The variations in the water area and water level of Cheonji, the caldera lake of Baekdu Mountain, serve as reliable indicators of volcanic precursors. However, the geographical and spatial features of Baekdusan make it impossible to directly observe the water area and water level. Therefore, it is crucial to rely on remote sensing data for monitoring purposes. Optical satellite imagery employs different spectral bands to accurately delineate the boundaries between water bodies and non-water bodies. Conventional methods for classifying water bodies using optical satellite images are significantly influenced by the surrounding environment, including factors like terrain and shadows. As a result, these methods often misclassify the boundaries. To address these limitations, deep learning techniques have been employed in recent times. Hence, this study aimed to create an AI dataset using Landsat-5/-7/-8 and Sentinel-2 optical satellite images to accurately detect the water body area and water level of Cheonji lake. By utilizing deep learning methods on the dataset, it is reasonable to consistently observe the area and level of water in Cheonji lake. Furthermore, by integrating additional volcanic precursor monitoring factors, a more accurate volcano monitoring system can be established.

As volcanoes are closely related to the living environment of humans, in particular via natural hazards and geothermal energy, scientific studies on volcanic edifice structures are required. Ulleung Island is a Quaternary volcanic island located in the mid-western East Sea (Sea of Japan). In this study, we conducted an audio-frequency magnetotelluric (AMT) survey to image the substructure of the Ulleung volcanic edifice. In addition, the electrical structure was interpreted from the geothermal system perspective, geochemical compositions of volcanic rocks, and the possibility of the presence of a magma reservoir. AMT data were obtained from 25 stations and processed using the remote reference technique. Then, the three-dimensional (3-D) approach was reasonably adopted according to dimensionality analysis. Before conducting the 3-D inversion, the effects on topography and ocean were analyzed using a simplified 3-D synthetic model, because Ulleung Island is surrounded by sea and the topography is undulating. Most AMT stations on Ulleung Island are distorted by topographical and oceanic effects; in particular, oceanic effects are significant at frequencies lower than 10 Hz. The 3-D inversion was conducted with full impedance components and vertical magnetic transfer functions in a frequency range of 10,000–1 Hz. The results show that the Ulleung volcanic edifice is characterized by two layers of electrical structures as follows: near-surface resistive anomalies and an underlying conductive layer. Considering the high geothermal gradient on Ulleung Island, we suggest that the conductive layer of the volcanic edifice is due to hydrothermal alteration of basaltic rocks, with a potential heat source underneath. Based on the geochemical characteristics of the Ulleung volcanic rocks, the possibility of heat transfer from a trachytic magma reservoir within the shallow crustal is suggested. In summary, this study presents a geological interpretation of shallow volcanic structures beneath Ulleung Island and the possibility of an active magma reservoir as a potential heat source for the volcanic edifice. Graphical Abstract

<p>On March 11, 2011, a magnitude 9.0 earthquake occurred in the Pacific Ocean off the Tohoku region, which greatly affected the crust of the Korean Peninsula. At the time of the Tohoku earthquake, the crust deformation of the Korean Peninsula moved about 30 mm in the eastward direction. This is the size corresponding to the annual average on the Korean Peninsula, and this displacement occurred in one day due to the earthquake. Therefore, in this study, the degree of impact of crustal movement on the Korean Peninsula before and after the Tohoku earthquake was analyzed using domestic and global GNSS observation data. The analysis was performed in two ways. The first was a time series analysis of the crustal movement on the Korean Peninsula based on the IGS observation station in order to globally observe the change in the crustal movement on the Korean Peninsula before and after the earthquake. Secondly, the movement of the inner crust of the Korean Peninsula was confirmed by analyzing the correlation of movement according to the distance from the epicenter. The analysis period is from January 1, 2005 to December 31, 2021. As a result, stations that moved in the southeast direction before the earthquake on the Korean Peninsula moved eastward after the earthquake. However, over time, it gradually moves in a southeasterly direction. The velocity was higher in the western region before the earthquake. At the time of the earthquake, the stations in the eastern area where closer to the epicenter moved more, but have since returned to a similar velocity to that before the earthquake. It seems to be recovering over time after the earthquake, but to be incompleted which means continuous monitoring is needed.</p>

The fluctuations in the area and level of Cheonji in Baekdu Mountain have been employed as significant indicators of volcanic activity. Monitoring these changes directly in the field is challenging because of the geographical and spatial features of Baekdu Mountain. Therefore, remote sensing technology is crucial. Synthetic aperture radar utilizes high-transmittance microwaves to directly emit and detect the backscattering from objects. This weatherproof approach allows monitoring in every climate. Additionally, it can accurately differentiate between water bodies and land based on their distinct roughness and permittivity characteristics. Therefore, satellite radar is highly suitable for monitoring the water area of Cheonji. The existing algorithms for classifying water bodies using satellite radar images are significantly impacted by speckle noise and shadows, resulting in frequent misclassification. Deep learning techniques are being utilized in algorithms to accurately compute the area and boundary of interest in an image, surpassing the capabilities of previous algorithms. This study involved the creation of an AI dataset specifically designed for detecting water bodies in Cheonji. The dataset was constructed using satellite radar images from TerraSAR-X, Sentinel-1, and ALOS-2 PALSAR-2. The primary objective was to accurately detect the area and level of water bodies. Applying the dataset of this study to deep learning techniques for ongoing monitoring of the water bodies and water levels of Cheonji is anticipated to significantly contribute to a systematic method for monitoring and forecasting volcanic activity in Baekdu Mountain.

The Korea Meteorological Administration(KMA) uses seismic and infrasound networks data identifying explosion, collapse, and earthquakes.We identify events using various methods . First, Vp/Vs spectral amplitude ratio in the frequency domain is calculated by applying Fast Fourier Transform (FFT) and Power Spectrum Density (PSD) analysis methods. The Vp/Vs ratio more than 50% is distinguished as an explosion.Second, Explosion, collapse, and earthquakes are distinguished by (using) the body wave method (Walter et al., 2018). The Pn/Lg, Lg/Lg ratios are used for studying the DPRK 6th test, collapse and induced earthquakes at Chuncheon, Sokcho, and Seohwa KMA seismic networks.Finally, the KMA operates five infrasound networks for monitoring DPRK nuclear test and Mt. Baekdu volcano in addition to the seismic networks.Infrasound analysis calculates the apparent speed and azimuth of the infrasound source by applying Progressive Multi-Channel Correlation(PMCC) algorithm.The calculated azimuth and apparent velocity are important factors in determining that the seismic and infrasound signals occurred at the same point during an explosion.

<p>Earthquake early warning (EEW) is a technology that aims to reduce damage by notifying an alarm message before a large shaking due to an earthquake. The EEW currently adopted by most national or local governments is a network-based method. Network-based EEW produces seismic source information based on seismic wave detection from at least three observation stations, so the density of the observation network is a very important factor in shortening the warning time. However, a huge budget and space are required to construct a dense seismic observation network. In order to compensate for such limitations, all sensors capable of detecting vibration are being expanded and applied to seismometers. The Korea Meteorological Administration (KMA) signed an MOU with a private telecommunication service provider and installed about 6,700 MEMS sensors across the country. This is about 22 times more sensors than the existing KMA seismic observation network. In this study, the seismic detection performance of MEMS sensors and the KMA seismometers installed across the country was analyzed from the perspective of time. Since it is difficult to apply the existing seismic wave detection technology to the MEMS sensor as it is, an artificial intelligence-based seismic detection technology was applied. We compared the analysis results of the KMA observation network and the MEMS observation network in real-time for earthquakes of M 3.5 or greater. As a result of real-time detection, it was found that the high-density of observation network was more effective in detecting earthquakes than the performance of the sensor.</p>

ABSTRACT Lee, E.; Shin, S.; Park, J.-N., and Park, S.-C., 2023. Meteorological water surface disturbance in tsunami frequency band near Ulleung Island on the East Sea of Korea. Journal of Coastal Research, 39(1), 191–198. Charlotte (North Carolina), ISSN 0749-0208. The probability of exposure to coastal hazards has been increasing recently due to climate change. Meteotsunamis, one of the coastal hazards, are generated around the world by meteorological forcing, and their frequency band is 2 minutes to 3 hours, similar to earthquake-induced tsunamis. Although the west coast of South Korea has been unexpectedly affected, incurring casualties and property losses in 2007 and 2008, there are no documented cases in the East Sea of Korea. However, the surge gauge located on Ulleung Island (the easternmost part of Korea), operated by Korea Meteorological Administration to detect tsunamis, observed two meteotsunamis in January and April 2020. So, this study aimed to quantify the two meteotsunamis based on sea-level measurements and atmospheric observations. The results of the study show the maximum peak-to-trough wave height was 99.86 cm greater than the wave height of the 2011 Tohoku tsunami obtained by the Ulleung surge gauge. This reveals that the meteotsunami monitoring system should be improved based on the deep analysis of sea level and atmospheric observations on the East Sea of Korea to respond to climate change.

Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a hightemperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

ABSTRACTThe city of Pohang in South Korea experienced substantial damage following the ML 5.4 earthquake on 15 November 2017. Damage surveys immediately after the earthquake revealed significant spatial variations in damage intensity across the epicentral area. We collected ambient noise data (three‐component seismic data) from 124 locations in Pohang, covering a total area of 17 km × 20 km, and applied the horizontal‐to‐vertical spectral ratio technique to estimate the resonance frequency, amplification factor and vulnerability of the ground to seismic shaking. Spatial variations exhibited by these three parameters are strongly correlated and consistent with variations in local geology. By comparing the distribution of the ground vulnerability index (Kg) with the damage distribution (or intensity) documented by the Korea Meteorological Administration following the earthquake, we confirm that the site effect is the prevailing effect that influences damage intensity in this region. We also demonstrate that Kg is an efficient proxy for identifying relatively fragile zones during ground shaking: in areas with a reported damage intensity of VIII, the proportion of the area with Kg > 20 is as high as 78.2%. Given that the study area has experienced a recent moderate earthquake, with significant economic consequences, our new results and analytical techniques will provide valuable information for future urbanization projects.

We investigated decaying post-seismic deformation observed on the Korean Peninsula associated with the 2011 Mw 9.0 Tohoku-Oki earthquake using Global Navigation Satellite System (GNSS). The GNSS velocity vectors were estimated in five periods from 2005 to 2019. A co-seismic offset of the Korean Peninsula caused by the 2011 earthquake was inversely proportional to epicentral distances. According to the temporal variations of two components (magnitude and direction) of the GNSS velocity vector with the epicentral distance, the difference between the eastern and western regions for the two components becomes smaller over time. For approximately nine years after the 2011 event, the direction for the crustal movement in South Korea showed a recovery pattern returning to the pre-earthquake motion. In addition, the recovery patterns of the crustal movement were observed differently with the regional geologic structure (e.g., the crustal thickness) and each period. Our estimates of the decay in post-seismic deformation of the Korean Peninsula suggest that post-seismic relaxation will be complete within 5–20 years after the 2011 earthquake. The results suggest that the crustal movement on the Korean Peninsula is gradually recovering to its pre-earthquake motion.

Over a decade since 2007, Korea Meteorological Administration (KMA) has constructed and modified tsunami scenario database in order to issue tsunami warning soon after a large offshore earthquake and when tsunami is expected to affect Korean coasts. Based on the scenario tsunami database, KMA has improved tsunami issuance criteria and notification process. Moreover, we have developed numerical simulation process using parallelized code of tsunami modeling program. This will be used to provide additional and detailed tsunami information with better accuracy. On the basis of this achievement, it is needed to improve the system and process to be faster and more accurate. Furthermore, researches on non-typical sources of tsunami and complexity and non-linearity near the coast are needed for the coming decade.

We detected the coseismic ionospheric disturbance generated by the earthquakes of magnitude 5.0 and greater in Korean Peninsula. We considered the seismic events such as Gyeongju earthquake in September 2016 with magnitude 5.8, the Pohang earthquake in November 2017 with magnitude 5.4, and the underground nuclear explosion from North Korea in September 2017 with magnitude 5.7. Although all GPS stations were not detected, the ionospheric disturbance induced by these earthquakes occurred approximately 10-30 minutes and 40-60 minutes after the events. We inferred that the time difference within each variation is due to the different focal depth and the geometry of epicenter, satellite, and GPS station. In the case of the Gyeongju earthquake, the earthquake had relatively deeper depth than the other earthquakes. However, the seismic magnitude was bigger and it occurred at nighttime when the ionospheric activity was stable. So we could observe such anomalous variations. It is considered that the ionospheric disturbance caused by the difference in velocity of the upward propagating waves generated by earthquake appears more than once. Our results indicate that the detection of ionospheric disturbances varies depending on the geometry of the GPS station, satellite, and epicenter or the detection method and that the apparent growth of amplitude in the time series varies depending on the focal depth or the site-satellite-epicenter geometry.

The volcanic ash can spread out over hundreds of kilometers in case of large volcanic eruption. The deposition of volcanic ash may induce damages in urban area and transportation facilities. In order to respond volcanic hazard, it is necessary to estimate efficiently the diffusion area of volcanic ash. The purpose of this study is to compare in-situ volcanic deposition and satellite images of the volcanic eruption case. In this study, we used Near-Infrared (NIR) channels 7 and 8 of Geostationary Ocean Color Imager (GOCI) images for Mt. Aso eruption in 16:40 (UTC) on October 7, 2016. To estimate deposit area clearly, we applied Principal Component Analysis (PCA) and a series of morphology filtering (Eroded, Opening, Dilation, and Closing), respectively. In addition, we compared the field data from the Japan Meteorological Agency (JMA) report about Aso volcano eruption in 2016. From the results, we could extract volcanic ash deposition area of about 380 km(2). In the traditional method, ash deposition area was estimated by human activity such as direct measurement and hearsay evidence, which are inefficient and time consuming effort. Our results inferred that satellite imagery is one of the powerful tools for surface change mapping in case of large volcanic eruption.

The Long Valley area and its surroundings are part of a major volcano system where inflation occurred in the resurgent dome in the 1990s. We used ENVISAT data to monitor surface deformation of the Long Valley area and its surroundings after the inflation, from 2003-2010. To retrieve the time series of the deformation, we applied the refined Small BAseline Subset (SBAS) algorithm which is improved using an iterative approach to minimize unwrapping error. Moreover, ascending and descending data were used to decompose the horizontal and vertical deformation in detail. To confirm refined SBAS results, we used GPS dataset. The InSAR errors are estimated as +/- 1.0 mm/yr and +/- 0.8 mm/yr from ascending and descending tracks, respectively. Compare to the previous study of 1990s over the Long Valley and its surroundings, Paoha Island and CASA geothermal area still subside. The deformation pattern in the Long Valley area during the study period (2003-2010) went through both subsidence (2003-2007) and slow uplift (2007-2010) episodes. Our research also shows no deformation signal near McGee Creek. Our study provided a better understanding of the surface changes of the indicators in the 1990s and 2000s.

We classified the areas in the eastern coast of South Korea which are highly likely to be affected by the tsunami that occurred along the northwestern coast of Japan using the improved tsunami scenario database of the Korea Meteorological Administration.To improve the accuracy of database, we used the latest data on bathymetry, coastline, and land information and increased grid resolution for numerical simulation.When tsunami occurred along the northwestern coast of Japan, the maximum wave height appeared in the around Imwon.And the wave height of Gangwon coastal area was higher than those of Gyeongsang.The tsunami height increased as the magnitude of the earthquake increased and decreased as it moved southward along the eastern coast.In conclusion, attention should be paid when an earthquake of magnitude 7.0 or greater occurs in the plate boundary region of the East Sea.And when a tsunami occurs in this region, we need to focus on the area near Imwon.