In this study we explored the application of synthetic aperture radar (SAR) intensity time series for urban flood detection. Our test case was the flood in Lumberton, North Carolina, USA, caused by the landfall of Hurricane Matthew on 8 October 2016, for which airborne imagery—taken on the same day as the SAR overpass—is available for validation of our technique. To map the flood, we first carried out normalization of the SAR intensity observations, based on the statistics from the time series, and then construct a Bayesian probability function for intensity decrease (due to specular reflection of the signal) and intensity increase (due to double bounce) cases separately. We then formed a flood probability map, which we used to create our preferred flood extent map using a global cutoff probability of 0.5. Our flood map in the urban area showed a complicated mosaicking pattern of pixels showing SAR intensity decrease, pixels showing intensity increase, and pixels without significant intensity changes. Our approach shows improved performance when compared with global thresholding on log intensity ratios, as the time series-based normalization has accounted for a certain level of spatial variation by considering the different history for each pixel. This resulted in improved performance for urban and vegetated regions. We identified smooth surfaces, like asphalt roads, and SAR shadows as the major sources of underprediction, and aquatic plants and soil moisture changes were the major sources of overprediction.

Nearby faults interact through stress changes induced by fault slip and viscoelastic flow. The process is, however, often elusive and can be geometry-dependent and time-variant. Here, we combine geodetic and field observations to characterize the interaction of two head-to-head, conjugate faults in eastern Taiwan during the 2022 Chihshang earthquake sequence. We map the coseismic slip on the Central Range fault and dynamically-triggered shallow slip on the Longitudinal Valley fault, which has been creeping interseismically. Overlapping of seismic and aseismic slip suggests that the Longitudinal Valley fault is capable of hosting a variety of distinct slip behaviors. Moreover, substantial slip on the Central Range fault suppresses Coulomb stress on the Longitudinal Valley fault, and vice versa, resulting in seismic bursts in an out-of-phase pattern on the two faults as seen in the hundred-year historical records. Such fault interaction implies the need for time-dependent seismic hazard reassessment for the complex fault system.

This study addresses one of the most commonly-asked questions in synthetic aperture radar (SAR)-based landslide detection: How the choice of datatypes affects the detection performance. In two examples, the 2018 Hokkaido landslides in Japan and the 2017 Putanpunas landslide in Taiwan, we utilize the Growing Split-Based Approach to obtain Bayesian probability maps for such a performance evaluation. Our result shows that the high-resolution, full-polarimetric data offers superior detection capability for landslides in forest areas, followed by single-polarimetric datasets of high spatial resolutions at various radar wavelengths. The medium-resolution single-polarimetric data have comparable performance if the landslide occupies a large area and occurs on bare surfaces, but the detection capability decays significantly for small landslides in forest areas. Our result also indicates that large local incidence angles may not necessarily hinder landslide detection, while areas of small local incidence angles may coincide with layover zones, making the data unusable for detection. The best area under curve value among all datatypes is 0.77, suggesting that the performance of SAR-based landslide detection is limited. The limitation may result from radar wave’s sensitivity to multiple physical factors, including changes in land cover types, local topography, surface roughness and soil moistures.

Unknown seismogenic structures lurking beneath convergent margins introduce substantial uncertainty in seismic hazard assessments. In northwestern Luzon, the M_w 7.0 Abra earthquake on 27 July 2022 highlights the seismic activity along an unmapped blind fault underneath the Cordillera Central. By integrating coseismic displacements constrained by radar satellite imagery and Global Navigation Satellite System, we image oblique coseismic slip at 11–22 km depth with peak slip of ∼1 m beneath the Philippine Fault Zone in northern Luzon. The southward propagation of coseismic slip and aftershocks terminated at a distance of 50 km from the northern end of the 1990 Luzon earthquake rupture, leaving a seismicity gap in between. Coulomb stress changes of reaching 2 bars are imparted at the shallow portions of the Vigan‐Aggao and Abra River faults, where the updated 100‐year seismic potential is increased to M_w 7.0–7.7, given the thick seismogenic layer of ∼30 km in northern Luzon.

Surface Related Multiple Elimination (SRME) usually suffers the issue of either over-attenuation that damages the primaries or under-attenuation that leaves strong residual multiples. This dilemma happens commonly when SRME is combined with least-squares subtraction. Here we introduce a more sophisticated subtraction approach that facilitates better separation of multiples from primaries. Curvelet-domain subtraction transforms both the data and the multiple model into the curvelet domain, where different frequency bands (scales) and event directions (orientations) are represented by a finite number of curvelet coefficients. When combined with adaptive subtraction in the time–space domain, this method can handle model prediction errors to achieve effective subtraction. We demonstrate this method on two 2D surveys from the TAiwan Integrated GEodynamics Research (TAIGER) project. With a careful parameter determination flow, our result shows curvelet-domain subtraction outperforms least-squares subtraction in all geological settings. We also present one failed case where specific geological condition hinders proper multiple subtraction. We further demonstrate that even for data acquired with short cables, curvelet-domain subtraction can still provide better results than least-squares subtraction. We recommend this method as the standard processing flow for multi-channel seismic data.