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Data Acquisition and Network Enhancements


Introduction

Stations from nearly all networks operated by the BSL transmit data continuously to UC Berkeley for analysis and archive. As part of our operations, we have developed procedures for data acquisition and quality control in order to ensure a complete and high quality data collection. In this chapter, a new addition to the Annual Report, we describe the data acquisition systems and quality control procedures. We also document the process of obtaining new station coordinates for the BDSN and HFN and new work in instrument testing.

Data acquisition

Central-site data acquisition for the BDSN and HFN is performed by two computer systems located at the BSL (Figure 8.1). These acquisition systems are also used for the Parkfield-Hollister electromagnetic array and for the BARD network. A third system used primarily as data exchange system with the USNSN receives a secondary feed from CMB, SAO, and WDC from the the NSN VSAT, and processes and transmits event data to the USNSN from HOPS, CMB, SAO, WDC, and YBH.


  
Figure 8.1: Data flow from the BDSN, HFN, and BARD network into the BSL central processing facility.
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Data acquisition and communication with the Quanterra dataloggers depends both on the software on the dataloggers and at the central site. At the dataloggers, several components of Quanterra's UltraSHEAR acquisition software and the underlying OS/9 operating system were not year 2000 compliant, and required upgrades in 1999. Over the past few months, we have reorganized aspects of the central site acquisition in order to facilitate REDI processing.

MultiSHEAR

In late 1998, Quanterra provided the first release of MultiSHEAR, an enhanced version of its data acquisition software that was year 2000 compliant, and updated components of the OS/9 operating system to address the year 2000 problem. MultiSHEAR contained a number of enchancements, especially in the area of multi-site data collection, and introduced a totally new configuration procedure. The BSL worked with Quanterra during 1999 to enhance the configuration procedures to address the diverse needs of the BDSN and HFN station configurations. During November and December 1999, all of the BSL Quanterra dataloggers were updated to MultiSHEAR with the corresponding OS/9 modifications, which addressed the year 2000 problems.

The two significant features of MultiSHEAR that affected the BSL were the correction of a systematic timing error of the decimated channels from SHEAR and UltraSHEAR software in the Quanterra datalogger, and the addition of multi-site data collection.

Quanterra timing correction

The timing description in the following section is provided courtesy of the USGS Albuquerque Laboratory, which provides data collection and quality control procedures for the portion of the GSN that uses Quanterra dataloggers.

The Quanterra digitizers initially sample at very high rates. In firmware the data are introduced to a filter cascade of a various number of stages where they are low-pass FIR filtered and decimated multiple times. Depending on the specific system the data are further FIR filtered and decimated by configurable software. Each applied FIR filter introduces to the data a nominal delay of half the FIR filter width which then requires subsequent corrections to the data time tags.

For these Quanterra systems the calculation of the time tag applied to the data is more complicated than the first order correction associated with the half widths of the FIR filters. There is a very small correction term associated with data buffering and a more substantive subjective correction to account for the delay in reading the 'first break' during signal onset. This second term attempts to bridge the gap between impulsive and steady state signals. The size of this term has been a function of each filter's transition band and is generally 1.5-2.0 output samples. The cumulative effect of these corrections has mistimed most of the seismic channels. The mistiming is obvious when a sine wave is input into the Quanterra system and time series from the various channels are overplotted.

The following tables provide the timing corrections required for the various Quanterra datalogger configurations used by the BSL.


 
Table 8.1: Time tag corrections for Quanterra Q680, Q980, and Q935 systems running SHEAR and UltraSHEAR software. The time corrections in the table should be added to the original timeseries timetags.
BDSN Quanterra
Q680, Q980, Q935 dataloggers
Channel Sample Rate Correction
  (Hz) (seconds)
H?? 80 0.000
B?? 40 0.0025
B?? 20 -0.005
L?? 1.0 -0.688
V?? 0.1 -0.189
 


 
Table 8.2: Time tag corrections for Quanterra Q4120 seismic systems running UltraSHEAR software. The time corrections in the table should be added to the original timeseries timetags.
BDSN Quanterra
Q4120 seismic dataloggers
Channel Sample Rate Correction
  (Hz) (seconds)
H?? 100 -0.00587
B?? 20 +0.03913
L?? 1.0 +1.06413
V?? 0.1 ???
 


 
Table 8.3: Time tag corrections for Quanterra Q4120 electro-magnetic systems running UltraSHEAR software. The time corrections in the table should be added to the original timeseries timetags.
BDSN Quanterra
Q4120 electro-magnetic dataloggers
Channel Sample Rate Correction
  (Hz) (seconds)
B?? 40 +0.02277
L?? 1.0 +1.08527
 


 
Table 8.4: Time tag corrections for Quanterra HFN Q4120 systems running UltraSHEAR software. The time corrections in the table should be added to the original timeseries timetags.
BDSN/HFN Quanterra
Q4120 dataloggers
Channel Sample Rate Correction
  (Hz) (seconds)
CL?,DP? 500 -0.00633
HL?,EP? 100 -0.02161
B?? 20 +0.02339
L?? 1.0 +1.04839
 

Multi-site data acquisition

Prior to the release of MultiSHEAR, a Quanterra datalogger could acquire, store, and telemeter data from only a single source such as its own A/D system. However, MultiSHEAR adds the new capability to acquire data from remote digital sources such as other Quanterra systems. The BSL will use this feature to create "hub" systems, which will acquire, store, and transmit data from several remote dataloggers as well as the hub's own data. The San Francisco/Oakland Bay Bridge network will consist of two Quanterra 4120 hubs (Figures 8.2 and 8.3), each of which will acquire data from its own digitizers as well data from three remote diskless Quanterra Q730 systems. Each hub will provide local storage for all four sites, and will transmit the real-time the data from all four sites to the BSL over a 512Kb spread spectrum radio. The BSL developed the initial MultiSHEAR hub configuration procedure, and worked with Quanterra to refine and test the hub configurations.


  
Figure 8.2: Planned data flow from the East Bay Bridge seismic monitoring hub.
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Figure 8.3: Planned data flow from the West Bay Bridge seismic monitoring hub.
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Comserv

The BSL uses the comserv program for central data acquisition, which was developed by Quanterra. The comserv program receives data from a remote Quanterra datalogger, and redistributes the data to one or more comserv client programs. The comserv clients used by REDI include datalog, which writes the data to disk files for archival purposes, cdafill, which writes the data to the shared memory region for REDI analysis, and other programs such as the seismic alarm process, the DAC480 system, and the feed for the Momento Mori Web page (Figure 8.4).

  
Figure 8.4: Dataflow in the REDI processing environment, showing waveform data coming in from the Quanterra dataloggers (Q) into comserv. From comserv, data are logged to disk (via datalog), distributed to other computers ( mserv), fed into the CDA for REDI processing, and spooled into a tracering for export.
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The two computers that perform data acquisition also serve as REDI processing systems. In order to facilitate REDI processing, each system maintains a shared memory region that contains the most recent 30 minutes of data for each channel used by the REDI analysis system. All REDI analysis routines first attempt to use data in the shared memory region, and will only revert to retrieving data from disk files if the requested data is unavailable in the shared memory region.

Most BDSN and HFN stations transmit data to only one or the other of the two REDI systems. In earlier system configurations, each station would transmit data to one of the two systems, which would write the data to local disk files, copy the data in its own shared memory region, and transmit the data via a socket to the other system's shared memory region. Each REDI system's shared memory region contained data from all stations, but each computer's filesystem contained data from only one half of the network. The REDI systems use the Network File System (NFS) to access remote files that reside on other computers. If a REDI analysis program required data that was not in the shared memory region, it would attempt to retrieve the data from the disk files from both REDI computers. If one of the two REDI computers was unavailable, this could cause the REDI processing to hang waiting for access to the other computer's files.

During the past year, we revised our data acquisition procedure to use two programs developed at Caltech. The comserv client program cs2m receives data from a comserv and multicasts the data over a private ethernet. The program mcast, a modified version of Quanterra's comserv program, receives the multicast data from cs2m, and provides a comserv-like interface to local comserv clients. This allows each REDI system to have a comserv server for every BDSN station. We added additional disk space to both of the REDI computers so that we use datalog to write a copy of data to each computer's filesystem. We reconfigured the REDI computers to only retrieve data from their own filesystems, thereby preventing a loss of one REDI computer from possibly hanging the other REDI computer's processing.

We extended the multicasting approach to handle data received from other networks such as the NCSN and UNR. These data are received by Earthworm data exchange programs, and are then converted to MiniSEED and multicast in the same manner as the BDSN data. We use mserv on both REDI computers to receive the multicast data, and handle it in an identical fashion to the BDSN MiniSEED data.

Background Noise

BDSN data are routinely monitored for state-of-health. An automated analysis is computed weekly to characterize the seismic noise level recorded by each broadband seismometer. The estimation of the Power Spectral Density (PSD) of the ground motion recorded at a seismic station, provides an objective measure of background seismic noise characteristics over a wide range of frequencies. When used routinely, the PSD algorithm also provides an objective measure of seasonal and secular variation in the noise characteristics and aids in the early diagnoses of instrumental problems. A PSD estimation algorithm was developed in the early 1990's for in-house use at the BSL for characterizing the background seismic noise and as a tool for quality control. As presently implemented, the algorithm sends the results via email to the engineering and some research staff members and it also generates a bargraph output which compares all the BDSN broadband stations by components. A summary of the results for 1999-2000 is displayed in Figure 2.3.

We have expanded our use of the weekly PSD results to monitor trends in the noise level at each station. In addition to the weekly bar graph, additional figures showing the analysis for the current year are produced. These cummulative PSD plots are generated for each station and show the noise level in 5 frequency bands for the broadband channels. These cummulative plots make it easier to spot certain problems, such as failure of a sensor. In addition to the station-based plots, a summary plot for each channel is produced, comparing all stations. These figures are presented as part of a noise analysis of the BDSN on the WWW at http://seismo.berkeley.edu/bdsn/psd/ and are part of the recently revamped BDSN Web pages at http://seismo.berkeley.edu/bdsn/.

We have recently developed an exportable version of the PSD algorithm for use by IRIS. In order to facilitate portability and ease of using the algorithm, it has been redesigned to acquire all requisite station transfer function information and seismic time series data from a SEED data volume.

In parallel with our effort to monitor the network state-of-health, we are looking at ways to remove noise sources. The measurement of atmospheric pressure fluctuations (infrasound) at a seismic station is essential for removing the pressure correlatable component of the seismic background noise and for monitoring natural and man-made pulsation of the atmosphere. Removal of the pressure correlatable component of the seismic background noise can reduce the background noise PSD in the seismic surface wave and normal mode bands and effectively increase the resolvability of small seismic signals. Recording of infrasonic signals also enables investigation of atmospheric excitation caused by natural (volcanic, seismic, atmospheric, etc) and man-made (missile launches, explosions, etc) sources.

PSD algorithm

Description

In order to facilitate portability, the PSD algorithm is designed to acquire the requisite station, sensor, and time series data from a SEED data volume (Halbert, et al., 1993). The "rdseed" algorithm developed by IRIS personnel is invoked to extract the station data, the sensor transfer function data, and to dump the time series data records in SAC binary format (Tapley and Tull, 1992). The resulting SAC binary format files are processed to calculate the background noise PSD.

The PSD algorithm uses a statistical approach to robustly estimate the background noise PSD. The PSD estimates are reported in dB relative to 1 (m/s2)2/Hz. The input time series is parsed into eight (possibly overlapping) time series and each of the resulting time series are appropriately windowed prior to calculating their PSD estimates. For short time series, less than 1.5 hours in length, the time series are detrended and sine tapered while for longer time series the dominant semi-diurnal gravitational tide signal is also removed to avoid biasing the long-period PSD estimates. The PSD estimates are smoothed and reported at twenty logarithmically spaced intervals per decade in period.

Owing to the statistical nature of the PSD algorithm, it is required that the time series to be processed contain at least 65,635 (216) contiguous samples. Shorter time series are not processed and a warning is issued. The PSD algorithm can process data with a wide variety of sampling rates (from <0.01 sps to >500 sps). A typical usage with broadband data is for the time series to contain one day of continuous LH (1 sps) data (86,400 samples), say. Since the sensor transfer function representation in the SEED data volume for a typical inertial seismometer does not include the static component of the response, the background noise PSD estimates for periods longer than approximately an hour will be biased high and hence they will be unreliable.

Source Code

The original BSL in-house version of the PSD algorithm was written in Fortran 77. At the request of IRIS, the f2c translator was used to convert the Fortran source code to C source files. The source code was cleaned up and the necessary subroutines, not included in the f2c libraries, were added to expedite the conversion process. The only f2c option used in the conversion was "-c" which embeds the original Fortran 77 source code as comments in the C source files.

The PSD code distribution along with examples of its usage are available via the Web at http://seismo.berkeley.edu/algorithms/.

Sample Output

Besides the information that is printed to the screen when PSD is executing, it outputs a station coordinate file ``instr.sdv.coord'', a sensor transfer function file ``instr.sdv.resp'', and a file with a SEED naming convention prefix and a ``.psd'' suffix for each data stream, that meets the minimum requirements of the PSD algorithm, extracted from the SEED data volume.

An abridged sample of the PSD output file ``YBH.BK.LHZ.XX.D.2000.178.0000.psd'' is given in Table 8.5:


  
Table 8.5: Abridged PSD output.
\begin{table}
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where: the first 17 lines contain header information and the 18th through the last line give the PSD estimates where the first column gives the period of the PSD estimate in seconds and the remaining columns give the lower bound (LB), minimum (min), median (med), maximum (max), and upper bound (UB) of the background noise acceleration PSD estimate. The minimum and maximum columns refer to the estimates which have the minimum and maximum sums over all periods (the time series corresponding to each is given in the 13th header line) while the lower bound (LB) and upper bound (UB) columns give the lowest and highest estimate for each period. The PSD estimate in the ``PSD min'' column is the most robust estimate of the background noise PSD.

A plot of the PSD minimum estimate versus period is given in Figure 8.5.


  
Figure 8.5: Background noise PSD estimate for the BDSN station YBH sited in an abandoned hard rock mine in the Klamath National Forest, 430 km N of Berkeley. The low and high background noise models ( Peterson, 1993) (dashed lines) are given for comparison.
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Infrasound Monitoring and Transducer Development

The measurement of atmospheric pressure fluctuations (infrasound) at a seismic station is essential for removing the pressure correlatable component of the seismic background noise and for monitoring natural and man-made pulsation of the atmosphere. Removal of the pressure correlatable component of the seismic background noise can reduce the background noise PSD in the seismic surface wave and normal mode bands and effectively increase the resolvability of small seismic signals. Recording of infrasonic signals also enables investigation of atmospheric excitation caused by natural (volcanic, seismic, atmospheric, etc) and man-made (missile launches, explosions, etc) sources.

Background and Motivation

Our primary motivation is to improve the infrasonic detection capabilities of the pressure transducers that are co-sited with the BDSN broadband seismometers to facilitate the reduction of the pressure correlatable component of the seismic background noise.

At the present time, the BSL records pressure signals at 18 BDSN stations which also house broadband seismometers. The sensor uses a Motorola MPX2010 silicon pressure transducer (http://mot-sps.com/) and the resolution is $\sim $0.95 mPa/LSB when recorded by a high-resolution (24-bit) channel on the Quanterra datalogger. The self-noise of this pressure sensor is inadequate to measure the background atmospheric noise at quiet sites, where, for example, noise levels of 5 mPa at a period of 1 second have been observed. While cross-correlation of the pressure and seismic signals results in noise reduction at long periods (longer than approximately 100 seconds), it works best at periods longer than an hour and it is limited at shorter periods by the lack of spatial averaging to reduce the wind generated noise and ultimately at the shortest periods (less than 20-50 seconds) by the sensor self noise.


  
Figure 8.6: Pressure transducer interface circuit.
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Pressure Transducer and Interface

After searching for a suitable pressure sensor, we found that the Druck model PTX 1240 pressure transducer (http://www.druck.com/usa/) had a suitably low noise level and reasonable cost. We acquired two of these transducers and set about designing a suitable interface to match the current output of the pressure transducer to the input voltage requirements of the Quanterra datalogger. The PTX 1240 pressure transducer is a current device with a 11.5-17.4 psi pressure range producing a 4-20 mA current signal while the Quanterra 4120 datalogger has a differential input with a $\pm$20 V range. The circuit shown in Figure 8.6 was subsequently designed and constructed. The pressure transducer and interface were installed in the Byerly Vault (BKS) for testing.

The interface has a gain of 40X and also a stable offset circuit so that the output can be zeroed at nominal atmospheric pressure. The resolution of the pressure transducer recorded by the 24-bit resolution datalogger, at a sample rate of one per second, is $\sim $0.6 mPa/LSB. This is in line with the resolution specifications set by the CTBT.

Comparison of Infrasonic Background Signals

The new pressure transducer has been operating in the Byerly Vault (BKS) for the past two months. Figure 8.7 shows the infrasonic background noise PSD recorded by the two sensors co-sited at BKS and by the sensors at two additional BDSN stations, sited in the Central Coast Ranges, MHC (79 km SE of BKS) and HOPS (149 km NW of BKS). The background noise PSD inferred by the two sensors at BKS are similar at all frequencies and nearly identical from 30 uHz to 5 mHz. The PSD peak at 12 uHz observed by all four sensors is due to the diurnal solar heating of the atmosphere. The differences in the $\sim $10-100 mHz range are due to differences in the sizes of the vaults at the three sites. BKS has the largest vault, a 32 m long by approximately 4 m diameter drift in the side of a hill in Strawberry canyon with a volume of $\sim $400 cubic meters, and it also has the longest time constant as evidenced by the $\sim $10 mHz frequency roll off. The vault at HOPS is constructed from a Sea-Land shipping container with a volume of $\sim $36 cubic meters and, correspondly, the shortest time constant with a $\sim $25 mHz frequency roll off. MHC, sited in a wing of the Lick Observatory building, has a $\sim $50 mHz frequency roll off. The average -2 slope observed in the background noise PSD is compatible with the expected 1/f characteristic of atmospheric noise. The high frequency asymptotic PSD level of approximately -20 dB is attributed to the self-noise of the sensors.

Future Plans

Ideally the sensor self-noise PSD ought to be no larger than the minimum acoustic noise at the quietest sites or $\sim $-50 dB at $\sim $1 Hz. Our goal is thus to lower the pressure transducer system self-noise by $\sim $30 dB. Towards this goal, we need to spatially average the infrasonic signal over dimensions of order 10-30 meters (using a manifold and permeable noise reduction hoses (http://www.ctbt.rnd.doe.gov/ctbt/), say, to reduce transient wind generated noise), to apply appropriate filtration to reduce electrically induced noise, and to increase the thermal insulation to reduce temperature related noise. At present we are sampling at 1 Hz but we need to investigate sampling at 10 Hz or perhaps 20 Hz to increase the infrasonic signal bandwidth for observing transient atmospheric phenomena.


  
Figure 8.7: Background infrasonic noise PSD observed at BKS, HOPS and MHC.
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Seismometer Testing

During the past year we had an unique opportunity to test a new type of broadband seismometer which utilizes a novel transduction technology. Three PMD broadband sensors were loaned to the BSL by Igor Abramovich, of PMD Scientific, Inc., for testing. These three-component broadband sensors use an unique Molecular Electronic Transfer (MET) transducer (Abramovich et al., 1997) to detect the velocity of a highly conductive electrolytic fluid. It is noteworthy that prior to the introduction of the PMD MET transducer, all seismic sensors with velocity transduction invariably used a coil and a magnet. The MET transducer has a peaked response with its greatest output at $\sim $1 Hz and thus the electronics are most likely to saturate at $\sim $1 Hz. These seismometers do not employ any feedback mechanism and our primary concern in testing them was to determine their noise floor, linearity, and dynamic range of their response to ground motions produced by local earthquakes.

Noise PSD and Response to Seismic Signals

The first part of the test was to characterize the noise floor of the PMD sensors and to record some local earthquakes. The sensors were installed in the Byerly Vault (BKS) for testing and operated for a few weeks to record background noise and some seismic events. The vertical component background noise PSD recorded by two of the PMD sensors track the background noise PSD recorded by the STS-1 in the microseismic band while at periods shorter than 1 second, the PMD sensor noise PSD is a few dB higher than the background noise. At periods longer than $\sim $ 15 seconds, the PMD inferred noise PSD levels are dominated by the self noise of the MET transducer. The $\sim $156 dB minimum in the observed PMD sensor self noise is is consistent with the specifications published by PMD.

For weak ground motions (ground velocities $\sim $5 um/sec) generated by a small ( $M_{L} \sim$2.9) earthquake that occurred 126 km from Berkeley, linear correlations between the signals recorded by the PMD sensors and the observatory quality Streckeisen STS-1 seismometers housed at BKS were all 0.95 or higher. The highest correlations were observed between the PMD sensors as expected given the higher frequency passbands of the PMD sensors and the high frequencies generated by the small earthquake.

A Mw 7.0 teleseism which occurred 93$^\circ $ west of Berkeley was also recorded by the PMD and STS-1 sensors and linear correlation analysis produced mixed results. Linear correlation of the (0.01-0.1 Hz 6PBP) signals recorded by one of the PMD sensors and the STS-1 was 0.999 while the linear correlation of the signals recorded by the other PMD sensor and the STS-1 was $\sim $0.67. Further analysis indicated that the factory supplied transfer function for the second PMD sensor was inaccurate near the low-frequency corner.

Linearity and Dynamic Range

Characterization of the linearity and dynamic range of a broadband seismic sensor is problematic because seismic signals are a wide bandwidth transient phenomena while typical linearity tests utilize a narrow bandwidth stationary signal. One figure of merit that is indicative of the quality with which a seismic sensor can reproduce the ground motion signal is Total Harmonic Distortion (THD). THD is defined as the ratio of the rms level of the harmonics that result from non-linear processes in the seismic sensor to the rms level of the fundamental drive signal. Given the difficulties in reliably determining the rms level of the sum of the harmonics, we opted instead to measure the size of the third-order harmonic as a function of the sinusoidal drive signal and to determine the third-order intercept point. The third-order intercept point, i.e., the projected asymptotic point at which the fundamental and third harmonic signal levels are equal in amplitude, provides a useful figure of merit for the quality of a ground motion signal recorded by a seismic sensor.

The problem of how to shake the PMD sensors in order to test the linearity was solved by constructing a vertical component shake table using a Johnson-Matheson (J-M) Model 6840 short-period vertical seismometer. A platform to hold the PMD sensor and extra springs to support the additional weight were added to the J-M seismometer so that vertical shaking could be induced by actively driving the signal coil. The natural frequency of the resulting system was $\sim $1.6 Hz. We found that the shake table could be driven over the useful range of $\sim $50 dB, from $\sim $3.4x10-6m rms (limited by the $\sim $1 Hz background noise PSD level of $\sim $-136 dB at BKS) to $\sim $10-3m rms (limited by the shake table suspension travel), and over a 0.2-5 Hz frequency range by driving the signal coil sinusoidally using a WaveTek function generator. Vertical and horizontal Wilcoxon 731A accelerometers were attached to the PMD sensor housing to directly measure the amplitude and spectral characteristics of the induced shaking. The table and sensors were arranged so that their center of mass was on the center line of J-M suspension axis in order to minimize cross-axis coupling.

The methodology for determining the third-order intercept poit is shown in Figure 8.8 where the fundamental and third harmonic output velocities were plotted as a function of the sinusoidal input velocity at a given frequency. Asymptotes are drawn through the fundamental and third harmonic slopes and their intersection is the third-order intercept point. As seen in Figure 8.8, the third-order intercept point for the vertical component on one of the PMD seismometers, at a frequency of 4 Hz, is 0.000185 m/s or -74.6 dB relative to 1 m/s. Note that at larger input velocities the fundamental and third harmonic velocities deviate significantly from the asymptotes as the sensor saturates. At input velocities of $\sim $0.0003 m/s and higher, the sensor is saturated and the third harmonic signal is actually larger than the fundamental. Spectral analysis of the shake table vertical motion, recorded by a Wilcoxon 731A mounted on top of the PMD sensor housing, shows that the amplitude of the third harmonic is 70 dB below the amplitude of the fundamental drive signal at an input velocity of 0.0002 m/s. Likewise, spectral analysis of shake table horizontal motion shows that the amplitude of horizontal shaking induced by non-linearities in the shake table suspension is 50 dB below the fundamental vertical amplitude and the third harmonic is below the noise floor of the Wilcoxon sensor. Thus the PMD sensor and not the shake table is the source of the observed harmonic distortion.


  
Figure 8.8: Third order intercept plot. The third-order intercept point is useful as a figure of merit for the quality of a ground motion signal recorded by a seismic sensor.
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A third-order intercept point of -74.6 dB at 4 Hz is equivalent to a ground velocity of $\sim $200 um/s. A 200 um/s ground velocity would be expected to be exceed by a ML 2.7 at 10 km, a ML 2.9 at 20 km, a ML 3.8 at 50 km, a ML 4.2 at 100 km, or a ML 4.7 at 200 km. These ML versus distance thresholds are more than a magnitude unit below the typical signal handling capabilities of broadband feedback seismometers which utilize velocity transduction. The harmonic distortion observed in the PMD sensors with MET transducers should be significantly reduced when feedback is employed.

Station locations

For many decades, BSL seismic station locations were determined using USGS 7.5-minute quadrangle series maps that use the North American Datum of 1927 (NAD-27, 1927) coordinates and Mean Sea Level (MSL) referenced elevations. The stated accuracies (NMAS, 1947) for these hypsographic maps are: not more that 10 percent of the well defined test points shall be in error by more than 1/40 inch horizontally, and; not more than 10 percent of the elevations tested shall be in error by more than 1/2 of the contour interval. This implies that, for a 1:24,000 scale map, the estimated horizontal uncertainty is $\sim $15+ meters and the estimated vertical uncertainty is $\sim $1.5+ meters. In the past 5+ years, the BDSN and HFN station coordinates have been determined mostly by Geodetic GPS measurements using the World Geodetic System 1984 (WGS-84, 1987) reference frame for the coordinates and the corresponding reference ellipsoid for elevation. For reference, the difference between the NAD-27 and WGS-84 (WGS-84 minus NAD-27) coordinates for BKS are: 95.2 m W in longitude, 8.7 m N in latitude, and -32.1 m in elevation.

During the past year, to avoid ambiguity in the BDSN and HFN station locations, to improve the accuracy of the locations, and to meet the SEED station control header requirements for generating SEED data volumes (Halbert et al., 1996), we decided to revise the master station coordinate file using the following guidelines:

The resulting master station location data are presented in Table 2.2. The coordinates are given to a precision of 5 decimal places ( $\pm$1 meter) and the estimated location accuracy is 1-2 meters. The elevation is given to a precision of 0.1 meters and the estimated elevation accuracy is 1-2 meters. The overburden thickness (the elevation difference between the pier and the surface grade directly above the pier) accuracy is estimated to be $\sim $1 meter.

We found that the errors in the reported locations for the earlier stations were of the order that we expected. For example, the original reported NAD-27 location for BKS, sited in the Byerly Vault in Strawberry Canyon, (37$^\circ $-52.6' N, 122$^\circ $-14.1' W, 276 m) (Lomnitz et al., 1965) was only given to the nearest 0.1' (equivalent to an uncertainty of $\sim $92 m in latitude and $\sim $73 m in longitude). The BKS location was later reported in decimal degrees as (37.877$^\circ $ N, 122.235$^\circ $ W, 276m). The GPS derived location for BKS (WGS-84: 37.87622$^\circ $, -122.23558$^\circ $, 243.9 m; NAD-27: 37.87630$^\circ $, -122.23450$^\circ $, 276.0 m). The GPS derived NAD-27 referenced location is 60 m S47$^\circ $E of the original location reported in the Bulletin.

Acknowledgements

Under Barbara Romanowicz's general supervision, and with Doug Neuhauser and Bob Uhrhammer as head gurus, Lind Gee, Steve Fulton, and Rick McKenzie are involved in the data acquisition and quality control of BDSN and HFN data. Lind Gee, Doug Neuhauser, and Bob Uhrhammer contributed to the preparation of this chapter. Development of the exportable version of the PSD algorithm was funded by the IRIS Consortium and performed by Bob Uhrhammer. The infrasound monitoring project is funded by the Defense Threat Reduction Agency.

References

Abramovich, I. A., V. M. Agafonov, M. E. Cobern, and V. A. Kozlov, Improved Wide-Band Molecular Electronic Seismometer and Data Acquisition system, Poster Session S31B-18, 1997 Fall AGU Meeting, San Francisco, 1997.

Halbert, S. E., R. Buland, and C. R. Hutt, Standard for the Exchange of Earthquake Data (SEED), Version V2.0, February 25, 1988. United States Geological Survey, Albuquerque Seismological Laboratory, Building 10002, Kirtland Air Force Base East, Albuquerque, New Mexico 87115, 82 pp., 1996.

Lomnitz, C, M. Otsuka, and H. Acharya, Bulletin of the Seismographic Stations, University of California, Berkeley, 32, pp. 89-140, 1965.

NAD-27, The North American Datum of 1927 is ``The horizontal control datum for the United States that was defined by a location and azimuth on the Clarke spheroid of 1866, with origin at (the survey station) Meades Ranch (in Kansas).'' The geoidal height at Meades Ranch was assumed to be zero. ``Geodetic positions on the North American Datum of 1927 were derived from the (coordinates of and an azimuth at Meades Ranch) through a readjustment of the triangulation of the entire network in which Laplace azimuths were introduced, and the Bowie method was used.'' During the 5-year period 1927-1932, all available primary data were adjusted into NAD-27.

NMAS, US National Map Accuracy Standard, 1947, http://rockyweb.cr.usgs.gov/nmpstds/nmas.html.

Peterson, J. Observations and Modeling of Seismic Background Noise, U.S. Geological Survey Open File Report 93-322, 94 pp., 1993.

Tapley, W. C. and J. E. Tull, SAC - Seismic Analysis Code: Users Manual, Lawrence Livermore National Laboratory, Revision 4, 388 pp., March 20, 1992.

WGS-84, Department of Defence World Geodetic System 1984 - Its Definition and Relationships With Local Geodetic Systems, DMA TR 8350.2, Headquarters, Defence Mapping Agency, Washington, DC, 30 September 1987.


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