Deconvolution for Acoustic Emission

Abstract

A new technique is presented for deconvolution of time series (digitally recorded temporal waveforms) such as obtained in acoustic emission. The method, called cross-cut deconvolution, combines two different least squares methods--one completely new, the other a recently developed variant of singular valued decomposition--to produce a potentially robust technique for treating ill-conditioned problems. A simple example is given for deconvolution of a Gaussian kernel in the presence of varying amounts of noise by each least squares method.

Computational methods for large-scale inverse problems: a survey on hybrid projection methods

“…Other algorithmic contributions included the use of cross-validation to determine stopping criteria and transformations to standard form based on Elden's earlier work [66]. Example problems from time series deconvolution were used in [164], but the computational technology at the time was still quite limited [193].…”