Jangwon Suh

Smart Compass-Clinometer: A smartphone application for easy and rapid geological site investigation

This study presents a smartphone application for geological site investigation. The application allows a smartphone to replace a diverse array of instrumentation and processes required for data measurement, visualization, and analysis. This application, named Smart Compass-Clinometer, consists of a digital compass-clinometer module, a data visualization module, a data analysis module, and a data management module. The compass-clinometer module measures the orientation of geological structures using data collected from built-in sensors. It converts the sensor data to orientation information using an algorithm developed specifically for this purpose. The visualization module plots the measured data on stereographic projections using three different methods, and can be used concurrently with the compass-clinometer module. The analysis module conducts instability analyses on the measured data, and can present the results in graphical and statistical forms. Users can send or receive data wirelessly with the data management module, even without a connection to a cellular network. To evaluate and validate the precision and accuracy of the compass-clinometer module, indoor and outdoor tests were conducted using Smart Compass-Clinometer and a conventional compass-clinometer. The minimum standard deviation of measured values with Smart Compass-Clinometer was 0.096^o for dip and 0.122^o for dip direction. The average difference between values measured using Smart Compass-Clinometer and the conventional compass-clinometer in the outdoor test was 1.70^o for dip and 2.63^o for dip direction. In an underground mine, the average discrepancies between Smart Compass-Clinometer and the conventional compass-clinometer were 2.57^o in dip and 4.57^o in dip direction. Smart Compass-Clinometer offers geoscientists a fast, reliable, and convenient tool for geological investigation.

Estimation of soil erosion and sediment yield from mine tailing dumps using GIS: a case study at the Samgwang mine, Korea

This paper presents a Geographical Information Systems (GIS)-based method for the estimation of soil erosion and sediment yield from mine tailing dumps at abandoned mining areas. A region of 21.27 km2 in size around the Samgwang mine in Korea was selected as a study area. GIS data sets (i.e. a Digital Elevation Model (DEM), a soil map and a land cover map) and the mean annual rainfall over 30 years recorded from the nearest observatory were used to create factor layers for calculating the mean annual rate of soil erosion using the universal soil loss equation model. The watershed areas were extracted from the DEM using hydrological analysis tools in GIS. The concept of sediment delivery ratio was used to determine the sediment yield from mine tailing dumps during storm events. As a result, the soil erosion and sediment yield from mine tailing dumps in the study area were calculated as 75.63–350.24 tons yr− 1 and 40.40–187.64 tons yr− 1 respectively.

ArcMine: A GIS extension to support mine reclamation planning

This paper presents a new GIS extension, named ArcMine, developed to support reclamation planning in abandoned mining areas. ArcMine provides four tools to (a) assess mine subsidence hazards, (b) estimate the erosion of mine wastes, (c) analyze flow paths of mine water at the surface, and (d) identify suitable tree species for mine reforestation. A spatial database incorporating a topographical map, geological map, mine drift map, and borehole data was designed and utilized in ArcMine to examine distributed mine hazards that can damage the surrounding environment. Application to abandoned mining areas in Korea shows that ArcMine could provide useful information on mine hazards to support reclamation planning. This paper reports the concept, development, and implementation of ArcMine.

Mapping Copper and Lead Concentrations at Abandoned Mine Areas Using Element Analysis Data from ICP–AES and Portable XRF Instruments: A Comparative Study

Understanding spatial variation of potentially toxic trace elements (PTEs) in soil is necessary to identify the proper measures for preventing soil contamination at both operating and abandoned mining areas. Many studies have been conducted worldwide to explore the spatial variation of PTEs and to create soil contamination maps using geostatistical methods. However, they generally depend only on inductively coupled plasma atomic emission spectrometry (ICP–AES) analysis data, therefore such studies are limited by insufficient input data owing to the disadvantages of ICP–AES analysis such as its costly operation and lengthy period required for analysis. To overcome this limitation, this study used both ICP–AES and portable X-ray fluorescence (PXRF) analysis data, with relatively low accuracy, for mapping copper and lead concentrations at a section of the Busan abandoned mine in Korea and compared the prediction performances of four different approaches: the application of ordinary kriging to ICP–AES analysis data, PXRF analysis data, both ICP–AES and transformed PXRF analysis data by considering the correlation between the ICP–AES and PXRF analysis data, and co-kriging to both the ICP–AES (primary variable) and PXRF analysis data (secondary variable). Their results were compared using an independent validation data set. The results obtained in this case study showed that the application of ordinary kriging to both ICP–AES and transformed PXRF analysis data is the most accurate approach when considers the spatial distribution of copper and lead contaminants in the soil and the estimation errors at 11 sampling points for validation. Therefore, when generating soil contamination maps for an abandoned mine, it is beneficial to use the proposed approach that incorporates the advantageous aspects of both ICP–AES and PXRF analysis data.

A Rapid, Accurate, and Efficient Method to Map Heavy Metal-Contaminated Soils of Abandoned Mine Sites Using Converted Portable XRF Data and GIS

The use of portable X-ray fluorescence (PXRF) and inductively coupled plasma atomic emission spectrometry (ICP-AES) increases the rapidity and accuracy of soil contamination mapping, respectively. In practice, it is often necessary to repeat the soil contamination assessment and mapping procedure several times during soil management within a limited budget. In this study, we have developed a rapid, inexpensive, and accurate soil contamination mapping method using a PXRF data and geostatistical spatial interpolation. To obtain a large quantity of high quality data for interpolation, in situ PXRF data analyzed at 40 points were transformed to converted PXRF data using the correlation between PXRF and ICP-AES data. The method was applied to an abandoned mine site in Korea to generate a soil contamination map for copper and was validated for investigation speed and prediction accuracy. As a result, regions that required soil remediation were identified. Our method significantly shortened the time required for mapping compared to the conventional mapping method and provided copper concentration estimates with high accuracy similar to those measured by ICP-AES. Therefore, our method is an effective way of mapping soil contamination if we consistently construct a database based on the correlation between PXRF and ICP-AES data.

BoreholeAR: A mobile tablet application for effective borehole database visualization using an augmented reality technology

Boring logs are widely used in geological field studies since the data describes various attributes of underground and surface environments. However, it is difficult to manage multiple boring logs in the field as the conventional management and visualization methods are not suitable for integrating and combining large data sets. We developed an iPad application to enable its user to search the boring log rapidly and visualize them using the augmented reality (AR) technique. For the development of the application, a standard borehole database appropriate for a mobile-based borehole database management system was designed. The application consists of three modules: an AR module, a map module, and a database module. The AR module superimposes borehole data on camera imagery as viewed by the user and provides intuitive visualization of borehole locations. The map module shows the locations of corresponding borehole data on a 2D map with additional map layers. The database module provides data management functions for large borehole databases for other modules. Field survey was also carried out using more than 100,000 borehole data.

An Overview of GIS-Based Modeling and Assessment of Mining-Induced Hazards: Soil, Water, and Forest

In this study, current geographic information system (GIS)-based methods and their application for the modeling and assessment of mining-induced hazards were reviewed. Various types of mining-induced hazard, including soil contamination, soil erosion, water pollution, and deforestation were considered in the discussion of the strength and role of GIS as a viable problem-solving tool in relation to mining-induced hazards. The various types of mining-induced hazard were classified into two or three subtopics according to the steps involved in the reclamation procedure, or elements of the hazard of interest. Because GIS is appropriated for the handling of geospatial data in relation to mining-induced hazards, the application and feasibility of exploiting GIS-based modeling and assessment of mining-induced hazards within the mining industry could be expanded further.