Enhancement of TEM Data and Noise Characterization by Principal Component Analysis

Abstract

Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. The research performed over the life-span of this project has progressed satisfactorily with the major research tasks completed approximately to project plan. In addition, we have identified an additional area of research critical to the application of PCA to TEM data and added these aspects to the research plan. REPORT DATE MAY 20102First, we developed a principal component analysis algorithm tailored to unexploded ordnance applications. Decay characteristics of TEM data preclude the standard Karhunen-Loéve transform; we have addressed these issues with algorithm modifications and incorporated these into the workflow.Secondly, we identified the optimum choice of principal components for the attenuation of both random noise and correlated noise, leaving the signal due to UXO intact. We show that the processed data is optimally prepared for automatic anomaly picking routines with a highly reduced number of false anomalies. We demonstrate this on both synthetic examples of UXO surveys, as well as on TEM data from Kaho'olawe, Hawaii, USA.iii Finally, we have identified a critical issue with inversion of processed data that results in extremely inaccurate recovered models without the incorporation of the PCA process into the forward model. We developed an inversion algorithm which takes the processing steps into account during construction of the inverse kernels.This leads to more accurate recovered models of inverted anomalies.iv The goal is to increase the signal-to-noise ratio of the TEM data as well as to generate an estimate of data noise characteristics (statistical distribution and associated parameters) for use in subsequent inversion-based discrimination.One of the most effective geophysical techniques for UXO application is the transient electromagnetic method. Using a loop as a source, a time-varying magnetic field is generated at the surface which in turn induces electrical currents in the ground, and more importantly in buried metallic objects. Once induced, these currents dissipate over time due to ohmic losses, leading to a measured transient decay in magnetic field flux density at the surface by geophysical sensors. The currents induced in the surrounding medium and in the buried metallic ob...

Noise Removal Based on Filtered Principal Component Reconstruction

“…Du et al (2007) achieved the reduction of spectral correlation and spectral dimension, improving the information preservation. PCA not only can suppress the noise of electromagnetic data, but also be applied to the inversion calculation (Kass and Li, 2007;Kass et al, 2010). PCA was applied to remove artifacts and noise of ecg signal, extracting the useful ecg signal effectively (Chawla, 2011).…”