Seismic Network Operations


Mt. Furi, Ethiopia

IU FURI commences operations on: 1997,244

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Host: Addis Ababa University
Latitude: 8.895
Longitude: 38.68
Elevation: 2570
Datalogger: Q330
Broadband: STS-1VBB_w/E300
Accelerometer: FBA_ES-T_EpiSensor_Accelerometer
Telemetry Status at the NEIC: Last Data In Less Than 10 Minutes
Station Photo Station Photo Station Photo Station Photo 

Site Description: Remote site located approxmately three-fourths up the road to the top of Mt. Furi., The data processing equipment is located at the Addis Ababa University. Communications, for data between these locations is via Cylink Radio Modems.

Vault Condition: 20 Meter horizontal tunnel with approximately 5 meters of overburden.

Site Geology: Vault dug into a dike consisting of mafic rock

Location CodeChannel CodeInstrumentFlagsSample RateDipAzimuthDepth
20LNZFBA ES-T EpiSensor AccelerometerCG1.00-
20LN2FBA ES-T EpiSensor AccelerometerCG1.000.0090.005.00
20LN1FBA ES-T EpiSensor AccelerometerCG1.
20HNZFBA ES-T EpiSensor AccelerometerTG100.00-
20HN2FBA ES-T EpiSensor AccelerometerTG100.000.0090.005.00
20HN1FBA ES-T EpiSensor AccelerometerTG100.
00VMZSTS-1VBB w/E300CH0.
00VM2STS-1VBB w/E300CH0.
00VM1STS-1VBB w/E300CH0.
00VHZSTS-1VBB w/E300CG0.10-
00VH2STS-1VBB w/E300CG0.100.0090.005.00
00VH1STS-1VBB w/E300CG0.
00LHZSTS-1VBB w/E300CG1.00-
00LH2STS-1VBB w/E300CG1.000.0090.005.00
00LH1STS-1VBB w/E300CG1.
00BHZSTS-1VBB w/E300CG20.00-
00BH2STS-1VBB w/E300CG20.000.0090.005.00
00BH1STS-1VBB w/E300CG20.
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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
00BH12012:304 2012:300 to No Ending TA0.020470.00878830.333040.096482 STS1VBBE3Random
00BH22012:304 2012:300 to No Ending TA0.0186670.00868430.331070.098198 STS1VBBE3Random
00BHZ2012:304 2012:300 to No Ending TA0.0195290.00862580.332210.098182 STS1VBBE3Random
10BHZ2012:305 2012:300 to No Ending TB0.0391910.0393890.329230.32105 STS-2.5Random
  1. 2012-10-28
    Station upgraded to Q330 digitizer.