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Computers & Geosciences 36 (2010) 977–986

Contents lists available at ScienceDirect

Computers & Geosciences

0098-30

doi:10.1

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journal homepage: www.elsevier.com/locate/cageo

Proschema: A Matlab application for processing strong motion recordsand estimating earthquake engineering parameters$

M. Segou n, N. Voulgaris

Department of Geophysics–Geothermics, Faculty of Geology and Geoenvironment, National and Kapodistrian University of Athens, Panepistimioupoli Zografou,

15784 Athens, Greece

a r t i c l e i n f o

Article history:

Received 31 March 2009

Received in revised form

3 July 2009

Accepted 14 September 2009

Keywords:

Corrected time series

Response spectra

Seismology

Smc format

CDMG format

European strong motion database format

04/$ - see front matter & 2010 Elsevier Ltd. A

016/j.cageo.2009.09.020

e available from server at http://www.iamg.o

esponding author. Tel.: +30 210 7274206; fa

ail addresses: msegou@geol.uoa.gr (M. Segou)

is@geol.uoa.gr (N. Voulgaris).

a b s t r a c t

Strong motion records are the original input data for earthquake engineering studies and earthquake

resistant building codes. Records originating from both, analogue and modern digital instruments

should be subjected to processing in order to derive credible engineering parameters, such as spectral

ordinates. A number of processing procedures have been proposed by many researchers through the

past few decades; the goal, however, is always the same, identification and removal of noise and

performing necessary adjustments related to the operating instrument or the record’s quality. A careful

inspection of previous processing schemes and the design of several development tests related to major

processing steps proved to be helpful in determining the necessary features that the presented software

should include. These have been incorporated in a Matlab application in order to produce a reliable and

user-friendly interface giving the researcher several options on major processing steps. It is noteworthy

that this computer program can be used either as an interactive process tool when individual record

processing corresponds to the researcher’s needs or, for processing a vast number of records without

user supervision and even through a scheduled task.

& 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Software packages capable to address the need for strongmotion record processing belong to two main categories; they areeither a product of a seismological instrument provider company,which in several cases do not incorporate the latest advances indigital signal processing, or a non-commercial research product.Proschema belongs clearly to the second category since theobjective behind its development has been to provide the user, aseismologist or an engineer, with the necessary ground motionparameters, through a prism of innovation regarding processingand computational techniques.

In case the derived spectral parameters are to be used inearthquake engineering research the processing scheme is offundamental importance since the effort is twofold; to removethe noise in periods of interest and to protect the actual frequencycontent of the record from the application of unnecessarysevere filters.

During the processing of any strong motion record severalactions are better carried out either in the time or frequencydomain; this criterion has been fundamental during Proschemadevelopment.

ll rights reserved.

rg/CGEditor/index.htm

x: +30 210 7274878.

,

Briefly, Proschema supports filter implementation in order toremove high and low frequency noise by applying carefullydesigned filters and inspection of the corrected acceleration,velocity and displacement time series, calculation of elasticresponse spectra in terms of pseudo spectral acceleration, velocityand displacement for basic damping values, and computation ofother spectral ground motion measures independent of the sensororientation (Boore et al., 2006). The user can also estimateearthquake engineering parameters such as peak ground motionparameters, effective peak acceleration and velocity, responsespectrum intensity, acceleration spectrum intensity as well ascumulative absolute velocity, root-mean squared acceleration andArias intensity.

The user can be either involved in an interactive processby providing all necessary choices, developing a personalstandardized procedure, or perform automatic processing basedin predefined choices.

The structure of the paper follows the description of the itemson the main menu bar of Proschema in order to familiarize thereader with the presented software.

2. Strong motion record processing with Proschema

This manuscript provides a thorough description of thefeatures supported by Proschema; but the user can also findadditional documentation, some theoretical aspects as well as a

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user guide in the Proschema User Manual (included with thesoftware) together with useful suggestions about strong motionprocessing.

2.1. Supported data formats

Proschema supports importing strong motion raw data inseveral formats such as Cosmos version 1.10, and 1.20 smc files(COSMOS Strong Motion Data Format, 2001), the format of theEuropean Strong motion Database, as well as CDMG format(Shakal and Huang, 1985). Since several networks for conveniencein distribution form multicomponent records derived from theconcatenation of single component smc format files (COSMOSStrong motion Record Format, 2001) a supplementary optionregarding the aforementioned files has been incorporated in thesoftware. Additionally, the user can import seismometer data inSAC binary format and proceed with processing and calculation ofresponse spectra and engineering parameters (Clinton andHeaton, 2002); but caution is advised since the user shouldprovide instrument corrected (Havskov and Alguacil, 2004)velocity time series in this case.

There are however two specific formats, referred as NOA andITSAK, used by Greek providers of strong motion data, that eventhough they bear characteristics of other standard formats theyare highly irregular due to the existence of many spacing errorsand non-standarized format requiring the implementation ofspecial scripts to address this inhomogeneity. A number ofutilities simplifying the conversion between formats can be alsofound in the software package for third party usage.

For reasons of minimizing computer storage requirementsProschema keeps both uncorrected and processed time series,their corresponding spectra and estimated parameters in matMatlab binary files in the form of a standardized Matlab structure.The aforementioned .mat files can be also loaded into Proschemaif the user wishes to reprocess or view the corrected or raw timeseries once again.

Before importing raw data the user can also visually inspectstrong motion records in order to decide which should be furtherprocessed. After importing a strong motion record the user mayopt to review earthquake and station metadata as well asavailable processing features.

2.2. Time and frequency domain processing

As it was briefly mentioned in the introduction Proschemasoftware supports actions carried out in the time and frequencydomain. Several actions such as the construction of an equallysampled time series, performing re-sampling techniques, orinstrument and baseline adjustment can be thought as timedomain related processing but when it comes to mathematicalintegration or the implementation of a previously designed filter,frequency domain related processing is advisable. An analyticaldescription of the features related with Time and FrequencyDomain Processing serve the purpose of informing the readerabout Proschema capabilities.

2.2.1. Time domain processing

Time domain processing is related to the first processing stepsincluding the construction of an equally spaced time series, theremoval of the records’ mean, the application of re-samplingtechniques and performing necessary adjustments which arereferred for many years as instrument and baseline correction.

The user is encouraged to inspect the raw time series since theexistence of spurious spikes appears to be a common problemthat should be dealt within the pre-processing stage. The

algorithm used for identification and removal of spikes has twostages; taking into consideration the severity of the problemwhile at the same time protecting the original information thatthe raw time series provides. During the first stage spikeidentification is accomplished by a running window withoverlapping segments, tracking unexpected exceedances of athreshold acceleration, which is re-computed for each window.The peaks indentified earlier as spikes are replaced with anaverage amplitude value, between preceding and followingsamples. In cases where spurious spikes remain, a second stageof spike removal can be implemented. At this point the part ofthe signal that does not contain spurious spikes is protected fromthe de-spiking effort.

In a number of cases uncorrected strong motion records do notcorrespond to equally sampled time series. In order to solve thisproblem equally sampled series are constructed with samplinginterval equal to the minimum digitization time step of theoriginal time series. If the minimum interval tends to be veryclose to zero then the sampling interval equals the average ofdigitization time steps.

An additional interesting feature is related with the removal ofthe mean of the user-defined pre-event segment of digital strongmotion records.

The application of re-sampling techniques, such as over-sampling and decimation of a time series, is provided in orderto change the initial sample rate. Proschema implementsresampling by employing a flexible public-domain resamplingalgorithm, found in MATGPR software (Tzanis, 2006) described indetail in Smith and Gosset (1984). According to the theory of idealbandlimited interpolation during sampling rate conversion theproblem is to correctly compute signal values at arbitrarycontinuous times from a set of discrete time samples of thesignal amplitude. After considerable research has been devoted tothe problem of interpolating discrete points the method followedin order to reconstruct the re-sampled time series uses a cardinalsine function evaluated at specified times. According to Karl(1989) changing the sampling rate without previous oversam-pling can lead to significant loss of higher frequency content ofthe strong motion record. The degree and method of up-samplinghas been investigated through development tests by acceptingminimum impact on the original strong motion record as theappropriate criterion for choosing the method of interpolationand the degree of re-sampling. The default oversampling supportsan intermediate sampling rate at 600 samples-per-second bycardinal sine interpolation (Karl, 1989) whereas before down-sampling, at 200SPS, a low-pass filter is implemented inpreparation of decimation for ensuring that no aliasing occurs inthe decimated data. The choice of arbitrary oversample anddecimation allows the user to define the desired degree ofup-sampling and decimation.

Instrument adjustment is performed by a second orderdifferential equation representing the motion of a single degreeof freedom, damped, harmonic oscillator which is common-practice since the late 70s (Hudson, 1979). Since most of theavailable strong motion databases incorporate both digital andanalogue records, instrument adjustment should be performedwhen needed. Although strong motion records from this kind ofanalogue instruments suffer artificial high frequency noise due tohand digitization (Trifunac et al., 1999) in most cases, especiallywhen the natural frequency of the accelerograph is less than20 Hz, instrument adjustment should not be avoided. Develop-ment tests showed that in the case of an analogue instrumentwith natural frequency of 17 Hz the lower frequency adjustedis of the order of 10.5–12 Hz; pointing out the importance ofperforming instrument adjustment (Fig. 1). The suggested processcan adjust for the instrument response to frequencies reaching up

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Fig. 1. Influence of performing instrument adjustment. Fourier amplitude spectrum of an uncorrected acceleration time series against the spectrum of an instrument

adjusted time series of a typical analogue accelerograph with natural frequency 17 Hz. Instrument adjustment begins for frequencies significantly lower than nominal

frequency of instrument.

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to a quarter of the Nyquist frequency (Boore and Bommer, 2005)so the user is encouraged, in case of analogue recording, toprovide a 200SPS original frequency in order to compensate, atleast partially, for the analogue instrument’s deficiency to recordstrong motion near its nominal natural frequency.

Baseline adjustment involves a quadratic fit to the velocitytime series which is then applied to the acceleration time series; asimplification of the procedure formerly proposed by Graizer(1979).

2.2.2. Frequency domain processing

The frequency domain processing holds the most significantpart of any processing scheme since it involves frequencyfiltering.

Primary inspection of the Fourier amplitude spectrum of theuncorrected acceleration, velocity and displacement time seriesprovide an evaluation tool for the frequency content of the record.

Mathematical calculations such as integration, in order toobtain velocity time series, are carried out in the frequencydomain to produce more stable results. Taking into considerationthe invertibility of the Fourier transform, according to Parseval’ stheorem, the acceleration time series can be represented throughits Fourier transform. Extending the above, the integration of asinusoidal input signal in the frequency domain corresponds tothe convolution with the frequency response of the perfectintegration operator which is 1/io whereas for differentiationthe frequency response of the operator is just the inverse of theintegrator, simply io (Karl, 1989).

Another tool provided to evaluate the record’s frequencycontent supports the visual inspection of the Fourier amplitudespectrum of the uncorrected acceleration time series of recordingscorresponding to the same earthquake, selected by means ofdistance and site characteristics. This option aids the user for theinvestigation of the ‘‘actual’’, on acceptance that this term exists,frequency content of the recording. It should be noted thatrecords corresponding to near field recordings are excluded from

this primary inspection in order to avoid high frequency content,due to near-source effects, and nonlinear de-amplificationphenomena. The latter are more prominent in soft sites,characterized with shear wave velocity less than 360 m/s, atepicentral distances less than 10 km; which only supports furtherthe authors’ decision to exclude near field recordings. The selectedrecordings are eventually sorted by means of inter-stationdistance and the Fourier spectrum of the uncorrected accelerationof the homonymous components of the three closest stations arefinally provided.

2.2.3. Filter design

The most time consuming procedure when processing strongmotion records involves filter design and implementation.Concerning the design of a frequency filter, the authors believethat the user should be free to decide which is the preferable filtertype and its specifications, but within limits. The limits, control-ling such sensitive a procedure, had been set during thedevelopment tests of Proschema and lead to the conclusion thatinfinite impulse response filters of Butterworth and elliptic typecan adequately be used in strong motion record processing.

During the first steps of the present study the theoreticalaspects of filter characteristics played an important role inselecting which filter types should be incorporated in thesoftware. The minimum required filter order and the nature ofthe transition band of the filter type had been the main criteria forchoosing the filter types used during development tests (Fig. 2).Elliptic filters are the most efficient in achieving performancespecifications with the minimum filter order (Proakis andManolakis, 1996). For a given filter order and performancespecifications elliptic filters exhibit the smallest transition band,in other words they have the steepest roll off ; a factor that whichcan also influence the spectral ordinates.

Butterworth filter type is characterized by a magnituderesponse that is maximally flat in the pass band and monotonic

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Fig. 2. Impact of different filter types and implementation techniques on spectral ordinates. Pseudo acceleration response spectra for 5% damping value after four different

types of filtering, all supported by Proschema software. Two filter types, such as Butterworth and elliptic, are combined with two types of filter implementation, such as

causal and phase preserving, in order to illustrate filtering capabilities of this code. All four filtering options have common cut-off frequencies. Usable Data Bandwidth

between 0.0416 and 5.3334 s corresponds to the most strict choice for Butterworth filters; elliptic filters are sharper than other but they show ripples across whole

bandwidth which suggests further caution when designing and implementing the latter.

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overall (Ingle and Proakis, 1997) and as a consequence provides asmoother transition band with low filter order.

Thus, although elliptic filters meet given performance speci-fications with the lowest order of any filter type, Butterworthfilter is a better choice for several applications since it has betterphase response, whereas the first one is highly non linear near thedesirable data bandwidth (Proakis and Manolakis, 1996).

The concept of parsimony applied in filter design suggests thatButterworth filter stands as a better choice since a smaller numberof designing factors is required, providing the user with more stableand replicable results. The definition of the values of pass bandripple and stop band attenuation are two factors required whendesigning an elliptic filter, which can lead to different peak groundacceleration and Arias intensity values even if the order of the filterhas not been changed (Mollova, 2007). Therefore, selecting Butter-worth combines the smaller filter order with less steep roll off thanan elliptic filter. The user is free to decide however between thesetwo filter types when designing a frequency filter.

Except filter type there is a number of filter specifications, suchas the filter order, five pairs of possible low and high cutfrequencies, the nature of the filter’s implementation, that iswhether a causal or phase-preserving implementation is requiredand if the time series should be tapered or not, that the usershould enter before the implementation of the filter. Proschemafacilitates the definition of filter’s specifications through a figurewindow, in which the Usable Data Bandwidth (UDB) is also stated,

next to the possible pairs of cut off frequencies. The Usable DataBandwidth for a strong motion record lies between the high andlow corner frequencies of the selected filter. According to Shakalet al. (2003) outside this band, filters have removed a greatamount of information making data practically unusable inmodelling structure response.

2.2.4. Filter implementation

In order to create a less severe processing scheme each pair ofthe low and high cut off frequencies are combined in a bandpassfilter. Development tests supported the idea that a bandpassimplementation in contrast to a high pass followed by a low passfilter can produce differences in the time histories. It is ourobservation that implementing two filters can influence frequen-cies more severely than a bandpass filter, which appears to be amore stable solution.

To implement infinite impulse filtering of an acceleration timeseries in the frequency domain, Proschema multiplies the Fouriertransform of the input sequence with the frequency responsefunction of the filter followed by inverse Fourier Transform(Scherbaum, 1996).

Acausal implementation in the frequency domain is stronglysuggested since it will prevent phase delay (Scherbaum, 1996) butcausal implementation for specific applications should not beavoided (Fig. 3). Any recursive filter, such as Butterworth and

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elliptic, can be converted to zero phase with bidirectionaltechnique of filter implementation (Smith, 1997). Bidirectionalfiltering in the frequency domain involves further convolution theFourier transform of the data of the first pass with the conjugatefrequency response of the filter and subsequent inverse Fouriertransform.

In the case of the acausal filter implementation special cautionis advised concerning the length of the input sequence so as toaccommodate the filter’s infinite impulse response. Concerninginfinite impulse response filters with acausal implementation,padding symmetrically the time series with a number of zerosamples is of great importance (Boore, 2005; Boore and Akkar,2003; Karl, 1989). For a given low cut off frequency the filteredtime series can be severely influenced when no zero padding hasbeen performed before filtering or even when non adequatenumber of zero samples has been added. The number of zerosamples is a function of the low cut off frequency and the order ofthe filter (Converse and Brady, 1992; Boore, 2005).

Regarding tapering before padding with zero samples takesplace, in case the length of the original time series is adequateexceeding 10 seconds, a cosine taper is applied symmetrically tothe 20% of the time series. In any other case the zero padding isperformed before and after the first and last zero-crossing,respectively.

2.2.5. Selection of the appropriate cut-off frequencies

The stability and easy implementation of spectral divisiontechniques and frequency domain filtering allows the user toinspect the velocity and displacement time series once filtering ofthe acceleration time series is completed. The selection of theappropriate pair of cut off frequencies is accomplished through

Fig. 3. Filter implementation techniques. Acceleration time series filtered with causal an

cut-off frequencies at 0.15 Hz, are common for both implementations. Phase preserving

other hand equals a one-pass eight-pole filter implementation.

the comparison of acceleration, velocity and displacement filteredtime series which are simultaneously produced after the imple-mentation of five different band pass filters, which are designedaccording to the user defined filters’ specifications at the corres-ponding window. For clarification reasons the authors state that,Proschema combines user defined, filter specifications such asfilter type, order, implementation details with each—user defi-ned—pair of cut off frequencies; designing a unique frequencyresponse for each of the five filters. The next step involves filteringthe uncorrected acceleration time series with each one of the fivefilters and producing velocity and displacement time series afterintegration of the each filtered acceleration time series. Togetherwith the uncorrected acceleration, velocity and displacement timeseries the user can inspect five differently filtered acceleration,velocity and displacement time series and decide which onecorresponds to an adequately filtered time series (see UserManual for details about corresponding figure windows). Afterselecting the most suitable filtered time series the user can reviewclosely the latter by inspecting the corrected acceleration, velocityand displacement time series at a dedicated figure (Fig. 4).

2.3. Spectra

2.3.1. Calculation of response spectra

The authors note that in case of a previous phase preservingimplementation, where padding with a number of zero samplestook place, the filtered time series remains padded thought outthe response spectra calculation.

Spectra calculation comes after reviewing the selected filteredtime series which involves inspection of the uncorrected Fourier

d phase preserving implementation; notice phase shift. Butterworth filter type and

implementation corresponds to a two-pass four-pole filter whereas causal in the

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Fig. 4. Inspection of corrected acceleration, velocity and displacement time series with available header and metadata information.

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amplitude spectrum versus the filtered spectrum of theacceleration time series to verify the adequate implementationof the designed filter.

The calculation of elastic response spectra, in terms ofpseudo-acceleration, velocity and displacement, for the basicdamping levels over a wide range of period estimators (Fig. 5)follows the numerical method proposed by Nigam and Jennigs(1969) based on the exact solution of the differential equationdescribing ‘‘the maximum response of a simple oscillatorsubjected to base-acceleration a(t) ’’.

The authors selected this particular method because there areobservable differences between time and frequency domaincalculations (Robinson et al., 2006) of elastic response spectra.However small the arithmetic differences are, the smooth natureof time domain-derived response spectra lead to the finalselection of the well-documented solution provided by Nigamand Jennigs (1969).

Further response spectra calculation involves the computationof the measures of the geometric mean, introduced as GMRotD50and GMRotI50 (Fig. 6), which are independent of the sensororientation as defined in Boore et al. (2006). As stated thereinboth measures are ‘‘based on a set of geometric means computedfrom the as-recorded orthogonal horizontal motions rotatedthrough all possible non-redundant rotation angles’’. The readercan refer to the aforementioned paper for the detailed definitionof these two innovative measures since further analysis is beyondthe scope of the present paper.

2.3.2. Calculation of earthquake engineering ground motion

parameters

Concerning engineering parameters Proschema supports thecalculation of various ground motion parameters related withthe duration, the amplitude and the frequency content of theaccelerogram, such as peak ground acceleration and velocity,effective peak acceleration and velocity, response spectrumintensity, acceleration spectrum intensity, root-mean squaredacceleration and Arias intensity.

The effective peak acceleration (EPA) is defined as the averagespectral acceleration over the period range 0.1—0.5 s dividedby 2.5, which is the standard amplification factor for a res-ponse spectrum of 5% damping value. The effective peakvelocity (EPV) is proportional to the average of pseudo-velocityspectral ordinates at period of about 1 sec divided by a factorof 2.5. According to the Applied Technology Council (1978)both parameters could be considered as response spectranormalization factors and they are further used in Pro-schema during calculation of normalized response accelerationspectrum.

The response spectrum intensity (SI), as defined in Housner(1959) corresponds to the area under the pseudo-velocityresponse spectrum between 0.1 and 2.5 s for any fraction of thecritical damping.

Von Thun et al. (1988) introduced the acceleration spectrumintensity (ASI) defined as the area under the acceleration responsespectrum for 5% value of the critical damping between the 0.1 and0.5 s period estimators.

Since it is widely used in seismic hazard studies Proschemaincludes calculation of cumulative absolute velocity (CAV), whichis the area under the absolute accelerogram (Kramer, 1996).

The remaining engineering parameters include informationabout the amplitude and frequency content of the strong motionrecord to which root mean squared acceleration and Ariasintensity are closely related. The latter, defined by Arias (1970),is obtained by integration over the entire duration rather thanover the duration of strong motion (Kramer, 1996).

Even though modern studies proposed a variety of measuresregarding the evaluation of the duration of strong motion; theauthors feel that a widely used measure such as the bracketedduration will serve Proschema purposes well. The bracketedduration (Bolt, 1969) is defined as the time between thefirst and last exceedances of a threshold acceleration value,usually 0.05 g (Kramer, 1996). However the authors stress outthat engineering parameters such as root mean squaredacceleration are sensitive to the method used to determine strongmotion duration

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Fig. 5. Inspection of pseudo acceleration response spectra for basic damping values; cut off periods are indicated together with suggested usable data bandwidth, which for

Butterworth phase preserving implementation is considered to be the short and long period cut off multiplied by a factor of 1/0.8 and 0.8, respectively. It is strongly advised

to refrain from using spectral ordinates outside suggested usable data bandwidth, which lies between 0.0416 and 5.3334 s.

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Calculating engineering parameters, such as the responsespectrum intensity and acceleration spectrum intensity, bydefinition suggests definite integration; but the correspondingfunction is not only discontinuous, between the given set ofperiod estimators, but its arithmetic values under evaluation arethe solution in terms of local maxima of a set of non-linearequations providing the response of the system only atdiscrete values of period estimators; the above remark makesthe mathematical approach towards the area computation ademanding one.

The current approach supports the use of cubic splines for thesolution of the integral equation of an unknown function (Fig. 7).The boundary integral method is implemented after the approxi-mation of the unknown function by means of cubic splineinterpolation along the boundary. Analytically, a vector-valuedspline evaluated at planar points (x(i), y(i), i¼1yn), on thisoccasion the (x, y) stands for pairs of (T, PSA or PSV), forms aspline curve using chord-length parametrization and cubic splineinterpolation.

Cubic spline interpolation is characterized by continuous firstand second derivatives which allows for an accurate definition ofthe function under study. The use of cubic splines provides abetter approach than a high order polynomial because the firstlacks abrupt fluctuations.

The definition of the spline curve is followed by the construc-tion of the indefinite integral and the final evaluation of theindefinite integral at the endpoints of the basic interval of periodestimators. The difference between the aforementioned integrals

equals the arithmetic value of the definite integral over the basicinterval of interest between period estimators.

The use of cubic splines in addressing problems of mathema-tical calculations is extended to different fields of earth scienceother than engineering seismology, as hydraulics and hydrology(Liggett and Salmon, 1981).

3. Available Proschema modes

Proschema also includes several operation modes enabling theuser to perform automatic processing, with predefined or varyingprocessing parameters either for a number of records or for theremaining components of a multi-component strong motionrecord as well as semi-automatic processing through an algorithmfor the selection of the low cut-off corner frequency.

The aforementioned algorithm holds twenty three possiblelow cut-off frequencies ranging from 0.08 to 0.3 Hz selecting themost suitable by testing the stability of the peak grounddisplacement value, as derived from the adjacent filtereddisplacement time series. That is for every pair of successivelow cut-off’s the difference in peak ground displacement value isevaluated. At the point where the differences in peak grounddisplacement values tend to reach a minimum the strong motionrecord is very close to be considered ‘‘noise free’’. The calibrationof the automatic algorithm in terms of the definition of asubjective threshold, for which the differences in peak grounddisplacement values are considered minimized, came through

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Fig. 6. Orientation independent ground motion measure GMRotI50, introduced by Boore et al. (2006), compared with standard pseudo acceleration response spectra of

two horizontal components for 5% damping value.

Fig. 7. Cubic spline approximation of response spectra. Computation of earthquake engineering known as acceleration spectrum intensity (ASI) by means of vector valued

splines for achieving approximation of response spectra calculated at an earlier processing stage.

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comparing the user selected low cut off frequencies against thesuggested from the automatic algorithm using an actual strongmotion data set (Segou et al., 2008) including world wideanalogue and digital strong motion records. It should be noted

that a standard deviation of 0.06 Hz on the selection of the lowcut-off frequency is expected at this time; but Proschema offersthe user the ability to personalize the default threshold after thelatter has processed an adequate number of strong motion

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records. It comes within understanding that the recalculation ofthe threshold depicts the user’s behaviour towards strong motionprocessing and it is strictly user-dependant.

The user can also perform processing without supervision of avast number of strong motion records. Therefore, a standarizedprocessing structure, that incorporates a number of defaultprocessing parameters such as the filter specifications, informa-tion about the implementation of baseline or instrument adjust-ment and re-sampling techniques as well as the calculation ofengineering parameters, is provided; the user however can easilymodify them so as to formulate a processing scheme according tothe research needs.

In case the user wishes to process the remaining component ofa multi-component recording Proschema uses exactly the sameprocessing parameters as the ones being used during processingof the current strong motion record.

Proschema also provides the functionality of a scheduledprocessing task for files under the specifications of the EuropeanStrong motion Database (Ambraseys et al., 2002), for smc formatfiles and records converted to mat files through utilities includedin the software. This can be achieved thought a third partyscheduler which initiates Proschema and performs processingwith predefined parameters, as described earlier in this para-graph.

4. Concluding remarks

Strong motion record processing is a significant component ofearthquake engineering and seismic hazard studies. Even thoughseveral agencies provide corrected acceleration time series, in anumber of cases the researcher should follow a differentprocessing scheme. The advances of digital signal processingduring the last decades lead the authors to the incorporation thisknowledge related to the processing of earthquake records, in acomputer code that can be used in seismological and engineeringpractice. Performing necessary adjustments and implementinguser-designed filters, instead of black box filter implementation,provides the user the ability to study the effects of processingtechniques and evaluate almost any suggested processingscheme. Detailed documentation of important aspects regardingProschema operation can be found in the Proschema User Manual.Following current seismological trends Proschema allows the userto import seismological data collected by broad band velocitysensors and derive strong motion parameters. Processing ofstrong motion records without user supervision can be also auseful option in order to handle large data sets.

Comments

The user is urged to read the Proschema User Manual forfurther documentation, technical references and review of sometheoretical aspects related to the software. An instruction guidetogether with useful comments about processing sample datafiles included in the software can be found in the last chapter ofthe Proschema User Manual. The reader may also communicatethe corresponding author for providing Supplementary codes forimporting strong motion data in other formats.

1 http://nsmp.wr.usgs.gov/2 http://www.strongmotioncenter.org/

Acknowledgments

Sample data included in the software have been provided eitherby Greek institutions providing strong motion data, such as theNational Observatory of Athens (Kalogeras and Stavrakakis, 2007)

and the ITSAK institute for earthquake engineering studies(Theodulidis et al., 2004) or international data providers, such asthe United States Geological Survey, through the National StrongMotion Project,1 and the Center for Engineering Strong MotionData.2

The first author would like to thank Dr. David Boore forguidance and comments about strong motion data processingduring the early stage of this research and for providing theFortran code for calculating response spectra and orientationindependent measures, the original code can be found in Boore(2008), the authors give credit in the function reference chapter ofProschema User Manual as well as at the descriptive header oftranscribed in Matlab codes. Useful comments and suggestionsmade by Assistant Professor of the Department of Geophysics-Geothermics Dr. Tzanis during the first steps of signal processingproved to be invaluable for the authors. This research had beenfunded by the Greek Scholarship Foundation (IKY).

The final version of this paper benefited significantly from thecomments and suggestions made by Dr. Boore and an anonymousreviewer.

Appendix A. Supplementary material

Supplementary data associated with this article can be foundin the online version at doi:10.1016/j.cageo.2009.09.020.

References

Ambraseys, N., Smit, P., Sigbjornsson, R., Suhadolc, P., Margaris, B., 2002. -Internet-Site for European Strong-Motion Data, European Commission,Research-Directorate General, Environment and Climate Programme./http://www.isesd.cv.ic.ac.uk/S, (accessed 7 March 2010).

Applied Technology Council, 1978. Tentative provisions for the development ofseismic regulations for buildings, Applied Technology Council report ATC-3-06(NBS SP-510). United States Government Printing Office, Washington 505 pp.

Arias, A., 1970. A measure of earthquake intensity. In: Hansen, R.J. (Ed.), SeismicDesign for Nuclear Power Plants. MIT Press, Cambridge, MA, pp. 438–483.

Bolt, B.A., 1969. Duration of strong motion. In: Proceedings of the 4th WorldConference on Earthquake Engineering, Santiago, Chile, pp. 1304–1315.

Boore, D.M., 2005. On pads and filters: processing strong-motion data. Bulletin ofthe Seismological Society of America 95 (2), 745–750.

Boore, D.M., 2008. TSSP—A collection of FORTRAN for processing and manipulat-ing time series, version 1.5, United States Geological Survey, Open-File Report1111, 40 pp.

Boore, D.M., Akkar, S., 2003. Effect of causal and non causal filters on elastic andinelastic response spectra. Earthquake Engineering and Structural Dynamics32, 1729–1748.

Boore, D.M., Bommer, J.J., 2005. Processing of strong-motion accelerograms: needs,options and consequences. Soil Dynamics and Earthquake Engineering 25,93–115.

Boore, D.M., Watson-Lamprey, J., Abrahamson, N.A., 2006. Orientation indepen-dent measures of ground motion. Bulletin of the Seismological Society ofAmerica 96, 1502–1511.

Clinton, F.H., Heaton, T.H., 2002. Potential advantages of a strong motion velocitymeter over a strong motion accelerometer. Seismological Research Letters 73(3), 322–342.

Consortium of Organizations for Strong-Motion Observation Systems, 2001.COSMOS Strong motion data format, p. 16, /http://www.cosmos-eq.org/menu/2_Publications/cosmos_format_1_20.pdfS (accessed 7 March 2010).

Converse, A.M., Brady, A.G., 1992. BAP: Basic strong-motion accelerogramprocessing software; Version 1.0. Open File Report 92-296A, United StatesDepartment of the Interior, United States Geological Survey, Denver, CO,180 pp.

Graizer, V., 1979. Determination of the true ground displacement by using strongmotion records. Physics of the Solid Earth 15, 875–885.

Havskov, J., Alguacil, G., 2004. Instrumentation in Earthquake Seismology.Springer, Dordrecht 358 pp.

Housner, G.W., 1959. Behaviour of structures during earthquakes. Journal of theEngineering Mechanics Division 85, 109–129.

ARTICLE IN PRESS

M. Segou, N. Voulgaris / Computers & Geosciences 36 (2010) 977–986986

Hudson, D.E., 1979. Reading and interpreting strong motion accelerogramsEarthquake Engineering Research Institute, Berkeley, CA, 112 pp.

Ingle, V.K., Proakis, J.G., 1997. Digital Signal Processing Using MATLAB V.4.0.Bookware Companion Series. PWS Publishing Company, Boston 420 pp.

Kalogeras, I., Stavrakakis, G., 2007. Analysis of Greek accelerograms recorded atstations of N.O.A. ‘s network time period 2000–2007 Special Publication 17.Institute of Geodynamics, National Observatory of Athens, Greece 219 pp.

Karl, J.H., 1989. An Introduction to Digital Signal Processing. Academic Press Inc.,San Diego, CA 341 pp.

Kramer, S.L., 1996. Geotechnical Earthquake Engineering. Prentice Hall Interna-tional Inc., New Jersey 652 pp.

Liggett, J.A., Salmon, J.R., 1981. Cubic spline boundary elements. InternationalJournal for Numerical Methods in Engineering 17, 543–556.

Mollova, G., 2007. Effects of digital filtering in data processing of seismicacceleration records. Journal on Advances in Signal Processing 2007,29502–29511.

Nigam, N.C., Jennigs, P.C., 1969. Calculation of response spectra from strong-motion earthquake records. Bulletin of Seismological Society of America 59 (2),909–922.

Proakis, J.G., Manolakis, D.G., 1996. Digital Signal Processing: Principles, Algo-rithms and Applications 3rd ed. Prentice-Hall International Inc., EnglewoodCliffs, NJ 968 pp.

Robinson, D., Dhu, T., Scheinder, J., 2006. SUA: a computer program to computeregolith site-response and estimate uncertainty for probabilistic seismichazard analyses. Computers & Geosciences 32, 109–123.

Scherbaum, F., 1996. Of Poles and Zeros: Fundamentals of Digital Seismology.Springer, Dordrecht 256 pp.

Segou, M., Voulgaris, N., Makropoulos, K., Stavrakakis, G., 2008. A review ofthe Greek Strong Motion Database: Needs, improvements and future

development. In: Proceedings of the 31st General Assembly of the EuropeanSeismological Commission, Crete, Greece, pp. 422–427.

Shakal, A.F., Huang, M.J., 1985. Standard tape format for CSMIP strong-motion datatapes. California Strong Motion Instrumentation Project. Report OSMS 85-03,Office of Strong Motion Studies, California Division of Mines and Geology,Sacramento, CA, 26 pp.

Shakal, A.F., Huang, M.J., Graizer, V.M. 2003. Strong motion data processing. In:Lee, W.H.K., Kanamori, H., Jennings, P.C., Kisslinger, C. (Eds.) InternationalHandbook on Earthquake and Engineering Seismology, Academic Press,pp. 967–981.

Smith, S.W., 1997. The Scientist and Engineer’s Guide to Digital Signal Processing.California Technical Publishing, San Diego, USA 643 pp.

Smith, J.O., Gossett, P., 1984. A flexible sampling-rate conversion method.In: Proceedings of the International Conference on Acoustics, Speech, andSignal Processing, ICASSP-84 (2), IEEE Press, New York. pp. 1941-1942./http://www-ccrma.stanford.edu/� jos/resample/S, (accessed 27 March 2010).

Theodulidis, N., Kalogeras, I., Papazachos, C., Karastathis, V., Margaris, V.,Papaioannou, Ch., Skarlatoudis, A., 2004. HEAD v1.0: a unified hellenicaccelerogram database. Seismological Research Letters 75, 41–51.

Trifunac, M.D., Lee, V.W., Todorovska, M.I., 1999. Common problems in automaticdigitization of strong motion accelerograms. Soil Dynamics and EarthquakeEngineering 18, 519–530.

Tzanis, A., 2006. MATGPR: a freeware MATLAB package for the analysis ofcommon-offset GPR data. Geophysical Research Abstracts 8, EGU06-A-09488./http://www.cosis.net/abstracts/EGU06/09488/EGU06-J-09488.pdfS.

Von Thun, J.L., Roehm, L.H., Scott, G.A., Wilson, J.A., 1988. Earthquake ground motionsfor design and analysis of dams. In: Von Thun, J.L. (Ed.), Earthquake Engineeringand Soil Dynamics II—Recent Advance in Ground-Motion Evaluation, Geotechni-cal Special Publication 20. ASCE Publications, New York, pp. 463–481.