Objective of the BMBF funded compound project DESMEX (Deep Electromagnetic Sounding for Mineral Exploration) is the development of a semi-airborne, i.e. ground transmitters and a combined ground and airborne receivers, electromagnetic exploration method that is able to image ore deposits down to 1 km depth. The subproject IV deals with modelling and inversion algorithms.
In the first phase until 2019, the main focus of the project (DESMEX 1) was on the successful application the new method and the demonstration at two locations – an Antimone deposit near Schleiz, Germany, and the Iron ore deposit in Kiruna, Sweden. In the second phase of the project we demonstrate the effectiveness of the method to a number of deposits in Germany, Schweden and Namibia, using also new and optimized sensor types such as optically-pumped magnetometers (OPM) and next generation SQUID systems. In addition to the primary helicopter-towed semi-airborne CSEM system, we conduct so-called AFMAG measurements based on natural-source currents on larger scales and apply UAV-towed receiver systems as a cost-effective alternative on smaller scales (Figure 1). The LIAG participates in the field work with pre-investigation, ERT ground surveys, and contributions to the semi-airborne experiments in form of transmitter - and ground-receiver support.
We developed a 3D CSEM inversion with irregular meshes on arbitrary geometries (Rochlitz et al. 2023). The inversion is applied by the project partners to the data from the investigation areas. In the follow-up project DESMEX-real we apply the technology to the exploration of the real laboratory Oberharz in the district scale. We optimize survey design (Nazari et al. 2023) and inversion strategies for large-scale imaging.
The main task of LIAG in the first DESMEX project was to development a 3D frequency-domain finite-element modelling code for arbitrary CSEM setups – the Python toolbox custEM (https://custem.readthedocs.io/). In the follow-up project DESMEX II, the LIAG is responsible for maintaining and advancing the custEM software by adding additional functionalities for transient electromagnetic (TEM) (Figure 2), induced-polarization (Figure 3) EM and natural-source (magnetotelluric , MT) (Figure 4) data. Another goal is to optimize, in particular, the freqeuency-domain forward modeling algorithms, in terms of accuracy, computational performance, and robustness of custEM to develop inverse modeling workflows.
The main responsibility of the LIAG in DESMEX II is the implementation of flexible, multidimensional inversion routines for semi-airborne CSEM data. We aim to test different inversion techniques suitable to build upon the forward modeling solver custEM and the inversion framework pyGIMLi which are both developed at the LIAG. The goal is to include the real topographic information of our survey areas and invert magnetic field data, measured with the helicopter- or UAV-towed receivers, together with magnetic and electric field data from surface stations.
A suited possibility based on forward solutions with a direct solver is to re-use the already computed factorization of the system matrix to calculate the sensitivity matrix explicitly, which requires affordable computational effort due to the exploitation of comparatively cheap back-substitutions for this task. It is convenient to define the inversion domains as crowds of tetrahedral, e.g., by building a tetrahedral mesh by subdividing larger blocks or prisms, corresponding to the inversion domains, into multiple tetrahedra for an accurate forward solution. As an example, we used about 750 prisms with +-500 m elongation in y-direction for discretizing a mesh to invert synthetic data of a shallow 2D dipping plate model (Figure 5).
In a similar manner, this procedure can be extended to build meshes for 3D inversions with thousands of domains. This is work in progress before applying our developed tools on real survey data. We expect computation times of about than 1 day for simpler 3D inversion setups with our approach based on explicit Jacobian calculations. For more complex models, we are going to consider alternative or approximative techniques for obtaining the sensitivities to keep the computational efforts manageable.
Federal Ministry of Education and Research (BMBF) in the frame of the program Fona-r4