Satellite radar observation and advanced interpretation for stability monitoring of open pits: the Manefay failure (Kennecott Copper Mine), Utah, USA
Bibliographic record
Abstract
Slope stability monitoring is a very important aspect in open pit mining processes, where landslides without warning may cause huge loss of life, injuries and infrastructure damage, interfering with mine planning and causing significant increased costs and economic losses. Slope monitoring, modeling and stability analysis help to improve the safety of mining activities and to minimize these economic effects. For slope monitoring, many techniques are available, including the use of prisms, GNSS, total stations, extensometers, inclinometers, infrasound sensors, and ground-based radar. All those techniques only give observation data from the epoch over which the sensors have been installed and cover only the specific areas where they are installed. Both aspects can be important, conditioning the results and their applicability.  To complement these observation techniques and overcome their limitations remote satellite interferometric synthetic aperture radar (InSAR) analysis can be applied to detect and characterize unstable areas, although it normally is not used in an operative way.  Even if the deformation data are obtained in a continuous (or nearly continuous) way, normally they are not inverted using methodologies which allow determination of the initial stages of ground fracturing, the 3D characteristics of the sources acting to produce the observed deformation, their location- and time-evolution. A study of this type could facilitate early detection, in some cases a long time before a potential landslide, helping to support decision making about preventive and/or corrective measures, and to avoid disasters, minimizing impacts. We present here a new methodology that would complement the current operational ones. This methodology implies the use of two complementary aspects in the open pits monitoring: operational monitoring of the pit and its surroundings using InSAR observation looking for precursory small line of sight (LOS) displacements; and the use of an interpretation methodology to estimate the source’s location and characteristics and their time evolution. This interpretation methodology is able to invert simultaneously ascending and descending time-series of InSAR LOS displacement data, assuming the existence of possible offset values in these data sets which will be estimated during the inversion process. 3-D sources for pressure and dislocations (strike-slip, dip-slip, and tensile, representing fractures and faults) are adjusted without having any a priori hypotheses on the source characteristics (number, nature, shape or location). This approach automatically assigns the number of sources, their type, magnitude values (MPa for pressure and cm for dislocations), as well as their position and orientation (angles of dislocation planes). The inversion methodology is nonlinear, based on an exploratory approach of the model space.  To evaluate the applicability of this new approach we consider a very well-known test-case, the Manefay landslide at Bingham Canyon open pit mine, happened on April 10th, 2013, in southwest of Salt Lake City, Utah, USA. This research has been supported by grants G2HOTSPOTS (PID2021-122142OB-I00), STONE (CPP2021-009072) and Defsour-PLUS (PDC2022-133304-I00) from the MCIN/AEI/10.13039/501100011033/FEDER, UE with funds from NextGenerationEU/PRTR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".