Revealing the untold stories of sinkhole land subsidence over the remains of an underground river in tuff soil by Ground Penetrating Radar
Bibliographic record
Abstract
Sinkhole-induced land subsidence poses a critical hazard in urban areas underlain by pyroclastic tuff formations. This study applies a calibrated Ground Penetrating Radar (GPR) method to detect subsurface anomalies in Malang city, Indonesia, where residual underground river flow contributes to stratigraphic instability. Using a 90 MHz GPR OKO AB-90 system, calibrated with a portable Vector Network Analyzer (VNA), dielectric values ranging from 4.3 to 13.6 were obtained across dry tuff, saturated clay, and concrete layers. These values yielded reflection coefficients (R) between −0.023 and 0.035, indicating clear contrasts between restored culverts, suspected new sinkholes, and groundwater zones. GPR imaging revealed subsidence features at depths of 4 – 7 m , with high-density signal zones exceeding 0.85 normalized amplitude units. The signal accuracy was validated via Signal-to-Noise Ratio (SNR) and Normalized Root Mean Square Error (NRMSE), with SNR values above 22 dB and NRMSE values below 0.07, confirming reliable anomaly detection. A regression analysis ( R 2 = 0.64 , p < 0.0001 ) between flood severity and subsidence events further supported the hydrogeological linkage and erosion-driven subsurface instability in pyroclastic terrains. These findings demonstrate that calibrated GPR effectively distinguishes subsurface anomalies in moisture-variable tuff environments. This approach enhances early detection of sinkhole-prone zones and supports urban geohazard mitigation strategies. • GPR delineates subsurface anomalies in sinkhole-prone urban volcanic tuff zones. • VNA calibration improves permittivity accuracy and signal reliability for GPR scans. • SNR > 22 dB and NRMSE < 0.07 confirm high-quality subsurface anomaly detection. • Rainfall and flood recurrence contextualize subsidence vulnerability in urban terrain. • Calibrated GPR offers a replicable method for urban geohazard mapping in tuff areas.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Geophysical survey using ground penetrating radar to detect sinkhole subsidence.
This applies ground-penetrating radar to sinkhole detection, not research practice.
Geotechnical GPR study of sinkhole land subsidence; domain engineering.
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 | 0.000 | 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) | 0.000 | 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".