Mixed Signal Processing for Regional and Teleseismic Arrays

Shumway, Robert H.; Smart, Eugene; Clauter, Dean A.

doi:10.1785/0120060140

Cited by 13 publications

(9 citation statements)

References 15 publications

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“…At a frequency of 0.25 Hz aliasing is not a problem but the main lobe of the response is quite broad, taking about 5 -6 s/deg to decay to half-power ( Figure 2). This causes a decrease in the precision of slowness estimates, and can sometimes cause peaks from two plane waves incident in the same time window to smear into one apparent peak with a slowness maximum in between the two true slownesses [e.g., Shumway et al, 2008]. Because of this problem, and the aliasing problem mentioned above, we focused on frequencies between 0.25 and 0.75 Hz in the square of slowness space defined by ±50 s/deg.…”

Section: Methodsmentioning

confidence: 99%

Composition and variation of noise recorded at the Yellowknife Seismic Array, 1991–2007

2009

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[1] We analyze seismic noise recorded on the 18 short-period, vertical component seismometers of the Yellowknife Seismic Array (YKA). YKA has an aperture of 23 km and is sited on cratonic lithosphere in an area with low cultural noise. These properties make it ideal for studying natural seismic noise at periods of 1-3 s. We calculated frequency-wave number spectra in this band for over 6,000 time windows that were extracted once per day for 17 years (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007). Slowness analysis reveals a rich variety of seismic phases originating from distinct source regions: R g waves from the Great Slave Lake; L g waves from the Atlantic, Pacific, and Arctic Oceans; and teleseismic P waves from the north Pacific and equatorial mid-Atlantic regions. The surface wave energy is generated along coastlines, while the body wave energy is generated at least in part in deep-water, pelagic regions. Surface waves tend to dominate at the longer periods and, just as in earthquake seismograms, L g is the most prominent arrival. Although the periods we study are slightly shorter than the classic doublefrequency microseismic band of 4-10 s, the noise at YKA has clear seasonal behavior that is consistent with the ocean wave climate in the Northern Hemisphere. The temporal variation of most of the noise sources can be well fit using just two Fourier components: yearly and biyearly terms that combine to give a fast rise in microseismic power from mid-June through mid-October, followed by a gradual decline. The exception is the R g energy from the Great Slave Lake, which shows a sharp drop in noise power over a 2-week period in November as the lake freezes. The L g noise from the east has a small but statistically significant positive slope, perhaps implying increased ocean wave activity in the North Atlantic over the last 17 years.

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Section: Methodsmentioning

confidence: 99%

Composition and variation of noise recorded at the Yellowknife Seismic Array, 1991–2007

2009

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“…In this case, the best estimator for the signal can be approximated: first by filtering the data into the signal band, and then by delaying and averaging, that is, we compute the estimator In the geophysical literature, this is called 'beam-forming,' and it depends critically on the assumption that the signal is a plane wave propagating across the array with a fixed velocity and azimuth (direction in degrees measured clockwise from north). For details on this computation and extensions to multiple signal and noise sources, see Shumway et al (2008). Figure 3 shows the estimated signal compared with the raw signal observed at the first sensor, and we find improvements in signal-to-noise ratio, particularly when the signal arrives; the first arrival is enhanced, and a clear representation of the first two cycles can be important for discrimination between earthquakes and nuclear explosions.…”

Section: Introductionmentioning

confidence: 95%

“…The signals arrive at different times T i , i = 1,2,…, N , relative to some arbitrary start point, with the time delays

T_{i} = {bold-italicr}_{i}^{'} bold-italicθ

giving the relation of the array coordinate vector r i =( r i 1 , r i 2 )′ in km to a slowness parameter θ ′=( θ 1 , θ 2 ) in second/km. The slowness parameter is directly related to the velocity and azimuth of a propagating plane wave (see Shumway et al () for details). The coordinates for the nine vertical recording channels of the Makanche are shown in Figure .…”

Section: Introductionmentioning

confidence: 99%

“…For details on this computation and extensions to multiple signal and noise sources, see Shumway et al (). Figure shows the estimated signal compared with the raw signal observed at the first sensor, and we find improvements in signal‐to‐noise ratio, particularly when the signal arrives; the first arrival is enhanced, and a clear representation of the first two cycles can be important for discrimination between earthquakes and nuclear explosions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Deconvolution of Signals Observed on Arrays

Lin

Suess

Shumway

et al. 2016

Journal Time Series Analysis

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Time series data collected from arrays of seismometers are traditionally used to solve the core problems of detecting and estimating the waveform of a nuclear explosion or earthquake signal that propagates across the array. We consider here a parametric exponentially modulated autoregressive model. The signal is assumed to be convolved with random amplitudes following a Bernoulli normal mixture. It is shown to be potentially superior to the usual combination of narrow band filtering and beam forming. The approach is applied to analyzing series observed from an earthquake from Yunnan Province in China received by a seismic array in Kazakhstan.

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“…Partially this is due to this specific distance being near a branch intersection on the traveltime curve ( Figure 7.2), however it is also indicative of the small aperture of CMAR as discussed in the introduction. More sophisticated techniques (i.e., Capon, 1969;Schmidt, 1986;Shumway et al, 2008) could potentially provide more accurate estimates of slownesses for triplicated arrivals; however, all slowness estimators are likely to have problems when the signals arrive from similar directions at similar times (Shumway et al, 2008).…”

Section: Chinese Nuclear Testsmentioning

confidence: 99%

Model P Wave Multipathing at Regional Distances in Southeast Asia

Koper¹,

Fatehi²

2008

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Mixed Signal Processing for Regional and Teleseismic Arrays

Cited by 13 publications

References 15 publications

Composition and variation of noise recorded at the Yellowknife Seismic Array, 1991–2007

Composition and variation of noise recorded at the Yellowknife Seismic Array, 1991–2007

Bayesian Deconvolution of Signals Observed on Arrays

Model P Wave Multipathing at Regional Distances in Southeast Asia

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