Rapid inversion of 2-D geoelectrical data by multichannel deconvolution
Bibliographic record
Abstract
Abstract Modern geoelectrical data acquisition systems can record more than 100 000 data values per field day. Despite the growth in computer power and the development of more efficient numerical algorithms, interpreting such data volumes remains a nontrivial computational task. We present a 2-D one-pass inversion procedure formulated as a multichannel deconvolution. It is based on the equation for the electrical potential linearized under the Born approximation, and it makes use of the 2-D form of the Fréchet derivatives evaluated for the homogeneous half-space. The inversion is formulated in the wavenumber domain so that the 2-D spatial problem decouples into many small 1-D problems. The resulting multichannel deconvolution algorithm is very fast and memory efficient. The inversion scheme is stabilized through covariance matrices representing the stochastic properties of the earth resistivity and data errors. The earth resistivity distribution is assumed to have the statistical characteristics of a two-parameter, self-affine fractal. The local apparent amplitude and fractal dimension of the earth resistivity are estimated directly from geoelectrical observations. A nonlinearity error covariance matrix is added to the conventional measurement error covariance matrix. The stochastic model for the dependence of nonlinearity error on electrode configuration as well as resistivity amplitude and fractal dimension is determined pragmatically through nonlinear simulation experiments. Tests on synthetic examples and field cases including well control support the conclusion that for long data profiles this method automatically produces linearized resistivity estimates which faithfully resolve the main model features.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".