Non-conventional arrays for self-potential surveys
Bibliographic record
Abstract
SUMMARY The exponential growth of electrical resistivity tomography (ERT) methods for exploring the subsurface at large depths widened the applicability of the self-potential (SP) method, a passive geoelectrical technique suitable for a variety of purposes like mapping ore bodies or inferring fluid flow in the subsurface. Several new-generation resistivity meters have been designed to continuously log the electric potentials thus allowing for the identification of weak amplitude signals and resulting in deeper inversion models. In such approaches, long SP time-series are collected but are totally ignored as only marginal intervals are retained and analysed in the ERT procedure. The discarded SP records could be valuable although not collected using the traditional methodology, based on a reference electrode. We present an SP forward modelling feasibility study of different array techniques, based on numerical finite-element methods. The SP has been modelled in a variety of electrical settings to assess the imaging potentials of non-conventional (i.e. sparse gradient and full sparse gradient) arrays in comparison to traditional (i.e. fixed-base and the leapfrog) arrays. The analytic signal amplitude (ASA) algorithm was employed to compare numerical modelling results obtained from the different type of arrays, highlighting the great potentials of non-conventional arrays for the recognition of several sources of SP anomalies. The ASA maps, presenting a single peak centred over the targets, can significantly help in identifying the source anomalies for all the analysed array techniques. The cost-effectiveness along with the imaging capability of these non-conventional arrays constitute important benefits that could be exploited resulting in a systematic inclusion of SP analysis when collecting deep ERT data using distributed systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".