Electrical resistivity ground imaging (ERGI): a new tool for mapping the lithology and geometry of channel‐belts and valley‐fills
Bibliographic record
Abstract
Abstract Efforts to map the lithology and geometry of sand and gravel channel‐belts and valley‐fills are limited by an inability to easily obtain information about the shallow subsurface. Until recently, boreholes were the only method available to obtain this information; however, borehole programmes are costly, time consuming and always leave in doubt the stratigraphic connection between and beyond the boreholes. Although standard shallow geophysical techniques such as ground‐penetrating radar (GPR) and shallow seismic can rapidly obtain subsurface data with high horizontal resolution, they only function well under select conditions. Electrical resistivity ground imaging (ERGI) is a recently developed shallow geophysical technique that rapidly produces high‐resolution profiles of the shallow subsurface under most field conditions. ERGI uses measurements of the ground's resistance to an electrical current to develop a two‐dimensional model of the shallow subsurface (<200 m) called an ERGI profile. ERGI measurements work equally well in resistive sediments (‘clean’ sand and gravel) and in conductive sediments (silt and clay). This paper tests the effectiveness of ERGI in mapping the lithology and geometry of buried fluvial deposits. ERGI surveys are presented from two channel‐fills and two valley‐fills. ERGI profiles are compared with lithostratigraphic profiles from borehole logs, sediment cores, wireline logs or GPR. Depth, width and lithology of sand and gravel channel‐fills and adjacent sediments can be accurately detected and delineated from the ERGI profiles, even when buried beneath 1–20 m of silt/clay.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".