Ambient Noise Probability Density Functions
Sources of Noise and Signal in the PDFs
A new system for analyzing data quality is
now available to the seismology community allowing users to evaluate
the long-term seismic noise levels for any broadband seismic data
channel streaming into the buffer of uniform data (BUD)
within the Incorporated Research Institutions for Seismology (IRIS) data management system (DMS). BUD is the IRIS DMS's
acronym for the online data cache from which the DMC distributes
near-real time miniSEED data holdings prior to formal archiving.
The new noise processing software uses a probability density function
(PDF) to display the distribution of seismic power spectral density
(PSD) and has been implemented against the entire
continuous data-stream available within the BUD
utilizing the QUACK framework. QUACK is the software
package at the IRIS DMC, responsible for managing the quality control
(QC) of the real-time seismic data flowing into the BUD (see http://www.iris.washington.edu/servlet/quackquery/).
This noise processing system is unique in that there is no need to
screen the data for earthquakes, system glitches or general data
artifacts, as is commonly done in seismic noise analysis. Instead with
this new analysis, system transients map into a low-level background
probability while ambient noise conditions reveal themselves as high
probability occurrances. In fact, examination of artifacts
related to station operation and episodic cultural noise allows us to
estimate both the overall station quality and a baseline level of earth
noise at each site.
PDF noise plots are useful for characterizing
the current and past performance of existing broadband sensors, for
detecting operational problems within the recording system, and for
evaluating the overall quality of data for a particular station. The
advantages of this
new approach include:
1) provides an analytical view
representing the true ambient noise levels rather than a simple
2) provides an assessment of the
overall health of the instrument/station; and,
3) provides an assessment of the
health of recording and telemetry systems.
Below are PDF examples, with some artifacts and signals identified.
HLID - Typical ANSS
station ~10km from Hailey Idaho.
LTX - Lajitas TX. This station
was instrumental in the orginal Peterson Low Noise Model, however due
to increased cultural noise (0.1-1s, 1-10Hz) the highest probabilty
power levels (i.e. mode, black line) are now significantly higher than
the LNM. The minimum (red line) will approach the LNM
<2% of the time.
Additional features and artifacts observed in the
PDFs will be discussed below. (For a more
detailed discussion of the PDFs see McNamara
and Buland (2004).)
Sources of Noise and Signal in the PDFs
The most common source of seismic noise is
from the actions of human beings at or near the surface of the Earth.
This is often referred to as “cultural noise” and originates primarily
from the coupling of traffic and machinery energy into the earth.
Cultural noise propagates mainly as high-frequency surface waves
(>1-10Hz,1-0.1s) that attenuate within several kilometers in
distance and depth. For this reason cultural noise will generally be
significantly reduced in boreholes, deep caves
and tunnels. Cultural noise shows very strong diurnal variations and
characteristic frequencies depending on the source of the disturbance.
HLID - automobile
traffic along a dirt road only 20 meters from station HLID creates a
20-30dB increase in power at about 0.1 sec period (10Hz). This type of
cultural noise is observable in the PDFs as a region of low probability
at high frequencies (1-10Hz, 0.1-1s).
ANMO - Borehole station in Albuquerque
NM, with very low noise levels. For periods >1s the minimum closely
tracks the Peterson LNM.
DWPF - Borehole station at Disney World
Florida. Cultural noise levels are high due to human activity and
microseism are high due to proximity to the Atlantic ocean.
ISCO - Abandoned mine tunnel station in
Idaho Springs CO. Noise levels are low with minimal variations in
cultural noise and microseisms.
Our approach differs from many previous
noise studies in that we make no attempt to screen the continuous
waveforms to eliminate body and surface waves from naturally occurring
earthquakes. Earthquake signals are included in our processing because
they are generally low probability occurrences even at low power levels
(small magnitude events). We are interested in the true noise that a
given station will experience, thus we include all signals. For
example, including events tells us something about the probability of
teleseismic signals being obscured by small local events as well as
various noise sources. Large teleseismic earthquakes can produce powers
above ambient noise levels across the entire spectrum and are dominated
by surface waves >10s, while small events dominate the short period,
<1s. Earthquakes are observed in the PDFs as low probability smeared
signal at short and
HLID - Body waves
occur as low probabily signal in the 1sec range while surface waves are
generally higher power at longer periods.
DWPF - Noise levels are high enough
(0.1-1s) that body waves from smaller earthquakes are not readily
Since we make no attempt to screen
system transients such as data gaps and sensor glitches, the PDF plots
numerous system generated artifacts that can be very useful for network
control purposes. We have attempted to determine the source of several
high power, low probability noise artifacts in the PDF plots. Several
in the PDFs are easily explained and may be useful to the network
For example, data-gaps (due to telemetry drop-outs) and automatic mass
(necessitated by “drift” in sensor mass position) are easily
in the PDFs. Should the
mass re-centering and/or telemetry drop-outs drastically increase, a
network operator could readily diagnose the problem.
HLID - Automatic
mass re-centering and calibration pulses show up as low probability
occurances in the PDF.
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BINY - Binghamton
NY, BHN component. Numerous mass recenters due to
unstable sensor pad. We can determine that mass
re-centers are automatically issued aproximately 1% of the time.
LTX - Lajitas TX. Telemetry dropouts are
somewhat common (~1-2%).
Power Spectral Density
Employing the algorithm used to develop the USGS Albuquerque
Seismological Laboratory (ASL) low noise model (LNM; Peterson,
1993), we compute the power spectral density (PSD) for broadband
stations streaming into the IRIS DMC BUD system.
In this algorithm, hour-long, continuous, and over-lapping (50%)
time series segments are processed. (N.B. There is no removal of
earthquakes, system transients and/or data glitches.) The instrument
transfer function is removed from each segment, yielding ground
acceleration (for easy comparison to the LNM). Each hour-long
time series is divided into 13 segments, each about 15 minutes long and
overlapping by 75%, with each segment processed by:
1) removing the mean;
2) removing the long period
3) tapering using a 10% sine
4) transforming using an FFT
algorithm (Bendat and Piersol, 1971).
Segments are then averaged to provide a PSD for each one-hour time
Probability Density Functions
For each channel, raw frequency distributions are constructed by
gathering individual PSDs in the following manner:
1) binning periods in 1/8 octave
2) binning power in 1 dB intervals.
Each raw frequency distribution bin is then normalized by the total
number of PSDs to construct a Probability Density Function (PDF). The
probability of occurrence of a given power at a particular period is
plotted for direct comparison to the Peterson high and low noise models
We also compute and plot the minimum, mode, and maximum powers for
each period bin. A wealth of seismic noise information can be obtained
from this statistical view of broadband seismic noise. For a more
discussion of the noise processing methods see McNamara and Buland (2004).
Click on the following links for sample PDFs with
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or Return to IRIS
Bendat, J.S. and A.G. Piersol (1971). Random data: analysis and
measurement procedures. John Wiley and Sons, New York, 407p.
McNamara, D.E. and R.P. Buland, Ambient
Noise Levels in the Continental United States, Bull. Seism. Soc.
Am., 94, 4, 1517-1527, 2004.
Peterson, J., Observation and modeling of seismic background noise,
Geol. Surv. Tech. Rept., 93-322, 1-95, 1993.
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The algorithm and initial
software were first developed by Dan McNamara and Ray Buland, at the
United States Geological Survey (USGS) as a part of the data and
network quality control (QC) system for the Advanced National Seismic
System (ANSS). Further
development, supported by IRIS through funds it receives from the
National Science Foundation (NSF) allowed for Richard Boaz to develop
the system for implementation against the BUD dataset. Additonal
installation and implementation support was provided by Bruce Weertman
and Tim Ahern at the IRIS DMC.
Webpage authors: D. McNamara (USGS ANSS) and R. Boaz (www.boazconsultancy.com)
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