Single-path ground motion amplifications during the 2020 Haenam, South Korea, swarm

Kwak, Dongmin; Ahn, Jae-Kwang; Seo, Hwanwoo; Kang, Sinhang; Kim, Byungmin

doi:10.1007/s10518-022-01386-z

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…This study was restricted to drilled stations and epicenters within 120 km, and only light and medium earthquakes (3.0 ≤ M L < 6.0) were considered, with amplitude attenuation trends confirmed. The site effect was not trained, and for estimating IM at a specific site surface, passive methods such as transfer functions or one-dimensional ground response analysis (Kwak et al [49]) should be used.…”

Section: Data Limitationsmentioning

confidence: 99%

Virtual Scenarios of Earthquake Early Warning to Disaster Management in Smart Cities Based on Auxiliary Classifier Generative Adversarial Networks

Ahn,

Kim,

et al. 2023

Sensors

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Effective response strategies to earthquake disasters are crucial for disaster management in smart cities. However, in regions where earthquakes do not occur frequently, model construction may be difficult due to a lack of training data. To address this issue, there is a need for technology that can generate earthquake scenarios for response training at any location. We proposed a model for generating earthquake scenarios using an auxiliary classifier Generative Adversarial Network (AC-GAN)-based data synthesis. The proposed ACGAN model generates various earthquake scenarios by incorporating an auxiliary classifier learning process into the discriminator of GAN. Our results at borehole sensors showed that the seismic data generated by the proposed model had similar characteristics to actual data. To further validate our results, we compared the generated IM (such as PGA, PGV, and SA) with Ground Motion Prediction Equations (GMPE). Furthermore, we evaluated the potential of using the generated scenarios for earthquake early warning training. The proposed model and algorithm have significant potential in advancing seismic analysis and detection management systems, and also contribute to disaster management.

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Section: Data Limitationsmentioning

confidence: 99%

Virtual Scenarios of Earthquake Early Warning to Disaster Management in Smart Cities Based on Auxiliary Classifier Generative Adversarial Networks

Ahn,

Kim,

et al. 2023

Sensors

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“…The modified Mercalli intensity (MMI) system of the KMA uses the SAC of the Borcherdt model (Borcherdt, 1994). However, the site coefficient is not suitable as a ground surface prediction model for borehole stations, as the within-motion in the rock around the borehole differs from the rock outcrop motion in the free field (Kwak et al, 2022;Lai et al, 2022). Second, the ground response can be interpreted via numerical analysis.…”

Section: Introductionmentioning

confidence: 99%

“…This ground surface analysis method has been shown to be highly effective in predicting ground surface vibrations (Park and Hashash, 2004). Kwak et al (2022) proposed estimation of the ground surface response by combining the ground motion model (GMM) and 1D site response. However, this required a prior site characterize.…”

Section: Introductionmentioning

confidence: 99%

Horizontal seismic wave at ground surface from transfer function based on ambient noise

Lim

Ahn

2023

Front. Earth Sci.

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Earthquake detection can be improved by ensuring that seismometer sites experience little artificial noise in the surrounding environment. To minimize noise, seismological stations should be positioned in rocky mountainous areas without nearby valleys, away from significant human activity. However, such surface sites may be scarce when constructing dense monitoring networks, necessitating the use of underground sites to ensure low noise levels. The Korean Meteorological Administration is currently installing new underground seismometers to increase seismic monitoring capacity. However, seismic data on the ground surface are also required for engineering technological developments (to reduce damage to structural components). Therefore, borehole seismic stations without surface seismometers need to estimate ground surface motion from borehole record data. We propose a transfer function that converts motion within boreholes to surface seismic waves using ambient noise, thereby facilitating estimation of ground surface motions using borehole seismometers. As a result, predicting ground surface motion from borehole record data becomes possible.

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Geotechnical data based seismic microzonation in Seoul using region-specific and code-based site amplification models

Lee,

Park

2024

Bull Earthquake Eng

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Single-path ground motion amplifications during the 2020 Haenam, South Korea, swarm

Cited by 6 publications

References 28 publications

Virtual Scenarios of Earthquake Early Warning to Disaster Management in Smart Cities Based on Auxiliary Classifier Generative Adversarial Networks

Virtual Scenarios of Earthquake Early Warning to Disaster Management in Smart Cities Based on Auxiliary Classifier Generative Adversarial Networks

Horizontal seismic wave at ground surface from transfer function based on ambient noise

Geotechnical data based seismic microzonation in Seoul using region-specific and code-based site amplification models

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