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Record W2071360496 · doi:10.4138/4956

A Study of the SP Geophysical Technique in a Campus Setting

2007· article· en· W2071360496 on OpenAlexaffvenue
A. M. Leitch, Charles Boone

Bibliographic record

VenueAtlantic Geology · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAnomaly (physics)GeologySign (mathematics)GeophysicsMagnitude (astronomy)SeismologyPhysics

Abstract

fetched live from OpenAlex

The self potential (SP) method is a simple and inexpensive geophysical surveying technique, which involves measuring electrical potentials on the surface due to charge separations in the subsurface. These charge separations can arise from a number of different physical and electrochemical processes, and hence interpretations tend to be qualitative. Small anomalies are typically neglected as transient, inexplicable, or uninteresting. In contrast, large negative anomalies associated with ore bodies are noted for their constancy. In this study, an area on the campus of Memorial University was repeatedly surveyed in order to determine which natural and anthropogenic features generated SP anomalies, and whether these anomalies were constant or transient. We found anomalies associated with a building, a buried metallic pipe, trees, and subtler ground variations. The locations of anomalies, both large and small, were notably unvarying over a period of days and weeks. The building always generated a significant negative anomaly, but the sign of other anomalies (including that associated with the pipe) and the magnitude of all anomalies varied with time. In a second area dominated by a shallow sewer pipe, SP data allowed modeling of the burial depth and charge distribution of the pipe. Our results show that SP can be a useful and reliable method for shallow ground surveys, but that the time-varying nature of both sign and magnitude of small to moderate anomalies should be taken into account in data collection and interpretation.
 
 Résumé
 
 La méthode des potentiels spontanés est une technique d’exécution de levés géophysiques simple et peu dispendieuse consistant à mesurer les potentiels électriques à la surface liés aux séparations des charges dans le sous-sol. Ces séparations des charges peuvent découler d’un certain nombre de processus physiques et électrochimiques différents, de sorte que les interprétations données ont tendance à être qualitatives. On néglige généralement les petites anomalies, les considérant comme des anomalies transitoires, inexplicables ou peu intéressantes. En revanche, on note les anomalies négatives poussées qui sont associées aux corps minéralisés en raison de leur constance. Dans le cadre de cette étude, on a réalisé des levés répétés dans un secteur du campus de l’Université Memorial pour déterminer quelles particularités naturelles et artificielles produisaient des anomalies des PS et si ces anomalies étaient constantes ou transitoires. Nous avons découvert des anomalies associées à un bâtiment, à un tuyau en métal enfoui, à des arbres et à des irrégularités du terrain plus subtiles. Fait remarquable, les emplacements des anomalies, tant prononcées que minimes, n’ont pas changé au cours d’une période de plusieurs jours et semaines. Le bâtiment a toujours produit une anomalie négative marquée, mais le signal d’autres anomalies (notamment celle associée au tuyau) et la magnitude de toutes les anomalies ont varié au fil du temps. Dans un second secteur où était surtout présent un tuyau d’égout peu profond, les données des PS ont permis la modélisation de la profondeur d’enfouissement et de la distribution des charges du tuyau. Nos résultats révèlent que la polarisation spontanée peut s’avérer une méthode utile et fiable pour les levés terrestres peu profonds, mais qu’il faudrait tenir compte de la nature variable des signaux et de la magnitude des anomalies minimes à moyennes lors de la collecte et de l’interprétation des données.
 
 [Traduit par la redaction]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.248
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2007
Admission routes2
Has abstractyes

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