A Dynamic Prediction Method of Deep Mining Subsidence Combines D-InSAR Technique

XunChun, Wang; Zhang, Yue; XingGe, Jiang; Zhang, Peng

doi:10.1016/j.proenv.2011.09.394

Cited by 12 publications

(3 citation statements)

References 4 publications

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“…Among the 1059 articles stored in our database, 1051 include a specific area of interest. Eight of these papers did not indicate any specific area, as they were either more focused on the technical aspect of subsidence analysis e.g., Xing et al (2010) presented the advantage of Corner reflectors in InSAR processing or XunChun et al (2011) discussed a general framework for the integration of DInSAR with prediction model, or provided an overview of literature contributions (Cigna, 2018;Tomás and Li, 2017). Review papers (Ishwar and Kumar, 2017), discussing the applications of satellite SAR interferometric techniques on a specific subsidence type, also do not include site-specific analysis.…”

Section: Geographic Distribution Of the Insar Applicationsmentioning

confidence: 99%

Review of satellite radar interferometry for subsidence analysis

Raspini

Caleca²,

Soldato³

et al. 2022

Earth-Science Reviews

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Section: Geographic Distribution Of the Insar Applicationsmentioning

confidence: 99%

Review of satellite radar interferometry for subsidence analysis

Raspini

Caleca²,

Soldato³

et al. 2022

Earth-Science Reviews

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“…The time function of the dynamic ground movement is derived from Knothe's dynamic subsidence hypothesis. Many scholars have studied this model, including its limitations and ground subsidence prediction [1][2][3][4][5][6][7][8][9][10]. Lian reviewed and extended an existing classical prediction model of dynamic subsidence and proposed potential new research avenues offered by cellular automata (CA) models.…”

Section: State Of the Artmentioning

confidence: 99%

“…Wang Xunchun proposed a dynamic prediction model by combining the probability integral method with the D-InSAR measure technique based on the conclusion of mining subsidence prediction and the extant problems of monitoring technology. The subsidence rate and time threshold calculated by the mining dynamic prediction method could more accurately predict the level of deep mining subsidence and quantitatively estimate the regularity of damage evolution of environmental resources [4]. Xinrong Liu conducted a careful analysis on the regularity of surface subsidence in mined-out areas and proposed a new time function based on the Harris curve model by considering the shortage of current surface subsidence time functions [5].…”

Section: State Of the Artmentioning

confidence: 99%

Application Study on the Dynamic Prediction Model for Determining the Mining Subsidence

Hu¹,

Lian²,

LI³

2016

JESTR

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___________________________________________________________________________________________ AbstractThe movement and deformation of the overlying strata and surface induced by underground mining are processes involving temporal and spatial changes, which, in turn, cause damage to buildings. Estimating the dynamic ground movement and deformation is important to ascertain the damage degree of buildings. To predict the dynamic ground subsidence caused by underground coal mining and protect ground buildings from dynamic damage, a dynamic prediction model was established to calculate ground deformation. A derivation of the dynamic subsidence prediction model based on Knothe's dynamic subsidence hypothesis and Litwiniszyn's stochastic medium theory was proposed under the conditions of constant advancing speed mining and post-mining. The composition and significance of the dynamic model were analyzed and provided a basis for calculating the dynamics and programming implementation. Through case studies, the dynamic prediction model was verified and the expected results were obtained during mining and post-mining. Results showed the dynamic prediction model is fit for the normal geological condition of mining but is limited for the special geological condition, that is, thick loose layer and thick bedrock. The study can meet the demand of protecting ground buildings from deformation caused by underground mining. The proposed model solves the time issue in mining subsidence prediction.

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A new prediction model for mining subsidence deformation: the arc tangent function model

Wang

2014

Nat Hazards

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Ground subsidence in underground coal mining areas causes environmental damage and creates hazards on the ground surface, which is long-term, widely distributed, and can lead to large-scale geological disasters. Achieving a high-precision method to predict mining subsidence deformation is very important for assessing environmental damage and countermeasures. In this paper, based on the ''S''-type settlement curves of the monitoring points in the collapsed pit and the failure mechanism of rock strata on the goaf, the arc tangent function model was proposed and applied to the Taihe coal mine in Fushun, Liaoning Province, China. Using the Levenberg-Marquardt algorithm for nonlinear curve fitting of the data, the parameters of the model are obtained, and extending it in time, the prediction function will be obtained. Using different monitoring data to validate the model shows that the accuracy of the medium-and short-term forecasting is very good. With continuous updating of the monitoring data, the forecasting achieves higher accuracy and the function of dynamic track forecasting is achieved. A very high correlation coefficient was obtained (0.996) using all the available data from the monitoring point for the best-fit curve. This prediction model provides a reference for the evaluation and treatment of ground subsidence in the Taihe coal mining area.

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A Dynamic Prediction Method of Deep Mining Subsidence Combines D-InSAR Technique

Cited by 12 publications

References 4 publications

Review of satellite radar interferometry for subsidence analysis

Review of satellite radar interferometry for subsidence analysis

Application Study on the Dynamic Prediction Model for Determining the Mining Subsidence

A new prediction model for mining subsidence deformation: the arc tangent function model

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