Land subsidence induced by groundwater pumping, monitored by D-InSAR and field data in the Toluca Valley, Mexico
Bibliographic record
Abstract
Excessive groundwater pumping from compressible aquifers leads to land subsidence, potentially causing significant damage to buildings and infrastructure. Differential interferometry is applied to synthetic aperture radar (SAR) images (D-InSAR) of the Toluca Valley, Mexico, with the aim of measuring and monitoring land subsidence. D-InSAR results are verified with field data. Additionally, the different sensors are compared and contrasted. A total of 30 SAR images from various C-band sensors with dates ranging from December 1995 to May 2008 were used. Forty-four D-InSAR pairs were generated with 31 usable interferograms. ENVISAT ASAR generally had shorter baselines than RADARSAT-1, and thus more usable interferograms. Verifying InSAR results involved installing and taking measurements from two extensometer systems. The compressible clays compact in a relatively linear fashion, where varying compaction rates are a function of drawdown and geologic properties. The total maximum subsidence for a point location in the valley between November 2003 and May 2008 is approximately 40 cm. It is estimated that the maximum total subsidence since 1962 is over 2.0 m.
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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 | 0.000 |
| 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) | 0.000 | 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 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".