An autoregressive-moving average model in which all roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa is called an all-pass time series model. All-pass models are useful for identifying and modeling noncausal and noninvertible autoregressive-moving average processes. We establish asymptotic normality and consistency for rank-based es...

Layered reflectivity sequences have spectral signatures (impulse responses) in accordance with timefrequency transformations. These signatures are filtered by a source under the convolutional definition of a seismogram. Spectral signatures of wedge models indicate that thin layers have preferred source bandwidths needed to produce either a tuned reflection or separate interface reflections. Sou...

It is remarkable that the most recent nuclear test explosion, by North Korea (2006 October 9), although quite small, was well-detected teleseismically. We describe how this event was promptly identified as an explosion and not an earthquake, on the basis of regionally recorded signals, in a region that apparently would have permitted such identification even if the test had been much smaller. T...

An autoregressive-moving average model in which all roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa is called an all-pass time series model. All-pass models generate uncorrelated (white noise) time series, but these series are not independent in the non-Gaussian case. An approximate likelihood for a causal all-pass model is given a...

Coda waves arise from scattering to form the later arriving components of a seismogram. Coda-wave interferometry (CWI) is an emerging tool for constraining earthquake source properties from the interference pattern of coda waves between nearby events. A new earthquake location algorithm is derived which relies on coda-wave-based probabilistic estimates of earthquake separation. The algorithm ca...

This paper presents a method by which uncertainty in reservoir modeling can be reduced by integrating observed data in the estimation of model parameters. The problem is inverse and ill-posed. Further, since the observed data and the reservoir properties are non-linearly related, reducing uncertainty becomes a non-trivial task. The methodology involves generating multiple reservoir descriptions...

This report presents the results of applying an improved autocorrelogram migration to Exxon's Friendswood RVSP data. The improved autocorrelogram migration method is implemented in both the depth and time domains. Two imaging conditions are used: a primary re ection imaging condition and a surface-related ghost imaging condition. The raw traces in the common receiver gather domain are autocorre...

July 6, 2006 Abstract An autoregressive-moving average model in which all roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa is called an all-pass time series model. All-pass models are useful for identifying and modeling noncausal and noninvertible autoregressive-moving average processes. We establish asymptotic normality and consist...