• Geophysics and Geophysical Exploration
• /
• v.23 no.4
• /
• pp.230-242
• /
• 2020

We performed a magnetotelluric (MT) survey to delineate the geological structures below the depth of 20 km in the Gyeongju area where an earthquake with a magnitude of 5.8 occurred in September 2016. The measured MT data were severely distorted by electrical noise caused by subways, power lines, factories, houses, and farmlands, and by vehicle noise from passing trains and large trucks. Using machine-learning methods, we classified the MT time series data obtained near the railway and highway into two groups according to the inclusion of traffic noise. We applied three schemes, stochastic gradient descent, support vector machine, and random forest, to the time series data for the highspeed train noise. We formulated three datasets, Hx, Hy, and Hx & Hy, for the time series data of the large truck noise and applied the random forest method to each dataset. To evaluate the effect of removing the traffic noise, we compared the time series data, amplitude spectra, and apparent resistivity curves before and after removing the traffic noise from the time series data. We also examined the frequency range affected by traffic noise and whether artifact noise occurred during the traffic noise removal process as a result of the residual difference.

• Journal of Soil and Groundwater Environment
• /
• v.10 no.5
• /
• pp.52-60
• /
• 2005

The conceptual model of under-sampled study area will include a great amount of uncertainty. In this study, we investigate the applicability of Markov chain model in a spatial domain as a tool for minimizing the uncertainty arose from the lack of data. A new formulation is developed to generalize the previous two-dimensional coupled Markov chain model, which has more versatility to fit any computational sequence. Furthermore, the computational algorithm is improved to utilize more conditioning information and reduce the artifacts, such as the artificial parcel inclination, caused by sequential computation. A generalized 20 coupled Markov chain (GCMC) is tested through applying a hypothetical soil map to evaluate the appropriateness as a substituting model for conventional geostatistical models. Comparing to sequential indicator model (SIS), the simulation results from GCMC shows lower entropy at the boundaries of indicators which is closer to real soil maps. For under-sampled indicators, however, GCMC under-estimates the presence of the indicators, which is a common aspect of all other geostatistical models. To improve this under-estimation, further study on data fusion (or assimilation) inclusion in the GCMC is required.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.16 no.2
• /
• pp.1-12
• /
• 2017

The Seoul metropolitan transportation card's high value lies in its recording of total population movements of the public transit system. In case of recorded information on transit by bus, even though route information utilized by each passenger is accurate, the lack of passenger transfer information of the urban railway makes it difficult to estimate correct routes taken by each passenger. Therefore, pinpointing passenger path selection patterns arising in the metropolitan railway network and using this as part of a path movement estimation model is essential. This research seeks to determine that features of passenger movement routes in the urban railway system is comprised of M-similar routes with increasing number of transfer reflected as additional costs. In order to construct the path finding conditions, an M-similar route searching method is proposed, embedded with non additive path cost which appears through inclusion of the stepwise transportation parameter. As well, sensitivity of the M-similar route method based on transportation card records is evaluated and a stochastic trip assignment model using M-similar path finding is constructed. From these, link trip and transfer trip results between lines of the Seoul metropolitan railway are presented.