Seismic Network Operations

CU BCIP

Isla Barro Colorado, Panama

CU BCIP commences operations on: 2006,336

Country Flag
Host: Smithsonian Tropical Research Institute
Latitude: 9.166
Longitude: -79.837
Elevation: 61
Datalogger: Q330
Broadband: STS-2
Accelerometer: FBA
Telemetry Status at the NEIC: Last Data In Less Than 24 Hours And More Than 10 Minutes
Station Photo Station Photo Station Photo 
Location CodeChannel CodeInstrumentFlagsSample RateDipAzimuthDepth
20LN1Kinemetrics FBA ES-T EpiSensor AccelerometerCG1.000.000.000.00
20LN2Kinemetrics FBA ES-T EpiSensor AccelerometerCG1.000.0090.000.00
20LNZKinemetrics FBA ES-T EpiSensor AccelerometerCG1.00-90.000.000.00
00LHZStreckeisen STS-2 Standard-gainCG1.00-90.000.000.00
00LH2Streckeisen STS-2 Standard-gainCG1.000.0090.000.00
00LH1Streckeisen STS-2 Standard-gainCG1.000.000.000.00
00BHZStreckeisen STS-2 Standard-gainCG40.00-90.000.000.00
00BH2Streckeisen STS-2 Standard-gainCG40.000.0090.000.00
00BH1Streckeisen STS-2 Standard-gainCG40.000.000.000.00
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
20HNZKinemetrics FBA ES-T EpiSensor AccelerometerTG100.00-90.000.000.00
20HN2Kinemetrics FBA ES-T EpiSensor AccelerometerTG100.000.0090.000.00
20HN1Kinemetrics FBA ES-T EpiSensor AccelerometerTG100.000.000.000.00
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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
00BHZ2013:2632010:041 to No Ending TimeA0.0164250.0142870.0919140.068401STS-2-SGRandom
  1. 2013-06-08
    Batteries replaced.