Subsidence Prediction Model Research Articles

MEASUREMENT OF SEAWARD GROUND DISPLACEMENTS ON COASTAL LANDFILL AREA USING RADAR INTERFEROMETRY

Abstract. In order to understand the mechanism of subsidence and help reducing damage, researchers has been observed the line-of-sight subsidence on the Noksan industrial complex using SAR Interferometry(InSAR) and suggested subsidence prediction models. Although these researches explained a spatially uneven ground subsidence near the seaside, they could not have been explained the occurrence of the newly proposed seaward horizontal, especially nearly north-ward, displacement because of the geometric limitation of InSAR measurements. In this study, we measured the seaward ground displacements trend on the coastal landfill area, Noksan Industrial Complex. We set the interferometric pairs from an ascending and a descending orbits strip map data of ALOS PALSAR2. We employed InSAR and MAI stacking approaches for the both orbits respectively in order to improve the measurement. Finally, seaward deformation was estimated by retrieving three-dimensional displacements from multi-geometric displacements. As a results, maximally 3.3 and 0.7 cm/year of ground displacements for the vertical and seaward directions. In further study, we plan to generate InSAR and MAI stacking measurements with additional SAR data to mitigate tropospheric effect and noise well. Such a seaward observation approach using spaceborne radar is expected to be effective in observing the long-term movements on coastal landfill area.

A new model for predicting surface mining subsidence: the improved lognormal function model

Mining-induced problems in the coal field seriously threaten the normal operation of the mines and cause significant property losses and environmental disruption. Thus, high precision subsidence prediction is important on the processing of mining subsidence problems. In this paper, we analyzed the formation mechanism of skewed subsidence. The rock beam on the side of the gob and coal pillar presented different supporting reaction force, and the difference resulted in the asymmetric distribution of subsidence velocity, which further led to the formation of the surface skewed subsidence basin. The relationship between the wave curve and vibration curve was determined, and the skewed subsidence process of the surface point in the mining affected area was analyzed. The total duration of the initial and accelerated subsidence phases is smaller than that of the decelerated and end subsidence phases. Then, from the skewed subsidence characteristics, the skewed subsidence prediction model based on the lognormal function was built. An application example was selected to validate the feasibility and effectiveness of the proposed model. Results showed that the model has good prediction ability.

Characterizing strata deformation over coal pillar system in longwall panels by using subsurface subsidence prediction model

The ground control stability problem due to underground large mine openings often associates with large movements and deformations of strata. The situation becomes more complicated when relative subsidence research involves zones above the coal pillar. Continues fractures in strata created by such subsidence progress may become the pathway of fresh air delivery or water migration in the strata aquifer which would cause the problems of underground coal combustion or water resource losing. Hence, knowing the subsurface strata movement characteristics over gob and coal pillar to make any subsidence predictions are desired for mining engineers to estimate any damages happened in the subsurface strata in advance. This paper proposes a prediction model for investigating subsurface strata movements over the pillar. Using the calculated subsidence results, the related strata fraction distributions are characterised. For validation reason, extensometer boreholes are used to identify the subsurface strata caving progress in a long-wall panel. The measurement results agree with the predications derived by the model shows the reliability of the model. Such research efforts can well help to understand the moving behaviour of all strata over the coal pillars and assist mining engineer to efficiently deal with the possible crack pathways over the coal pillar.

Model for the prediction of subsurface strata movement due to underground mining

The problem of ground control stability due to large underground mining operations is often associated with large movements and deformations of strata. It is a complicated problem, and can induce severe safety or environmental hazards either at the surface or in strata. Hence, knowing the subsurface strata movement characteristics, and making any subsidence predictions in advance, are desirable for mining engineers to estimate any damage likely to affect the ground surface or subsurface strata. Based on previous research findings, this paper broadly applies a surface subsidence prediction model based on the influence function method to subsurface strata, in order to predict subsurface stratum movement. A step-wise prediction model is proposed, to investigate the movement of underground strata. The model involves a dynamic iteration calculation process to derive the movements and deformations for each stratum layer; modifications to the influence method function are also made for more precise calculations. The critical subsidence parameters, incorporating stratum mechanical properties and the spatial relationship of interest at the mining level, are thoroughly considered, with the purpose of improving the reliability of input parameters. Such research efforts can be very helpful to mining engineers' understanding of the moving behavior of all strata over underground excavations, and assist in making any damage mitigation plan. In order to check the reliability of the model, two methods are carried out and cross-validation applied. One is to use a borehole TV monitor recording to identify the progress of subsurface stratum bedding and caving in a coal mine, the other is to conduct physical modelling of the subsidence in underground strata. The results of these two methods are used to compare with theoretical results calculated by the proposed mathematical model. The testing results agree well with each other, and the acceptable accuracy and reliability of the proposed prediction model are thus validated.

Predicting Underground Strata Movements Model with Considering Key Strata Effects

Longwall coal mining causes a large scale of subsidence in both overlying strata and ground surface. No matter where subsidence or deformation happened, its damaging effects to underground mining work or surface infrastructures are not anticipated. To accurately predict surface subsidence over the mining area, surface subsidence prediction models based on influence function method are developed. Based on the surface subsidence model, in this paper, a prediction model for overlying strata movements is developed with innovatively optimizing three key parameters used for setting the boundary condition. A load is derived using the beam theory with the simple overlying strata model, which is important for getting the radius of major influence, one of the three key parameters. Many features of progress subsidence are reproduced in the model and finally found to fit reasonably well with numerical simulation results using the same geological parameters. The modelling study reveals that the newly proposed subsurface model is flexible and versatile to represent the subsurface subsidence profile under various possible conditions. The key strata, especially the hard rock strata, act like an elastic beam and can overhang the load from above strata so that the displacement of rock mass or stress distribution in strata will be drastically changed when close to key strata. The proposed subsurface subsidence prediction model discloses some basic laws of overlying strata subsidence with the mining of coal seam.

Strata movement and surface subsidence prediction model of dense solid backfilling mining

Surface subsidence is the most common disaster in coal mining areas, which threatens human lives and assets. In this study, a prediction model is proposed to predict the surface subsidence caused by dense solid backfilling mining. Firstly, the surface subsidence is monitored by high-resolution survey and its static and dynamic characteristics are analyzed. Then, the universal law of strata movement and load distribution on the main roof are analyzed by similar material simulation and numerical simulation, respectively. The strata movement and deformation of dense solid backfilling mining are totally different from caving mining. There is no caving zone but a limited height of fractured zone developed in the strata. The load distribution on main roof follows the piecewise function. Finally, the prediction model is created based on the combination of elastic foundation beams and volume invariant transfer principle, and the prediction values basically coincide with measured values. This prediction model can provide reference data for the evaluation and management of coal mining hazards.

Combining differential SAR interferometry and the probability integral method for three-dimensional deformation monitoring of mining areas

ABSTRACTWith the exploitation of coal resources, ground surface subsidence continues to occur in mining areas, destroying the ecological environment and significantly affecting the daily productivity and life of humans. The differential synthetic aperture radar interferometry (D-InSAR) technique is widely used to monitor ground surface deformation because of its unique advantages such as high accuracy and wide coverage. However, conventional D-InSAR technology provides only one-dimensional (1D) displacement monitoring along the radar line of sight (LOS). This article proposes a method based on an analysis of the mining subsidence law for true three-dimensional (3D) displacement monitoring by combining D-InSAR and a subsidence prediction model based on the probability integral method. In this approach, 1D displacement, obtained using D-InSAR, is then combined with the prediction model to obtain the 3D displacement of ground surface target points. Here, 3D displacement curves were obtained for the Fengfeng mining area (China) using RadarSat-2 images obtained on 9 January and 2 February 2011. True ground surface displacement was measured simultaneously by levelling when the 152under31 s working face was being exploited in Jiulong mine. Vertical displacement and inclined deformation calculated using the proposed method were compared with levelling survey data and the results showed average differences of 3.2 mm and 0.1 mm m−1, respectively; the calculated maximum displacement in the east–west and south–north directions were 106 and 73 mm, respectively. The spatial distribution of the displacements was in accordance with the mining subsidence law. Thus, the new method can retrieve highly accurate 3D displacements caused by mining subsidence.

Model Establishment for Ponding in Coal Mining Subsidence Areas and its Prediction: Case Study of Northern Jining, China

Mining in areas with a high phreatic water surface leads to groundwater exposure and the accumulation of the water in the subsidence basin. The calculation of the volume of water is of high importance. This paper analyzes the seasonal or perennial ponding caused by mining in areas with high phreatic water surfaces. With the help of digital elevation model, the estimated groundwater elevation, and the basis of a mining subsidence prediction model, a model of ponding in subsidence areas has been established. Using this model, the amount of mining subsidence ponding in northern Jining has been calculated. Furthermore, the evolution of ponding has been forecasted. Our experimental results indicate that the average depth of the ponding area is projected to be about 3 m after mine closure. The volume of the new perennial ponding is 0.2–2 million m3 per year and it is negligible to the river confluence in Jining. This model helps in the calculation of the amount of water in the subsidence area and the evaluation of the impact of mining subsidence area on the catchment, which would provide a certain basis for the reclamation and protection of water resources in the locality.

Surface dynamic subsidence prediction model of solid backfill mining

Solid backfill mining is a filling mining method that integrates subsidence control and solid waste disposal and is an effective way to extract coal under buildings, railways and water bodies. In this study, we propose a prediction model to accurately predict the surface dynamic subsidence process of solid backfill mining and assess mining damage. First, a dynamic subsidence function of roof in solid backfill mining was established, whose validity was verified using in situ measured subsidence data from the roof of two backfill working faces. Then, this function and the Knothe time function were combined to create a dynamic surface subsidence prediction model of solid backfilling mining, which accumulates surface subsidence caused by backfilling materials’ compression deformation at different times. Finally, an engineering case study demonstrates that the model is effective. This prediction model provides scientific reference for guidance in the design of solid backfill mining and the reduction in mining damage.

An improved influence function method for predicting subsidence caused by longwall mining operations in inclined coal seams

Prediction of surface subsidence caused by longwall mining operation in inclined coal seams is often very challenging. The existing empirical prediction methods are inflexible for varying geological and mining conditions. An improved influence function method has been developed to take the advantage of its fundamentally sound nature and flexibility. In developing this method, the original Knothe function has been transformed to produce a continuous and asymmetrical subsidence influence function. The empirical equations for final subsidence parameters derived from collected longwall subsidence data have been incorporated into the mathematical models to improve the prediction accuracy. A number of demonstration cases for longwall mining operations in coal seams with varying inclination angles, depths and panel widths have been used to verify the applicability of the new subsidence prediction model.

Time-dependent subsidence prediction model and influence factor analysis for underground gas storages in bedded salt formations

Taking into account the impurity and creep of rock salt, a new model is proposed to predict the time-dependent subsidence of the surface above underground gas storage caverns in bedded salt formations. In this model, the shape of the storage cavern is assumed to be a sphere. Formulae are developed for the volume and volume reduction of the cavern over time during both the construction and the operating periods. A probability integral method is adopted to determine the shape of the subsidence regions, and corresponding formulae are derived for the time-dependent surface subsidence, radial tilt and radial curvature. FLAC3D was used to verify the proposed prediction model. Using the proposed model, parametric analyses are carried out to study the influences of draw angle, volume adjusting coefficient, brine withdrawal rate, internal pressure, cavern depth, material properties of the rock salt, cavern diameter, and operating time on the surface subsidence. The results show that: the subsidence increases with an increase of the draw angle, volume adjusting coefficient, brine withdrawal rate, cavern diameter, material properties of the rock salt, and time, but decreases with an increase of internal pressure. Subsidence increases first and then decreases with increasing cavern depth. Draw angle, volume adjusting coefficient, cavern depth, cavern diameter, material properties of the rock salt, time and internal pressure have a large influence on the subsidence, but there is little influence from the brine withdrawal rate and salt impurity.

A novel ground surface subsidence prediction model for sub-critical mining in the geological condition of a thick alluvium layer

A substantial number of the coal mines in China are in the geological condition of thick alluvium layer. Under these circumstances, it does not make sense to predict ground surface subsidence and other deformations by using conventional prediction models. This paper presents a novel ground surface subsidence prediction model for sub-critical mining in the geological condition of thick alluvium layer. The geological composition and mechanical properties of thick alluvium is regarded as a random medium, as are the uniformly distributed loads on rock mass; however, the overburden of the rock mass in the bending zone is looked upon as a hard stratum controlling the ground surface subsidence. The different subsidence and displacement mechanisms for the rock mass and the thick alluvium layer are respectively considered and described in this model, which indicates satisfactory performances in a practical prediction case.

Dynamic Prediction Model of Mining Subsidence Based on Cellular Automata

Cellular Automata is a discrete dynamic model based on space-time, and is one of the effective methods to study complex systems. The CA, a new method applied to coal mining subsidence dynamic evolution model, provide a new idea for the prediction research of mining subsidence. Definition and basic theory of CA were introduced briefly. According to the particularity of the research in the field of mining subsidence, the definition of CA is extended. The model of dynamic evolution of mining subsidence is built on the CA. Then modeling method and the structure of model are elaborated, and the advantages of the CA are applied to coal mining subsidence prediction model is analyzed.

Surface Subsidence Prediction Model based on BP Neural Network

Surface Subsidence Prediction Model based on BP Neural Network

Experimental investigation on the train-induced subsidence prediction model of Beiluhe permafrost subgrade along the Qinghai–Tibet Railway in China

Abstract Based on dynamic triaxial test at low temperature of the frozen clay from the Beiluhe permafrost subgrade along the Qinghai–Tibet Railway (QTR), residual deformation laws and dynamic subsidence prediction model of permafrost subgrade under train traffic were studied. First, time history curves of residual strain of frozen soil are obtained and analyzed under different temperatures, frequency, confining pressure and moisture content. And conclusions can be drawn that the axial strain rate is greatly affected by the amplitude of dynamic stress, as well as increases with dynamic to static stress ratio and temperature rising, while it decreases with the raise of frequency and moisture content. Hereby, the power functions were adopted to fit the relationships of axial dynamic strain rate vs. stress ratio, temperature, frequency, and moisture content, respectively. Simultaneously, the permafrost dynamic subsidence mechanism was interpreted rationally and the variation of fitting parameters was discussed. Furthermore, the long-term traffic loading subsidence model was established through observing the axis residual strain time histories of frozen specimens under the longtime cyclic loading and adopting the amendment of vibratory number of times. The model can comprehensively consider the effects of stress state, temperature, moisture content, and confining pressure of frozen soil, as well as the vibratory frequency and vibration number of longtime cyclic loading. Consequently, the model can be applied to the train-induced subsidence forecast investigation of permafrost subgrade. The paper has an important significance for rational safety evaluation on long-term operation of permafrost regions railway such as QTR. Meanwhile, the investigation provides basic data for the further research on dynamic damage constitutive model of frozen soil under train traffic and the gradual improvement of railroading criterion in cold regions.

An influence function method based subsidence prediction program for longwall mining operations in inclined coal seams

The distribution of the final surface subsidence basin induced by longwall operations in inclined coal seam could be significantly different from that in flat coal seam and demands special prediction methods. Though many empirical prediction methods have been developed, these methods are inflexible for varying geological and mining conditions. An influence function method has been developed to take the advantage of its fundamentally sound nature and flexibility. In developing this method, significant modifications have been made to the original Knothe function to produce an asymmetrical influence function. The empirical equations for final subsidence parameters derived from US subsidence data and Chinese empirical values have been incorporated into the mathematical models to improve the prediction accuracy. A corresponding computer program is developed. A number of subsidence cases for longwall mining operations in coal seams with varying inclination angles have been used to demonstrate the applicability of the developed subsidence prediction model.

Advanced interpretation of subsidence in Murcia (SE Spain) using A-DInSAR data – modelling and validation

Abstract. Subsidence is a natural hazard that affects wide areas in the world causing important economic costs annually. This phenomenon has occurred in the metropolitan area of Murcia City (SE Spain) as a result of groundwater overexploitation. In this work aquifer system subsidence is investigated using an advanced differential SAR interferometry remote sensing technique (A-DInSAR) called Stable Point Network (SPN). The SPN derived displacement results, mainly the velocity displacement maps and the time series of the displacement, reveal that in the period 2004–2008 the rate of subsidence in Murcia metropolitan area doubled with respect to the previous period from 1995 to 2005. The acceleration of the deformation phenomenon is explained by the drought period started in 2006. The comparison of the temporal evolution of the displacements measured with the extensometers and the SPN technique shows an average absolute error of 3.9±3.8 mm. Finally, results from a finite element model developed to simulate the recorded time history subsidence from known water table height changes compares well with the SPN displacement time series estimations. This result demonstrates the potential of A-DInSAR techniques to validate subsidence prediction models as an alternative to using instrumental ground based techniques for validation.

Ground Subsidence in Areas of Loose Porous Aquifers

Abstract Groundwater is one of the important water resources in northern China's plain areas. Many severe geological hazards have occurred in these areas due to ground subsidence which is caused by over exploitation of groundwater. This paper introduces and analyses the ground subsidence caused by groundwater exploitation and its mechanism in the northern China's plains. A ground subsidence prediction model has been developed based on the consolidation theory. The authors have tested this model in a case study of Fuyang City, Anhui Province, where ground subsidence is a severe environmental problem. In the case study, the model results match very well with those of the actual measurement. Two schemes of groundwater exploitation are assessed. The conclusion from the study could be used in the long‐term water and economical management planning. The strategies for the control of ground subsidence are discussed.

Seam dip angle based mining subsidence model and its application

The novel mining subsidence prediction model is developed with the dip angle of a bedded ore deposit as a variable. It is applicable for coal seams with a dip angle ranging from 0 to 90°. Application of the model for seams with varying dip angles well demonstrates it's good adaptability.

Drought Subsidence Prediction Model for the Western San Joaquin Valley, California

Land subsidence due to groundwater extraction in the western San Joaquin Valley has been studied and monitoredfor almost six decades. Although its subsidence rates diminished after 1970, the problem is still of great concern during droughtperiods. Knowing how big the social impact and cost of subsidence were in the past decades, the paper proposes an innovative approach to predicting its further occurrence. Extrapolation is based on analysis of historical groundwater subsidence relationship, drought pattern, and acceptance of new explanatory mechanisms for clay inelastic compaction. Average predicted magnitudes of subsidence in the western San Joaquin Valleyfor the next five decades are in the range 4 m to 8 m.