Analysis of Mining-Induced Subsidence Prediction by Exponent Knothe Model Combined With Insar and Leveling

Chen, Lei; Zhang, Liguo; Tang, Yixian; Zhang, Hong

doi:10.5194/isprs-annals-iv-3-53-2018

Cited by 7 publications

(7 citation statements)

References 15 publications

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“…Both techniques, in contrast to the vast coverage of DInSAR remote sensing, provide data for discrete sparsely located points and are also time and cost-consuming. Different approaches have been proposed for fusing these inhomogeneous results regarding datum, time, and space [22][23][24]. Nevertheless, these studies are limited to linear deformations or are in an early stage of development.…”

Section: Insar Deformationsmentioning

confidence: 99%

Mining Deformation Life Cycle in the Light of InSAR and Deformation Models

et al. 2019

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The Sentinel-1 constellation provides an effective new radar instrument with a short revisit time of six days for the monitoring of intensive mining surface deformations. Our goal is to investigate in detail and to bring new comprehension of the mine life cycle. The dynamics of mining, especially in the case of horizontally evolving longwall technology, exhibit rapid surface changes. We use the classical approach of differential radar interferometry (DInSAR) with short temporal baselines (six days), which results in deformation maps with a low decorrelation between the satellite images. For the same time intervals, we compare the radar results with prediction models based on the Knothe–Budryk theory for mining subsidence. The validation of the results with ground levelling measurements reveals a high level of resemblance of the DInSAR subsidence maps (−0.04 m bias with respect to the levelling). On the other hand, aside from the explicable exaggeration, the location of the subsidence trough needs improvement in the forecasted deformations (0.2 km shift in location, a deformation velocity four times higher than in DInSAR). In addition, a time lag between DInSAR (compatible with extraction) and prediction is revealed. The model improvement can be achieved by including the DInSAR results in the elaboration of the model parameters.

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Section: Insar Deformationsmentioning

confidence: 99%

Mining Deformation Life Cycle in the Light of InSAR and Deformation Models

et al. 2019

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“…When estimating vertical deformation using the LOS deformation derived from In-SAR, several researchers argue that the vertical deformation resulting from coal mining significantly surpasses the horizontal movement [27,28]. Consequently, they often overlook the influence of horizontal movement on LOS deformation.…”

Section: Analysis Of the Effect Of Horizontal Movement On Los Deforma...mentioning

confidence: 99%

“…Although these methods offered high accuracy, they were characterized by being time-consuming, labor-intensive, expensive, and yielded low spatiotemporal resolution of monitoring results. Due to InSAR's efficiency in obtaining a wide range of surface deformation information, many researchers have turned to InSAR to capture surface subsidence in mining zones [25][26][27][28]. However, InSAR only provides line of sight (LOS) deformation, making it challenging to determine the actual surface deformation.…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers suggest that vertical subsidence from coal mining far outweighs horizontal movement. Therefore, when deducing vertical deformation from LOS deformation, if one disregards the influence of horizontal deformation, dynamic surface subsidence can be ascertained via time-series InSAR [27,28]. However, real-life scenarios exhibit significant differences in horizontal movement across subsidence basin areas.…”

Section: Introductionmentioning

confidence: 99%

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Application of the Time Function Model for Dynamic Deformation Prediction in Mining Areas under Characteristic Constraints

Wang,

Dai,

Yan

et al. 2023

Sustainability

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The fundamental model for dynamically predicting surface subsidence is the time influence function. However, current research and the application of time functions often neglect the comprehensive characteristics of the entire surface deformation process, leading to a less systematic representation of the actual deformation law. To rectify this, we explore ground point deformation along the strike line from two perspectives: dynamic subsidence and dynamic horizontal movement. Moreover, we develop prediction models for dynamic subsidence and dynamic horizontal movement at any point along the strike line, utilizing the probability integral method (PIM) and considering the surface deformation features. We then use characteristic constraints based on the prediction models to constrain the time influence function. For this purpose, we employ the Richards time function which has strong universality to establish the time functions for dynamic subsidence and horizontal movement under these constraints. We provide an illustrative example of its application in the 12,401 working face. Additionally, we explore the suitability of interferometric synthetic aperture radar (InSAR) technology for acquiring dynamic subsidence data on the surface. The experimental findings reveal the following key observations: the Richards model, when applied for dynamic subsidence prediction under constraints, exhibits high accuracy with an R-squared (R2) value of 0.997 and a root mean squared error (RMSE) of 94.6 mm, along with a relative mean square error of 1.9%. Meanwhile, the dynamic horizontal movement prediction model exhibits an accuracy in fully mined areas with an R2 of 0.986, an RMSE of 46.2 mm, and a relative mean square error of 2.6%.

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“…e conventional method of monitoring is to place observation stations in the form of discrete points along the working surface's trend and inclination lines, establishing a monitoring mode with "points" as "lines" and "lines" as "polygons" [6,7]. Despite its excellent precision, the limited monitoring scope, low e ciency, big workload, and expensive cost cannot be overlooked [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Deformation Monitoring and Spatiotemporal Evolution of Mining Area with Unmanned Aerial Vehicle and D-InSAR Technology

Tan

Zhu

et al. 2022

Mobile Information Systems

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The extraction of underground mineral sources has a significant negative impact on the local environment, results in land surface subsidence. As far as subsidence monitoring technology is concerned, leveling is the most accurate. However, leveling can only obtain discrete point data but not the whole area information of the subsidence basin. In this study, Differential Interferometric Synthetic Aperture Radar (D-InSAR) combined with Unmanned Aerial Vehicle (UAV) technology is used to study the subsidence characteristics of the whole working panel. In this analysis, the Huainan mining area is tested as a research area; measured data are compared to the elevation accuracy of the Digital Surface Model (DSM) data, which can be considered for the subsequent works of the mining area. Based on the subsidence affected area, the ground object-type information is recorded to provide basic information for the ecological restoration work after mining so that the data before and after mining can be obtained synchronously. Finally, the differential interference results and Digital Orthophoto Map (DOM) data are combined to assess the spatiotemporal evolution of working panel subsidence and its influence on surface features. The main novelty of the proposed work is combining UAV and D-InSAR to get more accurate analysis of mining subsidence. It can be done using the proposed method.

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Analysis of Mining-Induced Subsidence Prediction by Exponent Knothe Model Combined With Insar and Leveling

Cited by 7 publications

References 15 publications

Mining Deformation Life Cycle in the Light of InSAR and Deformation Models

Mining Deformation Life Cycle in the Light of InSAR and Deformation Models

Application of the Time Function Model for Dynamic Deformation Prediction in Mining Areas under Characteristic Constraints

Deformation Monitoring and Spatiotemporal Evolution of Mining Area with Unmanned Aerial Vehicle and D-InSAR Technology

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