To manage groundwater effectively, it is important to understand subsurface water systems. Geophysical methods can characterize subsurface layers, but relying on just one method can be misleading. This study combines two methods – transient electromagnetics and surface nuclear magnetic resonance – in a K-means clustering scheme to better resolve freshwater and saltwater zones. Two case studies showed how a combined approach improves characterization of these hydrogeologically important layers.

This work presents simulations, modelling, and experimental verification of a novel steady-state surface nuclear magnetic resonance (NMR) transmitter used for the non-invasive exploration of groundwater. The paper focuses on three main aspects of high-current transmitter instrumentation: thermal management, current-drooping, and pulse stability. This work will interest readers involved in geoscientific instrument prototyping for groundwater exploration using portable geoscientific instruments.

Data recorded with transient electromagnetics are typically gated to improve the signal-to-noise ratio. Gating corresponds to low-pass filtering of data with the shape of the gate giving the frequency response. We show that standard gate shapes can lead to significant correlation between gates caused by distortion from VLF radios. A multi-objective cost function is used to select optimum gate shapes with less influence from radio noise. Performance is demonstrated using synthetic and field data.

In this paper, we use a novel surface nuclear magnetic resonance (SNMR) method for rapid high-quality data acquisition. The SNMR results from more than 100 soundings in three different case studies were used to map groundwater. The soundings successfully track the water table through the three areas and are compared to boreholes and other geophysical measurements. The study highlights the use of SNMR in hydrological surveys and as a tool for regional mapping of the water table.

To apply a deep learning (DL) algorithm to electromagnetic (EM) methods, subsurface resistivity models and/or the corresponding EM responses are often required. To date, there are no standardized EM datasets, which hinders the progress and evolution of DL methods due to data inconsistency. Therefore, we present a large-scale physics-driven model database of geologically plausible and EM-resolvable subsurface models to incorporate consistency and reliability into DL applications for EM methods.