2D Joint Inversion Algorithms for Semi-Airborne and LOTEM Data: A Data Application from Eastern Thuringia, Germany

Various electromagnetic (EM) measuring techniques were developed to fulfil the requirements in diverse earth or resources explorations, such as the long-offset transient electromagnetic (LOTEM) and the semi-airborne EM methods. The novel semi-airborne frequency-domain electromagnetic system takes advantages of both ground and airborne techniques by combining ground-based high power sources with large scale and spatially dense covered data. However, its signal-to-noise ratio is still smaller in comparison with the ground-based method like LOTEM due to the limited stacking time. From the perspective of inversion, the data of different EM methods have distinct resolutions towards the subsurface resistivity structures and therefore they can provide complementary earth information. However, these distinct resolutions could also lead to different inversion results if each dataset is inverted individually, which may introduce confusions to the following interpretations. To reduce the ambiguities and parameter uncertainties, joint inversion algorithms are developed to couple spatially dense sampled semi-airborne data and horizontal electric fields (Ex) measured using LOTEM. Nevertheless, the 1D joint inversion faces convergence problems due to 2D effects in the field data. The synthetic modelling suggests that the 2D effects in different datasets lead to distinct artificial structures in the 1D inversion, which makes the 1D joint inversion unfeasible. Therefore, a 2D joint inversion algorithm was further developed for the frequency-domain semi-airborne EM data and the LOTEM transient electric fields. With its application, the newly developed 2D joint inversion of the semi-airborne and LOTEM Ex field data acquired in eastern Thuringia,Germany, converged successfully and a 2D conductivity model could be derived for the survey area. In the consequent 2D synthetic modelling studies, it is demonstrated that part of the discrepancies between the individual inversion result of each field dataset can be explained by the resolution differences leaded by the different observed quantities and by the measurement configurations, and the 2D joint inversion result of field data is validated to be one effective equivalent model.