Seismic Network Operations

CU GRGR

Grenville, Grenada

CU GRGR commences operations on: 2006,346

Country Flag
Host: Natural Disaster Management Agency
Latitude: 12.132
Longitude: -61.654
Elevation: 195
Datalogger: Q330
Broadband: STS-2
Accelerometer: FBA
Telemetry Status at the NEIC: Last Data In Less Than 10 Minutes
Station Photo Station Photo Station Photo Station Photo Station Photo Station Photo 
Location CodeChannel CodeInstrumentFlagsSample RateDipAzimuthDepth
20HN2FBATG100.000.0090.000.00
20HN1FBATG100.000.000.000.00
00LHZSTS-2CG1.00-90.000.000.00
00LH2STS-2CG1.000.0090.000.00
20HNZFBATG100.00-90.000.000.00
20LN2FBACG1.000.0090.000.00
20LN1FBACG1.000.000.000.00
20LNZFBACG1.00-90.000.000.00
00LH1STS-2CG1.000.000.000.00
00BHZSTS-2CG40.00-90.000.000.00
00BH2STS-2CG40.000.0090.000.00
00BH1STS-2CG40.000.000.000.00
00 BH1 Monthly PDF
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00 BH2 Monthly PDF
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00 BHZ Monthly PDF
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00 LH1 Monthly PDF
Image Unavailable

00 LH2 Monthly PDF
Image Unavailable

00 LHZ Monthly PDF
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Heliplot
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Latency
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Availability, Year
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Availability, Since 1972
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Availability, 2 Month
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As part of the annual calibration process, the USGS runs a sequence that includes a random, a step, and several sine wave calibrations.  The USGS analyzes the random binary calibration signal in order to estimate the instrument response.  The figures below show the results from the analysis of the most recent processed calibration at the station.

We use an iterative three-step method to estimate instrument response parameters (poles, zeros, sensitivity and gain) and their associated errors using random calibration signals. First, we solve a coarse non-linear inverse problem using a least squares grid search to yield a first approximation to the solution. This approach reduces the likelihood of poorly estimated parameters (a local-minimum solution) caused by noise in the calibration records and enhances algorithm convergence. Second, we iteratively solve a non-linear parameter estimation problem to obtain the least squares best-fit Laplace pole/zero/gain model. Third, by applying the central limit theorem we estimate the errors in this pole/zero model by solving the inverse problem at each frequency in a 2/3rds-octave band centered at each best-fit pole/zero frequency. This procedure yields error estimates of the 99% confidence interval.

LocChanCal DateEpoch-SpanGradeAmp Nominal Error (dB)Amp Best Fit Error (dB)Phase Nominal Error (degree)Phase Best Fit Error (degree)SensorCal Type
00BHZ2011:069 2010:041 to No Ending TiA0.0162390.0150290.123960.12261 STS-2-SGRandom
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