Stations from nearly all networks operated by the BSL transmit data continuously to the BSL facilities on the UC Berkeley campus for analysis and archive. In this chapter, we describe activities and facilities which cross-cut the individual networks described in Chapters 4 - 10, including the facilities in McCone Hall, procedures for data acquisition and quality control, sensor testing capabilities and proceedures, and a collaborative experiment in early warning.
While some of these activities are continuous from year to year, we have identified changes or activities which are specific to 2001-2002.
Because of the mission-critical nature of the automated earthquake processing, most computer systems operated by the BSL run on circuits with both UPS and generator power. Air conditioning is provided through both "building air" and a separate room AC unit.
Over the years, the BSL has experienced problems with the McCone generator system, including a failure in 1999 due to a combination of a weakened power system and a leak in the water pump.
During this time, BSL staff made several attempts to bring the McCone generator online. The initial failure of the generator was traced to a weak battery. When BSL staff replaced the battery, the generator started up and then shut itself off after several minutes, due to a leak in the water pump.
As a result of the failure of the generator, the BSL earthquake monitoring system went off the air around 5:30 PM when the UPS system shut down due to a low battery condition (the UPS is designed to carry the electrical load until the generator comes online). A subset of critical computers were brought back online when a personal generator belonging to Bob Uhrhammer was brought in around 8:00 PM. A temporary fix to the generator was provided by PPCS around 8:30 PM, which allowed the rest of the processing system to be restored. The generator was not fully repaired until March 26th, 19 days after the power outage.
The failure of the McCone generator was due to poor maintenance. Similar to the situation in 1999, it failed due to problems in the power system combined with a leak in the water pump. The BSL is working with PPCS to establish a routine of regular load tests, which should improve screening for problems such as this, as well as working with other groups to relocate the critical activities to more robust campus facilities.
The failure of the generator at Byerly Vault was traced to PPCS error. The generator had been left in a mode where it would not automatically start when power was lost. A BSL staff member went up to Byerly during the power outage and brought the generator online.
Central-site data acquisition for the BDSN/NHFN/MPBO is performed by two computer systems located at the BSL (Figure 11.1). These acquisition systems are also used for the Parkfield-Hollister electromagnetic array and for the BARD network. A third system s used primarily as data exchange system with the USNSN receives a feed from CMB, MOD, SAO, and WDC from the the NSN VSAT. This system transmits data to the USNSN from HOPS, CMB, SAO, WDC, and YBH. Data acquisition for the HRSN follows a more complicated path, as described in Chapter 6.
Data acquisition and communication with the Quanterra data loggers depends both on the software on the recording systems and at the central site.
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 data loggers 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 data logger (previously described in the 1999-2000 and 2000-2001 Annual Reports), and the addition of multi-site data collection.
The BSL will use this multi-site data collection feature to create "hub" systems, which will acquire, store, and transmit data from several remote data loggers as well as the hub's own data. The San Francisco/Oakland Bay Bridge network will consist of two Quanterra 4120 hubs (Figure 11.2), 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.
The BSL uses the comserv program for central data acquisition, which was developed by Quanterra. The comserv program receives data from a remote Quanterra data logger, 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 Memento Mori Web page (Figure 11.3).
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 stations transmit data to only one or the other of the two REDI systems. 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 station.
We have 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 BSL data. We use mserv on both REDI computers to receive the multicast data, and handle it in an identical fashion to the BSL MiniSEED data.
BSL seismic 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 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 generates a bargraph output which compares all the BDSN broadband stations by components. A summary of the results for 2001-2002 is displayed in Figure 4.3.
Last year, we 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/.
The PSD algorithm has been documented in previous annual reports. As reported in the past, this tool was originally written in Fortran and recently converted to C using the f2c utility in response to interest from the community. However, the resulting code was inpenetrable. As a result, a BSL undergraduate, Steve Chu, worked with Bob Uhrhammer in the spring of 2002 to write the code in C.
The BSL has set up an instrumentation test facility in the Byerly Seismographic Vault in order to systematically determine and to compare the characteristics of up to eight sensors at a time. The test equipment consists of an eight-channel Quanterra Q4120 high-resolution data logger and a custom interconnect panel that provides isolated power and preamplification when required to facilitate the connection and routing of signals from the sensors to the data logger with shielded signal lines. Upon acquisition of the 100 samples-per-second (sps) data from the instruments under test, PSD analysis and spectral phase coherency analysis are used to characterize and compare the performance of each sensor. Tilt tests and seismic signals with a sufficient signal level above the background seismic noise are also used to verify the absolute calibration of the sensors. A simple vertical shake table is used to access the linearity of a seismic sensor.
BSL personnel have tested numerous sensors during the past several years and each test has been ad hoc in its implementation and execution. In order to expedite the setup and testing of the instruments for both BSL in-house use and well as for other groups, such as IRIS, we dedicated an eight-channel Quanterra Q4120 data logger (see Figure 11.4) and constructed cabling and a patch panel (see Figure 11.5) to enable the simultaneous testing of up to eight sensors with high (24-bit integer) resolution sampling at 100 samples per second (for a usable bandwidth of 0-32 Hz). We housed the test equipment in the Byerly Seismographic Vault, located in an old mining drift in the Claremont shales and cherts formation in Strawberry Canyon behind the Botanical Garden. The Byerly Seismographic Vault is ideal for a test facility because it is seismically quieter than the BSL facilities on the Berkeley campus, it has all the necessary infrastructure, and because the BDSN station BKS, housed in the same vault, includes a set of Streckeisen STS-1 broadband seismometers and a Kinemetrics FBA-23 strong motion accelerometer which provide reference seismic recordings of the ground motion.
Sensor testing has three aspects: 1) data acquisition; 2) sensor calibration; and, 3) sensor performance.
The acquisition of high resolution digital data is absolutely essential for the testing of modern sensors. The Quanterra Q4120 data logger, which has 24-bit integer resolution (144 dB dynamic range) with a LSB of 2.38 V and a 40 V P-P signal handling capability, is ideally suited for this task. Data acquisition has two modes, active and passive. In the active acquisition mode, a signal is induced by appropriate means as described below. In the passive acquisition mode, the sensors signals are recorded for extended periods of time, typically from a day or so to a couple of weeks or more, to acquire samples of both the background noise level as well as various natural seismic, gravitational tide, and atmospheric signals.
The calibration methodology employed is sensor dependent. In the case of broadband seismometers, induced tilting, dynamic shaking, dynamic driving of a calibration coil, the gravitational tide signal, and comparison with ground motions inferred from known sensors are used as appropriate. In the case of strong motion accelerometers, induced tilting, dynamic shaking, and comparison with ground motions inferred from known sensors are used as appropriate. In the case of barometric pressure sensors, static elevation changes and comparison with the atmospheric pressure inferred from known sensors are used as appropriate.
Performance of a sensor is primarily characterized by determining the usable dynamic range, the linearity, and the noise characteristics. The usable dynamic range is the difference, usually expressed in dB, between the sensor saturation signal level and the sensor or background noise level. The usable dynamic range is usually frequency dependent. Characterization of the linearity and dynamic range of a broadband seismic sensor is problematic because seismic signals are a wide bandwidth transient phenomena (see the linearity and dynamic range section below). The linearity is characterized by determining the third-order intercept point. The usable dynamic range is quantified by the difference between the sensor saturation (full scale) signal level and the background signal or sensor noise floor and it is usually expressed in dB. PSD estimates of the background seismic (or sensor self noise), plotted as a function of frequency, are used to concisely quantify the performance of a broadband sensor (and also to quantify differences in the noise levels observed in various seismic vaults housing broadband seismic sensors). Another approach for characterizing the bandwidth and performance of a particular type of sensor is to simultaneously record two identical sensors placed on the same pier and calculate the phase coherency of their outputs as a function of frequency.
All of the above approaches to sensor testing have been employed in our analysis during the past year.
The problem of how to shake the seismic sensors in order to test the linearity was solved by constructing a vertical component shake table from a Johnson-Matheson (J-M) Model 6840 short-period vertical seismometer as shown in Figure 11.6. The large and stiff suspension of the J-M was ideally suited for modification to a simple shake table. A platform to hold the 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 J-M signal coil at relatively low current levels. The natural frequency of the resulting shake table was 1.6 Hz. We found that the shake table could be driven over a useful range of 50 dB, from m rms (limited by the background noise PSD level of -136 dB at BKS) to m rms (limited by the shake table suspension travel), and over a 0.2-5 Hz frequency range by driving the signal coil sinusoidally with a WaveTek function generator. Vertical and horizontal Wilcoxon 731A accelerometers are attached to the housing of the sensor under test to directly measure the amplitude and spectral characteristics of the induced shaking for reference to output of test sensor. The table and sensor were arranged so that their center of mass was on the center line of the J-M suspension axis in order to minimize cross axis coupling.
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 continuous signal. One figure of merit that is indicative of the quality with which a seismic sensor can record the ground motion signal is Total Harmonic Distortion (THD). The THD, in percent, is defined as:
where: is the rms level of the fundamental drive signal and the are the rms levels of the harmonics that result from non-linear processes in the seismic sensor. Given the difficulties in reliably determining the sum of the 's, we opted to measure instead 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 methodology for determining the third-order intercept point is shown in Figure 11.7 for a PMD sensor where the fundamental and third harmonic output velocities are 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.
Prior to deployment of the Guralp CMG-1TD OBS (Serial Number T1045) sensor package (Figure 11.8) on the ocean floor in Monterey Bay (MOBB - Chapter 10), we did extensive testing to verify the operation of the seismometers and the wide range leveling system (Figure 11.9), to verify the calibration of the seismometer, to determine the drift rate of the OBS clock, to characterize the background noise PSD performance of the seismometers and to calibrate the magnetometer used to determine the sensor orientation on the ocean floor.
The testing of the OBS system in the Byerly Seismographic Vault (BKS) started on 2 November with the arrival and installation of the CMG-1TD seismometer and learning the commands for unlocking, centering and checking the status of the seismometers. We determined then that the internal flux gate magnetometer was not operating properly. The test data from the CMG-1TD was telemetered to McCone Hall via a frame relay serial port. A plastic garbage can was placed over the CMG-1TD to keep dust and breezes off of the exposed sensors and electronics.
On 14 December, Cansun Guralp visited BSL to replace the flux gate magnetometer and determine the offset of the CMG-1TD internal clock. The new magnetometer was calibrated using a Guralp-supplied turntable. The Garmin GPS receiver was connected to the CMG-1TD to check the clock offset and, based on the software reports, it appears that the CMG-1TD internal clock was 18-19 msec faster than the GPS clock. We verified the offset by connecting a 1 PPM signal (generated by external diagnostics board from Guralp's Garmin GPS receiver) into the Guralp Z digitizer, in place of the Z component seismometer and observed the same 18-19 msec difference.
During January, the CMG-1TD was operated continuously and the ground motions, inferred from the CMG-1TD and from the STS-1's operating at BKS, were compared for some large teleseisms and regional earthquakes and we verified that the transfer function of the CMG-1TD was correct. However background noise PSD analysis showed that the Z component of the CMG-1TD was very noisy at periods longer than 10 seconds, as shown in Figure 11.10. The high noise level observed on the CMG-1TD vertical component is most likely caused by air circulation around the seismometer and the noise is expected to decrease significantly when the CMG-1TD is installed in the pressure vessel. During this time it was also determined that the CMG-1TD internal clock drift rate is -236.591.98 microseconds/day.
The CMG-1TD was installed in the titanium pressure vessel on 30 January and the pressure vessel was placed on a bed of sand as shown in Figure 11.11. Subsequent background noise PSD analysis indicated that the CMG-1TD vertical component was still very noisy at periods longer than 10 seconds. Communication with Guralp suggested several possible causes for the observed long-period noise: 1) the sand may be creaking under the weight of the pressure vessel and seismometer; 2) one or more of the calibration relays may have been left in the cal-enable position at boot-up; 3) the air inside the pressure vessel may be convecting; or 4) something is amiss with the Z component seismometer. Tests subsequently ruled out the sand creaking and the calibration relays as the source of the problem.
In a one week interval, starting on 26 February, a series of tests were done to determine their effect on the CMG-1TD background noise PSD level. The first test was to construct and install a baffle and insulating sleeve around and over the seismometer inside the pressure vessel as shown in Figure 11.12. The result was that background noise PSD at periods longer than 10 seconds decreased by 17 dB on the Z component. This is evidence that the air inside the pressure vessel was in fact convecting and inducing noise on the Z component. The insulation and baffle served to stratify the air and inhibit convection inside the pressure vessel. The second test was to purge the pressure vessel with dry argon gas (argon is 37 denser than air and has thermal conduction properties which tends to inhibit convection). The argon gas indeed lowered the background noise PSD level by another 6+ dB at long periods. To test the idea that the power supply may be inducing some long period noise into the system, we disconnected the power supply and powered the CMG-1TD solely from a 12 Volt battery. The background noise PSD level subsequently decreased by another 6+dB at long periods. Finally we added insulation to the outside of the pressure vessel by draping a space blanket over the vessel and covering the vessel with a two inch thick foam box with all seams taped. The background noise PSD performance of all three CMG-1TD seismometer components are now within 5 dB of the performance of the BKS STS-1 sensors (which are more heavily insulated and installed in an nearly ideal observatory environment), as shown in Figure 11.13. The result of this testing is that CMG-1T must be appropriately insulated and the pressure vessel purged with argon gas in order to obtain the performance of which the CMG-1T is capable.
For the final phase of the testing, the CMG-1TD and the pressure vessel were transported to MBARI on 26 March for testing in their cold room at the 4 degrees C temperature of the ocean floor. The cultural environment at MBARI, owing to its being situated on sand and in close proximity to the surf, makes for noisy seismic recordings with a large seven-second microseismic peak. The clock drift while operating in the cold room increased by an order of magnitude to -3191.392.656 microseconds/day. This was a surprise because the expectation was that the drift rate would be smaller at 4 degrees C than at room temperature. There was insufficient time, however, to retrain the clock prior to the scheduled deployment on the ocean floor. We also did a rough check of the calibration of the flux gate magnetometer because there was some concern that the metal in the BKS Vault may have biased the original calibration results.
For details on the deployment of the MOBB system, please see Chapter 10.
The question arose as to what is the typical self-noise of a Quanterra 680-family Very-Broad-Band data acquisition system (aka "Pumpkin"). The digitizer package contains identical sets of independently optically-isolated three-channel 24-bit digitizers operating at 80 Hz sampling with a least significant bit (LSB) of 2.38 microvolts and a 20 Volt full scale range. The self-noise was tested by operating a Q680 (s/n 921209) in the BSL lab for a couple of weeks with open inputs to the six high-resolution channels. The result, shown in Figure 11.14, is a composite based on the noise PSD observed on all six channels. The lowest self-noise occurs at 2-4 Hz frequencies and the lowest median self-noise PSD of -136.6 dB relative to 1 V/Hz occurs at 2.5 Hz. Most of the self-noise of the Q680 occurs at high frequencies (above 10 Hz). A -136.6 dB PSD level integrated over 1 Hz (the 2 to 3 Hz interval) is equivalent to a rms noise level of 0.067 LSB (or 0.159 microvolts) and the median value integrated over the 0-32 Hz bandwidth limited by the FIR anti-aliasing filters of the 80 Hz sampled channel is 0.601 LSB (or 1.43 microvolts). The 1.43 microvolt rms 0-32 Hz self-noise of the Q680 is approximately an order of magnitude lower than the quietest broadband sensors used by BDSN. The asymmetric variation in the inter-quartile range, given by the spacing between the medium dashed lines, is due to individual differences in the self-noise of the six high-resolution digitizer channels. There is a 6 dB variation in the observed self-noise PSD which is mostly due to differences in the self-noise of the individual digitizers and partly due to variations in the environmental conditions (primarily temperature variation) in the lab.
In addition to the above testing, we tested several new broadband seismometers and strong motion accelerometers. New, and recently repaired, sensors are routinely tested before deployment in the field. We also had a few BDSN sensors, both accelerometers and broadband sensors, which either become noisy or malfunctioned during the past year and they were tested at the test facility in the Byerly Seismographic Vault to identify and characterize the problem. Most notably we have two horizontal Streckeisen STS-1 broadband seismometers which became noisy over the past year and the problem was traced to rusty hinges, probably caused by operating at atmospheric pressure in a humid environment for several years. We are currently experimenting with different hinge materials for replacing the hinges.
The established joint notification system in Northern California (Chapter 12) provides accurate and reliable determination of earthquake parameters, but there is a time delay between the occurrence of an event and the determination of its size. In an emergency, this time delay prevents actions which could mitigate damage from strong ground shaking. The present configuration of the system could be improved with the capability of rapid size determination. In addition, it will be advantageous to have detection capabilities independent of the dense short-period network, especially in an emergency when communications may be disrupted.
In an effort to develop such capability with the BDSN, we have started an experiment collocating a set of UrEDAS (Urgent Earthquake Detection and Alarm System; see Nakamura, 1996), an integrated real-time earthquake warning system, at BKS. The UrEDAS detects an earthquake using only 3 sec of the P-wave recorded at a single station and has been used in Japan for over a decade to alert bullet trains to strong shaking in progress. While the weakness of UrEDAS is that it is less accurate in event parameter determination, its advantage is speed. Through this experiment we explore the development of a single-station capability of rapid earthquake detection and location/magnitude estimate using data from the BDSN stations.
UrEDAS was developed to mitigate earthquake hazards for Japanese railways, especially bullet trains that travel at a peak speed close to 200 kph. Because of the speed of the bullet trains, it is extremely important to rapidly detect hazardous earthquakes and send alarm signals to the automatic train stop system. Empirically, damage to railways occurs for events with M 5.5, with the damaged area generally confined within a certain distance range of the epicenters. For example, earthquakes with M6, M7 and M8 can cause damages within epicentral distances of 12, 60, and 300 km, respectively. The target area of event detection of UrEDAS is designed to be 200 km from its location along the railways. Dr. Yukata Nakamura, who was formerly at the Railway Technical Research Institute and is now the president of System and Data Research (SDR) Co., Ltd. in Tokyo, Japan, is the leader of the UrEDAS system development.
The philosophy of the UrEDAS "front alarm" system is to detect earthquake motions as early as possible before strong ground shaking reaches the site (Nakamura, 1996). The concept of such a system was originally published by Dr. J. D. Cooper in the San Francisco Daily Evening Bulletin, November 1868. At the time Cooper addressed the importance of an automated early warning system alarm as well as the likely problems of false alarms and the necessity of public education for the warning. However, the technical expertise at the time was inadequate for construction such an alarm system. In 1972 the Earthquake Research Institute of the University of Tokyo proposed the development of an automated system similar to that suggested by Cooper. The Japanese National Railways (JNR) was interested in this concept and they promoted the system development. The JNR completed the development of a Coast-line Detection System for the Tohoku Bullet Train Line in 1982 (Nakamura and Saito, 1982), and it was the prototype that has the same basic function of the present UrEDAS system.
UrEDAS utilizes a single system that consists of two sets of three-component seismometers, a set of velocity sensors with =1 sec (short-period sensors with moving coil designed for events up to M6) and a set of force feedback velocity sensors with a 0.1-10 Hz frequency passband (long-period sensors designed for larger events), for stable determination of small to large events. The system uses a velocity threshold to detect events and the epicentral azimuth is estimated from the direction of the initial motion projected on the horizontal plane. An estimate of the distance and magnitude is based on frequency content and amplitudes of the first three seconds of the P-wave motion. If the S-wave is detected, the estimates of distance and magnitude are revised. The epicentral distance () is estimated using the relation where is the amplitude of the initial P-wave motion (in mkine), its prominent period, and and are constant. The magnitude is estimated from the prominent period () of the initial P-wave motion using the relation .
An alarm can be issued if hazardous earthquake is detected from the P-wave motions. However, it is not always easy to detect an event from the P-wave alone and there is a trade-off between the detection threshold and the rate of false alarms. The primary advantage of UrEDAS is that the system can provide event information in 3 seconds following the detection of a P-wave at a single site. The quality of the parameter determination depends on the conditioning and tuning of the system for site specific effects and thus the pre-conditioning is important in the determination procedure. Also, the magnitude estimation using just the first three seconds of the P-wave motions may not always be reliable. It is also possible that the station location happens to be near a P-wave node of the focal mechanism. To overcome the weakness of the single system algorithm, each of the UrEDAS stations distributed along the railways is linked to a centralized system to provide better estimates of the event parameters.
Since 1984, JNR has tested the UrEDAS system in various areas where high-quality location and magnitude determinations are available from dense regional short-period networks. In November 1999 the crew of SDR visited BSL to discuss a possible joint experiment with UrEDAS. The seismologists at BSL agreed to test the UrEDAS performance by collocating it at one of the BDSN stations. We chose the Byerly Vault (station BKS) for its low noise level and accessibility and because it includes a set of Streckeisen STS-1 seismometers and a Kinemetrics FBA-23 strong motion accelerometer and because the station has been operational since June 1962 and the site specific characteristics of the seismic wave propagation are fairly well known.
The initial system installation was completed with the event detection and notification in February 2001 and was upgraded to transmit waveform data to the BSL in July 2001. The SDR crew visited the site in July 2002 to check on the equipment and to revise the values of the parameters used by the UrEDAS algorithms. Figure 11.15 shows the UrEDAS sensors and Figure 11.16 shows an illustration of the UrEDAS network configuration.
Since February 2001, there have been 283 UrEDAS detected events, and 151 of these correspond to events in the NCSN earthquake catalog (within a 500 km radius and with a theoretical P-wave onset time at BKS within 20 seconds of the UrEDAS detection time). The 132 uncorrelated UrEDAS events are presumably a mix of teleseisms (which UrEDAS has a tendency to mislocate as local events), some small local events near Berkeley (which are below the NCSN magnitude threshold), and a few random noise triggers.
To date UrEDAS readily detects the occurrence of local/regional events from the P-wave signal as shown in Figure 11.17. It also does a fair job of determining the source distance out to 160 km or so (see Figure 11.18) but the azimuth determination is basically unusable as shown in Figure 11.19. UrEDAS also has biased magnitude estimates as shown in Figure 11.20. The UrEDAS algorithm assumes a one-dimensional velocity model with straight line propagation paths and a three-dimensional model of the crustal structure will likely be required to significantly improve the azimuthal estimates. Also, the magnitude estimation algorithm needs further tuning.
Assuming that the primary goal is to determine the event location and size as rapidly as possible, the fastest approach will prove to be a hybrid approach where the remote stations determine the azimuth and ramp growth rate and associated uncertainties and the central site uses a fuzzy logic algorithm to determine the location and size of the event. The primary advantage of this hybrid method is that the ramp growth rate can be reliably determined before the S-wave arrives. In the limiting case, and with a sufficiently high station density, one could even go so far as to determine and report from the remote sites using only the broadband P-wave impulse, and associated azimuth and apparent angle of incidence and estimates of their resolution and have the central site coalesce the data into a viable and rapid event report.
The critical issue for a successful installation of a UrEDAS type system in the BDSN is the calibration of specific site effects at individual stations. A joint use of the single station detection system with the current northern California earthquake notification system would significantly increase the capability of real-time earthquake warning system.
The current UrEDAS system uses a dedicated PC and the SDR personnel have indicated that a UrEDAS/UNIX version of the algorithm is available. The UNIX version generates the same message format as does the PC-based system. The advantage of the UNIX based system is that, besides running on the BSL computers, it can process data from any seismometer or seismometer/filter system that can output ground acceleration in the 0.1-10 Hz passband at 100 sps. The UNIX version of the UrEDAS algorithm is hard wired for 100 sps acceleration data provided in Gals (to four decimal places) so we would need to generate a structure which supplies the algorithm with a date/time stamp amd the three acceleration components (Z,N,E) in Gals and a rate of 100 per second with a specific comma delimited format: "20020607114810.00, +24.4356, -21.5642, +14.9547" (for example). The primary disadvantage of the UrEDAS/UNIX processing is that it will lag several seconds behind real-time (5-10, say), due to the packet transmission protocol that we use to transmit the data to BSL. The UrEDAS/UNIX source code is proprietary property of SDR and will not be made available to BSL.
Doug Neuhauser, Bob Uhrhammer, Lind Gee, Pete Lombard, and Rick McKenzie are involved in the data acquisition and quality control of BDSN/NHFN/MBPO data.
Development of the sensor test facility and analysis system was a collaborative effort of Bob Uhrhammer, Tom McEvilly, John Friday, and Bill Karavas. IRIS and DTRA provided, in part, funding and/or incentive to set up and operate the facility and we thank them for their support.
Bob Uhrhammer led the testing and problem solving effort of the MOBB sensor, with help from John Friday, Doug Neuhauser, and Bill Karavas.
Fumiko Tajima initiated the collaboration with SDR on testing the UrEDAS system, which is now coordinated by Bob Uhrhammer. Doug Neuhauser, Bill Karavas, John Friday, and Dave Rapkin helped with installation and maintenance. We thank Yutaka Nakamura and his colleagues at SDR for providing us with the installation of UrEDAS system and information on the accumulated data by this system.
Bob Uhrhammer, Lind Gee, and Doug Neuhauser contributed to the preparation of this chapter.
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, AGU Fall 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.
Nakamura, Y. and A. Saito, Train stopping system for the Tohoku Shinkansen (in Japanese), Proc. of Semiannual meeting of the Seismological Society of Japan, 82, 244, 1982.
Nakamura, Y., Real-time information systems for seismic hazards mitigation UrEDAS, HERAS, and PIC, Quarterly Report of Railway Technical Research Institute, 37, 112-127,1996.
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.
Uhrhammer, R. A. and T. V. McEvilly, IRIS Sensor Tests: an interim report, Poster Presentation, 2001 IRIS Workshop, Jackson Lake Lodge, Moran, WY, June 6-9, 2001.