Informing Flood Dyke Resiliency Strategies Through Electrical Resistivity Inversion: A Case Study from the Upper Bay of Fundy
Bibliographic record
Abstract
Flood defense structures are becoming increasingly vulnerable to failure from escalating threats of climate change. This challenge is evident in the network of agricultural earthen dykes along the Bay of Fundy coastline in Atlantic Canada, which safeguard economically critical infrastructure in the region. Addressing these vulnerabilities requires assessment methods to guide engineering interventions ranging from rehabilitation to reconstruction. Non-invasive geophysical techniques, such as electrical resistivity imaging (ERI), are gaining prominence for assessing flood embankments. ERI can detect subsurface electrical resistivity anomalies that are potentially indicative of internal zones of weakness.This study investigates the application of ERI in evaluating and guiding dyke rehabilitation strategies in the Upper Bay of Fundy. The objectives are to: 1) develop a rapid screening approach capable of imaging potential internal weak zones; 2) assess the effectiveness of ERI in identifying structural vulnerabilities; 3) examine the primary factors influencing resistivity variations, including grain size distribution and pore water salinity; and 4) evaluate the impact of tidal level fluctuations on ERI imaging. A series of geophysical field investigations were conducted at Shepody dykelands in southern New Brunswick, between 2022 and 2024. This included a shallow EM apparent conductivity mapping, 2D ERI and a time-lapse 3D ERI survey. The latter was carried out over a period of 3.5 hours during which time the megatidal Bay of Fundy rose about 3 m, advancing approximately 100 m over tidal mudflat and grassland before rising up against the side of the roughly 2.5 m high dykes. The increasing tide level was anticipated to influence resistivity measurements. The timelapse 3D ERI survey utilized a novel electrode array configuration to enhance sensitivity without severely compromising survey efficiency. Furthermore, complementary geotechnical data were collected in 2024 through Standard Penetration Tests (SPT) using a split spoon sampler. Laboratory analysis of the samples measured resistivity, grain size distribution and pore water conductivity.The 2D ERI inversion results reveal significant subsurface resistivity anomalies within the dyke, highlighting localized zones of elevated conductivity within the dyke. The correlation of various laboratory measurements indicates a stronger relationship between soil resistivity and pore water conductivity than grain size distribution. We conclude that the increased conductivity observed by 2D ERI is primarily caused by the presence of highly conductive saline water that has intruded into the dyke in areas of higher hydraulic conductivity during high tide. Such regions could be at risk from seepage-induced internal erosion, piping, or other anomalous geotechnical conditions. Time-lapse 3D ERI inversion results demonstrated the real-time effects of tidal variations on resistivity profiles, providing insights into measurement deviations caused by tidal influences. These findings underscore the effectiveness of ERI in assessing coastal flood embankments by identifying critical regions within flood dykes that should be prioritized for monitoring or further sampling to determine their potential impact on structural integrity. The results of this study demonstrate the capability of ERI to provide valuable insights that can enhance the resiliency strategies of flood defense structures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".