Neural Network Based Interpretation Algorithm for Combined Induced Polarization and Vertical Electrical Soundings of Coastal Zones
Bibliographic record
Abstract
Abstract The problem of fresh water availability in coastal aquifers is a reality. For in-situ and dynamic characterization of seawater encroachment into coastal aquifers, electrical geophysical methods are better suited. Vertical Electrical sounding (VES) when combined with induced polarization soundings (IPS) can resolve saline sands from moist clays. Our feed forward back-propagation neural network (BPNN) based approach automates the analysis of combined vertical electrical and induced polarization soundings to suit practical needs. Our method is initially tested on synthetic data computed from available geo-electric sections and geological information concerning coastal aquifers of the East Coast of India. The synthetic data comprised 18 combined Schlumberger IPS and VES soundings (504 apparent resistivity and chargeability samples) spread over five profiles in the study region. Fictitious apparent resistivity (product of apparent resistivity and apparent chargeability) soundings are derived from them. We used 118 carefully selected discrete fictitious apparent resistivity values from 210 sample sets gathered from 15 (420 samples) combined soundings to train the BPNN, while 33 samples from 3 separate combined soundings, and 26 random samples from 92 unused training samples of 15 soundings were used for testing. Our trained BPNN involved one input node and one bias-unit at the input layer stage, one node in the output layer, and 18 nodes and one bias-unit in hidden layer. The trained neural net showed an overall success rate of 83% in testing phase for distinguishing clays from saline sands in the synthetic example. Our method is also tested on real data concerning a shaly groundwater aquifer in Bahia, Brazil yielding an overall accuracy of 85%, quite comparable to that of synthetic case. Thus, both synthetic and field data analysis validate our neural network based algorithm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".