Decision-making fusion of InSAR technology and offset tracking to study the deformation of large gradients in mining areas-Xuemiaotan mine as an example

Abstract

The multi-level disturbance of underground and surface caused by coal mining activities intensifies the deterioration of the ecological environment in the mining area. Among them, the uneven settlement caused by coal mining is the most intuitive manifestation of surface environmental damage. The uneven settlement in the mining area has the characteristics of large settlement magnitude and severe deformation. Therefore, based on 15 Sentinel-1A image data, this paper uses three methods: SBAS InSAR, continuous D-InSAR and offset tracking technology to monitor the surface deformation of the mining area. The results show that the continuous D-InSAR technology SBAS-InSAR technology is applied to the small deformation in the edge area of the subsidence basin. The mining area with low gradient subsidence of SBAS-InSAR can obtain better performance than continuous D-InSAR technology. The offset tracking technique is used to monitor the large gradient deformation in the center of the subsidence basin. Therefore, this paper proposes to expand the quantitative analysis through the spatial coherence threshold and the accuracy and successful image elements of the interference fringe displacement. Combine the advantages of the three methods and overcome the shortcomings of each method, fuse the deformation information of the three methods, and obtain the deformation law of the whole surface subsidence. The results show that the mean absolute error (MAE1-1) of continuity D-InSAR is 0.92 m, the mean absolute error (MAE2-1) of SBAS-InSAR is 0.94 m, and the mean absolute error (MAE3-1) of Offset-tracking is 0.25 m. The results of this fusion method are in good agreement with the measured data, and the mean absolute error (MAE4-1) of vertical displacement is 7 cm. Therefore, the fusion method has advantages over individual methods and provides a new idea in monitoring the large gradient deformation of coal mining subsidence in mining areas.

An Extraction Method for Large Gradient Three-Dimensional Displacements of Mining Areas Using Single-Track InSAR, Boltzmann Function, and Subsidence Characteristics

“…where 𝑘 is the integer cycle ambiguity of the predicted phase. Next, the residual phase of the monitored phase is obtained by removing the predicted phase from the monitored phase, as shown in Equation (14). The method of unwrapping the residual phase is adopted to reduce the difficulty of monitoring phase unwrapping, as shown in Equation (15): Furthermore, using the geological mining parameters and the prediction parameters, the 3-D displacements (W j , U j E , and U j N ) at the corresponding pixel of the target area is predicted based on the BPM.…”

“…At present, to overcome the above difficulties, the following methods have been developed by scholars: Method (I) multi-track InSAR [7][8][9][10], InSAR + Offset Tracking/MAI [11][12][13][14], and InSAR + GPS [15][16][17]; Method (II) Prior model + InSAR [18][19][20]; Method (III) InSAR + probability integral method (PIM) [21][22][23][24]. Method (I) can theoretically obtain onedimensional or two-dimensional observations in multiple directions, thereby the number of additional observation equations is increased and the 3-D deformations observation information is supplemented.…”

An Automatic Spatial-Temporal Evolution Inversion Method of Mining Goaf Based on the Improved Hotspot Analysis and Probability Integral Method

“…InSAR measures the surface deformation along the line of sight (LOS), which restricts the understanding of the movement mechanisms of the mining goaf [18]. Efforts [19], [20] have been made to overcome this limitation, particularly the integration with relevant data, such as offset tracking data [21], [22], airborne data [23], [24], leveling data [25], GPS data [26], and other auxiliary data [27], [28]. Another widely used strategy is the probability integral method (PIM) [29], [30], which is based on the surface movement model, and is frequently combined with TS-InSAR for subsidence analysis in mining areas.…”

Coal Structure Prediction Based on Type-2 Fuzzy Inference System for Multi-Attribute Fusion: A Case Study in South Hengling Block, Qinshui Basin, China

“…Other studies have implemented an elastic impedance inversion method for the fluid factor and brittleness coefficient, using both as effective pore-fluid bulk moduli to estimate coal structural distribution [9,10]. However, the use of multiple solutions for the inversion reduces the accuracy of the predictions, whereas they become more accurate with the addition of rich logging parameters, but the well extrapolation process still requires the inversion of seismic waves in order to solve for the relevant parameters, resulting in a certain error accumulation [11,12].…”