Comparison of Manual and Automatic Onset Time Picking

Leonard, Mark

doi:10.1785/0120000026

Cited by 127 publications

(62 citation statements)

References 9 publications

(5 reference statements)

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“…For the AR-model picker of Win system, both the forward and backward AR models (Takanami and Kitagawa, 1988;Leonard, 2000) are used to derive the AIC. The result of the AR-model picker is also shown in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…Algorithms based on wavelet analysis (Anant and Dowla, 1997;Zhang et al, 2003) and polarization analysis (Vidale, 1986;Reading et al, 2001) have also been suggested. The most commonly used algorithm is the autoregressive (AR) model (Yokota et al, 1981;Maeda, 1985;Takanami and Kitagawa, 1988;Sleeman and Eck, 1999;Leonard and Kennett, 1999;Leonard, 2000). Methods using the autoregressive model are based on the assumption that seismograms can be divided into two locally stationary intervals at the time of an arrival of seismic signal, with each interval satisfying a different autoregressive process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic seismic wave arrival detection and picking with stationary analysis: Application of the KM2O-Langevin equations

et al. 2007

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An automatic detection and a precise picking of the arrival times of seismic waves using digital seismograms are important for earthquake early detection systems. Here we suggest a new method for detecting and picking Pand S-wave signals automatically. Compared to methods currently in use, our method requires fewer assumption with properties of the data time series. We divide a record into intervals of equal lengths and check the "local and weak stationarity" of each interval using the theory of the KM 2 O-Langevin equations. The intervals are stationary when these include only background noise, but the stationarity breaks abruptly when a seismic signal arrives and the intervals include both the background noise and the P-wave. This break of stationarity makes us possible to detect P-wave arrival. We expand the method for picking of S-waves. We applied our method to earthquake data from Hi-net Japan, and 90% of P-wave auto-picks were found to be within 0.1 s of the corresponding manual picks, and 70% of S-wave picks were within 0.1 s of the manual picks. This means that our method is accurate enough to use as a part of the seismic early detection system.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic seismic wave arrival detection and picking with stationary analysis: Application of the KM2O-Langevin equations

et al. 2007

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“…This estimate is frequently a very good approximation of the arrival time (e.g. Leonard, 2000) with the significance of the AIC minimum increasing with increasing contrast in the amplitude and spectral content between the signal and noise. In the absence of a signal onset hypothesis, we can simply perform this procedure on consecutive overlapping segments of the seismogram, calculating short AIC traces at regular intervals (e.g.…”

Section: Strategies For Characterizing Repeating Sources and Aftershocksmentioning

confidence: 99%

Iterative Strategies for Aftershock Classification in Automatic Seismic Processing Pipelines

Gibbons

Kværna

Harris

et al. 2016

Seismological Research Letters

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Aftershock sequences following very large earthquakes present enormous challenges to nearrealtime generation of seismic bulletins. The increase in analyst resources needed to relocate an inflated number of events is compounded by failures of phase association algorithms and a significant deterioration in the quality of underlying fully automatic event bulletins. Current processing pipelines were designed a generation ago and, due to computational limitations of the time, are usually limited to single passes over the raw data. With current processing capability, multiple passes over the data are feasible. Processing the raw data at each station currently generates parametric datastreams which are then scanned by a phase association algorithm to form event hypotheses. We consider the scenario where a large earthquake has occurred and propose to define a region of likely aftershock activity in which events are detected and accurately located using a separate specially targeted semi-automatic process. This effort may focus on so-called pattern detectors, but here we demonstrate a more general grid search algorithm which may cover wider source regions without requiring waveform similarity. Given many well-located aftershocks within our source region, we may remove all associated phases from the original detection lists prior to a new iteration of the phase association algorithm. We provide a proof-of-concept example for the 2015 Gorkha sequence, Nepal, recorded on seismic arrays of the International Monitoring System. Even with very conservative conditions for defining event hypotheses within the aftershock source region, we can automatically remove about half of the original detections which could have been generated by Nepal earthquakes and reduce the likelihood of false associations and spurious event

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“…When the order of the AR process is fixed, the AIC function is a measure for the model fit. The point where the AIC is minimized determines the optimal separation of the two stationary time series in the least squares sense, and thus is interpreted as the phase onset (Sleeman and van Eck, 1999); this picker is known as AR-AIC picker (Leonard, 2000). Different than AR-AIC picker, calculates the AIC function directly from the seismogram without using the AR coefficients.…”

Section: Akaike Information Criterion (Aic) Pickermentioning

confidence: 99%

Processing and review interface for strong motion data (PRISM) software, version 1.0.0—Methodology and automated processing

Jones¹,

Kalkan²,

Stephens³

2017

Open-File Report

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For more information on the USGS-the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment-visit http://www.usgs.gov/ or call 1-888-ASK-USGS (1-888-275-8747).For an overview of USGS information products, including maps, imagery, and publications, visit http://store.usgs.gov/.Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.Suggested citation: Jones, J., Kalkan, E., and Stephens, C., 2017, Processing and review interface for strong motion data (PRISM) software, version 1.0.0-Methodology and automated processing: U.S. Geological Survey Open-File Report 2017-1008, 81 p., https://doi.org/10.3133/ofr20171008. ISSN 2331ISSN -1258 iii AcknowledgmentsThe retrieval, processing, and dissemination of seismic data involve a large number of individuals with a range of scientific and technological skills. We thank our colleagues at the U.S. Geological Survey, and in particular David Boore for providing his Fortran codes, the technicians of the National Strong-Motion Project (NSMP) network who install and maintain the strong motion instrumentation, and those involved in collecting and vetting data and providing easy access to the recordings. We also wish to thank the NSMP Working Group members for initiation of the Processing and Review Interface for Strong Motion data (PRISM) project. Special thanks are extended to Jamie Steidl, Robert Darragh, and Brad Aagaard for their reviews, and for providing their constructive comments and suggestions. Finally, we would like to thank Sarah Nagorsen for editing. Graphs showing a final suite of pseudo-spectral acceleration, pseudo-spectral velocity, and spectral displacement spectra computed at different damping levels (0, 2, 5, 10, and 20 percent of critical) for three channels of MLI record with 100 samples-per-second from the 2014 M4. Graphs showing a final suite of pseudo-spectral acceleration, pseudo-spectral velocity, and spectral displacement spectra computed at different damping levels (0, 2, 5, 10, and 20 percent of critical) for three channels of SAO record with 100 samples-per-second from the 2014 M4. Graphs showing a final suite of pseudo-spectral acceleration, pseudo-spectral velocity, and spectral displacement spectra computed at different damping levels (0, 2, 5, 10, and 20 percent of critical) for three channels of C002 record with 200 samples-per-second from the 2014 M6. Graphs showing a final suite of pseudo-spectral acceleration, pseudo-spectral velocity, and spectral displacement spectra computed at different damping levels ( 40.Graphs showing a final suite of pseudo-spectral acceleration, pseudo-spectral velocity, and spectral displacement spectra computed at different damping levels ( AbstractA continuall...

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Comparison of Manual and Automatic Onset Time Picking

Cited by 127 publications

References 9 publications

Automatic seismic wave arrival detection and picking with stationary analysis: Application of the KM2O-Langevin equations

Automatic seismic wave arrival detection and picking with stationary analysis: Application of the KM2O-Langevin equations

Iterative Strategies for Aftershock Classification in Automatic Seismic Processing Pipelines

Processing and review interface for strong motion data (PRISM) software, version 1.0.0—Methodology and automated processing

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