ENHANCED ELECTRODE SEQUENCES FOR 2D ERI: FORWARD MODELING AND FIELD RESULTS
Bibliographic record
Abstract
Abstract We have designed and evaluated enhanced electrode sequences for efficient collection of multiple array type data using the multi-channel advantages of modern electrical resistivity systems. Electrode sequences for 2D-ERI data collection attempt to balance tradeoffs between depth of investigation, resolution, quality, and time for data collection to adequately image subsurface resistivity variations. Often data collection time is controlled by site limitations or budgets and only Wenner or Wenner-Schlumberger data are collected. With the goals of efficient data acquisition and combining the advantages of different array types for 2D inversion, we have designed enhanced data sequences which provide combined dipole-dipole and Wenner-Schlumberger data in the same data collection time as for a Wenner-Schlumberger survey. Using a synthetic geological resistivity model from a previous ERI survey, we ran forward models using a variety of combined arrays and developed an optimized sequence. We used a 10-Channel IRIS120 with 88 electrodes to collect the enhanced data set over the same geology as used for the forward modeling. The initial investigation at the site used a Wenner-Schlumberger array that recorded 1773 measurements with the objective of imaging to a depth of approximately 40 meters. The survey provided reasonable resolution and was able to adequately characterize the bedrock but did not provide the more complex variations that were anticipated at the site and seen in limited drilling results. The enhanced data set collected over 4998 data points in the same 3 hour time period. For evaluation, we inverted the data set in three forms, using: only dipole-dipole data, only Wenner-Schlumberger data, and the combined data set. We used DCIP2D software provided by the University of British Columbia and RES2DINV software from Geotomo. The inversions demonstrate that the full (combined) data set provides the advantages of each array type, resulting in sharper structural boundaries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".