1. ANSS/NSMP STRONG- MOTION RECORD PROCESSING AND PROCEDURES Christopher D. Stephens and David M. Boore US Geological Survey Menlo Park, CA COSMOS/NSF International Workshop on Strong-Motion Record Processing May 26-27, 2004

2. Network consists of both analog (265) and digital (675) recorders Installations include ground motion reference sites and structures Located in 32 states and Puerto Rico, but about half are in California About 320 have continuous or dial-up connections

3. Digitizing Analog Records Scan in grayscale, 600 dpi (236pixels/sec) Enhance image contrast (no smoothing) and convert to black and white Semi-automatic trace following Interpolate to 200 sps for output Check for relative offsets and tilts of baselines for multi-segment traces Correct baselines using robust L1 fit

4. Processing scheme is simple and ensures compatibility: Baseline correction End conditioning and padding Acausal filtering of acceleration only –High cut cosine taper and instrument response correction in spectral domain –Low cut Butterworth filter in time domain Integration to velocity and displacement in time domain Compute response spectra Review for physically reasonable result

5. Baseline shifts occur on recordings of strong and weak motion

6. Many possible causes Mechanical: –Hysteresis (mechanical/ electrical) –“Popcorn” noise –Other Ground deformation –Tilt near earthquakes –Differential settlement –Other Analog-Digital Conversion (ADC)

7. After Iwan et al, 1985

8. Baseline corrections for different fit parameters

9. Although the results look physically plausible, the residual displacements can be sensitive to t1, t2

10. Need for low-cut (high-pass) filtering There can be many reasons for the shifts, and as a result it is not possible to design a single correction scheme Residual displacements can be sensitive to parameters of baseline correction Filtering is often required (and in many cases is all that is required) to remove unwanted low-frequency noise

11. Acausal filtering is preferred over causal At periods much shorter than the corner period, both the waveforms and response spectra are less sensitive to the corner Particularly true for inelastic response!

12. Boore and Akkar, 2003

13. Choosing filter corners Subjective Often guided by shape of Fourier spectrum, but this can lead to excessive removal of long periods Noise can be estimated from: - digitized fixed trace for analog records - pre-event of sufficient duration for digital In many cases, digital instruments allow choice of filter corners longer than periods of engineering interest, but peak displacement may be sensitive to the filter corner

14. M w 6.9 Loma Prieta at Anderson Dam DS, 333 o comp

16. Conditioning for bandpass filter Values extending inward from each end and up to first zero-crossing are set to 0. Pads are added symmetrically to both leading and trailing edges to accommodate filter transients Pad length at each end is determined from the empirical relation: tpad = 1.5 * nroll / f c Pads are removed after final processing

18. Selecting filter order is a compromise between: Effectively removing unwanted long- period noise Avoiding the introduction of excessive ringing by using too high an order

20. Integrating pad-stripped filtered data causes distortion

21. Uniform filtering (same filter corners and rolloff) is usually applied to all channels from a particular site or structure Disadvantage is that long-period content is controlled by channel with weakest signal But motions involving more than one channel (e.g., inter-story drift, torsion) can be computed directly from processed records

22. M w 7.2 Hector Mine  = 160 km, inter-station = 1.6 km One method used to validate choice of filter parameters is to compare long- period waveforms at nearby stations