Subspace modelling for structured noise suppression

TL;DR

This paper presents a subspace modeling approach combined with nonlinear techniques to effectively suppress structured noise, demonstrated in broadband seismic signals, offering a novel method for noise cancellation.

Contribution

It introduces a new framework that models signal subspaces and applies nonlinear separation techniques, specifically targeting low frequency noise in seismic data.

Findings

Effective suppression of low frequency seismic noise

Applicable to various structured noise scenarios

Improves signal clarity in seismic analysis

Abstract

The problem of structured noise suppression is addressed by i)modelling the subspaces hosting the components of the signal conveying the information and ii)applying a non-extensive nonlinear technique for effecting the right separation. Although the approach is applicable to all situations satisfying the hypothesis of the proposed framework, this work is motivated by a particular scenario, namely, the cancellation of low frequency noise in broadband seismic signals.