Estimating the parameters of simple models from two-component on-time airborne electromagnetic data

The horizontal and vertical components of the on-time electromagnetic (EM) response can be used to estimate the parameters of simple models like thin sheets, half-spaces, thin sheets over a lower half-space and a two-layer model. The formulae used in these methods are valid in areas where the on-time response is essentially proportional to the conductivity or conductance, the so called "resistive limit". The half-space and thin-sheet over a lower half-space models can be combined to give an estimate of the conductivity for a lower half-space below a thick sheet that might be reasonable for the whole of the survey area. With this estimation an equation solver can be used to estimate the thickness and conductivity of the overlying thick sheet over the whole survey area. This latter approach seemed most appropriate for the Russell South area in the Athabasca Basin, Canada, where GEOTEM data has been collected. The output of the algorithm was generally stable. Although it did not always reliably reproduce the overburden thicknesses as measured in a set of reference drill holes, it did give an estimate that was reasonable in the relatively conductive areas.

Imaging subsurface orebodies with airborne electromagnetic data using a recurrent neural network

Conventional interpretation of airborne electromagnetic data has been conducted by solving the inverse problem. However, with recent advances in machine learning (ML) techniques, a one-dimensional (1D) deep neural network inversion that predicts a 1D resistivity model using multi-frequency vertical magnetic fields and sensor height information at one location has been applied. Nevertheless, bacause the final interpretation of this 1D approach relies on connecting 1D resistivity models, 1D ML interpretation has low accuracy for the estimation of an isolated anomaly, as in conventional 1D inversion. Thus, we propose a two-dimensional (2D) interpretation technique that can overcome the limitations of 1D interpretation, and consider spatial continuity by using a recurrent neural network (RNN). We generated various 2D resistivity models, calculated the ratio of primary and induced secondary magnetic fields of vertical direction in ppm scale using vertical magnetic dipole source, and then trained the RNN using the resistivity models and the corresponding electromagnetic (EM) responses. To verify the validity of 2D RNN inversion, we applied the trained RNN to synthetic and field data. Through application of the field data, we demonstrated that the design of the training dataset is crucial to improve prediction performance in a 2D RNN inversion. In addition, we investigated changes in the RNN inversion results of field data dependent on the data preprocessing. We demonstrated that using two types of data, logarithmic transformed data and linear scale data, which having different patterns of input information can enhance the prediction performance of the EM inversion results.

Three-Dimensional Anisotropic Inversions for Time-Domain Airborne Electromagnetic Data

Rocks and ores in nature usually appear macro-anisotropic, especially in sedimentary areas with strong layering. This anisotropy will lead to false interpretation of electromagnetic (EM) data when inverted under the assumption of an isotropic earth. However, the time-domain (TD) airborne EM (AEM) inversion for an anisotropic model has not attracted much attention. To get reasonable inversion results from TD AEM data, we present in this paper the forward modeling and inversion methods based on a triaxial anisotropic model. We apply three-dimensional (3D) finite-difference on the secondary scattered electric field equation to calculate the frequency-domain (FD) EM responses, then we use the inverse Fourier transform and waveform convolution to obtain TD responses. For the regularized inversion, we calculate directly the sensitivities with respect to three diagonal conductivities and then use the Gauss–Newton (GN) optimization scheme to recover model parameters. To speed up the computation and to reduce the memory requirement, we adopt the moving footprint concept and separate the whole model into a series of small sub-models for the inversion. Finally, we compare our anisotropic inversion scheme with the isotropic one using both synthetic and field data. Numerical experiments show that the anisotropic inversion has inherent advantages over the isotropic ones, we can get more reasonable results for the anisotropic earth structures.

Geophysical Contributions to Gold Exploration in Western Mali According to Airborne Electromagnetic Data Interpretations

The Birimian of West African Craton (WAC) is known for its gold potential. Among Birimian structures, N-S and NE-SW trends have been defined as controlling gold mineralizations in Kedougou-Kenieba Inlier (KKI), whereas some of these structures are not gold-bearing. To determine structures related to gold mineralization, airborne electromagnetic data collected during the “Système Minier” of Mali have been processed and interpreted. Identified lineaments have been followed in the ground to establish their link with gold showings in the Kenieba area. Geophysical interpretations show trends similarity for different orders of conductivity. They allowed for characterizing resistance and conductive structures with prevalent N-S and NE-SW directionalities. Conductive structures are qualified as good conductors and are strongly related to known Artisanal Mining Sites (AMS). They coincide with tourmaline sandstones and quartz-albite veins, which are both often artisanally recognized as indicators of gold mineralization in Western Mali. Field observations show that resistance structures correspond to felsic rocks. These structures can bear gold only when silicified and they have spatial relations with Artisanal Mining Sites (AMS) within the Kenieba area. This study shows the efficiency of electromagnetic methods to characterize Birimian structures in relation to the gold mineralization in Kedougou-Kenieba Inlier (KKI).