Integrating SBAS-InSAR and AT-LSTM for Time-Series Analysis and Prediction Method of Ground Subsidence in Mining Areas

Abstract

Ground subsidence is a significant safety concern in mining regions, making large-scale subsidence forecasting vital for mine site environmental management. This study proposes a deep learning-based prediction approach to address the challenges posed by the existing prediction methods, such as complicated model parameters or large data requirements. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was utilized to collect spatiotemporal ground subsidence data at the Pingshuo mining area from 2019 to 2022, which was then analyzed using the long-short term memory (LSTM) neural network algorithm. Additionally, an attention mechanism was introduced to incorporate temporal dependencies and improve prediction accuracy, leading to the development of the AT-LSTM model. The results demonstrate that the Pingshuo mine area had subsidence rates ranging from −205.89 to −59.70 mm/yr from 2019 to 2022, with subsidence areas mainly located around Jinggong-1 (JG-1) and the three open-pit mines, strongly linked to mining activities, and the subsidence range continuously expanding. The spatial distribution of the AT-LSTM prediction results is basically consistent with the real situation, and the correlation coefficient is more than 0.97. Compared with the LSTM, the AT-LSTM method better captured the fluctuation changes of the time series for fitting, while the model was more sensitive to the mining method of the mine, and had different expressiveness in open-pit and shaft mines. Furthermore, in comparison to existing time-series forecasting methods, the AT-LSTM is effective and practical.

Prediction of the Deformation of Heritage Building Communities under the Integration of Attention Mechanisms and SBAS Technology

“…Therefore, it is necessary to conduct investigations on the heritage building in the study area and play a proactive role in prevention. (2) The LSTM neural network prediction model is built based on time series SBAS-InSAR results to predict the dynamic surface subsidence in the mining area using the Richards model. Through model design, training, and parameter selection, the LSTM neural network model achieves relatively high accuracy.…”

“…It aims to detect the risk of deformation of heritage buildings in a particular region of the country [1]. InSAR technology is a set of interferences and measurements as a single remote sensing technology, for urban ground sediment monitoring; the most widely applied of these are timescale InSAR technologies, such as permanent scatterer synthetic aperture radar interferometry (PS-InSAR) and Small Baseline Subset InSAR (SBAS-InSAR) [2]. These set reasonable time and spatial base thresholds, combine different SAR images into several interference pairs, and then calculate the corresponding surface sedimentation value for each intervention, so as to obtain the time of execution of the study area.…”

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm–Convolutional Neural Network–Long Short-Term Memory Model

“…LSTM is a kind of neural network with a unique time series processing ability in order to prevent the problem of gradient disappearance and the explosion of a recurrent neural network, the core of which lies in the forgetting gate, input gate, and output gate, which is able to make full use of the historical time series data and capture the temporal features in the data, as well as selectively retaining and forgetting the information to predict the future data more accurately [ 37 , 38 , 39 , 40 ]. The structure of its network model unit is shown in Figure 10 .…”

Prediction Method for Dynamic Subsidence Basin in Mining Area Based on SBAS-InSAR and Time Function

“…Yang et al combined the WeiBull model with Kalman filtering to predict the line-of-sight displacement caused by mining activities, and based on prior knowledge, they performed three-dimensional deformation decomposition to obtain information on the three-dimensional surface deformation caused by mining activities [24]. The second approach involves using deep learning or machine learning methods, such as Long Short-Term Memory (LSTM) models or Support Vector Machine (SVM) models, for predicting the dynamic subsidence in mining areas [25][26][27]. However, due to issues such as overfitting and an unclear physical significance, it is difficult to widely apply these methods in engineering.…”