Application of Full-Waveform Acoustic Borehole Logging to Detect and Characterize Rock Mass Fracture
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".