Wavefield decomposition using ML-probabilities in modelling single-site 3-component records

Abstract

This paper presents a new approach to the analysis of three-component digital seismograms.Earlier approaches used techniques such as Principal Components to estimate particlemotion using models of P and S waves. In this paper the Maximum-Likelihood (ML) estimator is preferred because this allows the use of X2-probabilities to test whether energy of a specific wave type (P, S, Love or Rayleigh) is present. In addition, this analysis allows the joint estimation of azimuth of approach and in cases of P-and SV-waves also apparent angle of incidence (and, hence, information on apparent velocity). For a single three-component seismogram, the covariance matrix provides only six independent observations, thus restricting analysis to rather simple wave models. The technique works satisfactorily for P-waves whereas shear and surface-wave models sometimes prove cumbersome to handle due to correlation between radial and transverse components reflecting complex propagation characteristics in inhomogeneous media. This technique has been tested on synthetic data and in such cases works perfectly for all wave types.An important aspect of this work has been the visual display of the probability and velocity information as functions of time and azimuth. Displaying the data in this form provides information on the ray path in a manner similar to analysis performed by seismic arrays.Practical examples on a variety of siesmic data are given to illustrate the viability of the technique: the data cover a broad spectrum of frequencies and applications from broad-band teleseismic data (1 Hz), regional seismic data (10-40 Hz), seismic profiling data (125 Hz) and VSP (500 Hz) recordings.

Seismic $P$ Phase Picking Using a Kurtosis-Based Criterion in the Stationary Wavelet Domain

“…Over the last two decades, numerous algorithms have been developed for P arrival identification based on energy analysis [1]- [5], polarization analysis [6]- [10], artificial neural networks [11], [12], maximum likelihood methods [13], [14], fuzzy logic theory [15], autoregressive techniques [16]- [19], higher-order statistics [20]- [24], sample of a sequence.…”

Semiannual Technical Summary, 1 April-30 September 1987

“…This problem has to a large extent been overcome by introducing flexible particle motion models in combination with effective maximum likelihood (ML) schemes for signal parameter estimation from 3C recordings (Christoffersson et al, 1988;Roberts et al, 1989;and Roberts and Christoffersson, 1990). Their techniques allow us to produce quite precise automatic preliminary bulletins for single 3C (digital) stations (Ruud and Husebye, 1991).…”

Seismic Event Location: Nonlinear Inversion Using a Neighbourhood Algorithm

“…The task of phase association is not entirely straightforward but can be aided by the use of array information (see e.g., KVAERNA and DOORNBOS, 1986;RINGDAL and KVAERNA, 1989) or by using multiple attribute analysis for three-component broadband records (CHRISTOFFERSSON et al, 1988;KENNETT, 1995, 1996). In this work we will assume that the identity of each observed phase is known but allow for the possibility of using dierent classes of phases and types of information.…”