An overview of a highly versatile forward and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric data

Abstract

We present an overview of a mature, robust and general algorithm providing a single framework for the inversion of most electromagnetic and electrical data types and instrument geometries. The implementation mainly uses a 1D earth formulation for electromagnetics and magnetic resonance sounding (MRS) responses, while the geoelectric responses are both 1D and 2D and the sheet's response models a 3D conductive sheet in a conductive host with an overburden of varying thickness and resistivity. In all cases, the focus is placed on delivering full system forward modelling across all supported types of data. Our implementation is modular, meaning that the bulk of the algorithm is independent of data type, making it easy to add support for new types. Having implemented forward response routines and file I/O for a given data type provides access to a robust and general inversion engine. This engine includes support for mixed data types, arbitrary model parameter constraints, integration of prior information and calculation of both model parameter sensitivity analysis and depth of investigation. We present a review of our implementation and methodology and show four different examples illustrating the versatility of the algorithm. The first example is a laterally constrained joint inversion (LCI) of surface time domain induced polarisation (TDIP) data and borehole TDIP data. The second example shows a spatially constrained inversion (SCI) of airborne transient electromagnetic (AEM) data. The third example is an inversion and sensitivity analysis of MRS data, where the electrical structure is constrained with AEM data. The fourth example is an inversion of AEM data, where the model is described by a 3D sheet in a layered conductive host

Quantifying the effect of primary field modelling on TEMPEST data - The importance of uncertainty

“…The results have all been produced with the Aarhus Workbench (Auken et al, 2009) which uses the AarhusInv algorithm as the inversion and forward modelling work horse (Auken et al, 2015), and compared with results from the GA-LEI algorithm which has been widely used for the inversion of TEMPEST data ( Roach, 2012). We invert for a 30 layer model and include the system geometry in the inversion as well.…”

“…We invert for a 30 layer model and include the system geometry in the inversion as well. Lateral constraints are applied between models on both conductivities and system geometries , Auken et al, 2015.…”

Quasi-3D inversion of full size AEM datasets

“…The presented algorithm is a further development of the AarhusInv program package (Auken et al, 2014), which manages both large scale AEM and ground-based surveys. In particular, AarhusInv supports airborne TEM data and FEM data (Siemon et al, 2009) and several ground based methods, i.e.…”

Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

“…The model responses were simulated using AarhusInv neglecting lateral heterogeneity. In other words, the inverse model is 1-D, following the state of practice (Viezzoli et al, 2010a;Auken et al, 2014).…”

“…In other words, the sounding loses resolution with depth because of its increasing footprint. In the following, we use the AarhusInv 1-D simulation code (previously called em1Dinv; Auken et al, 2014) to simulate the geophysical responses. To mimic the loss of resolution with layer depth, we use the logarithmic average resistivity of all model cells inside the radius of the footprint at a given depth.…”