Search

The most common approach to seismic triggering is to compare short-term averages (STA) with long-term averages (LTA) of transformed amplitudes. In recording environments where this technique is of limited use, hidden Markov models (HMMs) are increasingly used for statistical event detection and classification, but these require training data and are often susceptible to false positive detection errors. In this work, we introduce an adaptive STA-LTA triggering algorithm that uses STA and LTA of state probabilities defined by restricting an HMM to a two population model of outliers in background noise. Monte Carlo simulations of noise and synthetic events are used to investigate detector sensitivity using statistical properties of latent states. We compare our method with traditional STA-LTA triggering on real data recorded by a 12-station vertical borehole array near Hoadley gas field, Alberta, Canada. These tests suggest that our method is more accurate when dealing with closely spaced events and is less susceptible to false positive detection errors. When existing picking algorithms are adapted for HMM STA-LTA, the result is improvement in total picks, accuracy, and consistency. A narrow range of detection thresholds is optimal for a wide range of signal-to-noise ratios; this suggests HMM STA-LTA may be less sensitive to analyst parameter choices than even traditional STA-LTA. [ABSTRACT FROM AUTHOR]

We present a method to extract seismic signals from three-component array data and estimate their polarization properties at each station. The technique is based on a singular value decomposition (SVD) of the complex three-component analytic signal and applies to linearly as well as elliptically polarized seismic phases. To increase accuracy we simultaneously analyze data from different stations and apply a noise weighting based on prearrival data. For polarization analysis, an automated routine is also included. The automated routine selects the data window with the best signal-to-noise ratio from which to obtain a polarization. A linearity measure and a confidence interval accompany the polarization estimate at each station in the array. We test our technique for automated polarization analysis on synthetic P-wave data and compare results with those from other methods. A microseismic dataset from the North Sea provides a unique opportunity to statistically compare previous and independently obtained P-wave polarizations with those provided by the automated technique presented here. We conclude that, for P-wave polarization analysis, our method is robust and significantly more accurate than conventional, mainly manual methods. This is especially so on data with polarized and correlating background noise. It is also faster and provides meaningful quality estimates. [ABSTRACT FROM AUTHOR]

The propagation of two orthogonally polarized shear waves, or shear-wave splitting, is arguably the most robust indication of seismic anisotropy in the Earth. This splitting can be parameterized in terms of the polarization of the fast shear-wave φ and the lag time between fast and slow components δt. These two parameters provide constraints on the mechanism causing the anisotropy. All methods of calculating splitting require a shear-wave analysis window to be selected. Then the φ and δt that best account for the splitting in that window are calculated. Conventionally the shear-wave analysis window is picked manually. However, manual window selection is laborious and also very subjective; in many cases different windows give very different results. We present a method for automating the selection of the window. First, the splitting analysis is performed for a range of window lengths. Then a cluster analysis is applied in order to find those measurements that are stable over many different windows. Once clusters of stable results have been found, the final choice of shear-wave analysis window corresponds to the measurement with the lowest error in the cluster with the lowest variance. Resulting estimates of φ and δt are objective, and very large datasets can be analyzed easily. The success of the technique is illustrated with application to a microseismic dataset of 324 events, which confirms previously published results using manually selected analysis windows. [ABSTRACT FROM AUTHOR]