Help: noise-psd v.1

Description

The noise-psd web service returns Power Spectral Density estimates for seismic channels.

Summary

The Power Spectral Density (PSD) data returned by this service describe seismic channel spectral characteristics for hour long data windows calculated every half hour. The service can return instrument response corrected, as well as uncorrected PSD estimates. The PSD data returned by this service is stacked to build Power Density Function (PDF) estimates returned by the noise-pdf service. The algorithm for this metrics follows NcNamara and Boaz (2005).

McNamara, D.E and Boaz, R.I., 2005, Seismic Noise Analysis System Using Power Spectral Density Probability Density Functions – A Stand-Alone Software Package, U.S.G.S. OFR 2005-1428.

Peterson, J, 1993, Observations and Modeling of Seismic Background Noise, U.S.G.S. OFR-93-322

Uses

The primary purpose of storing PSDs in this form is so that PDF generation can be performed using the latest metadata with a minimum amount of recalculation. The PDFs, in turn, can be used to evaluate the general noise characteristics of a channel, providing data quality information as a function of frequency.

Data Analyzed

Traces – one N.S.L.C (Network.Station.Location.Channel) per measurement
Window – 1 hour for sample rates >= 10 Hz; 2 hours for sample rates between 1 and 10 Hz; 3 hours for 1 Hz sample rates
Data SourceIRIS SEED archive

SEED Channel Types – ?H?, ?L?, ?N?, ?G?, ?P? | High Gain, Low Gain, Accelerometer, Gravimeter, Geophone | excluding very long period channels

Algorithm

  • Request 24 hours of data for the current N.S.L.C.
  • Divide the trace into windows having 50% overlap where the window length is
    • 1 hour for sample rates >= 10 Hz,
    • 2 hours for sample rates between 1 and 10 Hz,
    • 3 hours for 1 Hz sample rates.
  • For each window,
    • Truncate the time series to the nearest power of 2 samples.,
    • Smooth and average the PSD to reduce variance by
      • Dividing the window into 13 segments having 75% overlap.
      • For each segment,
        • Removing the trend and mean,
        • Apply a 10% sine taper,
        • FFT,
        • Calculate the normalized PSD.
      • Average the 13 PSDs & scale to compensate for tapering.
      • Frequency-smooth the averaged PSD over 1-octave intervals at 1/8-octave increments,
      • Convert power to decibels.

Metric Values Returned

target – the trace analyzed, labeled as N.S.L.C.Q (Network.Station.Location.Channel.Quality)
start – beginning of averaged PSD window in UTC
end – end of averaged PSD window in UTC
freq, power – frequency in Hz, power in dB

Notes

During smoothing, there is a tradeoff between enhancing frequency resolution and reducing variance of the PSDs (i.e. increasing repeatability). These PSDs were smoothed to reduce variance. This choice gives a clearer general picture of station noise by reducing power smear, it is consistent with steps used to generate the Peterson new high and low noise models to which they are being compared and it optimizes data storage. Consequently, features requiring detailed frequency resolution will appear smeared in these PSDs.

Limitations

The noise-psd service limits the number of PSDs that can be returned in a single request. The limitation will cause a 413 Request too large errors to be returned if more than 20,000 PSDs are requested. This corresponds to a bit over a years worth of data from one channel. To reduce load to back end servers and increase overall through put, it is preferable to request data in smaller intervals, for example, one month and to then combine the result from those queries.

Automating data retrieval

The /availability method of the noise-pdf-browser service can be used to automate the retrieval of PSD data. The help section of the service gives a simple illustrative example of how this can be done.

Using /availability method to automate PSD and PDF downloads

Contact

dmc_qa@iris.washington.edu

See Also

dead_channel_lin,
pct_above_nhnm, pct_below_nlnm, transfer_function

Citations and DOIs

To cite the MUSTANG system or reference the use of MUSTANG metrics:

  • Assuring the Quality of IRIS Data with MUSTANG
    Robert Casey, Mary E. Templeton, Gillian Sharer, Laura Keyson, Bruce R. Weertman, Tim Ahern
    Seismological Research Letters (2018) 89 (2A): 630-639.
    DOI: https://doi.org/10.1785/0220170191

Problems with this service?

Please send an email report of which service you were using, your URL query, and any error feedback to:
ws-issues AT iris.washington.edu
We will address your issue as soon as possible.

Page built 07:26:24 | v.e60cbb73