Tidal forcing influences groundwater flow and salt distribution in shallow coastal aquifers, with the interaction between sea level variations and geology proving fundamental for assessing the risk of seawater intrusion (SI). Constraining the relative importance of each is often confounded by the influences of groundwater abstraction and geological heterogeneity, with understanding of the latter often restricted by sampling point availability and poor spatial resolution. This paper describes the application of geophysical and geotechnical methods to better characterize groundwater salinity patterns in a tidally dominated ~ 20 m thick sequence of beach sand, unaffected by groundwater abstraction. Electrical resistivity tomography (ERT) revealed the deposit to consist of an upper wedge of low resistivity (< 3 Ωm), reaching over 8 m thick in the vicinity of the low water mark, overlying a higher resistivity unit. Cone penetrometer testing (CPT), and associated high-resolution hydraulic profiling tool system (HPT), coupled with water quality sampling, revealed the wedge to reflect an intertidal recirculation cell (IRC), which restricts freshwater discharge from a relatively homogeneous sand unit to a zone of seepage within the IRC. The application of CPT and HPT techniques underscored the value of geotechnical methods in distinguishing between geological and water quality contributions to geophysical responses. Survey results have permitted a clear characterization of the groundwater flow regime in a coastal aquifer with an IRC, highlighting the benefit of combining geophysical and geotechnical methods to better characterize shallow SI mechanisms and groundwater flow in coastal hydrogeological environments.

Deriving saltwater concentrations from the light intensity values of dyed saline solutions is a long-established image processing practice in laboratory scale investigations of saline intrusion. The current paper presents a novel methodology that employs the predictive ability of machine learning algorithms in order to determine saltwater concentration fields. The proposed approach consists of three distinct parts, image pre-processing, porous medium classification (glass bead structure recognition) and saltwater field generation (regression). It minimizes the need for aquifer-specific calibrations, significantly shortening the experimental procedure by up to 50% of the time required. A series of typical saline intrusion experiments were conducted in homogeneous and heterogeneous aquifers, consisting of glass beads of varying sizes, to recreate the necessary laboratory data. An innovative method of distinguishing and filtering out the common experimental error introduced by both backlighting and the optical irregularities of the glass bead medium was formulated. This enabled the acquisition of quality predictions by classical, easy-to-use machine learning techniques, such as feedforward Artificial Neural Networks, using a limited amount of training data, proving the applicability of the procedure. The new process was benchmarked against a traditional regression algorithm. A series of variables were utilized to quantify the variance between the results generated by the two procedures. No compromise was found to the quality of the derived concentration fields and it was established that the proposed image processing technique is robust when applied to homogeneous and heterogeneous domains alike, outperforming the classical approach in all test cases. Moreover, the method minimized the impact of experimental errors introduced by small movements of the camera and the presence air bubbles trapped in the porous medium.