Seismic Network Operations

CU TGUH

Tegucigalpa, Honduras

CU TGUH commences operations on: 2006,263

Country Flag
Host: Dept. of Natural Resources and Environment
Latitude: 14.057
Longitude: -87.273
Elevation: 1151
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 
Location CodeChannel CodeInstrumentFlagsSample RateDipAzimuthDepth
20LN2Kinemetrics FBA ES-T EpiSensor AccelerometerCG1.000.0094.000.00
20LN1Kinemetrics FBA ES-T EpiSensor AccelerometerCG1.000.004.000.00
20HN2Kinemetrics FBA ES-T EpiSensor AccelerometerTG100.000.0094.000.00
20HN1Kinemetrics FBA ES-T EpiSensor AccelerometerTG100.000.004.000.00
00VMWStreckeisen STS-2 Standard-gainCH0.100.000.000.00
00VMVStreckeisen STS-2 Standard-gainCH0.100.000.000.00
00VMUStreckeisen STS-2 Standard-gainCH0.100.000.000.00
00LH2Streckeisen STS-2 Standard-gainCG1.000.0090.000.00
00LH1Streckeisen STS-2 Standard-gainCG1.000.000.000.00
00BH2Streckeisen STS-2 Standard-gainCG40.000.0090.000.00
00BH1Streckeisen STS-2 Standard-gainCG40.000.000.000.00
20HNZKinemetrics FBA ES-T EpiSensor AccelerometerTG100.00-90.000.000.00
20LNZKinemetrics FBA ES-T EpiSensor AccelerometerCG1.00-90.000.000.00
00LHZStreckeisen STS-2 Standard-gainCG1.00-90.000.000.00
00BHZStreckeisen STS-2 Standard-gainCG40.00-90.000.000.00
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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
00BHZ2011:236 2010:153 to No Ending TiA0.0142850.0142960.0715040.067645 STS-2-SGRandom
  1. Current Issues
    Frequent data drop outs possibly due to batteries.