An automatic, adaptive algorithm for refining phase picks in large seismic data sets

Department of Earth and Environmental Science, New Mexico Institute of Mining and Technology, Сокорро, New Mexico, United States
Bulletin of the Seismological Society of America (Impact Factor: 2.32). 06/2002; 92(5):1660-1674. DOI: 10.1785/0120010224


We have developed an adaptive, automatic, correlation- and clustering-based method for greatly reducing the degree of picking inconsistency in large, digital seismic catalogs and for quantifying similarity within, and discriminating among, clusters of disparate waveform families. Innovations in the technique include (1) the use of eigenspectral methods for cross-spectral phase estimation and for providing subsample pick lag error estimates in units of time, as opposed to dimensionless relative scaling of uncertainties; (2) adaptive, cross-coherency-based filtering; and (3) a hierarchical waveform stack correlation method for adjusting mean intercluster pick times without compromising tight intracluster relative pick estimates. To solve the systems of cross-correlation lags we apply an iterative, optimized conjugate gradient technique that minimizes an L1-norm misfit. Our repicking technique not only provides robust similarity classification-event discrimination without making a priori assumptions regar

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Available from: Brian Borchers, Oct 01, 2015
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    • "In order to improve the S-phase identification, a technique which combines polarization analysis of single three components recording of a seismic event with the analysis of lateral waveform coherence in a trace gathers (Amoroso et al., 2012) was applied. As a further improvement, a refined re-picking algorithm (Rowe et al., 2002) based on the waveforms cross-correlation was applied providing with a high-accurate observed travel-times data set. The used tomographic method performs a simultaneous inversion of P and S arrival times to estimate the event location coordinates, the origin times, and P and S velocities at the nodes of a 3D discretized volume. "
    2ECEES, At Istanbul, Turkey; 08/2014
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    • "At local and very local scale there is no guarantee that the incident S waves is nearly vertical. In such cases, better results can be obtained by computing the instantaneous covariance matrix using the three-component traces (Rowe et al. 2002). Nevertheless, in this study we obtained better results by using a covariance matrix based on the horizontal components only. "
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    ABSTRACT: Automated location of seismic events is a very important task in microseismic monitoring operations as well for local and regional seismic monitoring. Since microseismic records are generally characterised by low signal-to-noise ratio, automated location methods are requested to be noise robust and sufficiently accurate. Most of the standard automated location routines are based on the automated picking, identification and association of the first arrivals of P and S waves and on the minimization of the residuals between theoretical and observed arrival times of the considered seismic phases. Although current methods can accurately pick P onsets, the automatic picking of the S onset is still problematic, especially when the P coda overlaps the S wave onset. In this paper we propose a picking free earthquake location method, based on the use of the Short-Term-Average/Long-Term-Average (STA/LTA) traces at different stations as observed data. For the P phases we use the STA/LTA traces of the vertical energy function, while for the S phases, we use the STA/LTA traces of a second characteristic function, which is obtained using the principal component analysis technique. In order to locate the seismic event, we scan the space of possible hypocentral locations and origin times, and stack the STA/LTA traces along the theoretical arrival time surface for both P ans S phases. Iterating this procedure on a three-dimensional grid we retrieve a multidimensional matrix whose absolute maximum corresponds to the spatial coordinates of the seismic event. A pilot application was performed in the Campania-Lucania region (southern Italy) using a seismic network (Irpinia Seismic Network) with an aperture of about 150 km. We located 196 crustal earthquakes (depth < 20 km) with magnitude range 1.1<Ml<2.7. A subset of these locations were compared with accurate manual locations refined by using a double difference technique. Our results indicate a good agreement with manual locations. Moreover, our method is noise robust and performs better than classical location methods based on the automatic picking of the P and S waves first arrivals.
    Geophysical Journal International 01/2014; DOI:10.1093/gji/ggt477 · 2.56 Impact Factor
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    • "Significant improvement in resolution and reliability of local to regional tomographic studies can be made by automatically repicking and weighting data (Di Stefano et al., 2006; Diehl et al., 2009), resulting in either adjusted picked onsets or increased accuracy differential times. Picking error can additionally be reduced using cross correlation (CC) of similar events (Got et al., 1994; Dodge et al., 1995; Shearer, 1997; Rubin et al., 1998; Waldhauser and Ellsworth, 2000; Rowe et al., 2002). "
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    ABSTRACT: We have integrated waveform and arrival-onset data collected in Costa Rica as part of the National Science Foundation (NSF)-sponsored Costa Rica Seismo-genic Zone Experiment (CRSEIZE) and along central Costa Rica and Nicaragua as part of the German SFB 574 program. The five arrays, composed of different sensor types (one-and three-component land and ocean bottom seismometers and hydrophones), were archived using different software packages (Antelope and SEISAN) and were automatically and manually picked using various quality criteria resulting in a dispa-rate set of pick weights. We evaluate pick quality using automated arrival detection and picking algorithm based on the wavelet transform and Akaike information cri-terion picker. The consistency of the arrival information over various scales provides a basis for assigning a quality to the analyst pick. Approximately 31% of P arrival times and 26% of S times have been classified as high-quality picks (quality 0–1). An additional 21% of P times and 27% of S arrivals are good quality (quality 2–3). The revised quality picks are mapped directly into new pick weights for inversion studies. We explore the effect of new weighting and removal of poor data by relocating hypo-centers through a minimum 1D velocity model and conducting double-difference lo-cal earthquake tomography (LET). Analysis of the hypocenter relocation and seismic velocity tomography results suggest that using the improved quality determinations have a greater effect on improving sharpness in the velocity images than on the mag-nitude of hypocentral movement. Online Material: Figures of waveforms, event statistics, and tomography; and tables of station and event parameters, station qualities, velocity model, and hypocen-tral parameters.
    Bulletin of the Seismological Society of America 10/2013; 103(5):2752-2766. DOI:10.1785/0120120274 · 2.32 Impact Factor
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