The risk of collapse in abandoned mine sites: the issue of data uncertainty

Longoni, Laura; Papini, Monica; Brambilla, Davide; Arosio, Diego; Zanzi, Luigi

doi:10.1515/geo-2016-0022

Cited by 19 publications

(4 citation statements)

References 48 publications

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“…River network (Fig 2I) has been generated from DEM by computing flow accumulation, and then distance to river and river density also has been calculated by buffering river sections [20]. The NDVI (normalized difference vegetation index, Fig 2J), NDWI (normalized difference water index) and ULI (urban land-use index) have been extracted from Landsat 8 imagery using ENVI software (https://landsat.usgs.gov/landsat-8) [21]. The lithology and distance to faults were prepared using a geological map at a scale of 1:200,000 provided by China Geological Survey (http://www.cgs.gov.cn/).…”

Section: Study Areamentioning

confidence: 99%

Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors

et al. 2019

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The fragile ecological environment near mines provide advantageous conditions for the development of landslides. Mine landslide susceptibility mapping is of great importance for mine geo-environment control and restoration planning. In this paper, a total of 493 landslides in Shangli County, China were collected through historical landslide inventory. 16 spectral, geomorphic and hydrological predictive factors, mainly derived from Landsat 8 imagery and Global Digital Elevation Model (ASTER GDEM), were prepared initially for landslide susceptibility assessment. Predictive capability of these factors was evaluated by using the value of variance inflation factor and information gain ratio. Three models, namely artificial neural network (ANN), support vector machine (SVM) and information value model (IVM), were applied to assess the mine landslide sensitivity. The receiver operating characteristic curve (ROC) and rank probability score were used to validate and compare the comprehensive predictive capabilities of three models involving uncertainty. Results showed that ANN model achieved higher prediction capability, proving its advantage of solve nonlinear and complex problems. Comparing the estimated landslide susceptibility map with the ground-truth one, the high-prone area tends to be located in the middle area with multiple fault distributions and the steeply sloped hill.

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Section: Study Areamentioning

confidence: 99%

Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors

et al. 2019

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“…When a mine is closed, the shafts are liquidated in a manner that depends on the country and time. Unfortunately, liquidated shafts pose danger to the surrounding area due to the factors indicated above as well as the following [1,6]:…”

Section: Introductionmentioning

confidence: 99%

Microgravity Survey to Detect Voids and Loosening Zones in the Vicinity of the Mine Shaft

Porzucek

Łój

2021

Energies

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In mining and post-mining areas, the assessment of the risks to the surface and its infrastructure from the opening or closed mine is of the utmost importance; particular attention should be paid to mine shafts. The risks include the occurrence of undetected voids or loosening zones in the rock mass. Their detection makes it possible to prevent their impact on a mine shaft and surface infrastructure. Geophysical methods, and in particular, a microgravity method lend themselves for the detection of changes in the distribution of masses (i.e., the density) due to voids and loosening zones. The paper presents the results of surface microgravity surveys in the vicinity of three mine shafts: under construction, working, and a liquidated one. Based on the gravity anomalies, the density distribution of the rock mass for all three cases was recognized. The properties of the anomalies allowed to determine which of the identified decreased density zones may pose a threat to the surface infrastructure or a mine shaft. The microgravity survey made inside the working mining shaft provided information on the density of rocks outside the shaft lining, regardless of the type of lining. No significant decrease of density was found, which means that there are no larger voids outside the shaft lining. Nevertheless, at a depth of 42 m in running sands layer, the decreasing density zone was located, which should be controlled. Additionally, measurements in two vertical profiles gave the possibility of directional tracking of density changes outside shaft lining. Such changes were observed on three boundaries of geological layers, with two of them being on the boundary of gypsum and other rocks.

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“…In case of a progressive ground subsidence simulation during undermining, GIS would be effective and efficient in computing such 4D ground movement and deformation if a prediction system could be properly developed. It also should be addressed that the GIS approach could result in an uncertain issue once it relies on just a small amount of information (Longoni et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Computational implementation of a GIS developed tool for prediction of dynamic ground movement and deformation due to underground extraction sequence

Cai

Jiang

Liu

et al. 2016

Int J Coal Sci Technol

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In the last century, there has been a significant development in the evaluation of methods to predict ground movement due to underground extraction. Some remarkable developments in three-dimensional computational methods have been supported in civil engineering, subsidence engineering and mining engineering practice. However, ground movement problem due to mining extraction sequence is effectively four dimensional (4D). A rational prediction is getting more and more important for long-term underground mining planning. Hence, computer-based analytical methods that realistically simulate spatially distributed time-dependent ground movement process are needed for the reliable long-term underground mining planning to minimize the surface environmental damages. In this research, a new computational system is developed to simulate four-dimensional (4D) ground movement by combining a stochastic medium theory, Knothe time-delay model and geographic information system (GIS) technology. All the calculations are implemented by a computational program, in which the components of GIS are used to fulfill the spatial-temporal analysis model. In this paper a tight coupling strategy based on component object model of GIS technology is used to overcome the problems of complex three-dimensional extraction model and spatial data integration. Moreover, the implementation of computational of the interfaces of the developed tool is described. The GIS based developed tool is validated by two study cases. The developed computational tool and models are achieved within the GIS system so the effective and efficient calculation methodology can be obtained, so the simulation problems of 4D ground movement due to underground mining extraction sequence can be solved by implementation of the developed tool in GIS.

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The risk of collapse in abandoned mine sites: the issue of data uncertainty

Cited by 19 publications

References 48 publications

Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors

Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors

Microgravity Survey to Detect Voids and Loosening Zones in the Vicinity of the Mine Shaft

Computational implementation of a GIS developed tool for prediction of dynamic ground movement and deformation due to underground extraction sequence

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