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1. Record Processing Methods Utilized by Networks 1.4

PROCESSING OF EUROPEAN STRONG-MOTION RECORDS AT IMPERIAL COLLEGE LONDON

JULIAN J. BOMMER JOHN DOUGLAS Imperial College London

STRONG-MOTION RECORDING IN EUROPE AND ADJACENT AREAS Strong-motion recording in Europe and the Middle East began much later than in the USA and Japan, the earliest records from accelerographs being obtained in Yugoslavia on 2 December 1967 and then in Lisbon on 28 February 1969 [1]. In the decade that followed, large numbers of accelerograms were generated by earthquakes in Italy, and important records were also obtained in Greece, Iran, the USSR and Yugoslavia. Strong-motion accelerographs have now been installed in most of the seismically active countries of Europe, North Africa and the Middle East, as well as in many of the low seismicity areas of central and northern Europe, leading to a continually growing databank of accelerograms from the region. The instruments have been installed and operated by a variety of organizations, each of which has deployed different instruments and differing processing procedures for the records. The various strong-motion operating agencies have also had different approaches to data dissemination, some making digitized records available shortly after earthquakes, others refusing to release records to end users. Over time, the number of agencies operating in the second mode has decreased very significantly and data is now more generally released to the public. In the early years of European strong-motion recording, Imperial College London, acting in effect as a neutral broker, began to collect records, in both analogue and digitized formats, from throughout Europe and the Middle East. The work was originally undertaken as a ‘labor of love’, before being funded through a series of grants from the UK government’s Engineering and Physical Sciences Research Council and the Commission of the European Communities, the latter for collaborative projects with a number of European research centers. A major component of these projects has been the uniform evaluation of associated parameters including earthquake magnitudes and source-to-site distances [2], [3], [4]. For many years the databank and database were maintained at Imperial College London without access for external parties, and records were disseminated to end users in

Invited Workshop on Strong-Motion Record Processing

J. J. Bommer and J. Douglas

a responsive mode [5], but subsequently a CD-ROM of European accelerograms was produced and widely distributed by Ambraseys et al. [6] in 2000. In March 2002, the Internet Site for European Strong-Motion Data (ISESD) was launched [7] at www.isesd.cv.ic.ac.uk, providing direct access to the records on the CDROM and others, although a significant number of records are not accessible to users due to restrictions imposed by the contributing network operators. A second CD-ROM, with a reduced number of records but with a greatly improved search interface, was released by Ambraseys et al. [8] in April 2004; some of the search features of the second CD-ROM are described by Bommer and Acevedo [9]. This brief paper provides an overview of the procedures that have been adopted to process these records, work which has been motivated initially by the objective of disseminating high-quality data to end users and in which there has been significant interaction with the European partners in the database project, e.g. [10]. Additional motivation for work on record processing has been provided by the work carried out at Imperial College London on the development of European ground-motion prediction equations for peak ground motions and response spectral ordinates. DATA QUALITY AND PROCESSING OF NON-STANDARD ERRORS The databank of accelerograms from Europe, North Africa and the Middle East, has been generated by a wide variety of instruments, ranging from MO-2, AR-240, RFT-250 and SMA-1 analogue recorders to modern digital instruments, although the majority of the records of engineering significance in the databank are still those obtained from analogue recorders. The analogue records have been digitized using a variety of procedures, of varying quality and resolution, and the data has been subsequently distributed on a variety of media. Many of the early records were obtained by Imperial College London on punch cards and magnetic tape, the former occasionally presenting problems if some of the cards had been mislaid or damaged. Several of the records were obtained in different versions, resulting from different digitizations, and criteria had to be established to select one or the other as the representative record for the databank. Figure 1 shows two digitizations of an early accelerogram from Italy with different polarity assumed in each case. Figure 2 shows the velocity time-histories obtained from integration of the filtered acceleration timehistories of three different versions of the 1978 Tabas, Iran, accelerogram and Figure 3 compares the acceleration spectra from the three records. The polarity problem shown in Fig. 1 obviously can only be resolved by the instrument operator, and this example suggests that the polarities of many of the records may well be incorrect. The first criterion for selecting between multiple digitizations of an accelerogram was the quality of the digitization, where this could be easily judged; clearly for the records in Fig. 2, the uppermost trace was easily discarded but the differences between the other two are indiscernible and, as shown in Fig. 3, their spectra are identical. In such cases, the final choice was based on the length of the record (with a preference for longer records) and the sampling rate. The two lower traces of the Tabas record in Fig. 2 both had sampling intervals of 0.00244 seconds, so the lower of the two was chosen on the basis of being slightly longer. For the traces in Fig. 1, the upper traces has a very slightly better sampling interval (37.0 per second as opposed to 36.6 per second) but the lower trace has a longer total duration (11.9 s vs. 10.4 s) and a better sampling rate in the strong portion of the motion; on this basis, the lower trace was selected for the databank.

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Fig. 1.

Record Processing Methods Utilized by Networks

Digitizations of NS component of the Rocca accelerogram of the 21 June 1972 Ancona, Italy, earthquake; only the strong portion of motion is shown.

The first pre-processing of the European databank was carried out as part of the study to derive the predictive equations for spectral acceleration of Ambraseys et al. [11]; the non-standard errors identified, and the procedures adopted for their correction, are summarized by Simpson [12]. Those records identified as having insufficient sampling rates, or actually having missing sections of data points, were discarded. Many of the earlier records were digitized using semi-manual processes and in some of these it was found that in the higher amplitude portions of the traces there were zero, or even negative, time steps. Adjustments for these errors were made in two ways: if the point creating the negative time step was not a local maximum it was simply removed, otherwise its time was shifted by the minimum time increment possible within the resolution of the data that would remove the zero or negative time step. Some accelerograms were identified as containing spurious peaks, such as the recording from the Bajestan station of the 1978 Tabas earthquake, shown in Fig. 4. Comparison with a copy of the original SMA-1 trace (Fig. 5) clearly indicates that the spikes at 10.6, 15.8 and 26.0 seconds are spurious. Simpson [12] reports that the two approaches considered for dealing with such spurious spikes were to either remove the spike completely or else to reduce its amplitude to a level consistent with adjacent data points, with the latter option being preferred. In those cases where the analyst has access to the original analogue record it is a reasonably straightforward matter to correct the record, although this becomes more difficult if the spikes coincide with high-frequency portions of the motion. The influence of the spurious spikes in the Bajestan record on the response spectrum is shown in Fig. 6.

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Fig. 2.

J. J. Bommer and J. Douglas

Velocity time-histories obtained from filtered acceleration traces of the 1978 Tabas accelerogram obtained from three different digitizations; the uppermost record was digitized in Iran, the lower two traces were obtained with an automatic high-resolution laser digitizer by ENEA (Contravese) in Rome.

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Record Processing Methods Utilized by Networks

Fig. 3. Acceleration response spectra (5% damped) of the three versions of the Tabas accelerogram shown in Fig. 2.

Fig. 4. Transverse component of the Bajestan record of the 1978 Tabas earthquake.

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Fig. 5.

Reproduction of the original Bejstan record of the 1978 Tabas earthquake [13].

Fig. 6.

Acceleration response spectra (5% damped) of the record in Fig. 4, before and after removal of the spurious peaks.

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Record Processing Methods Utilized by Networks

A problem encountered with some digitized analogue records is shifts in the baseline, which are presumed to be the result of the record being digitized in sections and these then not being correctly spliced. An alternative explanation is that the film was moved during digitization. An example of a record with baseline shifts is shown in Fig. 7. The procedure adopted for correcting for these shifts was simply to fit a linear baseline, using least squares regression, through each segment of the record, and then join the sections by translation and rotation in order to align the individual baselines. For cases such as that shown in Figure 7, for which there are two shifts, the first adjustment is made to line up the segment from 5.6 to 8.3 seconds with the initial portion of the record, and then the final segment of the record is aligned in the same way. Douglas [14] examines the influence of two other non-standard errors, S-wave triggers and insufficient digitizer resolution, on response spectral ordinates. The purpose of the study was to establish if useful information could be recovered from weak accelerograms affected by these two features for which corrections cannot be applied. The results showed that for a wide range of periods of engineering interest, from 0.2 to 2.0 seconds, reasonable estimates of the response spectral ordinates can be obtained from S-wave triggered accelerograms provided the record has a minimum bracketed duration of 10 seconds at the triggering threshold of the instrument. The other non-standard error examined by Douglas [14] was that of insufficient digitizer resolution, a problem associated with weak motion recordings from digital accelerographs. In this case the usable range of data is dependent on the amplitude and specifically the ratio between PGA and the instrument resolution, defined in terms of the full-scale amplitude and the A/D converter bit range.

Fig. 7. NS component of the 21 May 1979 Italian earthquake (12:36:41 UTC) recorded at Nocera Umbra, showing shifts in the baseline at 5.6 and 8.3 seconds.

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ROUTINE RECORD PROCESSING PROCEDURES The first systematic processing of a large number of European strong-motion accelerograms was carried out for the derivation of predictive equations for ordinates of spectra acceleration [11]. The first stage was to apply pre-processing – or elimination – for the non-standard errors discussed in the previous section. The few very short duration (< 5 s) records were adjusted using a parabolic baseline obtained by least squares fitting. For records with durations of at least 10 s, the signal was first re-sampled to a constant interval of 0.01 s, a significant increase in time step for some of the records but a value consistent with the resolution of a large proportion of the records. The records were then filtered using the program ELLICOR, developed by Menu [15], based on the elliptical filter proposed by Sunder and Connor [16]. All of the records were band-passed filtered with cut-offs at 0.20 Hz and 25 Hz. For records with durations between 5 and 10 s, both procedures were applied and the results selected on the basis of the appearance of the resulting timehistories. No instrument corrections were applied for the simple reason that the transducer characteristics (frequency and damping) were not known for a large proportion of the records. The period range considered for regressions was limited to 0.1 s at the high-frequency end hence for the records obtained from instruments such as the SMA-1, where the distortion is limited to frequencies greater than 25 Hz, instrument corrections would have been of little value. The long-period limit for the regressions was limited, perhaps overcautiously, to just 2.0 seconds. The records made available via the first CD-ROM [6] and via the ISESD web site [7] were processed in the same way, using the elliptical filter with cut-offs at 0.25 Hz and 25 Hz. A systematic re-processing of the records was undertaken to derive predictive equations for spectral ordinates of displacement, for which, by virtue of the long periods considered using the substitute structure technique in displacement-based design, the ordinates at periods beyond 2.0 seconds were of particular interest [17]. The procedure adopted in this case was to begin filtering each record with a low-frequency cut-off at 10 seconds and then iteratively decreasing the period of the cut-off and inspecting the resulting velocity and displacement time-histories. The filter cut-off was reduced until the velocity and displacement time-histories were judged to be physically reasonable and further decreases in the cut-off period did not lead to appreciable improvements. Each component was treated separately hence sometimes different filter parameters were applied to the two horizontal components. Records were only used for regressions on spectral ordinates at periods lower than the cut-off applied. The same iterative filtering procedure was adopted for processing records to derive predictive equations for PGV and PGD [18]; unlike PGA, these parameters were found to be highly sensitive to the selected cut-off frequency, as shown in Fig. 8. A problem identified with this procedure is that for many records the low-frequency cut-off was determined to be greater than the corner frequency obtained from the equation of Joyner and Boore [19], which inevitably means that the filter has destroyed a significant part of the signal. This has almost definitely led to the under-estimation of PGV, PGD and displacement response ordinates, particularly for larger magnitude earthquakes. For the records included on the second CD-ROM [8] the BAP software developed by Converse [20] was employed. For the 123 analogue records for which digitized fixed traces are held at Imperial, the fixed trace was used to model the noise and the cut-off frequency selected where the signal-to-noise ratio was equal to 2.0. For digital recordings, the pre-event memory was used in the same way. For other records, the 8

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Record Processing Methods Utilized by Networks

Fig. 8.

Variation of PGV and PGD with low-frequency cut-off for two records, one analogue and one digital, of the 1999 Kocaeli earthquake [18].

Fig 9.

Displacement response spectra (5% damped) of the 1978 Tabas accelerogram in four different states of processing.

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cut-off frequency was estimated from where the Fourier amplitude spectrum no longer tended towards zero, as recommended by Zaré and Bard [21]. Following the application of the filter at this initial cut-off, and high-frequency cut-off and roll-offs at 23 and 25 Hz for analogue records and 50 and 100 Hz for digital records, the same iterative procedure described above, based on inspection of the velocity and displacement traces, was used to modify the low-frequency cut-off until reasonable results were obtained. The same filter parameters were applied to each of the three components of each record, after padding with zeros. Figure 9 compares the displacement response spectra of an uncorrected accelerogram and from the same record processed for the two CD-ROMs and for the displacement spectra study. Although there is no way of validating any of the versions, the figure does also point to a rather severe filter being used for the displacement spectra study. Records from analogue instrument, which require instrument correction due to the relatively low natural frequencies of such transducers, have been included on the second CD-ROM (after correcting for instrument response) only if the transducer characteristics are known. In addition, some records from digital instruments, with sufficiently high natural frequencies not to require instrument correction, have been included without correcting for instrument response because the transducer characteristics are not known. ALTERNATIVE PROCESSING PROCEDURES The filtering of digitized analogue strong-motion accelerograms inevitably removes long-period motion from the record, since the filters aim to remove the portion of the signal where the signal-to-noise ratio is considered to be low but do not differentiate between long-period motion and long-period noise. As indicated above, this will lead to the true value of PGV and PGD being under-estimated. This has led some researchers at Imperial to explore the use of alternative processing procedures, particularly the correction technique proposed by Grazier [22], modified from that proposed by Hudson et al. [23]. The underestimation of the PGV and PGD using filters rather than this technique is illustrated in Fig. 10. Although these results do point to the extended Grazier technique as an attractive alternative to filtering, the results obtained are still sensitive to the parameters selected and since there are very few cases of ‘ground truth’ (i.e. independently measured post-seismic displacements at the location of the recording station) it is not possible to verify results and hence to develop a robust and objective procedure for selecting the parameters. CONCLUSIONS This brief paper has provided an overview of the issues related to several decades of work at Imperial College London on the collection, processing and analysis of strong-motion accelerograms recorded in Europe, the Mahgreb and the Middle East. Although Imperial has been closely involved with strongmotion networks elsewhere in the world, within Europe and adjacent regions the College has effectively assumed the role of a data center bringing together recordings obtained from a large number of other agencies. The paper has illustrated various issues related to data quality that have been encountered in fulfilling this role; whereas network operators would generally be able to address several of the non-standard errors that arise with some of the recordings, the Imperial College London database project has been dependent on the quantity and quality of information supplied by the contributing agencies. As many as 50% of 10

Invited Workshop on Strong-Motion Record Processing

Fig. 10.

Record Processing Methods Utilized by Networks

Ratios of PGV (left) and PGD (right) from records filtered at 0.1 Hz to the values obtained using the Grazier [22] technique [24].

the more than 3,000 accelerograms in the European strong-motion databank are affected by non-standard errors, but Douglas [14] has shown that useful information, at least regarding acceleration spectral response ordinates at short and intermediate periods, can nonetheless be retrieved from these records. The outcome of this finding is that the ISESD web site contains a far larger number of records than were originally released on the first CD-ROM of European strong-motion data [6]. The paper has also given a brief overview of the standard processing procedures that have been applied to the records at different times using filters, both to provide reliable data to end users and to derive empirical prediction equations for spectral ordinates of acceleration and displacement. The key issue, as yet unresolved, is to define the frequency range in which reliable spectral ordinates can be obtained, particularly from the analogue recordings, which despite the rapid transfer to digital recording technology in the region, still dominate the existing databank. As far as long-period displacement ordinates are concerned, the spectra obtained from high-quality digital accelerograms of large earthquakes, and their relative insensitivity to processing, discussed, for example by Joyner and Boore [25], indicate a fundamentally different behavior from that observed using analogue accelerograms. The indications are that long-period spectral displacements, at periods of greater than 5 seconds say, probably cannot be reliably estimated from analogue accelerograms, regardless of the processing applied. ACKNOWLEDGMENTS The strong-motion databank and database project was initiated by Professor N.N. Ambraseys, who has led the work for more than a quarter of a century. During most of that time, valuable assistance and advice have been provided by Dr S.K. Sarma. The authors of this paper are just two of the many people who have been employed as Research Assistants on the project at different times; other individuals who have contributed 11

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significantly to the development of the databank and database include Dimitri Papastamatiou, Jim Crowder, Jean Menu, George Eleftheriadis, Matthew Free, Keith Simpson, Milutin Srbulov and Patrick Smit. The project has also benefited enormously from the work, insights and advice of the partners in the Europeanfunded projects, who are too many to list here. The agencies and individuals (listed on the ISESD web site) who have operated accelerograph networks throughout Europe and the Middle East, and contributed their data, have, of course, made the project possible and the importance and value of their contributions cannot be over-estimated. REFERENCES [1] Ambraseys NN (1978). “Preliminary analysis of European strong-motion data 1965-1978.” Bull. Eur. Assoc. Earthq. Engrg., 4(1):17-37. [2] Ambraseys NN (1990). “Uniform magnitude re-evaluation of European earthquake associated with strong-motion records,” Earthq. Engrg. Struc. Dyn., 19:1-20. [3] Ambraseys NN, Bommer JJ (1990). “Uniform magnitude re-evaluation for the strong-motion database of Europe and adjacent regions,” Eur. Earthq. Engrg., 4(2):3-16. [4] Ambraseys NN, Bommer JJ (1991). “Database of European strong-motion records,” Eur. Earthq. Engrg., 5(2):18-37. [5] Bommer JJ, Ambraseys NN. “An earthquake strong-motion databank and database”. Proc., 10th World Conf. Earthq. Engrg., Vol. 1: 207-210, Madrid, Spain. [6] Ambraseys NN, Smit P, Berardi D, Cotton F, Berge C (2000). “Dissemination of European strongmotion data”, CD-ROM Collection, Brussels: European Commission, Directorate-General XII, Environmental and Climate Programme, ENV4-CT97-0397. [7] Ambraseys NN, Smit P, Douglas J, Margaris B, Sigbjörnsson R, Ólafsson O, Suhadolc P, Costa G (2004). “Internet site for European strong-motion data,” Bolletino di Geofisica Teorica ed Applicata, in press. [8] Ambraseys NN, Douglas J, Rinaldis D, Berge-Thierry C, Suhadolc P, Costa G, Sigbjörnsson R, Smit P (2004). “Dissemination of European strong-motion data, Vol. 2,” Engineering and Physical Sciences Research Council, Swindon, U.K. [9] Bommer JJ, Acevedo AB (2004). “The use of real earthquake accelerograms as input to dynamic analysis,” J. Earthq. Engrg. 2004, in press. [10] ENEA-ENEL (1985). Proc., Workshop on Investigation of Strong Motion Processing Procedures, Rome, Italy. [11] Ambraseys NN, Simpson KA, Bommer JJ (1996). “Prediction of horizontal response spectra in Europe”. Earthq. Engrg. Struc. Dyn., 25:371-400. [12] Simpson KA (1996). “The attenuation of strong ground-motion incorporating near-surface foundation conditions.,” Ph.D. Thesis, University of London, London, U.K. 12

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[13] Moinfar AA, Adibnazari H (19820. “The Tabas earthquake of 16th. Sept. 1978: Main shock acceleration-time history and related uncorrected data,” Building and Housing Research Center, Tehran, Iran. [14] 156.

Douglas J (2003). “What is a poor quality strong-motion record?” Bull. Earthq. Engrg., 1(1):141-

{15] Menu JMH (1986). “Engineering study of near-field earthquake strong-motions,” Ph.D. Thesis, University of London, London, U.K. [16] Sunder SS, Connor J (1982). “A new procedure for processing strong-motion earthquake signals,”. Bull. Seism. Soc. Amer., 72: 643-661. [17] Bommer JJ, Elnashai AS (1999). “Displacement spectra for seismic design,” J. Earthq. Engrg., 1999; 3(1):1-32. [18] Tromans IJ, Bommer JJ (2002). “The attenuation of strong-motion peaks in Europe,” Proc., 12th Eur. Conf. Earthq. Engrg., London, Paper No. 394. [19] Joyner WB, Boore DM (1988). “Measurement, characterization and prediction of strong ground motion,” Proc., Earthq. Engrg. & Soil Dynamics II, ASCE GT Div., pp. 43-102. [20] Converse AM (1992). “BAP basic strong-motion accelerogram processing software, version 1.0,” USGS Open-File Report 92-296A. [21] Zaré M, Bard P-Y (2002). “Strong-motion dataset of Turkey: data processing and site classification,” Soil Dyn. Earthq. Engrg., 22:703-718. [22] Grazier VM (1979). “Determination of the true ground displacement by using strong motion records,” Izvestiya Academy of Sciences, USSR, Physics of the Sold Earth, 15(12): 875-885. [23] Hudson DE, Nigam NC, Trifunac MD (1969). “Analysis of strong-motion accelerograph records”. Proc., 4th World Conf. Earthq. Engrg., Vol. 1: A2,1-17. [24] Douglas J (2002). “On the recovery of peak ground velocity and peak ground displacement from strong-motion records,” Proc., 12th Eur. Conf. Earthq. Engrg., Paper No. 018, London, U.K. [25] Joyner WB, Boore DM (2000). “Recent developments in earthquake ground motion estimation,” Proc., 6th Inter. Conf. Seismic Zonation, Palm Springs, Calif.

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