Inversion of Amplitude from the 2-D Analytic Signal of Self-Potential Anomalies
Bibliographic record
Abstract
In the present study, analytic signal amplitude (ASA) or total gradient (TG) inversion of self-potential anomalies has been carried out using very fast simulated annealing (VFSA) global optimization technique. The results of VFSA optimization demonstrate the application and efficacy of the proposed method for idealized synthetic hypothetical models and real single and multiple geological structures. The model parameters deciphered here are the amplitude coefficient (k), horizontal location (x0), depth of the body (z), and shape (q). Inversion of the model parameter suggests that constraining the horizontal location and the shape factor offers the most reliable results. Investigation of convergence rate, histogram, and cross-plot examination suggest that the interpretation method developed for the self-potential anomalies is stable and the model parameters are within the estimated ambiguity. Inversion of synthetic noise-free and noise-corrupted data for single structures and multiple structures in addition to real field information exhibits the viability of the method. The model parameters estimated by the present technique were in good agreement with the real parameters. The method has been used to invert two field examples (Sulleymonkoy anomaly, Ergani, Turkey, Senneterre area of Quebec, Canada) with application of subsurface mineralized bodies. This technique can be very much helpful for mineral or ore bodies investigation of idealized geobodies buried within the shallow and deeper subsurface.
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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 | 0.000 |
| 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) | 0.005 | 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".