Quantitative Analysis and Interpretation of Transient Electromagnetic Data via Principal Component Analysis
Bibliographic record
Abstract
Transient electromagnetic (TEM) data in applied geophysics are invariably contaminated by random and coherent noise associated with acquisition, geology, and the environment. Advanced unexploded ordnance (UXO) detection and discrimination using TEM data require the suppression of the random noise and reliable separation of UXO signal from other coherent signals. The random noise is usually easier to remove, with coherent signal being more difficult to identify as well as separate. We have developed a method based on principal component analysis (PCA) to achieve both objectives. As a data-adaptive linear transformation, PCA is a fast and reliable method for the removal of random uncorrelated noise as well as for the separation of coherent undesired signals from those due to UXO and UXO-like anomalies. In this paper, we outline the PCA method, including the choice of data organization, construction of covariance matrices, and choice of principal components in reconstruction. We then show both synthetic and field data as examples of the efficacy of the method.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".