Investigating the Integration of Neural Networks in Least-Squares Method for Airborne Electromagnetic Data Inversion

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

Airborne time-domain electromagnetic surveys produce large datasets that may contain thousands of line kilometers of data. The inversion of such large datasets becomes a computationally expensive process due to the repeated calculations of the forward model data and the partial derivatives for solving the least-squares inverse problem. To improve the computational efficiency of the inversion process, we use neural networks to compute the forward model data and the partial derivatives for a broad range of resistivity structures and flight altitudes for an airborne setup. Experiments show that the integration of neural network based forward data modelling and partial derivative calculations within the inversion framework opens the possibility of faster inversions with little to no loss in inversion precision.
Original languageEnglish
Publication dateSept 2022
DOIs
Publication statusPublished - Sept 2022
EventNSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics - Beograd, Serbia
Duration: 18 Sept 202222 Sept 2022
Conference number: 3

Conference

ConferenceNSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics
Number3
Country/TerritorySerbia
CityBeograd
Period18/09/202222/09/2022

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