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Parkfield-Hollister Electromagnetic Monitoring Array

Subsections

Introduction

There are many reports of anomalous electric and magnetic fields, at frequencies from quasi DC to several 10's of Hertz, and changes in ground resistivity prior to earthquakes. Most reports are devoted to one or another of these phenomena using a variety of measurement configurations and data processing techniques. No such studies are reported using instrumentation capable of measuring all these properties simultanously at a network of sites. It is the objective of this study to determine whether significant changes in resistivity, quasi DC electric fields, or ULF electric and magnetic fields occur before earthquakes in California. In 1995 we installed two well-characterized electric and magnetic field measuring systems at two sites along the San Andreas Fault which are part of the Berkeley Digital Seismic Network. Since then, magnetotelluric (MT) data have been continuously recorded at 40 Hz and 1 Hz and archived at the NCEDC (Table 7.1 and 7.2). At least one set of orthogonal electric dipoles measures the vector horizontal electric field, E, and three orthogonal magnetic sensors measure the vector magnetic field, B. These reference sites, now referred to as electromagnetic (em) observatories, are co-located with seismographic sites so that the field data share the same time base, data acquisition, telemetry and archiving system as the seismometer outputs. Using a robust multiple station MT processing algorithm (Egbert, 1997), we have examined the long term stability of single site and interstation transfer functions. Using a precise transfer function obtained from long runs of the array data, we can effectively predict the fields at one site from those measured at another site. Subtracting the predicted fields from the measured fields at a site yields residuals which are more sensitive to anomalous signals local to the site. Residual analysis in both time and frequency is the primary goal of this project.

Figure 7.1: Map illustrating the location of operational (filled squares) and closed (grey squares) MT sites in central California.
\begin{figure}\begin{center}
\epsfig{file=em_map.ps, width=7cm}\end{center}\end{figure}

MT Overview

The MT observatories are located at Parkfield (PKD1, PKD) 300 km south of the San Francisco Bay Area and Hollister (SAO), halfway between San Francisco and Parkfield (Figure 7.1). In 1995, initial sites were established at PKD1 and SAO, separated by a distance of 150 km, and equipped with three induction coils and two 100 m electric dipoles. PKD1 was established as a temporary seismic site, and when a permanent site (PKD) was found, a third MT observatory was installed in 1999 with three induction coils, two 100 m electric dipoles, and two 200 m electric dipoles. PKD and PKD1 ran in parallel for one month in 1999, and then the MT observatory at PKD1 was closed.

Data at the MT sites are fed to Quanterra data loggers, shared with the collocated BDSN stations, synchronized in time by GPS and sent to the BSL via dedicated communication links.

Activities in 2001-2002

In the past year, significant energy was directed toward the maintenance of the MT network and toward the establishment of routine data processing.

Station Maintenance

SAO

SAO experienced problems with the power supplies for the B-field and E-field equipment. The B-field coils and the EFSC box were removed, calibrated, and returned. The voltage regulator circuit of the B-field power supply was replaced.

PKD

The site at Parkfield continued to have problems with electrodes drying out. Sierra Boyd visited the site several times to "water" the holes and experimented with using bentonite to help retain moisture. The electrodes were pulled in March and the copper-sulfate solution was replaced. In parallel, lead-lead-chloride electrodes were provided by John Booker of the University of Washington. It is hoped that the lead-lead-chloride electrodes will be less sensitive to the lack of moisture in the holes.

Instrument Responses

As part of the station maintenance, calibrations have been performed on various components of the MT systems. Sierra Boyd is working to ensure that the transfer function information at the NCEDC is correct and current.

Data quality control

During this year, BSL staff worked in collaboration with Gary Egbert to install software developed by him for automated data processing. The software, which is described more fully below, provides the capability of identifying problems and alerting staff.

Data Processing

A major part of the recent effort at Oregon State University has been to develop user friendly computer codes for routine processing of data from the UCB MT sites. The processing system is based on a graphical user interface, written in MATLAB, which allows the user to download MT data from the NCEDC and complete all routine processing steps (including the multi-site). The program also can be used to plot processing results, multi-channel time series and various simple diagnostics of data quality. Results (including daily estimates of MT impedances, inter-station transfer functions, estimates of noise amplitudes, and summaries of frequency and time domain residual amplitudes) are then automatically archived for statistical analysis and correlation in space and time with cluster events at Parkfield. There is now a daily printout of the signal to noise ratios (SNR) in dB for each channel of the array, Table 7.3. Currently, SNR's below 10 dB are flagged for inspection or repair by the array operators.

Any data in the NCEDC archive can be downloaded, but by default the program gets and processes the most recent unprocessed data. These codes will thus enable more-or-less automatic monitoring of system functionality, and make it easier to maintain a high rate of quality data return. The streamlined processing codes will also make it easier to reprocess existing data with any new schemes that will be implemented in the future. We are presently using the new system to process the backlog of data from the array, and to update analyses of residuals, and of MT impedance stability.

Several procedures for the residual analysis (the goal of the em array) have been developed: one uses a simple time-domain regression operator to estimate residuals in a moving time window. This algorithm is fast and provides a rapid method to scan incoming data for anomalies. We have tested this process on selected segments of data. No anomalous signals were detected, but there were no earthquakes during this interval.

Another approach is a frequency domain analysis using multiple station techniques (Egbert, 1977) which makes optimal use of data from all sites to estimate stable and reliable transfer functions. The multi-site analysis has also proven to be very useful for better understanding of signal and noise, and for separating coherent signals of differing spatial scales (Figure 7.2). For example, Egbert et al. (2000) used a multiple station analysis to show that the transfer function between SAO and PKD1 is systematically affected by both the DC train system in the Bay Area (BART) and by the non-uniformity of the natural fields in the Pc3 band (Figure 7.3). These results demonstrated that cultural noise sources can extend their effect over surprisingly large areas, and that at the same time natural ionospheric sources may exhibit significant spatial complexity. Because of this added spatial complexity, multiple sites are required for complete cancellation of the background (non-tectonic) em noise. Ideally three stations should be used to avoid bias errors in the transfer function estimates and to maintain better control over cultural noise.

Predictions based on data from at least three sites will significantly improve our ability to detect anomalous signals. Source complications, as well as local incoherent noise sources, are highly variable in time (e.g., Figure 7.3), making it a challenging task to verify that apparently anomalous signals truly originate in the earth. Thorough calibration and understanding of both local and distant noise sources is essential. This critical step has now been accomplished for the UCB array (Egbert et al., 2000; Eisel and Egbert, 2001). When a major Parkfield event does occur we will be in a very good position to detect and identify anomalous em emissions (if there are any) and to avoid the ambiguity of interpretation that has plagued much of the past search for em precursors.

So far we have focused on the frequency domain approach to study residuals. This analysis has revealed significant diurnal variations in the residual distributions. With a two site array, residuals are smallest between the hours of 0-4 am, making this a particularly good time to look for anomalous signals. Comparison of the temporal distribution of unusually large magnetic residuals to local earthquake catalogs has so far revealed no clear associations, but there have been few earthquakes of significant magnitude in the time period studied. Karakelian et al. (2000) analyzed data from some of their sites and ours and suggest that there was anomalous activity before the San Juan Bautista earthquake, but we have not been able to verify this.

The MT stations at PKD and SAO can also be used to monitor resistivity changes prior to earthquakes. Unlike the UC Riverside telluric array, the MT impedance can yield depth information because the depth of penetration of the em waves increases with period. Seasonal changes caused by precipitation would presumably be shallow and affect primarily the shorter periods, while deeper changes would be seen also at longer periods. The amplitude of the MT impedance may fluctuate at all periods in response to shallow changes (the so-called "static" shift problem), but the phase of the response is set by more regional structure at longer periods. Thus, variations in phase should be a sensitive indicator of resistivity variations at seismogenic depths ($\sim $10 km). Eisel and Egbert (2001) made a study of the stability of the MT impedances at PKD1. Typical deviations of estimates based on a single day of data differed from the long term average transfer function by 2-3% for T $<$ 300s and increasing to about 10% for T=2000s. Variations between contiguous days were nearly random, so significantly smaller variability can be obtained by longer averaging times. There is some evidence from this analysis for a slow variation of about 1% in impedance amplitude when an 11 day average is applied. Relative resistivity variations are nearly frequency independent, appear anti-correlated between the x-y and y-x modes, and are larger than variations in phase. These features together are suggestive of temporal variations in near-surface static distortion. Although it is difficult to make a definitive statement on the basis of the data analyzed so far, there does not appear to be any seasonal component to these variations, as might be expected if they were due to near surface hydrology.


Table 7.1: Sites of MT observatories
Site Net Latitude Longitude Elev (m) Date Location
PKD BK 35.945171 -120.541603 583 1999/02/05 - Bear Valley Ranch, Parkfield
PKD1 BK 35.8894 -120.426109 431.6 1995/06/06 - 1999/03/08 Haliburton House, Parkfield
SAO BK 36.76403 -121.44722 317.2 1995/08/15 - San Andreas Obs., Hollister



Table 7.2: Typical data streams acquired at each MT site, with channel name, sampling rate, sampling mode, and FIR filter type. C indicates continuous; T triggered; Ac acausal.
Sensor Channel Rate (sps) Mode FIR
Magnetic VT? 0.1 C Ac
Magnetic LT? 1.0 C Ac
Magnetic BT? 40.0 C Ac
Electric VQ? 0.1 C Ac
Electric LQ? 1.0 C Ac
Electric BQ? 40.0 C Ac



Table 7.3: SNR at selected periods (dB): Bad channels indicated *
Station Channel T=30S T=100S T=300S
PKD Hx 26 25 26
PKD Hy 23 22 24
PKD Hx 18 17 15
PKD Ex1 16 16 18
PKD* Ey1 9 10 11
PKD Ex2 23 23 26
PKD Ey2 21 23 25
SAO Hx 21 21 23
SAO Hy 21 20 24
SAO Hz 15 12 12
SAO Ex 22 21 22
SAO Ey 21 21 20


Figure 7.2: (a) Eigenvalues of the scaled Spectral Density Matrix (SDM) for the three site PKD1/PKD/SAO array, computed following the methods described in Egbert (1997). Briefly, cross-products of Fourier coefficients computed from short time segments of all 17 data channels are averaged for the 30 days. (b) The magnitude of incoherent noise power is estimated for each data channel, and these are used to non-dimensionalize the SDM. Eigenvalues of the 17x17 scaled SDM then given signal-to-noise (power) ratios of independent coherent EM sources. For idealized quasi-uniform MT sources, there should be only two eigenvalues significantly above the 0 dB noise level. Additional large eigenvalues, as seen here from 10-300 s, are a clear indication of "coherent noise", or temporally varying complications in source geometry. For the two dominant eigenvectors, the horizontal magnetic components are roughly uniform across the array, consistent with the usual MT assumptions. Eigenvectors three and four are dominated by gradients in the EM fields.
\begin{figure}\begin{center}
\epsfig{file=temp.eps, width=8cm}\epsfig{file=em_fig5b.eps, width=8cm}\end{center}\end{figure}

Figure 7.3: (a) Amplitude and phase of the Hx/Hx transfer function (TF) between PKD1 and SAO. Curves marked by symbols correspond to TFs estimated for different local times, as indicated in the legend. The heavy dashed line is the TF computed from all data (days 140-199, 1997), and the heavy grey solid line is the TF computed from the data collected during a strike by BART workers (days 150-156, 1997; see Egbert et al. (2000)). For periods outside of the band plotted, TFs computed for different data subsets are in close agreement. (b) Variations in the real part of the Hx/Hx TF as a function of local time and time of year, for data grouped into 10 day-long 2 hour bands.
\begin{figure*}\begin{center}
\epsfig{file=em_fig6a.eps, width=6cm}\epsfig{file=em_fig6b.eps, width=10cm}\end{center}\end{figure*}

Acknowledgements

Frank Morrison directs the MT program, and collaborates closely with Gary Egbert of Oregon State University and Steve Park of UC Riverside. During Frank's sabbatical last year, Gary Egbert worked extensively with Sierra Boyd on aspects of the data processing. John Friday, Lind Gee, and Doug Neuhauser also contribute to the operation of the MT observatories. Sierra Boyd, Lind Gee, and Gary Egbert contributed to the preparation of this chapter.

Support for the MT array is provided by the USGS through the NEHRP external grants program and by the Plato Malozemoff Chair in Mineral Engineering held by Frank Morrison.

References

Egbert, G.D., M. Eisel, O.S. Boyd and H.F. Morrison, DC trains and Pc3s: Source effects in mid-latitude geomagnetic transfer functions, Geophys. Res. Lett., 27, 25-28, 2000.

Egbert, G.D., Robust Multiple-Station Magnetotelluric Data Processing, Geoph. J. Int., 130, 475-496, 1997.

Eisel, M., and G.D. Egbert, On the stability of magnetotelluric transfer function estimates and the reliability of their variances, Geophys. J. Int., 144, 65-82, 2001.

Karakelian, D., S.L. Klemperer, G.A. Thompson, and A.C. Fraser-Smith, Results from electromagnetic monitoring of the $M_{w}$ 5.1 San Juan Bautista, California earthquake of 12 August 1998, Proc. of the 3rd Conference on Tectonics Problems of the San Andreas Fault System, Eds. G. Bokelmann and R.L. Kovach, Stanford University Publication, Geological Sciences, Vol. XXI, 2000.



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