Multi-component autoregressive techniques for the analysis of seismograms

Leonard, Mark

doi:10.1016/s0031-9201(99)00054-0

Cited by 231 publications

(107 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Theory and implementation of different AR-AIC picking algorithms are described, for example, in Takanami and Kitagawa (1991), Leonard and Kennett (1999), or Sleeman and van Eck (1999). Although multivariate AR approaches appear to be slightly more appropriate for robust S-wave picking (e.g., Takanami and Kitagawa, 1993;Leonard and Kennett, 1999), the application of the univariate method to single components (N, E, Q, and T) and combined components (E N) provides additional details about uncertainty of timing and phase identification.…”

Section: Polarization Detectormentioning

confidence: 99%

“…Although multivariate AR approaches appear to be slightly more appropriate for robust S-wave picking (e.g., Takanami and Kitagawa, 1993;Leonard and Kennett, 1999), the application of the univariate method to single components (N, E, Q, and T) and combined components (E N) provides additional details about uncertainty of timing and phase identification. Our implementation is mainly based on the method of Takanami and Kitagawa (1988), which uses an univariate AR fitting approach to derive automatic arrival times.…”

Section: Polarization Detectormentioning

confidence: 99%

“…In addition, a predictive AR-AIC picking algorithm is used on single (original and rotated) components and on a combination of both horizontal components as described, for example, in Takanami and Kitagawa (1991) or Leonard and Kennett (1999). The information provided by the different picking techniques are combined to yield robust S-wave arrival times and are used to derive in situ estimates of the uncertainty and quality of the corresponding phase pick.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic S-Wave Picker for Local Earthquake Tomography

Diehl

Deichmann

Kissling

et al. 2009

Bulletin of the Seismological Society of America

120

View full text Add to dashboard Cite

High-resolution seismic tomography at local and regional scales requires large and consistent sets of arrival-time data. Algorithms combining accurate picking with an automated quality classification can be used for repicking waveforms and compiling large arrival-time data sets suitable for tomographic inversion. S-wave velocities represent a key parameter for petrological interpretation, improved hypocenter determination, as well as for seismic hazard models. In our approach, we combine three commonly used phase detection and picking methods in a robust S-wave picking procedure. Information from the different techniques provides an in situ estimate of timing uncertainty and of the reliability of the automatic phase identification. Automatic picks are compared against manually picked reference picks of selected earthquakes in the Alpine region. The average accuracy of automatic picks and their classification is comparable with the reference picks, although a higher number of picks is downgraded to lower quality classes by the automatic picker. In the production-mode, we apply the picker to a data set of 552 earthquakes in the Alps recorded at epicentral distances ≤ 150 km. The resulting data set includes about 2500 S phases with an upper error bound of 0.27 sec.

show abstract

Section: Polarization Detectormentioning

confidence: 99%

Section: Polarization Detectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic S-Wave Picker for Local Earthquake Tomography

Diehl

Deichmann

Kissling

et al. 2009

Bulletin of the Seismological Society of America

120

View full text Add to dashboard Cite

show abstract

“…Leonard and Kennett, 1999, and references therein). In the procedure, an auto-regressive (AR) model is generated for the background noise prior to the detection time and a residual trace is formed containing, for each sample, the difference between the recorded value and the linear prediction from the preceding samples using this AR-model (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

View full text Add to dashboard Cite

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

show abstract

“…Gibbons et al (2005) applied the AR-AIC (Autoregressive -Akaike Information Criterion) method (Akaike, 1973;GSE/JAPAN/40, 1992;Leonard and Kennett, 1999) and found that this provided excellent estimates for the Pn-arrivals from Kovdor events due to the large contrast in amplitudes over a wide range of frequencies. Given the limited source region of interest, and the fact that the study was limited to a single array station, Gibbons et al (2005) fixed a time-frame relative to the autoregressive Pn-arrival time estimate and measured slowness for the secondary phases in time-windows fixed relative to the Pn onset estimate.…”

Section: Re-estimation Of Phase Arrival Timementioning

confidence: 99%