AusAEM Year 1: Some aspects of quality control and calibration. Issue 1 (1st December 2019)

• Record Type: Journal Article
• Authors: Brodie, Ross C.; Ley-Cooper, Yusen; Lockwood, Ray; Murray, David
• Is Part Of: ASEG Extended Abstracts (Online). Volume 2019:Issue 1(2019)
• Page Start: 1
• Page End: 5
• Publication Date: 2019-12-01
• Subjects: AusAEM -- TEMPEST -- airborne -- electromagnetic -- calibration -- AEM; Prospecting -- Geophysical methods -- Periodicals; Prospecting -- Geophysical methods; Periodicals
• DOI: 10.1080/22020586.2019.12073132
• Languages: English
• ISSNs: 2202-0586

Abstract

Summary: For the AusAEM Year 1 survey an inertial measurement unit (IMU) was installed for the first time on the TEMPEST receiver bird to measure its orientation and to augment GPS derived positioning of the receiver. This has given us the opportunity to develop better quality control and calibration procedures, which would otherwise not be possible. Theoretical modelling of the primary field on high altitude zero-lines, using the full position/orientation information, revealed discrepancies between observed and modelled data. It alerted us to time-lag parallaxes between EM and bird position/orientation data, some spurious IMU data on many pre-flight zero-lines, and a coordinate system sign convention inconsistency. The modelling also revealed systematic differences that we could attribute to the calibration of the receiver pitch and EM data scaling. We developed an inversion algorithm to solve for a receiver pitch offset and an EM scaling calibration parameter, for each zero-line, which minimised the systematic discrepancies. It eventuated that the calibration parameters fell into five distinct populations explicable by significant equipment changes. This gave us the confidence to use the medians of these populations as parameters to calibrate the data. The work shows the value of the new receiver bird orientation data and the importance of accurate IMU calibration after any modification. It shows the practical utility of quantitative modelling in the quality control workflow. It also demonstrates how modelling and inversion procedure can be used to successfully diagnose calibration issues in fixed-wing AEM data.