Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS

This study constructs a hazard map for ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using a probability (frequency ratio) model, a statistical (logistic regression) model, and a Geographic Information System (GIS). To evaluate the factors related to ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, Global Positioning System (GPS) data, land use map, lineaments, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed from field investigations and reports on the existing ground subsidence areas at the study site. Nine major factors causing ground subsidence were extracted from the probability analysis of the existing ground subsidence area: (1) depth of drift; (2) DEM and slope gradient; (3) groundwater level, permeability, and rock mass rating (RMR); (4) lineaments and geology; and (5) land use. The frequency ratio and logistic regression models were applied to determine each factor’s rating, and the ratings were overlain for ground subsidence hazard mapping. The ground subsidence hazard map was then verified and compared with existing subsidence areas. The verification results showed that the logistic regression model (accuracy of 95.01%) is better in prediction than the frequency ratio model (accuracy of 93.29%). The verification results showed sufficient agreement between the hazard map and the existing data on ground subsidence area. Analysis of ground subsidence with the frequency ratio and logistic regression models suggests that quantitative analysis of ground subsidence near AUCMs is possible.     

Mangerite magmatism associated with a probable Late-Permian to Triassic Hongseong–Odesan collision belt in South Korea

The Odesan area in the eastern Gyeonggi Massif, South Korea, consists principally of migmatitic and porphyroblastic gneisses intruded by mangerite. Mafic mangerites with SiO₂ contents from 52.40 to 54.20 wt.% have higher FeO* + MgO (14.98–18.28 wt.%) and CaO contents (5.80–7.84 wt.%) but lower total alkali contents (4.74 < Na₂O + K₂O < 5.80 wt.%) than felsic mangerites (55.9 < SiO₂ < 60.61 wt.%, 9.51< FeO* + MgO < 11.62 wt.%, 3.28 < CaO < 5.68 wt.%, 6.72 < Na₂O + K₂O < 8.05 wt.%). Fe-numbers (FeO* / [MgO + FeO*]) are 0.44–0.47 for mafic mangerites and 0.38–0.42 for felsic mangerites. The mangerites show calc-alkaline affinities in an AFM plot and resemble high-Ba–Sr granitoids with low Rb / Sr ratios of 0.25–0.10. Their MORB-normalized compositions show enrichment in LILE (decoupled LIL/HFS pattern) and negative anomalies in Ti–Nb–Ta. Their geochemical characters are consistent with their formation by partial melting of a basaltic source at temperatures higher than 1025 °C. The mangerites of the present study differ from mangerite formed in a typical within-plate tectonic setting in their high mg# and Sr concentrations and negative Nb and Ta anomalies. Their LILE enrichment and negative Ti–Nb–Ta anomalies could well have been inherited from a pre-collision subduction event. A mean U–Pb zircon age of 257 Ma for the mangerite demonstrates that the tectonic belt connecting the Hongseong and Odesan areas represents a probable extension of the late Permian–Triassic collision belt between the North China and South China blocks into South Korea, with collision occurred earlier in South Korea.     

Micropaleontological and seismic observations for early developmental stage of the southwestern Ulleung Basin, East Sea (Japan Sea)

Abstract   The Ulleung Basin is located in the southwestern part of the East Sea (Japan Sea) and contains thick Neogene sediment. Detailed examination of the stratigraphic distribution of dinoflagellates was carried out on samples from the onshore Pohang Basin (E well) and two wells (Gorae I and Dolgorae VII) in the southwestern Ulleung Basin, to investigate the early evolution of the basin. The results show that thick syn-rift sediments mainly consist of terrestrial deposits and are widespread over the basin. This supports an extensional tectonic origin for the basin. The initiation of the deposits dates back to 17–16.4 Ma. Furthermore, well-preserved Eocene to Oligocene dinoflagellate taxa found in Miocene deposits of wells implies that the age of initial rifting might be Oligocene or earlier. Our results provide constraints for understanding the opening process of the East Sea.     

Marine environmental impact assessment of abalone,Haliotis discus hannai,cage farm in Wan-do,Republic of Korea

To assess the marine environmental impacts of abalone, Haliotis discus hannai, cage farms in Wan-do, we monitored the benthic environment on top of the sediment underneath cage farm stations and reference stations. We applied two methods for this assessment. One was the A- and B-investigation of the MOM system (Modeling–On fish farm–Monitoring) developed in Norway. The other was a general environmental monitoring method which is widely used. In this study, we found benthic animals in all samples that belonged to condition 1 which were based on group 1(presence of macrofauna) of the B-investigation method. The values of redox potential (group 2–pH, redox potential) in all samples were above +65 mV belonging to condition 1. Based on sensory results (group 3–gas, color, odor, thickness of deposits), five out of seven experiment samples showed condition 1 while stations 2 and 7 showed condition 2, which have been cultured for 10 years in semi-closed waters. As group 2 takes precedence over group 3, the level of the conditions for B-investigation results consequently showed condition 1 in all stations. We found that pollutants and trace metals in the sediment underneath cage farms were lower than the pollution standard. This led us to conclude that the environmental impacts of the cage farms in this study were not significant.     

Semi-empirical Model for Estimating the Small-Strain Shear Modulus of Unsaturated Non-plastic Sandy Soils

The shear modulus at small-strain, G _max is a maximum value of shear modulus for a given stress state and void ratio, and is a key parameter to evaluate the dynamic response of geotechnical structures. However, the laboratory testing procedures for determining G _max are time-consuming, cumbersome and require elaborate equipment especially for unsaturated soil samples. A semi-empirical model is proposed in this paper that can be used to estimate the variation of G _max with respect to matric suction for non-plastic sandy soils (i.e. I _p  = 0 %). The proposed model uses the Soil–Water Characteristic Curve (SWCC) and the shear modulus at saturation condition along with two fitting parameters ζ and ξ. The proposed model permits estimation of the variation of G _max with respect to matric suction over different zones of the SWCC (i.e. boundary effect, transition, and residual zones) for various non-plastic sandy soils. The fitting parameters ζ and ξ required for the proposed semi-empirical model can be estimated from simple relationships derived from the grain size distribution curve.     

Creating an advanced backpropagation neural network toolbox within GIS software

An artificial neural network (ANN) toolbox is created within GIS software for spatial interpolation, which will help GIS users to train and test ANNs, perform spatial analysis, and display results as a single process. The performance is compared to that of the open source Fast Artificial Neural Network library and conventional interpolation methods by creating digital elevation models (DEMs) given that nearly exact solutions exist. Simulation results show that the advanced backpropagations such as iRprop speed up the learning, while they can get stuck in a local minimum depending on initial weight sets. Besides, the division of input–output examples into training and test data affects the accuracy, particularly when the distribution of the examples is skewed and peaked, and the number of data is small. ANNs, however, show the similar performance to inversed distance weighted or kriging and outperform polynomial interpolations as a global interpolation method in high-dimensional data. In addition, the neural network residual kriging (NNRK) model, which combines the ANN toolbox and kriging within GIS software, is performed. The NNRK outperforms conventional methods and well captures global trends and local variations. A key outcome of this work is that the ANN toolbox created within the de facto standard GIS software is applicable to various spatial analysis including hazard risk assessment over a large area, in particular when there are multiple potential causes, the relationship between risk factors and hazard events is not clear, and the number of available data is small given its performance for DEM generation.     

Morphological identification of <Emphasis Type="Italic">Alexandrium</Emphasis> species (Dinophyceae) from Jinhae-Masan Bay,Korea

Outbreaks of paralytic shellfish poisoning (PSP) and dense blooms caused by Alexandrium species in Jinhae-Masan Bay, Korea have been nearly annual events for many years. However, excluding some Alexandrium species responsible for PSP, there are no critical reports on the morphology of Alexandrium species in this bay. To identify the Alexandrium species based on detailed morphological features, vegetative cells collected water samples and established by the incubation of resting cysts isolated from sediment trap samples were analyzed. Four species of Alexandrium were identified: Alexandrium affine, A. fundyense, A. catenella, and A. insuetum. Morphological features of these species were basically consistent with those outlined in previous studies. However, the ventral pore and the connecting pore on the sulcal plate, which have been accepted as diagnostic characteristics for the identification of A. fundyense and A. catenella, need to be reevaluated, indicating that useful morphological features for identifying these two species should be recommended to avoid confusion in the classification of species in genus Alexandrium.