Complementary to the regional broadband network, a deployment of borehole-installed, wide-dynamic range seismographic stations is being established along the Hayward Fault and throughout the San Francisco Bay toll bridges network. This project is a cooperative development of the BSL and the USGS, with support from USGS, Caltrans, EPRI, the University of California Campus/Laboratory Collaboration (CLC) program, LLNL, and LBNL (Figure 5.1 and Table 5.1).
The purpose of the network is twofold: to lower substantially the
threshold of microearthquake detection and increase the recorded
bandwidth for events along the Hayward fault; and to obtain bedrock
ground motion signals at the bridges from small earthquakes for
investigating bridge responses to stronger ground motions. A lower
detection threshold will increase the resolution of fault-zone
structural features and define spatio-temporal characteristics in the
seismicity at 
, where occurrence rates are
dramatically higher than those captured by the surface sites of the
NCSN. This new data collection will contribute to improved working
models for the Hayward fault. The bedrock ground motion recordings are
being used to provide input for estimating the likely responses of the
bridges to large, potentially damaging earthquakes. Combined with the
improved Hayward fault models, source-specific response calculations
can be made.
The Hayward Fault Network (HFN) consists of two parts. The Northern Hayward Fault Network (NHFN) is operated by the BSL and currently consists of 20 stations, including those located on the Bay bridges. This network is considered part of the BDSN and uses the network code BK. The Southern Hayward Fault Network (SHFN) is operated by the USGS and currently consists of 5 stations. This network is considered part of the NCSN and uses the network code NC. This chapter is primarily focused on the NHFN and activities associated with the BSL operations.
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All sites of the HFN have six-component borehole sensor packages which were designed and fabricated at LBNL's Geophysical Measurement Facility by Don Lippert and Ray Solbau, with the exception of site SFAB. Three channels of acceleration are provided by Wilcoxon 731A piezoelectric accelerometers and three channels of velocity are provided by Oyo HS-1 4.5 Hz geophones (Table 5.2). Sensors are installed at depths of 100-300 m and provide signals to the on-site data loggers (Quanterra Q4120 and Q730, Nanometrics HRD24, or RefTek 72A-07 systems).
The 0.1-400 Hz Wilcoxon accelerometers have lower self-noise than the geophones above about 25-30 Hz, and remain on scale and linear to 0.5 g. In tests performed in the Byerly vault at UC Berkeley, the Wilcoxon is considerably quieter than the FBA-23 at all periods, and is almost as quiet as the STS-2 between 1 and 50 Hz.
Eight of the NHFN sites have Quanterra data loggers with continuous telemetry to the BSL. Similar to BDSN sites, these stations are capable of on-site recording and local storage of all data for more than one day and have batteries to provide backup power. Signals from these stations are digitized at a variety of data rates up to 500 Hz at 24-bit resolution (Table 5.3). In contrast to the BDSN implementation, the NHFN data loggers employ casual FIR filters at high data rates and acausal FIR filters at lower data rates. Because of limitations in telemetry bandwidth and disk storage, these 7 sites transmit triggered data at 500 sps, using the Murdock, Hutt, and Halbert (MHH) event detection algorithm (Murdock and Hutt, 1983), and continuous data at reduced rates (100, 20 and 1 sps) to the BSL.
The remaining 12 sites of the NHFN have in the past recorded data using RefTek data loggers. These sites do not have continuous telemetry for acquisition and require visits from BSL staff for data recovery. Seven of these sites located on the Bay Bridge are scheduled to be upgraded with Quanterra data loggers and continuous telemetry in the fall of 2002 (see Figure 11.2 in Chapter 11). The Bay Bridge component of the NHFN has been delayed during the past year, primarily due to the major effort required to upgrade the HRSN (Chapter 6).
Signals from the 5 SHFN stations are digitized by Nanometrics data loggers at 200 sps and transmit continuous data to Menlo Park by radio. The digital data streams are processed by the Earthworm system with the NCSN data and waveforms are saved when the Earthworm detects an event.
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As part of the USGS and BSL collaboration on the HFN, data from the NHFN and SHFN sites with continuous telemetry are shared in near real-time. NHFN data are transmitted to the USGS and SHFN data are transmitted to the BSL.
Experience has shown that the MHH detector does not provide uniform triggering across the NHFN on the smallest events of interest. In order to insure the recovery of 500 sps data for these earthquakes, a central-site controller has recently been implemented at the BSL using the 500 sps vertical component geophone data for event detection. Originally the 100 sps vertical component geophone data was used for event detection but the bandwidth proved to be inadequate for detection of the smaller events where most of the seismic wave energy was at frequencies above 40 Hz. Triggers from this controller will be used to recover the 500 sps data from the NHFN data loggers.
Data from the NHFN and SHFN are archived at the NCEDC. At this time, the tools are not in place to archive the Hayward fault data together. The NHFN data are archived with the BDSN data, while the SHFN are archived with the NCSN data (Chapter 13). However, the new central-site controller will provide the capability to both include SHFN data in the event detection and extract SHFN waveforms for these events in the future.
As originally planned, the Hayward Fault Network was to consist of 24 to 30 stations, 12-15 each north and south of San Leandro, managed respectively by UCB and USGS. This is not happening quickly, although west of the fault, Caltrans has provided sites along the Bay bridges. This important contribution to the Hayward Fault Network has doubled the number of sites with instrumentation. At times, Caltrans provides holes of opportunity away from the bridges (e.g., HERB), so we have plans for additional stations that will bring the network geometry to a more effective state for imaging and real-time monitoring of the fault.
As a check on the calibration and an example of the capabilities of a borehole installed network, we compare the bandpass filtered (0.3-2 Hz) ground displacements, as inferred from the vertical component accelerometer and from the vertical component geophone data streams recorded at BRIB, CMSB, CRQB, HERB, OHLN, SBRN (the newest MPBO station, sited on San Bruno Mtn, San Francisco Peninsula), and RFSB, for a M 7.7 deep focus earthquake that occurred in the Fiji Islands at a depth of 580 km. in Figure 5.2.
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Whenever a NHFN station is visited, the engineer at the site and a seismologist at the BSL work together to expedite the testing process, especially when attempting to identify and correct ground-loop faults which generally induce significant 60, 120, 180, and 240 Hz seismic signal contamination due to stray power line signal pickup, generally inductively coupled and aggravated by the presence of ground loops.
2.3 amps) into the Q4120 and gradual failure
of one of the circuits.
-1.0) some specialized algorithms are being developed.
The Murdock-Hutt detection algorithms used by MultiSHEAR, which
basically flags an event whenever the short-term average exceeds a
longer-term average by some threshold ratio, is neither appropriate for
nor capable of detecting the smallest seismic events. One solution is to
use a pattern recognition approach to identify small events associated
with the occurrence of an event which was flagged by the REDI system.
Tests have indicated that the pattern recognition detection threshold
is -1.0 for events occurring within 10 km of a
NHFN station. The basic idea is to use a quarter second of the initial
P-wave waveform, say, as a master pattern to search for similar
patterns that occur within 
one day, say, of the master event.
Experimentally, up to six small CMSB recorded events, at the -1.0 threshold and occurring within one day of a
master pattern, have been identified.
The pattern recognition method is CPU intensive, however, and it will require a dedicated computer to handle the pattern recognition tasks. To expedite the auto-correlation processing of the master pattern, an integer arithmetic cross-correlation algorithm has been developed which speeds up the requisite processing by an order of magnitude.
The phase onset time detector makes use of the concept that the complex spectral phase data, over the bandwidth of interest (ie, where the SNR is sufficiently high), will sum to a minimum at the onset of an impulsive P-wave. The algorithm searches for the minimum phase time via phase shifting in the complex frequency domain over the bandwidth where the SNR is above 30 dB, say, to identify the onset time of the seismic phase. The algorithm requires that the recorded waveforms be deconvolved to absolute ground displacement. This implicitly requires that any acausality in the anti-aliasing filtration chain, such as the FIR filters used in the BDSN Quanterra data loggers, be removed. The algorithm typically resolves P-wave onset times to one-fiftieth of the sample interval or better.
The spectral phase coherency algorithm was developed to facilitate high
resolution quantification of the similarities and differences between
highly similar Hayward fault events which occur occur months to years
apart. Figure 5.3 shows an example of three highly
similar 
1.3 events. The 0.997 complex spectral phase
coherency between the waveforms for events a (1998.202.132956) and c
(2000.170.171607) implies that the centroids of these two events are
not more than 20 cm apart spatially. Extrapolation of
magnitude versus fault rupture area, empirically derived using M
4-7 earthquakes, yields an expected rupture radius of
20 m for a M 1.3 event. However, the 0.997 phase coherency
implies that the source rupture time histories can not differ by more
than 6 
sec which, in turn, implies that either the
rupture spatial-temporal histories of the two sources are virtually
identical over a radius of 20 m or that the sources have a
high stress drop and a rupture radius of order a few meters at most.
Of these two possibilities, the latter is considered the most likely.
The resolution of the complex spectral phase coherency methodology is an order of magnitude better that the cross correlation method which is commonly used to identify highly similar events with resolution of order a few meters.
Figure 5.4 shows an example of a spectrogram derived
from the CMSB Z-component ground acceleration recording of a M 1.3
local earthquake (1998.202.132956). Most of the power is in the first
few seconds and coincides with the P-wave and S-wave arrivals and their
immediate coda. There is little energy above 110 Hz and after
5 seconds for this M 1.3 event and 10 km propagation path.
Spectrograms can be used as a tool to help in the characterization of
seismic sources and propagation paths.
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Under Bob Nadeau's and Doug Dreger's general supervision, Rich Clymer, Wade Johnson, Doug Neuhauser, Bob Uhrhammer, Bill Karavas, John Friday, and Dave Rapkin all contribute to the operation of the NHFN. Bob Uhrhammer and Lind Gee contributed to the preparation of this chapter.
Partial support for the NHFN is provided by the USGS through the NEHRP external grant program. Expansion of the NHFN has been made possible through generous funding from Caltrans, with the assistance of Pat Hipley. Larry Hutchings of LLNL has been an important collaborator on the project.
Murdock, J., and C. Hutt, A new event detector designed for the Seismic Research Observatories, USGS Open-File-Report 83-0785, 39 pp., 1983.