A Novel Method of Monitoring Surface Subsidence Law Based on Probability Integral Model Combined with Active and Passive Remote Sensing Data

Abstract

For the accurate and high-precision measurement of the deformation field in mining areas using different data sources, the probability integral model was used to process deformation data obtained from an Unmanned Aerial Vehicle (UAV), Differential InSAR (DInSAR), and Small Baseline Subset InSAR (SBAS-InSAR) to obtain the complete deformation field. The SBAS-InSAR, DInSAR, and UAV can be used to obtain small-scale, mesoscale, and large-scale deformations, respectively. The three types of data were all superimposed by the Kriging interpolation, and the deformation field was integrated using the probability integral model to obtain the complete high-precision deformation field with complete time series in the study area. The study area was in the WangJiata mine in Western China, where mining was carried out from 12 July 2018 to 25 October 2018, on the 2S201 working face. The first observation was made in June 2018, and steady-state observations were made in April 2019, totaling four UAV observations. During this period, the Canadian Earth Observation Satellite of Radarsat-2 (R2) was used to take 10 SAR images, the surface subsidence mapping was undertaken using DInSAR and SBAS-InSAR techniques, and the complete deformation field of the working face during the 106-day mining period was obtained by using the UAV technique. The results showed that the subsidence basin gradually expanded along the mining direction as the working face advanced. When the mining advance was greater than 1.2–1.4 times the coal seam burial depth, the supercritical conditions were reached, and the maximum subsidence stabilized at the value of 2.780 m. The subsidence rate was basically maintained at 0.25 m/d. Finally, the accuracy of the method was tested by the Global Navigation Satellite System (GNSS) data, and the medium error of the strike was 0.103 m. A new method is reached by the fusion of active and passive remote sensing data to construct efficient, complete and high precision time-series subsidence basins with high precision.

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

“…Remote sensing imaging methods mainly include aerial photography, aerial scanning, and microwave radar. Remote sensing images can be broadly separated into active and passive remote sensing based on various detecting techniques [86]. According to the capture spectral range of the sensor, it is divided into ultraviolet remote sensing, visible light remote sensing, infrared remote sensing, microwave remote sensing, multi-band remote sensing, and ect.…”

Novel Method for Monitoring Mining Subsidence Featuring Co-Registration of UAV LiDAR Data and Photogrammetry

“…UAV technology is highly appropriate for monitoring surface subsidence in coal mining. Generally, the monitoring accuracy is at the decimeter level, and a few studies have reported a centimeter capability [19,20]. Given that the level of accuracy reported in independent studies differ, it is desirable to use a variety of sensors, including LiDAR, to obtain multi-source monitoring data to facilitate the development of UAVs for multi-source monitoring [21].…”

Study on the Ground Fissure Development Law of Coal Mining Based on UAV Photogrammetry and the Particle Flow Theory

“…After batch processing, it can generate DOM (Digital Orthophoto Map), DEM (Digital Elevation Model), DLG (Digital Line Graphic), topographic map, and other thematic geographic information results. Compared with traditional satellite remote sensing and large aircraft aerial photography, UAV technology has the advantages of strong maneuverability, fast speed, and less dependence on weather and terrain (Colomina and Molina, 2014;Siebert and Teizer, 2014;Wang et al, 2022).…”