<title>Application of self-correcting tomographic inversion to a borehole radar test survey</title>
Bibliographic record
Abstract
Variations of transmitter power and instrumental time drift often observed during borehole radar surveys are not usually monitored by the commercially-available acquisition systems. These variations may create artifacts in tomograms and lead to erroneous interpretation if not taken into account. The Self- Correcting Tomographic Inversion (SCTI) is a technique that jointly recovers these source variations together with the velocity or attenuation distribution. It assumes that the transmitting time To and the 'source strength' Ao may be considered constant only at each transmitter position. The problem results in a linear system of equations where the usual Jacobian matrix should be augmented by sparse columns with non-null elements corresponding to the respective transmitter positions only; thus the parameter vector (slowness or attenuation coefficient distribution) can be appended with the to (or log Ao) values for these transmitter positions. Synthetic and survey data examples demonstrate that the conventional inversion algorithm produces artifacts mainly located along the transmitter and the receiver boreholes and towards the corners of the tomogram. The magnitude of the artifacts depends on the distance between transmitter and receiver boreholes. The SCTI technique reduces the amplitude of these artifacts while recovering the transmitter drift. Two crosshole surveys with inter-changed transmitter-receiver positions were also performed to evaluate reciprocity. The resulted tomograms are slightly different, but the overall images seem to be improved. However, the SCTI seem to diminish the discrepancy between the reciprocal values. Meanwhile, the SCTI also recovers a very suitable variation for the transmitter parameters. We have also attempted to monitor the drift of these transmitter parameters by control measurements at the ground surface at different times during the survey with different antenna separations. It shows that the variation of to and Ao is of the same order as resulted from the SCTI method.
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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 | 0.001 |
| 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) | 0.013 | 0.003 |
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".