Monitoring of land surface subsidence using persistent scatterer interferometry techniques and ground truth data in arid and semi-arid regions, the case of Remah, UAE

Research on the surface subsidence characteristics and prediction models caused by coal mining under the reverse fault

Predicting and understanding the phenomenon of surface subsidence caused by coal mining in working faces with faults are important issues for safe coal mining and efficient production. In numerical simulation experiments, it was found that the phenomenon of surface subsidence manifests when faults exist, and the degree of influence of faults with different dip angles on surface subsidence varies. This phenomenon is attributed to fault activation. According to the experimental results, the impact of faults with different dip angles on surface subsidence falls into three levels: level I for 35° faults, level II for 45° and 55° faults, and level III for 65° and 75° faults. Similarly, the relationship between the difficulty of fault activation and the dip angle of faults can be categorized as 35° faults prone to activation, 45° and 55° faults difficult to activate, and 65° and 75° faults not prone to activation. The probability integral correction model for fault mining, which integrates the surface subsidence values caused by fault-induced attenuation and the subsidence arising from separation spaces, was introduced, thereby constructing a surface subsidence prediction model. This proposed prediction model can accurately predict surface subsidence, with a root mean square error of 10.74 mm between the predicted and measured values, as validated using DInSAR results from the III 6301 working face in the Jincheng mining area.

An interpretable deep learning model to map land subsidence hazard

The main goal of this research is the interpretability of deep learning (DL) model output (e.g., CNN and LSTM) used to map land susceptibility to subsidence hazard by means of different techniques. For this purpose, an inventory map of land subsidence (LS) is prepared based on fieldwork and a record of LS presence points, and 16 features controlling LS were mapped. Thereafter, 11 effective features controlling LS were identified by means of a particle swarm optimization (PSO) algorithm, which was then used as input in the CNN and LSTM predictive models. To address the inherent black box nature of DL models, six interpretation methods (feature interaction, permutation importance plot (PFIM), bar plot, SHapley Additive exPlanations (SHAP) main plot, heatmap plot, and waterfall plot) were used to interpret the predictive model outputs. Both models (CNN and LSTM) had AUC > 90 and therefore provided excellent accuracy for mapping LS hazard. According to the most accurate model-the CNN predictive model-the range from very low to very high hazard classes occupied 20%, 20%, 25%, 16.3%, and 18.7% of the study area, respectively. According to three plots (bar plot, SHAP main plot, and heatmap plot), which were constructed based on the SHAP value, distance from the well, GDR and DEM were identified as the three most important features with the highest impact on the DL model output. The results of the waterfall plot indicate two effective features consisting of distance from the well and coarse fragment, and two effective features comprising landuse and DEM, contributed negatively and positively to LS, respectively. Overall, these explanation techniques can address critical concerns with respect to the interpretability of sophisticated DL predictive models.

Land surface deformation monitoring in the Al-Ain arid region (UAE) using microgravity and SAR interferometry surveys

The integration of geophysical and satellite-based monitoring techniques can yield new insights in land surface deformation (LSD) studies. In this study, we integrated the microgravity monitoring geophysical technique with Interferometry Synthetic Aperture Radar (InSAR) to reveal the possible sources of LSD changes. A microgravity survey was conducted over Al-Ain city for 1 year at one-month intervals, with time-lapse microgravity changes calculated based on the results. Over the same area of interest and time interval, InSAR analysis was performed using Sentinel-1 (C-band) data. The time-lapse microgravity changes for the whole studied period ranged from -36 to 365 μGal. The InSAR processing showed periodic land surface deformation over the area of interest varying with the season of the year. The InSAR technique detected land surface subsidence at the northeast and western parts of the study area (-7 mm/year and -8 mm/year, respectively) and land surface uplift in the central and southern parts of the study area (2 mm/year). The recorded subsidence may relate to water extraction in these areas. The integration of the two techniques showed a negative correlation, with coefficients of -0.43 and -0.39 for land surface subsidence and uplift, respectively. Furthermore, groundwater level drawdown zones were identified in the west and center of the study area. Overall, LSD is mainly stimulated by water volume exploitation in the Al-Ain region.

Bayesian Estimation of Land Deformation Combining Persistent and Distributed Scatterers

Persistent Scatterer Interferometry (PSI) has been widely used for monitoring land deformation in urban areas with millimeter accuracy. In natural terrain, combining persistent scatterers (PSs) and distributed scatterers (DSs) to jointly estimate deformation, such as SqueeSAR, can enhance PSI results for denser and better coverage. However, the phase quality of a large number of DSs is far inferior to that of PSs, which deteriorates the deformation measurement accuracy. To solve the contradiction between measurement accuracy and coverage, a Bayesian estimation method of land deformation combining PSs and DSs is proposed in this paper. First, a two-level network is introduced into the traditional PSI to deal with PSs and DSs. In the first-level network, the Maximum Likelihood Estimation (MLE) of deformation parameters at PSs and high-quality DSs is obtained accurately. In the secondary-level network, the remaining DSs are connected to the nearest PSs or high-quality DSs, and the deformation parameters are estimated by Maximum A Posteriori (MAP) based on Bayesian theory. Due to the poor phase quality of the remaining DSs, MAP can achieve better estimation results than the MLE based on the spatial correlation of the deformation field. Simulation and Sentinel-1A satellite data results verified the feasibility and reliability of the proposed method. Regularized by the spatial deformation field derived from the high-quality PSs and DSs, the proposed method is expected to achieve robust results even in low-coherence areas, such as rural areas, vegetation coverage areas, or deserts.

Land Subsidence in Qingdao, China, from 2017 to 2020 Based on PS-InSAR

Land subsidence is a global geological disaster that seriously affects the safety of surface and underground buildings/structures and even leads to loss of life and property. The large-scale and continuous long-time coverage of Interferometric Synthetic Aperture Radar (InSAR) time series analysis techniques provide data and a basis for the development of methods for the investigation and evolution mechanism study of regional land subsidence. Based on the 108 SAR data of Sentinel-1 from April 2017 to December 2020, this study used Persistent Scatterer InSAR (PS-InSAR) technology to monitor the land subsidence in Qingdao. In addition, detailed analysis and discussion of land subsidence combined with the local land types and subway construction were carried out. From the entire area to the local scale, the deformation analysis was carried out in the two dimensions of time and space. The results reveal that the rate of surface deformation in Qingdao from 2017 to 2020 was mainly −34.48 to 5.77 mm/a and that the cumulative deformation was mainly −126.10 to 30.18 mm. The subsidence areas were mainly distributed in coastal areas (along the coasts of Jiaozhou Bay and the Yellow Sea) and inland areas (northeast Laixi City and central Pingdu City). In addition, it was found that obvious land subsidence occurred near the Health Center Station of Metro Line 8, a logistics company in Qingdao, and near several high-rise residential areas and business office buildings. It is necessary for the relevant departments to take timely action to prevent and mitigate subsidence-related disasters in these areas.

Monitoring of Land Subsidence and Ground Fissure Activity within the Su-Xi-Chang Area Based on Time-Series InSAR

Serious land subsidence and ground fissure (GF) disasters have brought huge economic losses to the Su-Xi-Chang area (China) and threatened the safety of its residents. To better understand the development of these disasters, it is urgent to carry out long-term and large-scale deformation monitoring in this region. In this study, based on time-series interferometric synthetic aperture radar (InSAR) technology, ground deformation characteristics were obtained at different periods. Meanwhile, Fast Lagrangian Analysis of Continua in Three Dimensions (FLAC3D) version 5.00 was used to study the stress, seepage field, and displacement changes in the soil layers caused by pumping activities at the bedrock bulge. The results showed that three subsidence centers were located in Suzhou, Wuxi, and Changzhou from 2007 to 2010. The ground fissures in Guangming village had obvious differential settlements and intense activities. The land subsidence in the Su-Xi-Chang area was under control from 2018 to 2021, while there was a relative rebound in most areas. Combined with numerical simulation and geological data, we demonstrated that pumping activities would accelerate and intensify the land subsidence process, and differential subsidence was prone to occur at the buried hill, which in turn led to the formation of ground fissures. By comparing the characteristics of ground deformation in different periods, it was proven that banning groundwater exploitation is an effective measure for preventing and controlling such disasters.