MétaCan
Menu
Back to cohort
Record W4366506664 · doi:10.11159/icgre23.113

Application of Full-Waveform Acoustic Borehole Logging to Detect and Characterize Rock Mass Fracture

2023· article· en· W4366506664 on OpenAlex
Tartoussi Nourhan, Lataste Jean-François, Rivard Patrice, Barbosa Nicolás D

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsUniversité de Sherbrooke
FundersUniversité de BordeauxMitacs
KeywordsBoreholeGeologyWaveformRock mass classificationLoggingSonic loggingWell loggingFracture (geology)SeismologyAcousticsGeotechnical engineeringPetroleum engineeringComputer scienceTelecommunicationsPhysicsRadar

Abstract

fetched live from OpenAlex

The characterization of discontinuities in the rock mass is very important to evaluate the global geomechanical properties of the mass, as it aims at solving problems in rock mechanics, such as the design of civil engineering structures, preventing landslides, etc.The presence of fracture affects the propagation of compression, shear, and surface waves that are recorded with acoustic borehole logging.To understand how filled fractures affect acoustic waves in a borehole environment, numerical simulations of full-waveform sonic response was performed using COMSOL Multiphysics software.This paper deals with two factors that can be used to quantify the transmission losses generated by fracture presence: velocity variation and amplitude attenuation (in the frequency domain) of P and S waves.In addition, the impact of multiple parameters of the discontinuity on transmission losses factors, such as fracture width, length, compression velocity, shear velocity, and the density of the fracture filling materials, were examined.S waves were found to be more sensitive to fracture characteristics.The influence of fracture length on compression and shear wavelength responses, as well as their relationship with P and S wave wavelengths, were underlined.We show that the velocity variation is more indicative of fracture width than the amplitude attenuation.These characteristics allow using shear and compression waves to characterize fractures.

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.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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