Seismic Network Operations


Standing Stone, Pennsylvania

IU SSPA commences operations on: 1994,335

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Host: Pennsylvania State University
Latitude: 40.636
Longitude: -77.888
Elevation: 270
Datalogger: Q330
Broadband: KS-36000-I
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

Location CodeChannel CodeInstrumentFlagsSample RateDipAzimuthDepth
20LNZFBA ES-T EpiSensor AccelerometerCG1.00-
20HNZFBA ES-T EpiSensor AccelerometerTG100.00-
10VHZTrillium 240 broad bandCG0.10-
10LHZTrillium 240 broad bandCG1.00-
10HHZTrillium 240 broad bandTG100.00-
10BHZTrillium 240 broad bandCG40.00-
30LDOCI/PAS pressure sensorCW1.
20LN2FBA ES-T EpiSensor AccelerometerCG1.000.0090.000.00
20LN1FBA ES-T EpiSensor AccelerometerCG1.
20HN2FBA ES-T EpiSensor AccelerometerTG100.000.0090.000.00
20HN1FBA ES-T EpiSensor AccelerometerTG100.
10VH2Trillium 240 broad bandCG0.100.0090.000.00
10VH1Trillium 240 broad bandCG0.
10LH2Trillium 240 broad bandCG1.000.0090.000.00
10LH1Trillium 240 broad bandCG1.
10HH2Trillium 240 broad bandTG100.000.0090.000.00
10HH1Trillium 240 broad bandTG100.
10BH2Trillium 240 broad bandCG40.000.0090.000.00
10BH1Trillium 240 broad bandCG40.
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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
10BHZ2011:088 2010:270 to No Ending TA0.0152540.00704530.119150.081206 TR240Random
00BHZ2011:087 2010:270 to No Ending TA0.0418780.00849540.186380.11169 36000IRandom
00BH22011:087 2010:270 to No Ending TA0.0529530.00855020.244120.13889 36000IRandom
00BH12011:087 2010:270 to No Ending TA0.0476540.00897850.216760.1248 36000IRandom
  1. 2012-06-21
    Episensor replaced due to noise issues. Vault shading and insulation improved to address daily noise issues with T240.
  2. 2010-09-29
    Upgraded to Q330 digitizer.