Leveling airborne geophysical data using a unidirectional variational model

Abstract Airborne geophysical data leveling is an indispensable step in conventional data processing. Traditional data leveling methods mainly explore the leveling error properties in the time and frequency domain. A new technique is proposed to level airborne geophysical data in view of the image space properties of the leveling error, including directional distribution property and amplitude variety property. This work applied a unidirectional variational model to all the survey data based on the gradient difference between the leveling errors in flight line direction and the tie-line direction. Then, a spatially adaptive multi-scale model is introduced to iteratively decompose the leveling errors which effectively avoid the difficulty in parameter selection. Considering that anomaly data with large amplitude may hide the real data level, a leveling preprocessing method is given to construct a smooth field based on the gradient data. The leveling method can automatically extract the leveling errors of the entire survey area simultaneously without the participation of staff members or tie-line control. We have applied the method to the airborne electromagnetic and magnetic data and apparent-conductivity data collected by the Ontario Geological Survey to confirm its validity and robustness by comparing the results with the published data.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Leveling airborne geophysical data using a unidirectional variational model ; volume:11 ; number:1 ; year:2022 ; pages:183-194 ; extent:12 ; Geoscientific instrumentation, methods and data systems ; 11, Heft 1 (2022), 183-194 (gesamt 12)

Classification and Topics

• Classification: Politik

Contributors, Places and Time

• Creator: Zhang, Qiong ; Sun, Changchang ; Yan, Fei ; Lv, Chao ; Liu, Yunqing

Further information

• DOI: 10.5194/gi-11-183-2022
• URN: urn:nbn:de:101:1-2022050505213503467676
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:30 AM CEST