Surface Deformation Prediction Research Articles

InSAR-CTPIM-Based 3D Deformation Prediction in Coal Mining Areas of the Baisha Reservoir, China

Time series dynamic prediction of surface deformation in mining areas can provide reference data for coal mine safety and production, which has important impacts. The combination of interferometric synthetic aperture radar (InSAR) technology and the probability integral method (PIM) is commonly used for predicting deformation. However, most surface subsidence prediction in mining areas is based on the static PIM parameters, failing to achieve the three-dimensional (3D) dynamic deformation prediction. This paper proposed a 3D deformation dynamic prediction model (InSAR-3D-CTPIM) between InSAR deformation observations and dynamic coordinate-time PIM (CTPIM) parameters, which can realize the prediction of east–west, north–south, and vertical series deformation caused by mining. The method has been validated by simulation experiments and real experiments in the mining area of Jiansheng Coal Mine in Baisha Reservoir, Henan Province, China. The results showed that the modeling accuracy was improved by 34.3% compared to the traditional multi-rate model, and the accuracy was improved by 28.5% compared to the vertical deformation obtained by the traditional static PIM method. The InSAR-3D-CTPIM model can be used to predict the evolutionary history of basin-wide surface deformation dynamics in coal mining areas, and provide a reference for the early warning and prediction of geological hazards in coal mining areas.

Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

At present, due to the complex and changeable geological conditions, the precise deformation prediction technology of high and steep slope could not achieve an accurate prediction. In particular, the single forecasting model has some problems such as poor stability, low precision, and data fluctuation. In practice, excavating the complex nonlinear relationship between open-pit slope surface deformation monitoring data and various influencing factors and improving the accuracy of the deformation prediction of high and steep slopes is the key to safe open-pit mine production. It proposed to introduce the position factor and the velocity factor into a twin support vector machine (TWSVM). The adaptive subgroup optimisation (APSO) algorithm is selected for parameter optimisation. Through the comparative analysis of TWSVM, genetic algorithm-TWSVM (GA-TWSVM), and the proposed APSO⁃TWSVM, the experimental data show that the mean absolute error (MAE) values of the three models are 13.29 %，8.17 %, and 1.27 %, the RMSE - 47.83 %，6.52 %, and 3.02 %, respectively; the prediction time for APSO⁃TWSVM is improved by 62.5 % compared to GA-TWSVM.

GLER-BiGRUnet: A Surface Deformation Prediction Model Fusing Multiscale Features of InSAR Deformation Information and Environmental Factors

GLER-BiGRUnet: A Surface Deformation Prediction Model Fusing Multiscale Features of InSAR Deformation Information and Environmental Factors

Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR

The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data.

Analysis of machine learning-based applications for intelligent construction of shield tunnels

With the quick advancement of the level of information science in shield tunnel construction, the monitoring methods of shield equipment during tunnel boring work are increasingly improved and the recorded construction data includes not only information on the internal workings of the shield equipment, but also on its interaction with the external strata. Machine learning data analysis is powerful and has a wider range of applications and scope than traditional data analysis methods in the civil construction industry. Through the use of machine learning methods, the data and information collected can be mined and analysed in depth to find the intrinsic connections and linkages that can help improve the safety and efficiency ragarding shield tunnel construction. This work presents a literature analysis of current situations of machine learning for shield tunnel construction at home and abroad, briefly describes the basic principles of machine learning methods, summarises and analyses the research situation in shield tunnel construction, reviews the progress of machine learning-based shield equipment condition analysis, intelligent prediction and control methods for shield tunneling parameters and shield tunneling surface deformation prediction, and summarises the current research The study also summarises the shortcomings of current research. Finally, an outlook on the development of shield tunneling towards intelligence is presented.

Large-Scale Surface Deformation Monitoring Using SBAS-InSAR and Intelligent Prediction in Typical Cities of Yangtze River Delta

Large-scale short-term monitoring and prediction of surface deformation are of great significance for the prevention and control of geohazards in rapidly urbanizing developing cities. Most studies focus on individual cities, but it would be more meaningful to address large urban agglomerations and consider the relevance of the regions within them. In addition, the commonly used linear fitting prediction methods cannot accurately capture the dynamic mechanisms of deformation. In this study, we proposed an automatic PS extraction method (named PS-SBAS-InSAR) that improves SBAS-InSAR to extract surface deformation and an Informer-based short-term surface deformation prediction method for case studies in 16 typical cities of the Yangtze River Delta (YRD). The results show that PS-SBAS-InSAR successfully extracted accurate surface deformation sequences of the YRD. During the period from January 2019 to January 2021, the YRD experienced a slight deformation with an average deformation rate within [−4, 4] mm/year. Geographically neighboring cities may have associated deformation distributions and similar deformation trends, as indicated by average deformation rate maps and landscape metrics. Both types of deformation (i.e., subsidence/uplift) tend to occur simultaneously, with specific areas of subsidence/uplift occurring in close proximity to areas of concentrated deformation. The Informer model effectively captured the time-series variation in surface deformation, suggesting a slowdown of deformation over the next two months (February 2021–March 2021). Our work contributes to a better understanding of changes and trends in large-scale surface deformation and provides useful methods for monitoring and predicting surface deformation in coastal areas.

Prediction of surface deformation induced by mining thin coal seam: A case study of Guanshan coalfield in Sichuan

The problem of surface deformation caused by coal mining is acute and usually lasts for a long time. During coal mining, the movement of the overlying strata has a broad range of influences, which may cause surface deformation, surface cracking, and damage to structures (buildings). However, continuous deformation monitoring data are often scarce in practice, making it challenging to predict the surface deformation caused by coal mining. In this context, this paper takes Sichuan’s Guanshan coalfield as the research object and proposes a comprehensive method integrating interferometric synthetic aperture radar (InSAR) technology, the FLAC3D finite difference software, and the probability integral method to predict the surface deformation caused by mining a thin seam of coal. The results show that the trend of surface deformation estimated by the numerical simulation agrees well with the results of InSAR data and the probability integral model when using InSAR historical deformation data to invert the parameters of rock mechanics in the numerical simulation, which is beneficial to improving the reliability of the simulation results. The calculations of the probability integral method are close to the predictions of the FLAC3D numerical simulation, and a settlement deformation of 0.68 ​m is expected to occur in the Guanshan coalfield area. The comprehensive prediction method proposed in this paper effectively enhances the accuracy of surface deformation prediction under the action of mining and can provide a reference for predicting the surface subsidence of similar coal mines.

Variability of Exploitation Coefficient of Knothe Theory in Relation to Rock Mass Strata Type

Geodesic measurements of mining area deformations indicate that their description fails to be regular, as opposed to what the predictions based on the relationships of the geometric-integral theory suggest. The Knothe theory, most commonly applied in that case, considers such parameters as the exploitation coefficient a and the angle of the main influences range tgβ, describing the geomechanical properties of the medium, as well as the mining conditions. The study shows that the values of the parameters a = 0.8 and tgβ = 2.0, most commonly adopted for the prediction of surface deformation, are not entirely adequate in describing each and every mining situation in the analysed rock mass. Therefore, the paper aims to propose methodology for determining the value of exploitation coefficient a, which allows to predict the values of surface subsidence caused by underground coal mining with roof caving, depending on geological and mining conditions. The characteristics of the analysed areas show that the following factors affect surface subsidence: thickness of overburden, type of overburden strata, type of Carboniferous strata, rock mass disturbance and depth of exploitation. These factors may allow to determine the exploitation coefficient a, used in the Knothe theory for surface deformation prediction.

A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area

The accurate calculation of mining-induced surface deformation has important guiding significance for efficient and safe production in mining areas. The probability integral method (PIM) is a main prediction method in China, and the selection of its parameters is directly related to the prediction accuracy of surface deformation in mining areas. To overcome shortcomings of PIM and other methods, this paper proposed a prediction model of the parameters of PIM combining a multiple regression model and an extreme learning machine. In this paper, the Huainan mining area was selected as the research object, the influence factors of PIM parameters were analyzed and the accuracy of the model was verified. The influence of the number of hidden layer nodes, the selection of activation function and the proportion of training set and test set in the model were analyzed. The conclusions suggest that the PIM parameters calculated in this paper could be used to predict mining subsidence and obtain surface movement and deformation data. The research results provide an effective method for the selection of surface deformation prediction parameters of new working faces or faces lacking measured data.

Evaluating Permafrost Degradation in the Tuotuo River Basin by MT-InSAR and LSTM Methods.

Permafrost degradation can significantly affect vegetation, infrastructure, and sustainable development on the Qinghai-Tibet Plateau (QTP). The permafrost on the QTP faces a risk of widespread degradation due to climate change and ecosystem disturbances; thus, monitoring its changes is critical. In this study, we conducted a permafrost surface deformation prediction over the Tuotuo River tributary watershed in the southwestern part of the QTP using the Long Short-Term Memory model (LSTM). The LSTM model was applied to the deformation information derived from a time series of Multi-Temporal Interferometry Synthetic Aperture Radar (MT-InSAR). First, we designed a quadtree segmentation-based Small BAseline Subset (SBAS) to monitor the seasonal permafrost deformation from March 2017 to April 2022. Then, the types of frozen soil were classified using the spatio-temporal deformation information and the temperature at the top of the permafrost. Finally, the time-series deformation trends of different types of permafrost were predicted using the LSTM model. The results showed that the deformation rates in the Tuotuo River Basin ranged between -80 to 60 mm/yr. Permafrost, seasonally frozen ground, and potentially degraded permafrost covered 7572.23, 900.87, and 921.70 km2, respectively. The LSTM model achieved high precision for frozen soil deformation prediction at the point scale, with a root mean square error of 4.457 mm and mean absolute error of 3.421 mm. The results demonstrated that deformation monitoring and prediction using MT-InSAR technology integrated with the LSTM model can be used to accurately identify types of permafrost over a large region and quantitatively evaluate its degradation trends.

A ConvLSTM Neural Network Model for Spatiotemporal Prediction of Mining Area Surface Deformation Based on SBAS-InSAR Monitoring Data

The surface deformation caused by underground mining leads to damage to surface buildings and brings potential safety hazards and property losses. The demand for reliable prediction methods of surface deformation in mining areas is becoming increasingly significant. At present, most prediction methods are based on sampling points; however, these methods neglect to consider local and overall spatial features, and this oversight affects the spatial accuracy of prediction results. The data form of the prediction output is often discontinuous and not intuitive. In order to solve this problem, the spatiotemporal prediction method of surface deformation in mining areas is a very effective proposal. However, few scholars have proposed a solution based on this idea. In this study, a convolution long short-term memory (ConvLSTM) neural network for surface deformation spatiotemporal prediction based on time-series interferometric synthetic aperture radar (InSAR) is proposed to directly predict the overall spatial deformation of the surface in the mining area. First, based on Sentinel-1A images of the Jinchuan Mining Area, Jinchang, Gansu province, China, the time-series InSAR surface deformation data of the study area from January 2018 to October 2020 (81 scenes) are obtained using small baseline subset InSAR (SBAS-InSAR) technology. Because of the large value scale of surface deformation in the mining area, we propose a method to fit the maximum and minimum values of time-series deformation, respectively, and carry out piecewise numerical compression. Then, based on the ConvLSTM neural network layer, construct the spatiotemporal prediction model of time-series InSAR surface deformation. Support vector regression (SVR), multilayer perceptron (MLP) regression, and the gray model [GM (1,1)] are used as benchmark methods. The prediction results of our models are compared with the three benchmark methods. The comparison results show that the prediction effect of the ConvLSTM model with optimal input time steps is significantly better than the benchmark methods in the comprehensive performance of various evaluation metrics, especially for the metrics used to evaluate the error. This shows that the ConvLSTM model has relatively fine spatiotemporal prediction performance for time-series InSAR surface deformation. Based on the InSAR time-series deformation monitoring results of the Jinchuan Mining Area, we carry out the spatiotemporal prediction of surface deformation in the subsequent 50 time steps (600 days). The results agree with the natural deformation development law in band collection statistics and 3-D representation of deformation; moreover, the reliability of numerical spatial distribution is relatively high. This result can be used to intuitively evaluate the overall surface deformation of the mining area in the monitoring range, find potential hazards in time, and take measures to address these hazards quickly. At the same time, this research process also provides a new concept design for such problems.

InSAR time-series deformation forecasting surrounding Salt Lake using deep transformer models

The free and open data policy of Sentinel-1 SAR images enables Radar interferometry (InSAR) to perform time series surface deformation monitoring over large areas. InSAR deformation monitoring and prediction can investigate the freeze-thaw cycles of permafrost on the Qinghai-Tibet Plateau. However, the convolutional and recurrent neural networks cannot accurately model long-term and complex relations in multivariate time series data, it is challenging to implement time series deformation prediction with high spatial resolution. In this paper, an innovative InSAR deformation prediction integrated algorithm based on the transformer models is proposed to predict time series deformation more accurately surrounding Salt Lake. Compared with the other solutions, the unique feature of the proposed method is that: 1) this method takes advantage of the self-attention mechanism to study complicated dynamic deformation features of permafrost caused by temperature and other variables from InSAR time series deformation. 2) The transformer-based model can more accurately simulate seasonal and non-seasonal deformation signals, and is effective for short-term prediction of surface deformation in permafrost areas. The InSAR deformation prediction results demonstrate that the InSAR deformation prediction method achieves better prediction performance in predicting the deformation trends of permafrost with a point scale compared with the prediction results of other models. Based on the predicted deformation and the water extraction results, the expansion trends surrounding Salt Lake are discussed and evaluated. The total area of Salt Lake increased by 57.32 km2 during the period 2015–2019. And Salt Lake maintained slowing expansion trend from 2019 to 2022. The time series deformation forecasting method can be used as a generic framework for modeling nonlinear deformation processes in complex permafrost areas, and it reveals the potential impact of the Salt Lake outburst event on the deformation processes and the degradation of permafrost.

Investigating Surface Deformation Caused by Excavation of Curved Shield in Upper Soft and Lower Hard Soil

Existing studies on surface deformation prediction consider single soil layers and straight-line excavation when investigating deformation caused by metro shield construction. In this study, we provide a new prediction method for studying the soil deformation caused by curved shield tunnel construction in the upper soft and lower hard soil. The deformation equations are derived using the Mindlin solution and random medium theory and are verified using engineering examples and numerical simulation. The influencing factors and laws of the surface deformation caused by the excavation are also identified. The study found that the horizontal settlement trough on the ground surface was distributed asymmetrically during the curve construction, with maximum settlement on the inner side of the curve. The offset and settlement values were affected by the thrust difference coefficients α and β and the turning radius. When constructing in the upper soft and lower hard soil layers, the settlement trough tends to become wider and shallower. The results show that the derived equation is suitable for actual engineering calculations, and the measured data are in good agreement.

Time-Varying Surface Deformation Retrieval and Prediction in Closed Mines through Integration of SBAS InSAR Measurements and LSTM Algorithm

After a coal mine is closed, the coal rock mass could undergo weathering deterioration and strength reduction due to factors such as stress and groundwater, which in turn changes the stress and bearing capacity of the fractured rock mass in the abandoned goaf, leading to secondary or multiple surface deformations in the goaf. Currently, the spatiotemporal evolution pattern of the surface deformation of closed mines remains unclear, and there is no integrated monitoring and prediction model for closed mines. Therefore, this study proposed to construct an integrated monitoring and prediction model for closed mines using small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) and a deep learning-based long short-term memory (LSTM) neural network algorithm to achieve evolution pattern and dynamic prediction of spatiotemporal surface deformation of closed mines. Taking a closed mine in the western part of Xuzhou, China, as an example, based on Sentinel-1A SAR data between 21 December 2015, and 11 January 2021, SBAS InSAR technology was used to obtain the spatiotemporal evolution pattern of the surface during the 5 years after mine closure. The results showed that the ground surface subsided in the early stage of mine closure and then uplifted. In 5 years, the maximum subsidence rate in the study area is −43 mm/a, and the cumulative maximum subsidence is 310 mm; the maximum uplift rate is 29 mm/a, and the cumulative maximum uplift is 135 mm. Moreover, the maximum tilt and curvature are 3.5 mm/m and 0.19 mm/m2, respectively, which are beyond the safety thresholds of buildings; thus, continuous monitoring is necessary. Based on the evolution pattern of surface deformation, the surface deformation prediction model was proposed by integrating SBAS InSAR and an LSTM neural network. The experiment results showed that the LSTM neural network can accurately predict the deformation trend, with a maximum root mean square error (RMSE) of 5.1 mm. Finally, the relationship between the residual surface deformation and time after mine closure was analyzed, and the mechanisms of surface subsidence and uplift were discussed, which provide a theoretical reference for better understanding the surface deformation process of closed mines and the prevention of surface deformation.

Time-Series Analysis and Prediction of Surface Deformation in the Jinchuan Mining Area, Gansu Province, by Using InSAR and CNN–PhLSTM Network

Surface deformation poses a great threat to the safety of Jinchuan mining area production activities. At present, the spatio-temporal evolution law and mechanism of surface deformation in the Jinchuan mining area are unclear, and it is difficult to obtain reliable prediction results using the existing spatio-temporal prediction methods due to the lack of model parameters or relevant data. To solve these problems, this study proposes a new unified convolutional neural network with peephole long short-term memory (CNN-PhLSTM). Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was used to obtain the spatio-temporal evolution characteristics of surface deformation in the period of 2014–2021. Time series InSAR deformation data are merged into a unified network model in series with a time-distributed CNN segmentation and stacked PhLSTM. The InSAR measurement results are shown to be reliable by comparison and verification with the benchmark and InSAR results of different orbits. The proposed CNN-PhLSTM model was evaluated by mean absolute error and structural similarity (SSIM) evaluation indexes, and was compared with support vector regression (SVR), multilayer perceptron (MLP) and CNN-LSTM models. The results show three continuous subsidence areas, namely the Longshou, second western and third eastern mining areas. The cumulative surface deformation continued to increase from 2014 to 2021. Faults and lithology control the spatial distribution of surface deformation in the Jinchuan mining area. The prediction results demonstrate that the surface deformation range will continue to expand and that time-series surface deformation will show a slow deceleration trend in the next two years.

Design of Fully Controllable and Continuous Programmable Surface Based on Machine Learning

Programmable surfaces (PSs) consist of a 2D array of actuators that can deform in the third dimension, providing the ability to create continuous 3D profiles. Discrete PSs can be realized using an array of independent solid linear actuators. Continuous PSs consist of actuators that are mechanically coupled, providing deformation states that are more similar to real surfaces with reduced complexity of the control electronics. However, continuous PSs have been limited in size by the lack of the control systems required to take into account the complex internal coupling between actuators in the array. In this work, we computationally explore the deformation of a fully continuous PS with 81 independent actuation pixels based on ionic bending actuator. We establish a control strategy using machine learning (ML) regression models. Both forward and inverse control are achieved based on the training datasets which are derived from the finite element analysis (FEA) data of our PS. The prediction of surface deformation achieved by forward control with accuracy under 1% is 15000 times faster than FEM. And the real-time inverse control of continuous PSs that is to reproduce any arbitrary pre-defined surfaces, which possess high practical value for tactile display or human-machine interactive devices, is first proposed in the letter.

Surface deformation prediction based on TS-InSAR technology and long short-term memory networks

Surface deformation prediction based on TS-InSAR technology and long short-term memory networks

A prediction model for surface deformation caused by underground mining based on spatio-temporal associations

Accurate predictions of the surface deformation caused by underground mining are crucial for the safe development of underground resources. Although surface deformation has been predicted by artificial intelligence (AI) methods, most AI models are established based on the relationships between surface deformation and influential factors. The lack of consideration of the deformation state transition often leads to errors in the prediction results of catastrophic deformation by conventional AI methods. In this respect, this study introduces a surface deformation prediction model based on spatio-temporal association rule mining (STARM). Surface deformation is classified as excessive deformation zone (EDZ) and hysteretic deformation zone (HDZ), representing different surface deformation stage or state. The spatio-temporal association rules between the monitored EDZ and HDZ data are then mined. A surface deformation prediction model is established according to the spatio-temporal relationship between monitored EDZ and HDZ data. The proposed model is verified based on a practical case study of the Chengchao Iron Mine in China. The data collection of the influential factors is not requisite for the proposed model. It can achieve accurate prediction of the catastrophic deformation that was characterized by deformation state transition.

Study on Numerical Models in Predicting Surface Deformation Caused by Underground Coal Mining

Accurate prediction of surface deformation due to the extraction of underground coal seams is a significant challenge in geotechnical engineering. This task is further compounded by the growing trend for coal to be extracted under buildings by leaving coal pillars in place. To rationalize the design of mining schemes to ensure effective control of surface deformation, precise 3D numerical models of different mining schemes were developed in this paper. Two schemes were compared and analyzed based on the results of numerical simulations. Also, to verify the reliability of the prediction results from numerical simulations, the probability integral method was used for this prediction. The results show that the prediction results of the numerical simulations are close to those calculated by the probabilistic integral method, which proves the reliability of the numerical simulation prediction. The optimal mining scheme was determined by comparing the predicted results from the analysis of theoretical calculations and numerical simulations.

Development of a Hybrid Method to Predict the Slope Surface Deformation Utilizing the Time Series and GA-Elman Models

A reliable prediction of the surface deformation of slopes is vital to better assess the fatalities and economic losses caused by landslides. Many prediction methods have been proposed to estimate the surface deformation of landslides with nonlinear characteristics. However, these methods have low accuracy and poor applicability. In this paper, a new hybrid method for surface deformation prediction was proposed, which was deduced from the Wavelet Analysis, Genetic Algorithm (GA), and Elman Algorithm. In this method, the slope surface deformation was decomposed into a trend component and a periodic component using the time series model, which were trained and predicted utilizing the GA-Elman model. The predicted slope surface deformation was the combination of the trend component and the periodic component. Then, the predicted results of slope surface deformation through GA-Elman were compared with the predicted results through Support Vector Machines (SVM), Extreme Learning Machine (ELM), Back Propagation (BP), and Genetic Algorithm-Back Propagation (GA-BP) models. The comparison was made with reference to the data retrieved from the on-site slopes and the laboratory tests. The results revealed that the proposed method highlighted reliability and could be used with higher accuracy to forecast the slope surface deformation in the process of landslides.