Surface Deformation Prediction Research Articles

3D Visualization Monitoring and Early Warning of Surface Deformation in Subsidence Area Based on GIS

Due to the large-scale mining of mineral resources, the surface strata in many areas have collapsed and even developed into deformation and subsidence. Based on the conventional geotechnical deformation monitoring technology, the surface deformation prediction and disaster caused by the conventional geotechnical deformation monitoring technology can be avoided. In this paper, based on GIS, combined with the analysis of surface settlement, the surface settlement, slope value, curvature deformation value, horizontal displacement, and corresponding horizontal deformation formula are comprehensively analyzed and studied. In order to make the surface deformation visualization and realize the high approximation processing with the subsidence area, this paper innovatively introduces the 3D visualization settlement data analysis and realizes the calculation result analysis and corresponding retrieval multiple visualization in the subsidence area depth prediction process. On this basis, the prediction data of subsidence area can be optimized and analyzed so as to master the efficient and accurate law of surface deformation. In the experimental part, a mine is taken as the experimental object, and the 3D visual monitoring processing is carried out. In this paper, the main method to prevent electromagnetic leakage of the computer system is the source suppression method. The source suppression method solves the problem fundamentally by reducing or eliminating the emission of computer electromagnetic leakage source. In the actual experiment, the effect of this method is very effective. The experimental results show that the prediction results and corresponding early warning accuracy of the system are significantly improved compared with the traditional scheme, which provides practical significance for disaster prevention and corresponding environmental impact assessment.

Retrieving 3D Large Gradient Deformation Induced to Mining Subsidence Based on Fusion of Boltzmann Prediction Model and Single-Track InSAR Earth Observation Technology

Surface deformation of mining area caused by mining subsidence can be divided into three stages: the initial stage, the active stage and the decline stage. At the active stage, D-InSAR technology could be easily affected by the incoherence of large-gradient deformation, thus leading to the failure of conventional D-InSAR technology in monitoring mining subsidence.In order to solve the problem, this paper developed a 3D surface deformation prediction method of mining subsidence by integrating dynamic Boltzmann prediction model with D-InSAR. It was firstly to monitor the surface movement and deformation by D-InSAR technology during the decline stage and obtain the surface LOS deformation field data. Then, on the basis of Boltzmann model, the geometric equation between LOS deformation by D-InSAR monitored and 3D deformation was established. Next, the geometric equation was solved based on the GA theory, and the parameters of Boltzmann prior model were obtained. On the basis, the 3D deformation of mining area were obtained, proving that D-InSAR technology was effective to get 3D deformation of mining subsidence. Through simulation experiments, the effectiveness of the proposed method is verified. The surface deformation of 1613 working face in Guqiaonan coal mine was monitored by D-InSAR during the decline stage, and the LOS deformation values were obtained. The 3D deformation was obtained from the initial stage to the decline stage by using the method of DB-InSAR. The monitoring results showed that the fitting errors in the LOS deformation were mostly within 3 mm, the RMSES was ±2.37 mm, shown that the fitting accuracy was high. The RMSE of subsidence were ±120 mm. The results showed that the method of DB-InSAR has engineering application values.

Research on Prediction Model of Mining Subsidence in Thick Unconsolidated Layer Mining Area

Accurately forecasting the scope of coal mining subsidence area is of great significance to the protection of surface structures. Due to the peculiarities of the unconsolidated layers rock formation, the surface movement of the thick unconsolidated layers mining area converges slowly at the boundary of the basin, and the boundary of the subsidence basin is larger than under conventional conditions. It is found that the existing models have a poor prediction effect at the boundary, and the predicted subsidence basin range is smaller than the actual basin range. To solve this problem, a new surface deformation prediction model based on Boltzmann function (IB) is proposed in this paper. Aiming at the problem that the model function is highly nonlinear and difficult to obtain parameters, the multi-population genetic algorithm (MPGA) is introduced into the parameter solution of the prediction model, and the parameter calculation model based on multi-population genetic algorithm (MPGAIB) is constructed. The simulation experiment and engineering example analysis show that both the overall fitting effect and the fitting effect at the boundary of IB model are closer to the actual situation, The MPGAIB model has good ability to anti-random error and gross error, and the result is stable.

Investigations on longwall mining subsidence impacts on Pennsylvania highway alignments

Over 600 longwall panels have been mined in Pennsylvania in the last 50 years. Of those 600 panels, 25 panels undermined interstates highways. Despite this quantity of panels, much is still unknown regarded the detailed effects of undermining highways. The Gateway Mine, the Emerald Mine, and the Cumberland Mine undermined I-79 with 17 panels in 1982–1989 and in 2003–2008, respectively; Mine 84 undermined I-70 with 4 panels in 1987–2000. Through the examination of the panels that undermined I-70 and I-79, it is possible to determine which factors have most impacted the highway alignments. In some locations, the highway intersects with panels at angles ranging from 45° to 80°; and at the others, it runs between two panels, which simulates the effect of gateroads on the subsidence. The panel width to overburden ratio varies between 0.64 and 1.7, meaning that the interstates were influenced by both subcritical and supercritical subsidence basins. The face advance rates and overburden depths also vary between the panels. Unfortunately, specific information detailing highway impacts associated with unique characteristics of the subsidence basins are limited. In this paper, using the profile function model and influence function model within the surface deformation prediction system (SDPS), the effects of overburden, panel size, and orientation of the road on the highway can be indirectly assessed.

How Accurately Can We Model Magma Reservoir Failure With Uncertainties in Host Rock Rheology?

AbstractForecasting the onset of a volcanic eruption from a closed system requires understanding its stress state and failure potential, which can be investigated through numerical modeling. However, the lack of constraints on model parameters, especially rheology, may substantially impair the accuracy of failure forecasts. Therefore, it is essential to know whether large variations and uncertainties in rock properties will preclude the ability of models to predict reservoir failure. A series of two‐dimensional, axisymmetric models are used to investigate sensitivities of brittle failure initiation to assumed rock properties. The numerical experiments indicate that the deformation and overpressure at failure onset simulated by elastic models will be much lower than the viscoelastic models, when the timescale of pressurization exceeds the viscoelastic relaxation time of the host rock. Poisson's ratio and internal friction angle have much less effect on failure forecasts than Young's modulus. Variations in Young's modulus significantly affect the prediction of surface deformation before failure onset when Young's modulus is < 40 GPa. Longer precursory volcano‐tectonic events may occur in weak host rock (E < 40 GPa) due to well‐developed Coulomb failure prior to dike propagation. Thus, combining surface deformation with seismicity may enhance the accuracy of eruption forecast in these situations. Compared to large and oblate magma systems, small and prolate systems create far less surface uplift prior to failure initiation, suggesting that more frequent measurements are necessary.

Monitoring and analysis of mining 3D time-series deformation based on multi-track SAR data

ABSTRACT Accurate monitoring of the developing process of a surface subsidence basin is the basis of building damage assessment and surface deformation prediction. In this paper, the Synthetic Aperture Radar (SAR) data of three different imaging geometries, TerraSAR, Radarsat-2, and Sentinel-1A, were exploited. Firstly, two-dimensional (2D) time-series deformation of the surface subsidence basin caused by 15,235 working face mining was obtained based on Multidimensional Small Baseline Subset (MSBAS) technology from 19 December 2015 to 5 March 2016. By comparing vertical deformation with levelling data, it is shown that the root-mean-square error of vertical deformation is 3.2 mm and the standard deviation is 1.9 mm when the ascending-descending track SAR data is available. Otherwise, the root-mean-square error of vertical deformation is 18.1 mm and the standard deviation is 11.6 mm. Because of the low precision of the north–south horizontal movement monitored by the SAR sensor, the vertical deformation acquired by MSBAS technology and the rules of the mining subsidence (horizontal movement is proportional to tilt) were combined to obtain the north–south horizontal movement which was proven to be reliable by comparing the 2D time-series deformation obtained by MSBAS technology. Then, the deformation of the railway in the surface subsidence basin was analysed based on the three-dimensional (3D) time-series deformation. The results show that the subsidence, tilt, and horizontal movement strongly influence the railway in the monitoring period, but will not affect the normal traffic. This experiment lays a technical foundation for preventing the occurrence of mining disasters and verifies the ability to monitor the deformation of buildings and structures by interferometry synthetic aperture radar technology.

Analysis of surface subsidence due to longwall mining under weak geological conditions: Turgut basin of Yatağan-Muğla (Turkey) case study

Accurate prediction of surface subsidence due to the extraction of underground coal seams becomes a significant challenge in engineering. For a projected longwall coal mining operation, conditions of weak cover rock and thick coal seams (6–12 m) in Turgut basin in Yatagan-Turkey are described and discussed based on the field data, using surface deformation prediction system software. This paper aims at predicting the subsidence values only numerically by evaluating the influence of subsidence on irrigation pipeline structure to be built on the ground for the cases with pillars left in the coal seam.

Development of a Neural Network for Earth Surface Deformation Prediction

Coal industry is one of the largest parts of the world fuel and energy complex, and 36% of the world coal reserves are extracted by underground mining. The process of undermining leads to Earth surface deformations, and it can cause damage and destruction of buildings and structures. Therefore, it is necessary to monitor undermined objects with certain frequency and predict deformations of the territory. The article describes the solution of the prediction problem with the help of neural network technology. The creation of a neural network includes several steps, described in the article in detail: choice of network architecture, preparation and normalization of input data, development of a mathematical model for network calculation, training and testing of the network. Training and testing were done based on the materials of the gas pipeline undermining project. The developed tool allows predicting the Z coordinate for profile line benchmarks for any day of displacement based on the data of instrumental observations.

Development of a new algorithm for implementing the edge effect offset for subsidence calculations

The surface deformation prediction system (SDPS) program has been developed as an engineering tool for the calculation of subsidence deformation indices through the implementation of various prediction methods. From basic user-defined input parameters, SDPS can determine subsidence indices, such as mining induced displacements, strains, tilt, etc., at any elevation between the seam and the horizontal or varying surface topography. A fundamental parameter in obtaining reliable ground deformation results is the determination of the edge effect offset. The value assigned to the edge effect offset corresponds to a virtual offsetting of boundary lines delineating the extracted panel to allow for roof cantilevering over the mined-out area. The objective of this paper is to describe the methods implemented in updating the edge effect offset algorithm within SDPS. Using proven geometric equations, the newly developed algorithm provides a more robust calculation of the offset boundary of the extracted panel for simplistic as well as more complex mining geometries. Given that an extracted panel is represented by a closed polyline, the new edge offset algorithm calculates a polyline offset into the extracted panel with respect to the user defined edge effect offset distance. Surface deformations are then calculated using this adjusted panel geometry. The Matlab program was utilized for development and testing of the new edge effect offset algorithm. After completing rigorous testing regimes, the new offset algorithm will be integrated into SDPS further increasing the speed and reliability of the program resulting in a retrospective increase in capability and flexibility.

Assessment of potential impacts to surface and subsurface water bodies due to longwall mining

Ground movements due to longwall mining operations have the potential to damage the hydrological balance within as well as outside the mine permit area in the form of increased surface ponding and changes to hydrogeological properties. Recently, the Office of Surface Mining, Reclamation and Enforcement (OSMRE) in the USA, has completed a public comment period on a newly proposed rule for the protection of streams and groundwater from adverse impacts of surface and underground mining operations (80 FR 44435). With increased community and regulatory focus on mining operations and their potential to adversely affect streams and groundwater, now there is a greater need for better prediction of the possible effects mining has on both surface and subsurface bodies of water. With mining induced stress and strain within the overburden correlated to changes in the hydrogeological properties of rock and soil, this paper investigates the evaluation of the hydrogeological system within the vicinity of an underground mining operation based on strain values calculated through a surface deformation prediction model. Through accurate modeling of the pre- and post-mining hydrogeological system, industry personnel can better depict mining induced effects on surface and subsurface bodies of water aiding in the optimization of underground extraction sequences while maintaining the integrity of water resources.

Lung surface deformation prediction from spirometry measurement and chest wall surface motion.

The authors have developed and evaluated a method to predict lung surface motion based on spirometry measurements, and chest and abdomen motion at selected locations. A patient-specific 3D triangular surface mesh of the lung region was obtained at the end expiratory phase by the threshold-based segmentation method. Lung flow volume changes were recorded with a spirometer for each patient. A total of 192 selected points at a regular spacing of 2 × 2 cm matrix points were used to detect chest wall motion over a total area of 32 × 24 cm covering the chest and abdomen surfaces. QR factorization with column pivoting was employed to remove redundant observations of the chest and abdominal areas. To create a statistical model between the lung surface and the corresponding surrogate signals, the authors developed a predictive model based on canonical ridge regression. Two unique weighting vectors were selected for each vertex on the lung surface; they were optimized during the training process using all other 4D-CT phases except for the test inspiration phase. These parameters were employed to predict the vertex locations of a testing data set. The position of each lung surface mesh vertex was estimated from the motion at selected positions within the chest wall surface and from spirometry measurements in ten lung cancer patients. The average estimation of the 98th error percentile for the end inspiration phase was less than 1 mm (AP = 0.9 mm, RL = 0.6 mm, and SI = 0.8 mm). The vertices located at the lower region of the lung had a larger estimation error as compared with those within the upper region of the lung. The average landmark motion errors, derived from the biomechanical modeling using real surface deformation vector fields (SDVFs), and the predicted SDVFs were 3.0 and 3.1 mm, respectively. Our newly developed predictive model provides a noninvasive approach to derive lung boundary conditions. The proposed system can be used with personalized biomechanical respiration modeling to derive lung tumor motion during radiation therapy from noninvasive measurements.

A Neural Network based Intelligent Method for Mine Slope Surface Deformation Prediction Considering the Meteorological Factors

Accurate mine slope surface deformation forecasting can provide reliable guidance for safe mining production and mine construction planning, which is also important for the personnel safety of the mining staffs. The mine slope surface deformation forecasting is a non-linear problem. Generalized regression neural network (GRNN) has been proven to be effective in dealing with the non-linear problems, but it is still a challenge of how to determine the appropriate spread parameter in using the GRNN for deformation forecasting. In this paper, a mine slope surface deformation forecasting model combining artificial bee colony optimization algorithm (ABC) and generalized regression neural network was proposed to solve this problem. The effectiveness of this proposed forecasting model was proved by experiment comparisons. The test results show that the proposed intelligent forecasting model outperforms the BP neural network forecasting model, BP neural network with genetic algorithm optimization (GA-BPNN) and the ordinary linear regression (LR) forecasting models in the mine slope surface deformation forecasting. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4815

Towards quantitative modelling of surface deformation of polymer micro-structures under tactile scanning measurement

Contact stylus-based surface profilometry is capable of topography measurement whilst being independent of the physical, electrical and optical properties of the materials under test, and has therefore become an indispensable tool for dimensional measurement of transparent specimens. However, large measurement deviations will appear when soft specimens, especially specimens made of polymers, are measured by contact stylus profilometry. In this paper the surface deformation behaviour of two polymers for molding and one photoresist, i.e. Ormocomp, Ormoclad and SU-8, under different tactile measurement conditions have been experimentally investigated. An empirical analytical model is hereby proposed for the prediction of surface deformation of soft specimens under tactile (sliding) contact. Preliminary experimental results demonstrate that the proposed five-parameter model is applicable for describing the deformation behaviour of these thermoplastic materials under the scanning speed ranging from 2 to 200 μm s−1 and the probing force varying from 5 to 500 μN. In addition, thanks to quantitative topographical measurements of the layer thickness of the aforementioned photoresists, the scratch behaviour and the time-dependent mechanical properties of these materials have also been experimentally determined.

On the applicability of Persistent Scatterers Interferometry (PSI) analysis for long term CO2 storage monitoring

Persistent scatterers synthetic aperture radar interferometry (PSI) analysis is viewed as a promising monitoring technique for multiyear detection of ground surface displacements during long term CO2 injection into deep aquifers (of the order of 25 to 50years).This technique was successfully utilized at the CO2 storage site of In-Salah (Algeria). Nevertheless, this success may not be generalized to future onshore industrial-scale CO2 storage sites, which are planned in far more complex contexts mostly presenting natural terrains, either agricultural or vegetated areas, i.e. characterized by poor PS density, which might locally drop to zero, hence hampering the deployment of the PSI technique. The objective of the present paper is to discuss the constraints on the PSI application regarding the specificities (magnitude, spatial or temporal scales and evolution) of the deformation phenomenon associated with the CO2 injection into storage aquifers. This discussion is supported by the estimates of the expected spatio-temporal evolution of surface vertical displacements caused by CO2 injection in deep aquifers by combining the approximate solutions for pressure build-up and for the associated vertical displacements in an elastic half-space. A possible option to enhance the performance of the PSI technique is explored, namely the deployment of a network of corner reflectors CR corresponding to artificial devices installed on ground to complement the existing “natural” PS network. A methodology is then proposed to optimize the CR network (number and position) in an iterative strategy (e.g., every 10years) relying on the prediction of surface deformation. The methodology is illustrated by a CO2 injection scenario in the Paris basin context.

Application of Neural Network in Displacement Prediction of Dongping Tunnel

It has been proved that Multi-layer network computing can solve the nonlinear separable problem, but the problem which the hidden layer makes the learning more difficult limits the development of multi-layer network.Back-propagation (BP) algorithm solve this problem and promote multi-network research to regain attention. In this paper, A new method which dynamic matrix control method based on neural network is found. Its essence is that the resulting prediction signal is produced by the manner which regards neural network model as prediction model, and the predictive control of nonlinear systems would be realized by the control law which using the receding optimization algorithm. Neural network Selects the BP neural network which possess a good nonlinear function approximation capability.Aiming at dongping tunnel surface deformation prediction, the article adopt BP Neural Network to train the system basing on the given data. It shows the hiding neural node is close to precision; predictive value in good agreement with measured values, and to some extent be able to guide the construction.

A Hybrid Boundary/Finite Element Method for Simulating Viscous Flows and Shapes of Droplets in Electric Fields

A mixed boundary element and finite element numerical algorithm for the simultaneous prediction of the electric fields, viscous flow fields, thermal fields and surface deformation of electrically conducting droplets in an electrostatic field is described in this paper. The boundary element method is used for the computation of the electric potential distribution. This allows the boundary conditions at infinity to be directly incorporated into the boundary integral formulation, thereby obviating the need for discretization at infinity. The surface deformation is determined by solving the normal stress balance equation using the weighted residuals method. The fluid flow and thermal fields are calculated using the mixed finite element method. The computational algorithm for the simultaneous prediction of surface deformation and fluid flow involves two iterative loops, one for the electric field and surface deformation and the other for the surface tension driven viscous flows. The two loops are coupled through the droplet surface shapes for viscous fluid flow calculations and viscous stresses for updating the droplet shapes. Computing the surface deformation in a separate loop permits the freedom of applying different types of elements without complicating procedures for the internal flow and thermal calculations. Tests indicate that the quadratic, cubic spline and spectral boundary elements all give approximately the same accuracy for free surface calculations; however, the quadratic elements are preferred as they are easier to implement and also require less computing time. Linear elements, however, are less accurate. Numerical simulations are carried out for the simultaneous solution of free surface shapes and internal fluid flow and temperature distributions in droplets in electric fields under both microgravity and earthbound conditions. Results show that laser heating may induce a non-uniform temperature distribution in the droplets. This non-uniform thermal field results in a variation of surface tension along the surface of the droplet, which in turn produces a recirculating fluid flow in the droplet. The viscous stresses cause additional surface deformation by squeezing the surface areas above and below the equator plane.

Design considerations for subsidence control

Subsidence planning and control must be an integral part of mine design in order to minimize the economic impact of underground mining on the surface. Precalculation of surface deformations which may cause damage to surface structures must be performed and damage criteria must be established, if the response of structures to such movements is to be determined and the extent of damages to be assessed. In this paper the Surface Deformation Prediction System (SDPS), which can be used to predict accurately the vertical and horizontal parameters of surface movements due to underground mining, is discussed. The Subsidence Response Modelling Program (SRMP), which can be applied to evaluate the response of buildings to subsidence-induced movements, is also reviewed. Finally, an example is presented demonstrating the applicability of these techniques for subsidence design and control.

Prediction of surface deformation in a full scale test pavement

The mechano-lattice analysis method of analysis of pavements is based on elastoplastic behaviour of the multi-layered pavement wheras other methods of analysis usually utilise elastic behaviour. The method enables a study to be made of the build-up of residual stress and strain due to the variation of plastic behaviour between the various pavement layers. The authors describe the mechano-lattice analysis and then use the method to predict the performance of two thin crushed rock pavements. The resilient moduli and the permanent strain behaviour of the materials under repeated load triaxial testing are used as input. The resulting calculated rut depths, rebound deflections and transient longitudinal strains under load are compared with the field results after the passage of an estimated 250 000 equivalent standard axles.