Seismic Network Operations
CU TGUH
Tegucigalpa, Honduras
CU TGUH commences operations on: 2006,263
Host:  Dept. of Natural Resources and Environment 

Latitude:  14.057 
Longitude:  87.273 
Elevation:  1151 
Datalogger:  Q330 
Broadband:  STS2 
Accelerometer:  FBA 
Telemetry Status at the NEIC:  Last Data In Less Than 10 Minutes 
Location Code  Channel Code  Instrument  Flags  Sample Rate  Dip  Azimuth  Depth 

00  BHZ  Streckeisen STS2 Standardgain  CG  40.00  90.00  0.00  0.00 
00  BH2  Streckeisen STS2 Standardgain  CG  40.00  0.00  90.00  0.00 
00  BH1  Streckeisen STS2 Standardgain  CG  40.00  0.00  0.00  0.00 
20  HNZ  Kinemetrics FBA EST EpiSensor Accelerometer  TG  100.00  90.00  0.00  0.00 
20  HN2  Kinemetrics FBA EST EpiSensor Accelerometer  TG  100.00  0.00  94.00  0.00 
20  HN1  Kinemetrics FBA EST EpiSensor Accelerometer  TG  100.00  0.00  4.00  0.00 
20  LN2  Kinemetrics FBA EST EpiSensor Accelerometer  CG  1.00  0.00  94.00  0.00 
20  LN1  Kinemetrics FBA EST EpiSensor Accelerometer  CG  1.00  0.00  4.00  0.00 
20  LNZ  Kinemetrics FBA EST EpiSensor Accelerometer  CG  1.00  90.00  0.00  0.00 
00  LHZ  Streckeisen STS2 Standardgain  CG  1.00  90.00  0.00  0.00 
00  LH2  Streckeisen STS2 Standardgain  CG  1.00  0.00  90.00  0.00 
00  LH1  Streckeisen STS2 Standardgain  CG  1.00  0.00  0.00  0.00 
00  VMW  Streckeisen STS2 Standardgain  CH  0.10  0.00  0.00  0.00 
00  VMV  Streckeisen STS2 Standardgain  CH  0.10  0.00  0.00  0.00 
00  VMU  Streckeisen STS2 Standardgain  CH  0.10  0.00  0.00  0.00 
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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 threestep method to estimate instrument response parameters (poles, zeros, sensitivity and gain) and their associated errors using random calibration signals. First, we solve a coarse nonlinear 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 localminimum solution) caused by noise in the calibration records and enhances algorithm convergence. Second, we iteratively solve a nonlinear parameter estimation problem to obtain the least squares bestfit 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 2/3rdsoctave band centered at each bestfit pole/zero frequency. This procedure yields error estimates of the 99% confidence interval.
Loc  Chan  Cal Date  EpochSpan  Grade  Amp Nominal Error (dB)  Amp Best Fit Error (dB)  Phase Nominal Error (degree)  Phase Best Fit Error (degree)  Sensor  Cal Type 

00  BHZ  2011:236  2010:153 to No Ending Ti  A  0.014285  0.014296  0.071504  0.067645  STS2SG  Random 

Current IssuesFrequent data drop outs possibly due to batteries.