The reduction of sample‐bias in polarization estimators for multichannel geophysical data with anisotropic noise

Summary. Estimates of the bivariate coherence, γ2, and the degree of polarization, β2, are often used to determine the amount of correlation between different time series. Both of these measures can be used to determine the presence of polarized waves in geophysical data, or to determine whether the multichannel data can be accurately represented by a reduced number of channels. The parameter β2 is rotationally invariant, suggesting that it is a more suitable measure for analysing vector waves. If noise in the data is spatially anisotropic, then both γ2 and β2 can assume large values, even when no signal is present, and consequently the signal may be difficult to detect. This difficulty can be reduced by changing the metric in the instrument space. Similarly, since γ2 and β2 are estimated from a finite number of samples, or finite degrees of freedom v, the estimators can have large biases. Approximate corrections for these sample biases are given by expanding the expectation of the estimators in power series in v-1.

[1]  H. Hotelling New Light on the Correlation Coefficient and its Transforms , 1953 .

[2]  J. C. Samson,et al.  Descriptions of the Polarization States of Vector Processes: Applications to ULF Magnetic Fields , 1973 .

[3]  M. C. Pease,et al.  Methods of Matrix Algebra , 1965 .

[4]  Calyampudi R. Rao The use and interpretation of principal component analysis in applied research , 1964 .

[5]  T. W. Anderson ASYMPTOTIC THEORY FOR PRINCIPAL COMPONENT ANALYSIS , 1963 .

[6]  A. E. Maxwell,et al.  Factor Analysis as a Statistical Method. , 1964 .

[7]  J. Samson,et al.  Polarization characteristics of Pi 2 pulsations and implications for their source mechanisms: Location of source regions with respect to the auroral electrojets , 1981 .

[8]  T. C. Brown,et al.  Foundations of Linear Algebra. , 1968 .

[9]  J. C. Samson,et al.  Some comments on the descriptions of the polarization states of waves , 1980 .

[10]  D. G. Watts,et al.  Spectral analysis and its applications , 1968 .

[11]  N. R. Goodman Statistical analysis based on a certain multivariate complex Gaussian distribution , 1963 .

[12]  M. H. Quenouille NOTES ON BIAS IN ESTIMATION , 1956 .

[13]  W. R. Bennett,et al.  Spectra of quantized signals , 1948, Bell Syst. Tech. J..

[14]  W. C. Dean,et al.  Data processing techniques for the detection and interpretation of teleseismic signals , 1965 .

[15]  A. James Distributions of Matrix Variates and Latent Roots Derived from Normal Samples , 1964 .

[16]  Job Jan C. Samson Comments on polarization and coherence , 1980 .

[17]  A. Nuttall,et al.  Bias of the estimate of magnitude-squared coherence , 1976 .

[18]  George A. Anderson,et al.  An Asymptotic Expansion for the Distribution of the Latent Roots of the Estimated Covariance Matrix , 1965 .

[19]  J. C. Samson,et al.  Pure states, polarized waves, and principal components in the spectra of multiple, geophysical time-series , 1983 .

[20]  H. Kaiser The varimax criterion for analytic rotation in factor analysis , 1958 .

[21]  Richard Bellman,et al.  Introduction to Matrix Analysis , 1972 .

[22]  M. Taner,et al.  SEMBLANCE AND OTHER COHERENCY MEASURES FOR MULTICHANNEL DATA , 1971 .

[23]  D. Lawley TESTS OF SIGNIFICANCE FOR THE LATENT ROOTS OF COVARIANCE AND CORRELATION MATRICES , 1956 .

[24]  E. Wolf Coherence properties of partially polarized electromagnetic radiation , 1959 .