T49 GIS and Remote Sensing Applications in Environmental and Engineering Geology (Posters)

Sunday, 27 October 2013
9:00 AM - 6:30 PM
Hall D

GIS-BASED MINING-INDUCED SUBSIDENCE HAZARD MAPPING AND ITS VALIDATION

Paper number: T49-2
Program: Technical Sessions
Time: 9:00 AM – 6:30 PM

Authors

Abstract

This study presents a Geographic Information Systems (GIS)-based mine subsidence hazard assessment using various geospatial data combined with statistical methods and expert systems which can be effectively used to hazard mitigation and management planning in abandoned coal mine areas. For this aim, GIS-based analysis model was developed which can evaluate effect of contributing factors on subsidence events, determine vulnerability of subsidence from the perspective of relative rank in regional scale. During the two years (2011~2013), intensive site investigation work for abandoned mine areas in South Korea was carried out and then the data were brought to the Prof. Edward Keller's lab at the Earth Sciences Department of UC Santa Barbara for discussion during invited lecture given by Prof. H. D. Park. GIS database was compiled from a variety of spatial data (i.e., mine drift maps, topographic maps, geologic maps, borehole data, and subsidence inventory maps representing the locations of past subsidence occurrences). Eight factor layers (drift depth, drift density, distance from nearest drift, distance from nearest railroad, rock mass rating, groundwater depth, slope, and surface runoff accumulation) were extracted to evaluate correlation between the related factors and past subsidence occurrences (training dataset). Frequency ratio (FR) model was applied to establish rating classes for each factor and Analytic hierarchy process (AHP) model was used as a method to establish weightings for the factors. The, two models were integrated to combine multiple FR-layers of eight factor to generate a subsidence hazard map. For the validation of subsidence hazard map, it is spatially compared with the subsidence inventory map (validation dataset) obtained from the field survey through the collaboration work. As a result, FR model and FR–AHP integrated model showed 97% and 94% accuracies of predicting subsidence occurrences. The subsidence hazard map can be used by planners and developers to identify and prioritize areas requiring more detailed investigations of mine subsidence risks.

(Acknowledgments) The corresponding author, Prof. Park, wish to thank Professor Edward A. Keller for his valuable help and collaboration during Prof. HYEOND-DONG PARK's academic visits to U.C. Santa Barbara between 2011 and 2013.