0.12 4.1

Acknowledgements

AaronClausetforenlightening comments andclariﬁca-

tionsaboutdistributionswithheavy tailsandaspecial

thanksforthereviewersfortheirinformativecommentsin

comprehensiveanalysisoftheseismicprocess.

References

[1]AbumuradK.M.,Al-TaminmiM.,Emanationpowerof

radonanditsconcentrationinsoilandrocks.Radiat.

Measurem.,J.Geophys.Res.,2001,Vol34,pp.423-

426

[2]Adelﬁo G., Chiodi M., De Luca L., Luzio D., Vi-

tale M. South erntyrrhenian seismicity in space-

time-magnitudedomain.AnnalsofGeophysics,2005,

49(6),1245-1257.

[3]AlariﬁA.S.,AlariﬁN.S.,Earthquakemagnitudepre-

dictionusingArtiﬁcialneuralnetworkinnorthernred

seaarea,AmericanGeophysicalunion,fallmeeting,

2009.

[4]Andreas V. M. H., Hopﬁeld J., Earthquake Cy-

cles and Neural Reverberations: Collective Os-

cillations in Systems with Pulse-Coupled Thresh-

old Elements, Phys.Rev.Lett.75,1995,1222–1225,

DOI:10.1103/PhysRevLett.75.1222.

[5]Arjun C.R., Kumar A., Artiﬁcial neural network –

basedestimationofpeakgroundacceleration,ISET,

2009,501,Vol.46(1),pp.19–28.

[6]BensonD.A.,SchumerR.,MeerschaertM.M.Recur-

renceofextremeeventswithpower-lawinter-arrival

times,GRL,2007,34(16):Art.No.L16404.

[7]BettinelliP.A.,AvouacJ.P.B.,FlouzatM.,Bollinger

F.,RamillienG.,RajaureS., SapkotaS.,Seasonal

variations of seismicity and geodetic strain in the

Himalayainducedbysurfacehydrology.Earthand

PlanetaryScienceLetters,2008vol266pp.332–344.

[8]BoﬀettaG.,CarboneV.,GiulianiP.,VeltriP.,Vulpi-

aniA.Powerlawsinsolarﬂares:self-organizedcrit-

icalityorturbulence? Phys.Rev.Lett.1999,88,pp.

4662–4665.

[9]BufeC.G.,VarnesD.J.Predictivemodelingofseis-

mic cycle of the Great San Francisco Bay Re-

gion. J. Geophys Res., 1993. 98 pp. 9871–9883.

doi:10.1029/93JB00357.

[10]CampbellK.W.,ThenhausP.C.SeismicHazardAnal-

ysis EarthquakeEngineeringHandbookEditedby

Wai-FahChenandCharles ScawthornCRCPress

Print,ISBN:978-0-8493-0068-4eBookISBN:978-

1-4200-4244-3DOI:10.1201/9781420042443.ch.8

pg.2,2003.

[11]ChelidzeT.,RubeisV.D.,MatcharashviliT.,TosiP.,

Inﬂuence of strong electromagnetic discharges on

thedynamicsofearthquakestimedistributioninthe

Bishkek test area (Central Asia), Annals of Geo-

physics,2006,VOL.49,pp.4/5.

[12]Chen C.C., Rundle J.B., Holliday J.R., Nanjo,

K.Z., Turcotte D.L., Li S.C., Tiampo K.F., The

1999 Chichi,Taiwan, earthquake as a typical

example of seismic activation and quiescence,

Geophys. Res. Lett., 32(22), 2005, L22315,

doi:10.1029/2005GL023991.

[13]Chialvo D.R. Emergent complexity: What uphill

250

P. K. Dutta, O. P. Mishra, M. K. Naskar

analysis or downhill invention cannot do 2010.

doi:10.1016/j.newideapsych.2007.07.013.

[14] Chyi L.L., Chou C.Y., Yang F.T., Continuous Radon

MeasurementsinFault,WesternPaciﬁcEarthSci-

ences,2001Vol.1,No.2,242.

[15]Clauset A., Shalizi C.R., Newman M.E.J., Power

Law distribution in empirical data, 2009, arxiv.org

:0706.1062v2(physics.data-an).

[16]Corral A. Long-term clustering, scaling, and uni-

versalityinthetemporaloccurrenceofearthquakes.

Phys.Rev.Lett.2004,92,pp.108501–108504.

[17]CorralÁ.Dependenceofearthquakerecurrencetimes

andindependenceofmagnitudesonseismicityhistory

Tectonophysics,2006,424,177–193.

[18]CorralA.,ChristensenK.,Commenton“Earthquakes

De-scaled:OnWaitingTimeDistributionsandScal-

ingLaws,”Phys.Rev.Lett.,2006,96,109801.

[19]Dahmen K., Ertaş D.,Yehuda Ben-Zion.Gutenberg-

Richter and characteristic earthquake behav-

ior in simple mean-ﬁeldmodelsofheterogeneous

faults, Phys. Rev. E 58, 1998. 1494–1501,

doi10.1103/PhysRevE.58.1494.

[20]DelWayneR.,BohnenstiehlG.,TolstoyM.,Smith

K., Fox C.G., Dziak R.P., Time-clustering be-

havior of spreading-center seismicity between 15

and 35°N on the Mid-Atlantic Ridge: observa-

tionsfromhydroacousticmonitoring Physics of the

EarthandPlanetaryInteriors138,2003,pp.147–161

doi:10.1016/S0031-9201(03)00113-4.

[21]DinkelmanM.G.,GranathJ.,BirdD.,HelwigJ.,Ku-

marN.,EmmetP.,PredictingtheBrittle-Ductile(B-

D) Transition in Continental Crust Through Deep,

LongOﬀset,PrestackDepthMigrated(PSDM),2D

SeismicData*SearchandDiscoveryPostedIn:AAPG

International Conference and Exhibition, Rio de

Janeiro,Brazil,November15-18,2010.

[22]Dutta P.K., Mishra O.P., Naskar M.K. A Poisson

ProcessHiddenMarkovCellularAutomataModelin

EarthquakeGenesisandConﬂictAnalysis:APhysi-

calApproach,2012,JournalofSeismologyandEarth-

quakeEngineering,2012,Vol14(2),pp.81-90.

[23]Dutta,P.K.,M.K.Naskar,andO.P.Mishra."Testof

strainbehaviormodelwithRadonanomalyinearth-

quakepronezones."HimalayanGeology33.1(2012):

23-28.

[24]EftaxiasK.,AthanasopoulouL.,BalasisG.,Kalimeri

M., Nikolopoulos S., Contoyiannis Y., Kopanas J.,

AntonopoulosG., Nomicos C.Unfoldingtheproce-

dureofcharacterizingrecordedultralowfrequency,

kHZandMHzelectromageticanomaliespriortothe

L’Aquilaearthquakeaspre-seismicones.PartI,2009,

arXiv:0908.0686v1(physics.geo-ph).

[25]FaenzaL.,HainzlS.,ScherbaumF.,BeauvalC.,Sta-

tisticalanalysisofthetimedependentearthquakeoc-

currenceanditsimpactonhazardinlowseismicity

regions,Geophys.J.Int.,2007171.

[26]GabrielovA.,NewmanW.I.,TurcotteD.L.,Exactlysol-

ublehierarchicalclusteringmodel:inversecascades,

self-similarity,andscaling.PhysicalReview,1999,E

60,5293–5300.

[27]GhoshD.,DebA.,SenguptaR.,PatraK.K.,BeraS.,

Pronouncedsoil-radonanomaly–precursor ofrecent

EQsinIndia.RadiationMeasurements,2007,v.42.

[28]GutenbergB.,RichterC.Frequencyofearthquakes

inCalifornia,Bull.Seismol.Soc.Am.,194434,185–

188.

[29]Hainzl,S.,G.Zöller,J.Kurths."Self-organizedcriti-

calitymodelforearthquakes:quiescence,foreshocks

andaftershocks."InternationalJournalofBifurcation

andChaos9(12),1999: 2249-2255.

[30]Hainzl,S.,G.Zöller,andJ.Kurths.Self-organization

of spatio-temporal earthquake clusters. Nonlinear

ProcessesinGeophysics7(1),2000: 21-29.

[31]HainzlS.,Selforganization of earthquake swarms,

JournalofGeodynamics,2003,Vol35,pp.157-172.

[32]Hainzl,S.,Seismicitypatternsofearthquakeswarms

duetoﬂuidintrusionandstresstriggering.Geophysi-

calJournalInternational159(3),2004pp.1090-1096.

[33]KaganY.Y.Whydoestheoreticalphysicsfailtoexplain

and predict earthquake occurrence?, In: Modeling

CriticalandCatastrophicPhenomenainGeoscience:

A Statistical Physics Approach, Lecture Notes in

Physics,2006,705,pp.303-359.

[34]KaragiannisT.,BoudecJ.Y.L.,VojnovicM.,PowerLaw

andExponentialDecayofInterContactTimesbe-

tweenMobileDevices,In:MobiCom’07,2007,Mon-

tréal,Québec,Canada.

[35]KhattriK.,WyssM.,Precursoryvariationofseismic-

ityrateintheAssamarea,IndiaGeology,Vol6;no.

11;1978,pp.685-688;doi:10.1130/0091-7613.

[36]Knopoﬀ L., Levshina T., Kellis Borok V.J., Mat-

toniC.,Increaselong-rangeintermediate-magnitude

earthquake activity prior to strong earthquakes in

California. J Geophys Res., 1996, 101:5779–5796.

doi:10.1029/95JB03730.

[37]Li H.C., Chen C.C., Characteristics of long-

term regional seismicity before the 2008 Wen-

Chuan, China, earthquake using pattern in-

formatics and genetic algorithms, Nat. Haz-

ards Earth Syst. Sci., 11, 1003–1009, 27

Apr, 2011,

www.nat-hazards-earth-syst-sci.

net/11/1003/2011

, 2011, doi:10.5194/nhess-11-

1003-2011.

[38]LodiS.H.,RafeeqiS.F.A.,Earthquakes-Consequences

251

Evaluation of seismogenesis behavior in Himalayan belt using data mining tools for forecasting

andResponse,1996 viewed 1 March, 2011

www.

neduet.edu.pk/Civil/consequences.pdf

[39]LuaC.,Thedegreeofpredictabilityofearthquakesin

severalregionsofChina:Statisticalanalysisofhis-

toricaldata,JournalofAsianEarthSciences,2005,

25,379–385.

[40]MalamudaB.D.,TurcotteD.L.,Theapplicabilityof

power-lawfrequencystatisticstoﬂoods, Journal of

Hydrology,2006,322,pp.168–180.

[41]Matsuzawa T., Hirose H., Shibazaki B., Obara K.,

Modeling short- and long-term slow slip events

in the seismic cycles of large subduction earth-

quakes,J.Geophys.Res., 2010, 115,B12301,DOI:

10.1029/2010JB007566.

[42]Mega M.S., Allegrini P., Grigolini P., Latora V.,

Palatella L., Rapisarda A., Vinciguerra S. Power-

LawTimeDistributionofLargeEarthquakes,Phys-

icalReviewLetters, Volume 90, Number18, 2011,

DOI:10.1103/PhysRevLett.90.188501.

[43]Mitra S., PriestleyK., Bhattacharyya A.K., Gaur

V.K.,Crustalstructureandearthquakefocaldepths

beneath northeastern India and southern Tibet,

Article ﬁrst published online: 4 Nov. 2004,

DOI: 10.1111/j.1365-246X.2004.02470.x, Geophysi-

calJournalInternational,Volume160,Issue1,2005,

pp.227–248.

[44]MishraO.P.ThreeDimensionalTomographyofNorth

EastIndiaandIndoBurmaRegionandimplications

on Earthquake Risks. In: National Workshop on

EarthquakeRiskmitigationstrategyinNorthEast

India,2011.

[45]McGuireR.K.Seismichazardandriskanalysis.EQ

engineeringresearchinstitute(EERI)OklandCali-

forniaUSAMonographseries,2004.

[46]MolchanG.,andT.Kronrod."Seismicintereventtime:

aspatialscalingandmultifractality."PureandAp-

pliedGeophysics164(1),2007pp.75-96.

[47]MukhopadhyayB.,AcharyyaA.,DasguptaS.,Poten-

tialsourcezonesforHimalayanEarthquakes: Con-

straintsfromspatial–temporalclustersNatHazards,

2010,DOI10.1007/s11069-010-9618-2,pg3.

[48]NathS.K.,ThingbaijamK.K.S.,GhoshS.K.,EQ

catalogueofSouthAsiaagenericMW scaleframe-

work,2010,viewed7 Feb,2011,

www.earthqhaz.

net/sacat/

,2010.

[49] Panakkat A., Adeli H., Neural network models for

earthquakemagnitudepredictionusingmultipleseis-

micity indicators. International Journal of Neural

Systems,2007,Vol17(1),pp.13-33.

[50]PapadimitriouP.Identiﬁcation ofseismicprecursors

beforelargeearthquakes: Deceleratingandacceler-

atingseismicpatterns,J.Geophys.Res.,2008, vol:

113,B04306,doi:10.1029/2007JB005112.

[51]PapazachosB.C.,KarakaisisG.F.,PapadimitrouE.E.,

PpaioannouC.H.Theregionaltimeandmagnitude

predictablemodelanditsapplicationtotheAlpine-

Himalayanbelt bTectonophysics, Volume 271, Is-

sues3-4,1997,15pp.295-323,DOI:10.1016/S0040-

1951(96)00252-1.

[52]PapazachosB.C.,KarakaisisG.F.,ScordilisE.M.,Pa-

pazachosC.B., PanagiotopoulosD.G.,Presentpat-

ternsofdecelerating–accelerating seismic strainin

South Japan, J Seismol., 2010, 14:273–288, DOI

10.1007/s10950-009-9165.

[53]PaudyalaH., ShankerD.,SinghH.N.,Characteris-

ticsofearthquakesequenceinnorthernHimalayan

regionofSouthCentralTibet–Precursorsearchand

locationofpotentialareaoffutureearthquake.Jour-

nalofAsianEarthSciences,2011,Volume41(4-5),

Pages459-466,ValidationofEarthquakePrecursors-

VESTO,DOI:10.1016/j.jseaes.2010.11.019.

[54]Ramola R.C., Prasad Y., Prasad G., Kumar

S., Choubey V.M. Soil-gas radon as seis-

motectonic indicator in Garhwal Himalaya.

Appl. Radiat. Isot., 66, 1523-1530, 2008, DOI:

10.1016/j.apradiso.2008.04.006.

[55]Roman D.C., Heron P., Eﬀect of RegionalTectonic

SettingonLocalFaultResponsetoEpisodesofVol-

canicActivityGeologyFacultyPublications.Paper

2.,2007.

[56]SarlisN.V.,SkordasE.S.andVarotsosP.A.,Mul-

tiplicative cascades and seismicity in natural time

Phys.Rev.E80,022102,2009.

[57]SarlisN.V.,E.S.Skordas,VarotsosP.A.Nonextensiv-

ityandnaturaltime:Thecaseofseismicity.Physical

ReviewE82.2,2010:021110.

[58]Saichev,A.,SornetteD.,Theoryofearthquakerecur-

rencetimes.JournalofGeophysicalResearch:Solid

Earth(1978–2012),2007,112(B4).

[59]SavageM.K.Theroleofﬂuidsinearthquakegener-

ationinthe2009M6.3L’Aquila,Italy,earthquake

anditsforeshocks,2010,v.38no.11p.1055-1056,

DOI:10.1130/focus112010.1.

[60]Scholz C.H. Mechanism of seismic quies-

cences. Pure Appl Geophys. 26:701–718, 1998,

DOI:10.1007/BF00879016.

[61]SinghS.,SharmaD.K.,DharS.,RandhawaS.S.Ge-

ologicalsigniﬁcanceofsoilgasradon:acasestudy

ofNurpurarea, districtKangra,HimachalPradesh,

India.Radiat.Meas.,2006,41(4),482–485.

[62]SobolevG.A.,TyupkinY.S.,Low-seismicityprecursors

oflargeearthquakesinKamchatka,Volcanologyand

Seismology,1997,18,433–446.

[63]TiwariR.K.,LakshmiS.S.,Somecommonandcon-

252

P. K. Dutta, O. P. Mishra, M. K. Naskar

trastingfeaturesofEQdynamics in major tectonic

zonesofHimalayasusingnonlinearforecastingap-

proach,CurrentScience,2005,Vol.88,(4).

[64]Turcotte D.L., Self-organizedcriticality.Reportson

ProgressinPhysics62,1999,1377–1429.

[65]TurcotteD.L.,MalamudB.D.,MoreinG.,NewmanW.,

Aninverse-cascademodelforself-organizedcritical

behavior.,PhysicaA268,1999,pp.629–643.

[66] Varotsos P., Alexopoulos K., Physical properties of

thevariationsoftheelectricﬁeldoftheearthpreced-

ingearthquakes.II.Determinationofepicenterand

magnitude. Tectonophysics 110(1), 1984a, pp. 99-

125.

[67]Varotsos,P.,K.Alexopoulos.,Physicalpropertiesof

thevariationsoftheelectricﬁeldoftheearthpre-

cedingearthquakes.Determinationofepicenterand

magnitude.Tectonophysics110(1),1984bpp99-125.

[68]Varotsos,P.,LazaridouM.,Latestaspectsofearth-

quakepredictioninGreecebasedonseismicelectric

signals.Tectonophysics188(3),1991,pp.321-347.

[69]Varotsos, P., Alexopoulos K., LazaridouM., Latest

aspects of earthquake prediction in Greece based

onseismicelectricsignals,II.Tectonophysics224(1),

1993,pp1-37.

[70]VarotsosP. A., Sarlis N.V., Skordas E.S., Long-

rangecorrelationsintheelectricsignalsthatprecede

rupture.PhysicalReview,2002,E66:011902.

[71] Varotsos P. A. et al., Attempt to distinguish long-

rangetemporalcorrelationsfromthestatisticsofthe

incrementsbynaturaltimeanalysis.PhysicalReview

E74.2,2006:021123.

[72]VarotsosP.A.,SarlisN.V.,SkordasE.S.,Remark-

able changes in the distribution of the order pa-

rameter of seismicity before mainshocks EPL (Eu-

rophysics Letters), Volume 100(3), 2012a, 39002,

DOI:10.1209/0295-5075/100/39002.

[73]VarotsosP.A.,SarlisN.V.,SkordasE.S.,Orderpa-

rameterﬂuctuationsinnaturaltimeand-valuevari-

ationbeforelargeearthquakes,Nat.HazardsEarth

Syst.Sci.,12,3473-3481,2012b,DOI:10.5194/nhess-

12-3473-2012.

[74]VarotsosP.A.,N.V.Sarlis,andE.S.Skordas,"Scale-

speciﬁcorderparameterﬂuctuationsofseismicitybe-

foremainshocks: NaturaltimeandDetrendedFluc-

tuation Analysis." EPL (Europhysics Letters) 99(5)

2012c:59001.

[75] Walia V., VirkH.S., Yang T. F., Walia M., Bajwa

B.S.,RadonPrecursoryAnomaliesforSomeEQsin

N-WHimalaya,India,4thTaiwan-JapanWorkshop

onHydrologicalandGeochemical ResearchforEQ

Prediction,2010.

[76]WeatherlyD.,RecurrenceIntervalStatisticsofCellu-

larAutomatonSeismicityModels,PureandApplied

Geophysics,Volume163(9),1933-1947, 2001,DOI:

10.1007/s00024-006-0105-3.

[77]Wilson L. (eds), “Welcome to the Hi-

malayan Oregony” viewed 27 Feb, 2011,

www.geo.arizona.edu/geo5xx/geo527/

Himalayas/geophysics.html

[78]WyssM.,KleinF.,JohnstonA.C.,Precursorsofthe

KalapanaM=72EQ.J.Geophys.Res.86:1981,

3881–3900.DOI:10.1029/JB086iB05p03881.

[79]WyssM., HabermannR.E.,Precursoryseismicqui-

escence. Pure Appl. Geophys. 126:319–332, 1998,

DOI:10.1007/BF00879001.

[80]WyssM.,WiemerS.,HowCanOneTesttheSeismic

GapHypothesis? TheCaseofRepeated Ruptures

intheAleutians,Pureappl.geophys.155,259–278,

1999,DOI:0033–4553/99/040259–20.

[81]WyssM.,DmowskaR.(Eds.):EarthquakePrediction

–StateoftheArt,1997,272pp.,BirkhauserVerlag.

[82]ZhaoW.,NelsonK.D.,Deepseismicreﬂection evi-

dencecontinentalunder-thrustingbeneathsouthern

Tibet.Nature366,1993,557–559.

[83]Zöller G., Hainzl S., Kurths J., Zschau J., A

systematic test on precursory seismic quiescence

in Armenia. Nat Hazards, 2002, 26: 245–263.

DOI:10.1023/A:1015685006180.

[84]ZöllerG.,HainzlS.,KurthsJ.,ZschauJ.,Seismicqui-

escenceasanindicatorforlargeearthquakesina

system of self-organized criticality, Geophys. Res.

Lett.,2000Vol27,pp.597-600.

[85] Zúńiga, F. R., Stefan W., Seismicity patterns: Are

theyalwaysrelatedtonaturalcauses?.PureandAp-

pliedGeophysics155(2-4),1999:713-726.

253

Content uploaded by O. P. Mishra

Author content

All content in this area was uploaded by O. P. Mishra on Mar 08, 2014

Content may be subject to copyright.