Seismic Network Operations
IU AFI commences operations on: 1993,236
|Host:||Ministry of Natural Resources and Environment|
|Telemetry Status at the NEIC:||No Data In More Than 24 Hours|
Vault Condition: Vault is a small concrete block building set into the ground so that the roof is at ground level. The sides are filled in with dirt. The floor is basalt bedrock. Piers are not isolated from the floor. A dehumidifier is normally in operation.
Site Geology: Pleistocene Salani Volcanics.
|Location Code||Channel Code||Instrument||Flags||Sample Rate||Dip||Azimuth||Depth|
|20||LNZ||FBA ES-T EpiSensor Accelerometer||CG||1.00||-90.00||0.00||1.00|
|20||LN2||FBA ES-T EpiSensor Accelerometer||CG||1.00||0.00||90.00||1.00|
|20||LN1||FBA ES-T EpiSensor Accelerometer||CG||1.00||0.00||0.00||1.00|
|20||HNZ||FBA ES-T EpiSensor Accelerometer||TG||100.00||-90.00||0.00||1.00|
|20||HN2||FBA ES-T EpiSensor Accelerometer||TG||100.00||0.00||90.00||1.00|
|20||HN1||FBA ES-T EpiSensor Accelerometer||TG||100.00||0.00||0.00||1.00|
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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.
|Loc||Chan||Cal Date||Epoch-Span||Grade||Amp Nominal Error (dB)||Amp Best Fit Error (dB)||Phase Nominal Error (degree)||Phase Best Fit Error (degree)||Sensor||Cal Type|
|00||BH1||2010:357||2010:357 to present||A||0.04122||0.03141||0.31393||0.35696||STS1-VBBE3||RANDOM|
|00||BH2||2010:059||2010:054 to present||A||0.016089||0.010883||0.12342||0.11654||STS1-VBBE3||RANDOM|
|00||BHZ||2010:357||2010:357 to present||A||0.04122||0.02937||0.31393||0.35696||STS1-VBBE3||RANDOM|
|10||BHZ||2010:357||2010:357 to present||A||0.02778||0.01604||0.2125||0.21761||STS-2-HG||RANDOM|
Current IssuesStation power was knocked out by a cyclone in December 2012. Local support staff is working to restore station operation.
2010-02-28Upgraded to Q330 digitizer.