Being in a stable continental region (SCR) with a limited history of instrumentation, South Korea has not collected sufficient instrumental data for data-driven ground motion models. To address this limitation, we investigated the suitability of the hybrid ground motion simulation method that Graves and Pitarka (2010, 2015) proposed for simulating earthquake ground motions in South Korea. The hybrid ground motion simulation method used in this study relies on region-specific parameters to accurately model phenomena associated with the seismic source and the wave propagation. We initially employed relevant models and parameters available in the literature as a practical approach. We incorporated a three-dimensional velocity model developed by Kim et al. (2017) and a one-dimensional velocity model presented by Kim et al. (2011) to account for the crustal velocity structure of the Korean peninsula. To represent the earthquake source, we utilized Graves and Pitarka’s rupture generator algorithm along with a magnitude-area scaling relationship developed for SCR by Leonard (2014). Additionally, we assumed the stress and attenuation parameters based on studies of regional seismicity. Using the implemented platform, we simulated the 2016 Mw5.57 Gyeongju earthquake and the 2017 Mw5.4 Pohang earthquake. Subsequently, we compared results with recorded accelerations and an empirical ground motion prediction equation at strong motion stations. Our simulations had an overall satisfactory agreement with the recorded ground motions and demonstrated the potential of broadband hybrid ground motion simulation for engineering applications in South Korea. However, limitations remain, such as the underestimation of long-period ground motions during the 2017 Pohang earthquake and the lack of a model to predict the ground motion amplification associated with the near-surface site response accurately. These limitations underscore the importance of careful validation and refinement of region-specific models and parameters for practically implementing the simulation method.

In stable continental regions, selecting appropriate ground motions for seismic design and dynamic response analysis presents significant challenges. This study evaluates the liquefaction potential of the Nakdonggang delta region, South Korea, by generating synthetic ground motion scenarios and applying a scenario-based liquefaction assessment approach. We utilized a hybrid broadband ground motion simulation method proposed by Graves and Pitarka (2010, 2015) to create bedrock ground motions for three hypothetical earthquakes (Mw 6.2 and 6.0) occurring along the Dongrae and Miryang faults. The generated synthetic ground motions were used as input for onedimensional nonlinear site response analyses, incorporating shear wave velocity profiles derived from surface wave inversion. The simulated ground motions demonstrated higher responses at short periods and relatively weaker responses at long periods compared to the Korean design spectra. This amplification of long-period components was attributed to the dynamic response of deep sedimentary layers, while high-frequency components were generally deamplified due to damping effects in shallow silty layers. Liquefaction susceptibility was assessed using surface ground motions derived from the site response analyses, following the SPT-based simplified method proposed by Idriss and Boulanger (2008). Results indicated high liquefaction potential across most sites for the Dongrae earthquake scenario, while liquefaction was unlikely for all sites under the Miryang-1 scenario. For the Miryang-2 scenario, liquefaction was predicted at some sites. Overall, liquefaction is expected at PGA values of approximately 0.13 g or higher, with sites exhibiting lower shear wave velocities being more vulnerable to liquefaction

The damage to structures during an earthquake can be varied depending on the frequency characteristics of seismic waves and the geological properties of the ground. Therefore, considering such attributes in the design ground motions is crucial. The Korean seismic design standard (KDS 17 10 00) provides design response spectra for various ground classifications. If required for time-domain analysis, ground motion time series can be either selected and adjusted from motions recorded at rock sites in intraplate regions or artificially synthesized. Ground motion time series at soil sites should be obtained from site response analysis. However, in practice, selecting suitable ground motion records is challenging due to the overall lack of large earthquakes in intraplate regions, and artificially synthesized time series often leads to unrealistic responses of structures. As an alternative approach, this study provides a case study of generating ground motion time series based on the hybrid broadband ground motion simulation of selected scenario earthquakes at sites in the Nakdonggang delta region. This research is significant as it provides a novel method for generating ground motion time series that can be used in seismic design and response analysis. For large-magnitude earthquake scenarios close to the epicenter, the simulated response spectra surpassed the 1000-year design response spectra in some specific frequency ranges. Subsequently, the acceleration time series at each location were used as input motions to perform nonlinear 1D site response analysis through the PySeismoSoil Package to account for the site response characteristics at each location. The results of the study revealed a tendency to amplify ground motion in the mid to long-period range in most places within the study area. Additionally, significant amplification in the short-period range was observed in some locations characterized by a thin soil layer and relatively high shear wave velocity soil near the upper bedrock.

본 연구에서는 대파의 가락시장 도매가격을 이용하여 기존 시계열 모형인 ARIMA 모형, 홀트-윈터스 평활법과 대표적인 기계학습 방법인 랜덤 포레스트(Random forest) 분석 기법의 가격 예측력을 비교하였다. 세 모형의 예측력을 분석한 결과는 다음과 같다. 가장 예측력이 높게 나타난 모형은 3년(36개월)을 주기로 설정한 ARIMA 모형이었다. 또한 ARIMA 모형과 홀트-윈터스 평활법은 일별 데이터보다 월별 데이터를 이용한 예측 결과의 정확도가 더 높아 훈련 데이터에 대한 과적합(overfitting)이 오히려 예측력을 낮추는 현상을 보였다. 반면, 랜덤 포레스트는 월별 데이터 보다 일별 데이터를 사용한 모형의 예측력이 더 높았다. 이는 학습량이 많을수록 높은 예측력을 보여주는 기계학습의 특징을 보여주었다. 그러나 기계학습 방법을 활용한 가격 예측에는 가격에 영향을 주는 설명변수를 찾고, 양질의 훈련 데이터 축적이 필요하다는 것을 알 수 있었다. 향후 연구에서는 다양한 설명변수와 기계학습 및 딥러닝 기법을 적용한다면 농축산물 가격 예측력을 높이는데 도움이 될 것으로 판단된다.

본 연구는 스포츠팬의 심리적 특성이 스포츠 관전행동에 영향을 미친다고 가정하고, 스포츠 관전동기와 스포츠팬 정체성의 다양한 차원들 중 스포츠 관전행동을 결정하는 요인들을 밝히고자 하였다. 질문지 조사를 통한 연구 결과 정체성현출성, 친지관계동기, 대리만족동기, 성별의 네 가지 변인이 스포츠 관전행동을 결정하는 요인으로 밝혀졌다. 또한 관전자 특성에 따라 관저행동에 영향을 미치는 관전동기 및 스포츠팬 정체성 요인에 차이가 있는 것으로 나타났다. 즉, 스포츠팬의 성별 및 소극적 관전수준에 따라 관전행동에 영향을 미치는 관전동기 및 스포츠팬 정체성의 구성요소가 달라졌다. 이러한 연구 결과를 스포츠 마케팅에 활용한다면, 관전자 특성에 따라 보다 세분화된 마케팅 전략을 수립할 수 있을 것이다.