Seismic Network Operations


Chiang Mai, Thailand

IU CHTO commences operations on: 1992,244

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Host: Geo-Informatics and Space Technology Development Agency
Latitude: 18.814
Longitude: 98.944
Elevation: 420
Datalogger: Q330
Broadband: KS-54000
Accelerometer: FBA_ES-T_EpiSensor_Accelerometer
Telemetry Status at the NEIC: Last Data In Less Than 10 Minutes
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Vault Condition: Borehole

Site Geology: Granite bedrock of Triassic age

Location CodeChannel CodeInstrumentFlagsSample RateDipAzimuthDepth
20LNZFBA ES-T EpiSensor AccelerometerCG1.00-
20LN2FBA ES-T EpiSensor AccelerometerCG1.000.0090.000.00
20LN1FBA ES-T EpiSensor AccelerometerCG1.
10VHZSTS-2 High-gainCG0.10-
10VH2STS-2 High-gainCG0.100.0090.000.00
10VH1STS-2 High-gainCG0.
10LHZSTS-2 High-gainCG1.00-
10LH2STS-2 High-gainCG1.000.0090.000.00
10LH1STS-2 High-gainCG1.
10BHZSTS-2 High-gainCG40.00-
10BH2STS-2 High-gainCG40.000.0090.000.00
10BH1STS-2 High-gainCG40.
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.
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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
10BHZ2011:041 2009:262 to No Ending TiA0.0147710.0142250.119070.099533STS2Random
00BHZ2011:041 2009:262 to No Ending TiA0.0190240.00561010.0512770.1035854000Random
00BH22011:041 2009:262 to No Ending TiA0.0205410.00606880.0495090.1047754000Random
00BH12011:041 2009:262 to No Ending TiA0.0204170.00595320.0503190.1067254000Random
  1. Current Issues
    LNZ and LN2 channels have steps in data. The KS54000 horizontals are showing LP pulsing.
  2. 2009-09-19
    Upgraded to Q330 digitizer.