Many ruptures across many scales: The period of seismic quiescence in Southern California was rudely interrupted by the Ridgecrest earthquake sequence in July 2019. Ross et al. mapped the slip sequences during the magnitude 6.4 and 7.1 earthquakes that shook the region. They found that ruptures of a few larger, but many smaller, faults occurred during both earthquakes. The Ridgecrest sequence calls for rethinking seismic hazard, as multifault ruptures are not usually considered when assessing seismic risk. Science , this issue p. 346

This study aims to monitor the deformation of bridges, namely in the form of long-term deflection and thermal dilation, using multi-temporal interferometric synthetic aperture radar (InSAR) observations. To precisely estimate the vertical and longitudinal displacements, we used the InSAR time-series technique with multi-track stacks of Sentinel-1 SAR dataset and a single-track stack of COSMO-SkyMed SAR data over two extradosed bridge cases; Kimdaejung and Muyoung bridges between 2013 and 2017. The vertical and longitudinal displacements are estimated using multi-track Sentinel-1 SAR data and orientation angle of bridges, and we converted the displacements into thermal dilation and long-term vertical deflection. From COSMO-SkyMed data, we calculated the horizontal thermal dilation and long-term vertical deflection assuming that they dominantly contribute to the horizontal and vertical displacements, respectively. This assumption appeared reasonable based on the comparison with calculations from Sentinel-1 data. The deflection patterns exhibit downward movements at the mid-spans between towers. The results reveal that both bridges have been suffering long-term deflection over the observation period. Thus, this study verifies the potential to monitor the long-term deflection and implies that the bridges need to be monitored periodically.

This article presents a framework for semi-automated building damage assessment due to earthquakes from remote-sensing data and other supplementary datasets, while also leveraging recent advances in machine-learning algorithms. The framework integrates high-resolution building inventory data with earthquake ground shaking intensity maps and surface-level changes detected by comparing pre- and post-event InSAR (interferometric synthetic aperture radar) images. We demonstrate the use of ensemble models in a machine-learning approach to classify the damage state of buildings in the area affected by an earthquake. Both multi-class and binary damage classification are attempted for four recent earthquakes, and we compare the predicted damage labels with ground truth damage grade labels reported in field surveys. For three out of the four earthquakes studied, the model is able to identify over 50 % or nearly half of the damaged buildings successfully when using binary classification. Multi-class damage grade classification using InSAR data has rarely been attempted previously, and the case studies presented in this report represent one of the first such attempts using InSAR data.

Damage mapping using Synthetic Aperture Radar (SAR) imagery has been studied in recent decades to support rapid response to natural disasters. Many researches have been developing coherent and incoherent change detection. However, their performances can vary depending on the types of the damages, the characteristics of the scatterers and the corresponding capability of algorithms. In particular, the coherence-based methods have been used as promising detectors over urban areas where high coherences are observed, but their detection accuracies still remain controversial over the area where low coherences are mainly observed such as the 2018 Hokkaido landslides. In order to understand the characteristics of landslide (damage) detectors for low-coherence areas and find an alternative and complementary method, we designed the coherence difference, coherence normalized difference, log-ratio, intensity correlation difference, and normalized differences of the intensity correlation assuming limited availability of dataset, and also developed multi-temporal algorithms using the coherence, intensity, and intensity correlation. They were tested and evaluated using multiple polygons extracted from aerial photos. We were able to observe that the multi-temporal intensity correlation method has the potential to detect the landslides over the low coherence region and all types of land uses.

The relative sea-level changes from tide gauges in the Korean peninsula provide essential information to understand the regional and global mean sea-level changes. Several corrections to raw tide gauge records are required to account for coastal vertical land motion (VLM), regional and local coastal variability. However, due to the lack of in-situ measurements such as leveling data and the Global Navigation Satellite System (GNSS), making precise assessments of VLM at the tide gauges is still challenging. This study aims to address the above limitation to assess the VLM in the Korean tide gauges using the time-series Interferometric Synthetic Aperture Radar (InSAR) technique. For 10 tide gauges selected in the Korean peninsula, we applied the Stanford Method for Persistent Scatterers (StaMPS)—Small Baseline Subset (SBAS) method to C-band Sentinel-1 A/B Synthetic Aperture Radar (SAR) data acquired during 2014/10–2020/05, with the novel sequential interferograms pair selection approach to increase the slowly decorrelating filtered phase (SDFP) pixels density near the tide gauges. Our findings show that overall the tide gauges in the Korean peninsula are stable, besides the largest VLM observed at Pohang tide gauge station (East Sea) of about −26.02 mm/year; also, higher rates of uplift (>1 mm/year) were observed along the coast of Yellow Sea (Incheon TG and Boryeong TG) and higher rates of subsidence (<−2 mm/year) were observed at Jeju TG and Seogwipo TG. Our approach estimates the rate of VLM at selected tide gauges with an unprecedented spatial and temporal resolution and is applicable when the in-situ and GNSS observations are not available.

Vertical land motion at tide gauges influences sea level rise acceleration; this must be addressed for interpreting reliable sea level projections. In recent years, tide gauge records for the Eastern coast of Korea have revealed rapid increases in sea level rise compared with the global mean. Pohang Tide Gauge Station has shown a +3.1 cm/year sea level rise since 2013. This study aims to estimate the vertical land motion that influences relative sea level rise observations at Pohang by applying a multi-track Persistent Scatter Interferometric Synthetic Aperture Radar (PS-InSAR) time-series analysis to Sentinel-1 SAR data acquired during 2015–2017. The results, which were obtained at a high spatial resolution (10 m), indicate vertical ground motion of −2.55 cm/year at the Pohang Tide Gauge Station; this was validated by data from a collocated global positioning system (GPS) station. The subtraction of InSAR-derived subsidence rates from sea level rise at the Pohang Tide Gauge Station is 6 mm/year; thus, vertical land motion significantly dominates the sea level acceleration. Natural hazards related to the sea level rise are primarily assessed by relative sea level changes obtained from tide gauges; therefore, tide gauge records should be reviewed for rapid vertical land motion along the vulnerable coastal areas.