Modeling the electrical conductivity of hydrogeological strata using joint-inversion of loop-loop electromagnetic data

Abstract

In coastal-estuarine agricultural landscapes that are inherently rich in sulfidic sediments and saline water-tables, natural resource management data need to be collected to describe the heterogeneous nature of the soil, underlying regolith, and interactions with groundwater. Geophysical methods, such as electromagnetic (EM) induction instruments, are increasingly being used. This is because they measure apparent soil electrical conductivity [Formula: see text], which has previously been successfully used to map the areal distribution of soil (e.g., salinity) and hydrological (e.g., water-table depth) properties. We explored the potential of a next-generation DUALEM-421 and EM34 to be used independently and in conjunction with each other to provide information we can use to represent the pedological and hydrogeological setting of alluvial and estuarine sediments. A 1D laterally constrained joint-inversion algorithm can account for the nonlinearity of large [Formula: see text] (i.e., [Formula: see text]). We applied this algorithm to develop 2D cross sections of electrical conductivity ([Formula: see text]) from DUALEM-421 and EM34 [Formula: see text] data acquired across an estuarine landscape and situated within Quaternary fluvial sediments adjacent to Rocky Mouth Creek on the far north coast of New South Wales, Australia. We compared this joint-inversion model with inversions of the DUALEM-421 and EM34 [Formula: see text] data independently of each other. For the most part, the general patterns of the inverted models of [Formula: see text] compare favorably with existing pedological and hydrogeological interpretations, based on results achieved during a previous geoelectrical survey. However, the joint-inversion provides a more realistic model of the location and extent of a saline water-table and associated with the location of sulfidic sediments.

Pedometrics Research in the Vadose Zone—Review and Perspectives

“…The main reasons are that the instruments do not require physical contact with the soil and there are an ever‐increasing number of commercially available instruments that provide information about the soil (i.e., EM38 and DUALEM‐1), vadose zone (e.g., EM31 and DUALEM‐2), and deeper regolith (e.g., EM34). Electromagnetic induction instruments are also easy to set‐up, use and mobilize (e.g., Triantafilis et al, 2012). Electromagnetic induction instruments measure the apparent soil electrical conductivity (ECa), which has been shown to be influenced by various soil (e.g., clay and mineralogy) and hydrological (e.g., moisture) properties which are of high agricultural value.…”

“…To understand soil and hydrogeological interactions, Triantafilis et al (2012) jointly‐inverted EMI data collected across an estuarine or alluvial landscape using both the DUALEM‐421 and EM34 instruments. Figure 1 shows that the joint‐inversion of ECa data obtained from both of these instruments produces reasonably robust estimates of the true electrical conductivity.…”

Improved Geoarchaeological Mapping with Electromagnetic Induction Instruments from Dedicated Processing and Inversion

“…Given that any electromagnetic (EM) method in the low frequency range [16] is diffusive and integrates a large volume based on the footprint of the system, this approach will inevitably smoothen out real soil structures. A few authors have suggested to perform a full inversion of the EMI data without assuming the LIN approximation [29][30][31], but it is our impression that this is not common practice. For airborne instruments of a similar type, the industry standard has been to perform a full processing and inversion of the data [32,33].…”

Modeling Coastal Salinity in Quasi 2D and 3D Using a DUALEM‐421 and Inversion Software

“…The number of iterations are specified or continue until the misfit between observed apparent conductivity ( ) and calculated apparent conductivity ( ) is reduced to an acceptable level. Incorporating a Lagrangian multiplier ( λ ), also called a damping factor, achieves this in balancing data fit and closeness to the a priori initial earth model (Triantafilis et al ).…”

“…The number of iterations are specified or continue until the misfit between observed apparent conductivity (σ o a ) and calculated apparent conductivity (σ c a ) is reduced to an acceptable level. Incorporating a Lagrangian multiplier (λ), also called a damping factor, achieves this in balancing data fit and closeness to the a priori initial earth model (Triantafilis et al 2012). Evaluation of the optimum λ value is able to be performed empirically through comparison of models of contrasting λ with expected responses from the literature and sample validation procedures (Monteiro .…”