Seismic Network Operations


Guam, Mariana Islands

IU GUMO commences operations on: 1991,160

Country Flag
Host: US Air Force
Latitude: 13.589
Longitude: 144.868
Elevation: 170
Datalogger: Q330
Broadband: KS-54000
Accelerometer: FBA_ES-T_EpiSensor_Accelerometer
Telemetry Status at the NEIC: Last Data In Less Than 10 Minutes
Station Photo Station Photo Station Photo 

Vault Condition: Borehole. The strong motion system for the EL_ channels is located in the recording building about 20 meters distant from the borehole. The sensors are on the concrete floor inside the data processing cabinet.

Site Geology: Barrigada limestone believed to be about 250 meters thick underlain by basalts and volcanic congolmerates.

Location CodeChannel CodeInstrumentFlagsSample RateDipAzimuthDepth
10VHZSTS-2 High-gainCG0.10-
10VH2STS-2 High-gainCG0.100.0090.000.00
10VH1STS-2 High-gainCG0.
20LNZFBA ES-T EpiSensor AccelerometerCG1.00-
20LN2FBA ES-T EpiSensor AccelerometerCG1.000.0090.000.00
20LN1FBA ES-T EpiSensor AccelerometerCG1.
10LHZSTS-2 High-gainCG1.00-
10LH2STS-2 High-gainCG1.000.0090.000.00
10LH1STS-2 High-gainCG1.
20HNZFBA ES-T EpiSensor AccelerometerTG100.00-
20HN2FBA ES-T EpiSensor AccelerometerTG100.000.0090.000.00
20HN1FBA ES-T EpiSensor AccelerometerTG100.
10HHZSTS-2 High-gainTG100.00-
10HH2STS-2 High-gainTG100.000.0090.000.00
10HH1STS-2 High-gainTG100.
10BHZSTS-2 High-gainCG40.00-
10BH2STS-2 High-gainCG40.000.0090.000.00
10BH1STS-2 High-gainCG40.
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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
10BHZ2011:228 2010:012 to No Ending TA0.0152110.0142090.12840.1186 STS-2-HGRandom
00BHZ2011:227 2010:012 to No Ending TA0.00613690.00553460.0627290.056299 54000Random
00BH22011:227 2010:012 to No Ending TA0.00639070.00584920.0591070.066229 54000Random
00BH12011:227 2010:012 to No Ending TA0.0062790.00581030.0593180.065905 54000Random
  1. 2010-01-15
    Upgraded to Q330 digitizer.