An inversion approach to generate electromagnetic conductivity images from signal data

The alluvial clay plains of the Murray–Darling Basin (MDB) have been extensively developed for irrigated agricultural production. Whilst irrigation has brought economic prosperity, there have been isolated environmental impacts. This is because the plains were formed by a system of ancient streams (i.e. prior stream and palaeochannels) that are characterised by coarse textured sediments and which are susceptible to deep drainage. To improve irrigation efficiency and natural resource management outcomes, information is required to characterise the connectivity of prior stream channels with underlying migrational channel deposits (i.e. palaeochannels). One option is the use of electromagnetic (EM) induction instruments which measure the apparent soil electrical conductivity (σa – mS/m). In this paper, we describe how σa collected using a next-generation DUALEM-421 and an EM34 can be used in conjunction with a joint-inversion algorithm (EM4Soil) to generate a 2d model of electrical conductivity (σ – mS/m) across an irrigated cotton growing field located on Quaternary alluvial clay plain in the lower Gwydir valley of NSW (Australia). The results compare favourably with existing pedological and stratigraphic knowledge. On the clay alluvial plain the accumulation of Aeolian and cyclical salt in the root zone and depth of clay alluvium are discerned by the DUALEM-421 and EM34, respectively. In addition, the approach is able to resolve the location of buried migrational channel deposits (i.e. palaeochannel) underlying the clay plain and the connectivity of these coarser sediments with a prior stream channel. Quantitatively the best correlation between estimated σ and measured soil properties, was found to be greatest when the DUALEM-421 and EM34 data were jointly inverted and when predicting EC1:5 (r2 = 0.61).

► We model DUALEM-421 and EM34 data using a 1-dimensional EM inversion software (EM4Soil).
► We examine estimates of electrical conductivity and correlation with various soil properties (e.g. salinity).
► We compare estimates of electrical conductivity using various instrument configurations.
► Increasing information increases resolution of prediction of true electrical conductivity.
► Results of inversion understood in terms of soil variability and regolith stratigraphy.