3,421 research outputs found
Limiting the effects of earthquakes on gravitational-wave interferometers
Ground-based gravitational wave interferometers such as the Laser
Interferometer Gravitational-wave Observatory (LIGO) are susceptible to
high-magnitude teleseismic events, which can interrupt their operation in
science mode and significantly reduce the duty cycle. It can take several hours
for a detector to stabilize enough to return to its nominal state for
scientific observations. The down time can be reduced if advance warning of
impending shaking is received and the impact is suppressed in the isolation
system with the goal of maintaining stable operation even at the expense of
increased instrumental noise. Here we describe an early warning system for
modern gravitational-wave observatories. The system relies on near real-time
earthquake alerts provided by the U.S. Geological Survey (USGS) and the
National Oceanic and Atmospheric Administration (NOAA). Hypocenter and
magnitude information is generally available in 5 to 20 minutes of a
significant earthquake depending on its magnitude and location. The alerts are
used to estimate arrival times and ground velocities at the gravitational-wave
detectors. In general, 90\% of the predictions for ground-motion amplitude are
within a factor of 5 of measured values. The error in both arrival time and
ground-motion prediction introduced by using preliminary, rather than final,
hypocenter and magnitude information is minimal. By using a machine learning
algorithm, we develop a prediction model that calculates the probability that a
given earthquake will prevent a detector from taking data. Our initial results
indicate that by using detector control configuration changes, we could prevent
interruption of operation from 40-100 earthquake events in a 6-month
time-period
A feasibility study for long-path multiple detection using a neural network
Least-squares inverse filters have found widespread use in the deconvolution of seismograms and the removal of multiples. The use of least-squares prediction filters with prediction distances greater than unity leads to the method of predictive deconvolution which can be used for the removal of long path multiples. The predictive technique allows one to control the length of the desired output wavelet by control of the predictive distance, and hence to specify the desired degree of resolution. Events which are periodic within given repetition ranges can be attenuated selectively. The method is thus effective in the suppression of rather complex reverberation patterns. A back propagation(BP) neural network is constructed to perform the detection of first arrivals of the multiples and therefore aid in the more accurate determination of the predictive distance of the multiples. The neural detector is applied to synthetic reflection coefficients and synthetic seismic traces. The processing results show that the neural detector is accurate and should lead to an automated fast method for determining predictive distances across vast amounts of data such as seismic field records. The neural network system used in this study was the NASA Software Technology Branch's NETS system
Polarization filtering for automatic picking of seismic data and improved converted phase detection
Data-adaptive polarization filtering is used to improve the detection of converted seismic phases. Both direct waves and mode-converted PS and SP arrivals may be more
easily picked on the filtered records. An autopicking routine is applied that cuts the polarization filtered traces according to the modelled traveltime of each phase through an initial structure. Use of forward-modelled, source–receiver times reduces the likelihood
of an automatic pick being incorrectly made on spurious spikes in the polarization filtered trace. It is therefore a realistic way of automatically picking multiphase data
sets or, more generally, linearly polarized phases where low signal-to-noise ratios may be encountered. The method is suitable for any three-component seismic data and is here applied to local earthquakes recorded in North Island, New Zealand. Intermediate energy is observed between the direct P and S arrivals due to phase conversion at the interface between the Indo-Australian and subducting Pacific plates. The amplitudes of
these converted arrivals are often too low for them to be identified above the P-wave coda but polarization filtering of the records enables the yield of converted phase picks to be greatly increased
Neural Network Aided Glitch-Burst Discrimination and Glitch Classification
We investigate the potential of neural-network based classifiers for
discriminating gravitational wave bursts (GWBs) of a given canonical family
(e.g. core-collapse supernova waveforms) from typical transient instrumental
artifacts (glitches), in the data of a single detector. The further
classification of glitches into typical sets is explored.In order to provide a
proof of concept,we use the core-collapse supernova waveform catalog produced
by H. Dimmelmeier and co-Workers, and the data base of glitches observed in
laser interferometer gravitational wave observatory (LIGO) data maintained by
P. Saulson and co-Workers to construct datasets of (windowed) transient
waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian)
noise with different signal-tonoise ratios (SNR). Principal component analysis
(PCA) is next implemented for reducing data dimensionality, yielding results
consistent with, and extending those in the literature. Then, a multilayer
perceptron is trained by a backpropagation algorithm (MLP-BP) on a data subset,
and used to classify the transients as glitch or burst. A Self-Organizing Map
(SOM) architecture is finally used to classify the glitches. The glitch/burst
discrimination and glitch classification abilities are gauged in terms of the
related truth tables. Preliminary results suggest that the approach is
effective and robust throughout the SNR range of practical interest.
Perspective applications pertain both to distributed (network, multisensor)
detection of GWBs, where someintelligenceat the single node level can be
introduced, and instrument diagnostics/optimization, where spurious transients
can be identified, classified and hopefully traced back to their entry point
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