Automated vertical Self Potential gradient logging and analysis for the tracking of Saline Intrusion
- 1Department of Civil and Environmental Engineering, Imperial College London, London, UK (t.rowan@imperial.ac.uk)
- 2School of Natural and Built Environment, Queen's University Belfast, Belfast, UK
- 3Department of Earth Science and Engineering , Imperial College London, London, UK
- 4School of Civil Engineering, University College Dublin, Dublin, Ireland
Climate change associated sea level increases and projected growth in global water consumption of about 1% per year (WWAP, 2018) are expected to place further demands on already heavily utilized coastal groundwater supplies. Water stress is anticipated to become more critical over the next decades (Werner and Simmons, 2009). Society’s over-reliance on coastal freshwater abstraction had led to an increased threat of Saline Intrusion (SI). In spite of these challenges, no widely applicable methods of tracking saline fronts in the subsurface exist, even though this capability could prove critical to stopping over abstraction (pumping) before SI occurs; observational boreholes offer a limited warning, and resistivity imaging is often too expensive and logistically infeasible, (MacAllister et al. 2016). An alternative approach to detecting imminent SI is needed. The ongoing goal of this work is to develop a robust and low-cost method of tracking SI in the sub-surfaces.
Naturally occurring voltages, known as Self Potential (SP), occur when pressure and concentration gradients in the subsurface cause ion separations (Jackson et al., 2012). SP can be used to track SI, so long as the signal source mechanism is understood. There are two key sources of SP widely encountered in hydrology, those induced by pressure, electro-kinetic potentials (VEK), and exclusion-diffusion potentials (VED), due to ion concentration gradients moving through the subsurface.
SP signals are generated relative to static reference electrodes, offering a signal reading per electrode. However, these signals drift over time making interpretation and comparison challenging. We present findings and insights of an investigation using travelling SP electrodes, moving vertically inside boreholes or wells, to generate SP profiles. Results offer new insights into relationships between SP and SI when logged over time. Profiles taken over the last year at a variety of coastal and inland sites in the UK build upon results from a controlled pumping experiment in Northern Ireland, completed in 2020 and which attempted to interpret these patterns and signals through machine learning. Filtering out background noise sources, (such as electrical interferance, tides, Magneto Telluric effects etc.) has allowed signatures to be more confidently generated and related SI under contrasting hydrogeological regimes. This novel methodology and initial findings are presented and the scope for widely application of the method discussed.
References
Jackson, M. D., et al. (2012). Measurements of spontaneous potential in chalk with application to aquifer characterisation in the southern UK quarterly. J. Eng. Geol. Hydrogeol.
MacAllister, et al. (2016), Tidal influence on self-potential measurements, Journal Geophysical Research Solid Earth.
Werner, A.D., Simmons, C.T., (2009), Impact of sea‐level rise on seawater intrusion in coastal aquifers. Ground Water.
WWAP, (2018), The United Nations World Water Development Report 2018: Nature-Based Solutions for Water. Paris, UNESCO.
How to cite: Rowan, T., Flynn, R., Butler, A., Jackson, M., Hamill, G., and Donohue, S.: Automated vertical Self Potential gradient logging and analysis for the tracking of Saline Intrusion, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15408, https://doi.org/10.5194/egusphere-egu23-15408, 2023.
