Abstract
The interpretation of semi-airborne transient electromagnetic data is mainly based on a one-dimensional inversion algorithm. The continuity of the resistivity profile is generally poor when the data noise is strong because the resistivity is laterally unconstrained, and the constraint between the resistivity in the vertical distribution is usually taken as the L2 norm regular term. However, the main disadvantage of this regularization constraint is that it over-smoothens the model and cannot effectively describe the interface information of an abrupt electrical change. To address these issues, this paper proposes a mixed norm spatially constrained inversion (SCI) algorithm that adopts the L1 norm regularization term for the vertical spatial constraint and the L2 norm regularization term for the lateral spatial constraint. The Gauss-Newton iterative method is used to solve the objective function. Fixed and adaptive strategies are proposed for the regularization factors, and a parameter is used to adjust the weights of the two constraints. The influence of different constraint weights on the results is analyzed. The inversion of noisy synthetic data verifies that the proposed SCI algorithm has a higher resolution for electrical interfaces and better noise suppression effect than the L2 norm SCI algorithm. The inversion of field data shows a good effect and practicability.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
