Synthetic Aperture Radar Observations Research Articles

Construction of High Spatiotemporal Continuity Surface Water Bodies Dataset in the Haihe River Basin

Surface water bodies exhibit high dynamic variability on seasonal and interannual scales, and high spatiotemporal resolution water bodies extent data are crucial for studying surface water bodies’ evolution. Existing surface water bodies datasets are mainly based on optical data acquisition, which has the advantages of long temporal coverage and convenience but is susceptible to cloud contamination, leading to low spatiotemporal continuity. Although microwave remote sensing data are not affected by clouds, early SAR acquisition and short temporal coverage limit its use. Therefore, existing surface water bodies datasets face the problem of insufficient spatiotemporal resolution or low continuity. This research integrates Sentinel-2 optical data and Sentinel-1 Synthetic Aperture Radar (SAR) observations to reconstruct the surface water bodies dataset with a 6-day and 10-meter spatiotemporal resolution. Then, the proposed method introduces a spatiotemporal correlation model and predicts the land cover (water or land) of Sentinel-2 cloudy pixels, which improves the spatiotemporal continuity of the reconstructed surface water bodies dataset further. Based on the proposed method, we construct the Haihe River Water Dataset (HRWD) from 2016 to 2020 with a 6-day and 10-meter spatiotemporal resolution. Compared with the European Commission’s Joint Research Centre’s (JRC’s) Global Surface Water Explorer and Global Surface Water Extent Dataset (GSWED), the HRWD shows a rational accuracy (e.g., the overall accuracy of the HRWD is more than 93%) and a better spatiotemporal continuity, which provide an improved performance in identifying and monitoring surface water bodies in the Haihe River Basin. This indicates that the proposed method can improve the spatiotemporal continuity of surface water body mapping and meet the needs of accurate and long-term quantitative observation of the distribution of large-scale and high spatiotemporal continuity surface water bodies.

Enhanced Kinematic Inversion of 3‐D Displacements, Geometry, and Hydraulic Properties of a North‐South Slow‐Moving Landslide in Three Gorges Reservoir

AbstractComplete three‐dimensional (3‐D) movements of slow‐moving landslides are critical to enhancing the understanding of the landslide mechanism. Multi‐source synthetic aperture radar (SAR) observations provide an opportunity to derive 3‐D movements. However, deriving the complete 3‐D movements faces potential challenges of incoherent phases and an ill‐posed inverse problem, which may result in incomplete and inaccurate results, especially for slopes facing north/south. Here, we propose a topography‐constrained strain model, which exploits the spatial relationship of 3‐D deformations between neighboring points as well as the assumption of the surface parallel flow of landslide, to derive complete 3‐D movements. Both synthetic and real datasets over the north‐south Xinpu landslide complex are utilized, to assess if the implemented method can overcome the ill‐posed condition and retrieve the complete 3‐D movement field. With the multi‐source SAR datasets, the performance of various datasets and the potential of NISAR in deriving time series and 3‐D movements are assessed. Based on the derived complete 3‐D movements and long‐term InSAR measurements, the landslide metrics, including elementary parameters of landslide geometry, spatial‐temporal patterns of movement, thickness, and hydraulic diffusivity, are derived to reveal that (a) the thickest landslide mass concentrates in the toe of the landslide, and (b) the effects of precipitation are more significant than those of the water level fluctuation in the Xinpu landslide complex, Three Gorges Reservoir areas.

Observed Surface Wind Field Structure of Severe Tropical Cyclones and Associated Precipitation

Using the International Best Track Archive for Climate Stewardship (IBTrACS) dataset, this study assessed the surface wind fields from high spatial resolution Synthetic Aperture Radar (SAR) observations, the fifth generation ECMWF reanalysis for the global climate and weather (ERA5) data and the Tropical Cyclone Winds and Inflow Angle Asymmetry (TCIAA) wind model. The results showed that SAR data are sufficient to reveal the surface wind field near a TC center and can accurately describe TC intensity and size under severe TC conditions. Then, a new, improved statistical wind structure model was set up using ERA5 data alone based on the assessment. In addition, the warm sea surface (SST > 26.5 °C) produced stronger TC wind fields and heavier precipitation. When the SST was higher (lower), the heavy rainfall was located on the left (right) side of the TC track and the strong positive correlation between wind speed and precipitation increased as the SST decreased.

Elucidating the magma plumbing system of Ol Doinyo Lengai (Natron Rift, Tanzania) Using satellite geodesy and numerical modeling

Ol Doinyo Lengai, located in the southern Eastern Branch of the East African Rift had several eruptive episodes with ash falls and lava flows (VEI 3) that caused damage to the nearby communities between 2007 and 2010. The volcano is remote and access is difficult. Although this volcano has been studied for decades, its plumbing system is still poorly understood, in part, because of the lack of precise observations of surface deformation during periods of quiet and unrest. This study investigates the volcanic plumbing system of Ol Doinyo Lengai and its surroundings using data from the network of permanent Global Navigation Satellite System (GNSS) sites monitoring the volcano (the TZVOLCANO network) around the flanks of the volcano and Interferometric Synthetic Aperture Radar (InSAR) observations. We constrain surface motions using 6 GNSS sites distributed around Ol Doinyo Lengai, operating between 2016 and 2021, and InSAR data covering nearly the same time period. Because of the complex local tectonics, the interpretation of the deformation pattern is not straightforward. We first invert the GNSS deformation and InSAR observations independently to infer potential deformation sources. Then we perform a joint inversion of both GNSS and InSAR datasets to verify our findings. We compare the results from the joint inversion with the results from inverting each dataset independently. The GNSS, InSAR, and joint inversion results point to a deflating source, located east of Ol Doinyo Lengai and southwest of the dormant volcano Gelai at a depth of 3.49 ± 0.03 km (GNSS inversion), 5.2 ± 1.2 km (InSAR inversion) and 3.49 ± 0.06 km (joint inversion) relative to the summit (vent) and with a volume change ∆V of −0.04 ± 0.05 × 106 m3 (GNSS inversion), −0.39 ± 0.29 × 106 m3 (InSAR inversion), and − 0.04 ± 0.01 × 106 m3 (joint inversion). Although this is non-unique modeling of geodetic datasets with small signals, the inversion results suggest that Ol Doinyo Lengai could be fed by an offset multi-reservoir system that includes a shallow magma reservoir (<5 km) east of Ol Doinyo Lengai, possibly connected to a deeper magma reservoir.

Delineating Polynya Area Using Active and Passive Microwave Sensors for the Western Ross Sea Sector of Antarctica

A polynya is an area of open water or reduced concentration of sea ice surrounded by either concentrated sea ice or land ice. They are often seen as sites of intense ocean–atmosphere heat exchange and as ice production factories. Given their importance, it is crucial to quantify the accuracy of satellite-derived polynya information. Polynyas in their early evolution phase are generally narrow and occur at scales likely too fine to be detected by widely used passive microwave (PMW) radiometric sensors. We derived 40 m scale polynya information over the western Ross Sea from high-resolution Synthetic Aperture Radar (SAR) Sentinel-1 C-band data and examined discrepancies with larger-scale estimates. We utilized two automated algorithms, supervised (a rule-based approach) and unsupervised (a combination of texture analysis with k-means clustering), to accurately identify the polynya areas. We generated data for validation using Sentinel-1 data at instances where polynyas can be visually delineated. Results from PMW sensors (NSIDC and AMSR2) and SAR-based algorithms (rule-based and texture-based) are compared with manually delineated polynya areas obtained through Sentinel-1. Analysis using PMW sensors revealed that NSIDC overestimates larger polynyas and underestimates smaller polynyas compared to AMSR2. We were more accurately able to identify polynya presence and area using Sentinel-1 SAR observations, especially in clear cases and cases when PMW data miscalculates the polynya’s presence. Of our SAR-based algorithms, the rule-based approach was more accurate than the texture-based approach at identifying clear polynyas when validated against manually delineated regions. Altogether, we emphasize the need for finer spatio-temporal resolution data for polynya studies.

Fine Resolution Classification of New Ice, Young Ice, and First-Year Ice Based on Feature Selection from Gaofen-3 Quad-Polarization SAR

A new method of sea ice classification based on feature selection from Gaofen-3 polarimetric Synthetic Aperture Radar (SAR) observations was proposed. The new approach classifies sea ice into four categories: open water (OW), new ice (NI), young ice (YI), and first-year ice (FYI). Seventy parameters that have previously been applied to sea ice studies were re-examined for sea ice classification in the Okhotsk Sea near the melting point on 28 February 2020. The ‘separability index (SI)’ was used for the selection of optimal features for sea ice classification. Full polarization parameters (the backscatter intensity contains the horizontal transmit-receive intensity (σhh0), Shannon entropy (SEi), the spherical scattering component of Krogager decomposition (Ks)), and hybrid polarization parameters (horizontal receive intensity(σrh0), hybrid-pol Shannon entropy (CPSEi), the correlation coefficient (ρrh−rv) between the σrh0 and σrv0, and the surface scattering component of m − α decomposition αs) were determined as the optimal parameters for the different work modes of SAR. The selected parameters were used to classify sea ice by the random forest classifier (RFC), and classification results were validated by manually interpreted ice maps derived from Landsat-8 data. The classification accuracy of OW, NI, YI and FYI reached 95%, 96%, 98% and 85%, respectively.

Seven-year variation in glacier surface velocity at Narsap Sermia in Southwest Greenland

ABSTRACT Glacier velocity is a critical factor closely related to ice dynamics of the Greenland ice sheet (GrIS). We present variations of ice velocity using space-based synthetic aperture radar (SAR) observations from July 2013 to September 2020. We focus on a tidewater outlet glacier of Narsap Sermia (NS), located in southwest Greenland, which is experiencing significant ice loss. A time series of high spatiotemporal resolution maps of ice velocity was traced by using an offset tracking approach with seven-year SAR acquisitions to understand glacier dynamics in time. We observed seasonal variations showing a dramatic increase in late spring (May) and early summer (June) followed by a sharp decrease in late summer (September). The seasonal variation was confirmed through Seasonal and Trend decomposition using Loess (STL) analysis. Overall, the NS glacier experienced a gradual speed-up of ice velocities. The ice velocity increased by 0.1–0.3 km/year from 2015 to 2017, then after a temporary decrease in 2018, the velocity moved faster by 0.9 km/year in 2020. The speed of glacier movement accelerated as it approached the tidal terminus of the glacier, showing approximately 6.0 km/year in 2020. The ice speed in 2020 increased by 0.9 km/year at the terminus region compared with 2014. We also discuss the relationship between the glacier velocity and temperature data such as air, land surface, and sea surface temperatures.

Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment

The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s−1 and negative biases of −0.4 m s−1 and −1.0 m s−1, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms.

High Spatial and Temporal Soil Moisture Retrieval in Agricultural Areas Using Multi-Orbit and Vegetation Adapted Sentinel-1 SAR Time Series

The retrieval of soil moisture information with spatially and temporally high resolution from Synthetic Aperture Radar (SAR) observations is still a challenge. By using multi-orbit Sentinel-1 C-band time series, we present a novel approach for estimating volumetric soil moisture content for agricultural areas with a temporal resolution of one to two days, based on a short-term change detection method. By applying an incidence angle normalization and a Fourier Series transformation, the effect of varying incidence angles on the backscattering signal could be reduced. As the C-band co-polarized backscattering signal is prone to vegetational changes, it is used in this study for the vegetational correction of its related backscatter ratios. The retrieving algorithm was implemented in a cloud-processing environment, enabling a potential global and scalable application. Validated against eight in-situ cosmic ray neutron probe stations across the Rur catchment (Germany) as well as six capacitance stations at the Apulian Tavoliere (Italy) site for the years 2018 to 2020, the method achieves a correlation coefficient of R of 0.63 with an unbiased Root Mean Square Error of 0.063 m3/m3.

The 2021 Mw 5.2 Baicheng Earthquake: Implications for the Hazards of Extremely Shallow Earthquakes

Abstract On 23 March 2021, an Mw 5.2 earthquake struck Baicheng city at the southern foreland of the Tian Shan mountains and produced unexpected severe damages. Based on Interferometric Synthetic Aperture Radar observations, geological investigations, petroleum seismic-reflection profiles, and available seismological results, we found that (1) the earthquake rupture was sinistral-slip dominated and mostly concentrated at ≤2 km depth, suggesting an extremely shallow earthquake; (2) the earthquake produced a 4.1-km-long surface rupture zone, along which the lateral slip ranged from 0 to 135 cm and showed a significant short-wavelength variation; and (3) the causative fault is poorly developed (i.e., has low slip accumulation and slip rate) and cuts through a shallow-buried salt dome. These findings, along with those of previous studies, document some unique characteristics of moderate earthquakes at extremely shallow depths (≤3 km) relative to those at greater depths. First, shallow earthquakes occur on faults that are poorly developed and bracketed by rocks being able to behave as velocity weakening at shallow depth (including crystalline basement rocks, carbonites, volcanic rocks, or evaporites). Second, the surface rupture produced by shallow earthquakes has abnormally large length and slip, and possibly includes an abrupt slip variation. These characteristics highlight potential severe damages of the shallow moderate earthquake but enhance the challenge of identifying the earthquake causative fault (the earthquake source) prior to the event using traditional methods (e.g., geomorphic mapping, geophysical imaging surveys, and paleoseismic trenching).

Snowmelt characterization from optical and synthetic-aperture radar observations in the La Joie Basin, British Columbia

Abstract. Snowmelt runoff serves both human needs and ecosystem services and is an important parameter in operational forecasting systems. Sentinel-1 synthetic-aperture-radar (SAR) observations can estimate the timing of melt within a snowpack; however, these estimates have not been applied on large spatial scales. Here we present a workflow to combine Sentinel-1 SAR and optical data from Landsat-8 and Sentinel-2 to estimate the onset and duration of snowmelt in the La Joie Basin, a 985 km2 watershed in the southern Coast Mountains of British Columbia. A backscatter threshold is used to infer the point at which snowpack saturation occurs and the snowpack begins to produce runoff. Multispectral imagery is used to estimate snow-free dates across the basin to define the end of the snowmelt period. SAR estimates of snowmelt onset form consistent trends in terms of elevation and aspect on the watershed scale and reflect snowmelt records from continuous snow water equivalence observations. SAR estimates of snowpack saturation are most effective on moderate to low slopes (&lt; 30∘) in open areas. The accuracy of snowmelt duration is reduced due to persistent cloud cover in optical imagery. Despite these challenges, snowmelt duration agrees with trends in snow depths observed in the La Joie Basin. This approach has high potential for adaptability to other alpine regions and can provide estimates of snowmelt timing in ungauged basins.

Oil spills detection from SAR Earth observations based on a hybrid CNN transformer networks

Oil spills are the main threats to marine and coastal environments. Due to the increase in the marine transportation and shipping industry, oil spills have increased in recent years. Moreover, the rapid spread of oil spills in open waters seriously affects the fragile marine ecosystem and creates environmental concerns. Effective monitoring, quick identification, and estimation of the volume of oil spills are the first and most crucial steps for a successful cleanup operation and crisis management. Remote Sensing observations, especially from Synthetic Aperture Radar (SAR) sensors, are a very suitable choice for this purpose due to their ability to collect data regardless of the weather and illumination conditions and over far and large areas of the Earth. Owing to the relatively complex nature of SAR observations, machine learning (ML) based algorithms play an important role in accurately detecting and monitoring oil spills and can significantly help experts in faster and more accurate detection. This paper uses SAR images from ESA's Copernicus Sentinel-1 satellite to detect and locate oil spills in open waters under different environmental conditions. To this end, a deep learning framework has been presented to identify oil spills automatically. The SAR images were segmented into two classes, the oil slick and the background, using convolutional neural networks (CNN) and vision transformers (ViT). Various scenarios for the proposed architecture were designed by placing ViT networks in different parts of the CNN backbone. An extensive dataset of oil spill events in various regions across the globe was used to train and assess the performance of the proposed framework. After the detection performance assessments, the F1-score values for the standard DeepLabV3+, FC-DenseNet, and U-Net networks were 75.08 %, 73.94 %, and 60.85, respectively. In the combined networks models (combination of CNN and ViT), the best F1-score results were obtained as 78.48 %. Our results showed that these hybrid models could improve detection accuracy and have a high ability to distinguish oil spill borders even in noisy images. Evaluation metrics are increased in all the combined networks compared to the original CNN networks.

The 2022 Delingha, China, Earthquake Sequence and Implication for Seismic Hazard near the Western End of the Qilian–Haiyuan Fault

Abstract Although the Qilian–Haiyuan fault is known to be responsible for major earthquakes up to M 8, the potential of damaging earthquakes near its western end is not well understood. Since January 2022, three moderate earthquakes (M 5.8, M 6.0, and M 5.4) occurred around the Halahu region of Delingha, China, near the western end of the Qilian–Haiyuan fault. These earthquakes are unusual M 5+ events in this low-seismicity region, and both the U.S. Geological Survey and Global Centroid Moment Tensor solutions suggest that the focal mechanisms of the three mainshocks are distinct from the activity characteristics of the nearby mapped faults. Thus, determining the precise source parameters and identifying the causative fault of this earthquake sequence are important to analyze its seismogenic settings and seismic hazard in this region. In this article, we determined the point-source parameters of the three moderate events via regional waveform modeling and found that these earthquakes are strike-slip events with the nodal planes striking nearly north–south and east–west directions. We then resolved its seismogenic faults by analyzing the aftershock distribution and the rupture directivity. The results show that both the M 5.8 and M 6.0 events ruptured along the north–south nodal plane and expanded toward the south for ∼4 km and ∼3 km, respectively, and the ruptured fault of the M 5.4 is difficult to distinguish. Furthermore, we constrained the relative location using the Interferometric Synthetic Aperture Radar observation, and the result is consistent with that obtained from seismic waveform data. We proposed that this earthquake sequence ruptured along an unmapped dextral fault, which forms a conjugate fault system with the sinistral strike-slip Qilian–Haiyuan fault. Its deformation mechanism may be controlled by long-term, protracted, nearly north–south-trending, right-lateral simple shear in the Qilian Shan fold-thrust belt. The accumulated static Coulomb stress changes resulted in the northwest of this ruptured fault being closer to failure in the future.

Optimal constellation design based on satellite ground tracks for Earth observation missions

In this study, we propose an optimal constellation determination method for Earth observation satellites. First, orbit requirements for the interferometric synthetic aperture radar (InSAR) observation are mathematically formulated. From these requirements, we can obtain nadir trajectories (ground tracks) of the chief satellites of the constellation. The optimal constellation determination problem is modeled as a multi-objective mixed integer programming problem. Then, a metaheuristic optimization method is applied to the resultant problem to optimize the number and configuration of satellite ground tracks and orbital planes. The proposed method enables us to design optimal satellite constellation configurations according to the mission conditions such as the observation area and the acceptable altitude range.

Mapping lava flow from the Kilauea eruption of 2018 in the east rift zone using space-based synthetic aperture radar

ABSTRACT Lava flows from volcanoes can significantly destroy infrastructure, although they rarely threaten human life. Mapping lava flows is essential to managing the volcanic hazard during eruptions and understanding significant changes in geomorphology after eruptions. In 2018, the eruption of Kilauea, Hawaii’s youngest and most active volcano, destroyed more than 700 homes, farmlands, and roads. Active lava flow tracking is crucial for providing timely warnings to evacuate residents and protect infrastructure. We constructed a map of the lava flow in the East Rift Zone (ERZ) during the Kilauea volcanic eruption in 2018 using remotely sensed satellite synthetic aperture radar observations. We processed 23 C-band Sentinel-1 A/B interferometric wide mode acquisitions captured from May 1 to 17 September 2018 and applied a coherent change detection (CCD) technique to detect minute changes in the target structure. A time series of coherence maps provided detailed spatial information on the movement of the lava flow fields in the ERZ. The overall lava flow map had an area of approximately 33.3 km2 and was verified with a lava thickness map provided by the United States Geological Survey that was generated by two digital elevation models using airborne light detection and ranging observations. Excellent agreement (>90%) among the lava flow observations suggests that the CCD technique is effective in monitoring lava flows during a volcanic eruption.

Coherent change detection of fine traces based on multi-angle SAR observations

Coherent change detection (CCD) is an effective method to detect subtle scene changes that occur between temporal synthetic aperture radar (SAR) observations. Most coherence estimators are obtained from a Hermitian product based on local statistics. Increasing the number of samples in the local window can improve the estimation bias, but cause the loss of the estimated images spatial resolution. The limitations of these estimators lead to unclear contour of the disturbed region, and even the omission of fine change targets. In this paper, a CCD approach is proposed to detect fine scene changes from multi-temporal and multi-angle SAR image pairs. Multi-angle CCD estimator can improve the contrast between the change target and the background clutter by jointly accumulating singleangle alternative estimator results without further loss of image resolution. The sensitivity of detection performance to image quantity and angle interval is analyzed. Theoretical analysis and experimental results verify the performance of the proposed algorithm.

Late complex tensile fracturing interacts with topography at Cumbre Vieja, La Palma

Volcanic eruptions are often preceded by episodes of in ation and emplacement of magma along tensile fractures. Here we study the 2021 Tajogaite-Cumbre Vieja eruption on La Palma, Canary Islands, and present evidence for tensile fractures dissecting the new cone during the terminal stage of the eruption. We use synthetic aperture radar (SAR) observations, together with drone images and time-lapse camera data, to determine the timing, scale and complexities associated with a fracturing event, which is diverging at a topographic ridge. By comparing the field dataset with analogue models, we further explore the details of lens-shaped fractures that are characteristic for faults diverging at topographic highs and converging at topographic lows. The observations made at Cumbre Vieja and in our models are transferrable to other volcanoes and add further evidence that topography is substantially affecting the geometry and complexity of fractures and magma pathways, and the locations of eruptions.

BUILDING RECONSTRUCTION BASED ON A SMALL NUMBER OF TRACKS USING NONPARAMETRIC SAR TOMOGRAPHIC METHODS

Abstract. Nowadays, the synthetic aperture radar (SAR) tomography (TomoSAR) technique plays a notable role in the 3D reconstruction of urban buildings through several SAR acquisitions with slightly different positions. Nonparametric-based TomoSAR spectral estimation algorithms usually work well when a large number of SAR observations. In this study, with a limited number of SAR images, we have assessed the efficiency of the nonparametric spectral estimation methods, including maximum entropy (ME), singular value decomposition (SVD), linear prediction (LP), Capon, minimum norm (MN), and beamforming (BF) in the reconstruction of the third dimension of urban buildings. The experiments are conducted on both simulated and TerraSAR-X stripmap images to indicate the effectiveness of the LP proposed estimation algorithm. The analysis of the results proves that by minimizing the average output signal power over the antenna array elements, the LP spectral estimation achieves the discrimination of distinct scatterers inside an image pixel. In addition, this low computational estimator improves the sidelobe suppression and the height estimates of the scatterers in the complex multiple-scattering urban environment. Compared to SVD, maximum entropy, Capon, minimum norm, and beamforming, the height of the Eskan tower in Tehran, Iran, obtained with the LP technique, is considerably near to field-based measurement.

A Complete Map of Fine‐Scale Slip Rate Distribution and Earthquake Potential Along the Haiyuan Fault System

AbstractWe use Sentinel‐1 Interferometric Synthetic Aperture Radar observations to derive the first complete map of slip‐rate distribution at km‐scale along the 1,000‐km‐long Haiyuan fault system, and further to analyze its earthquake potential. Rate distribution reveals four highly locked asperities (slip rate &lt;1 mm/yr) and three creeping regions (slip rate ranges from 2 to 5 mm/yr) at depths of 0–20 km. Based on the rate distribution, we estimate seismic moment accumulation rate, and by combining historical earthquake records, we calculate the moment deficits on each fault segment. Results show that many fault segments have accumulated a seismic moment that equates to an Mw &gt; 7.0 earthquake. The westernmost fault segment has been rarely mentioned in previous studies, but our calculations show evidently that it is capable of producing Mw &gt; 7.0 earthquakes given the high seismic moment accumulation rate and a lack of small‐to‐moderate earthquakes.

Depth‐Varying Friction on a Ramp‐Flat Fault Illuminated by ∼3‐Year InSAR Observations Following the 2017 Mw 7.3 Sarpol‐e Zahab Earthquake

AbstractWe use interferometric synthetic aperture radar observations to investigate the fault geometry and afterslip evolution within 3 years after a mainshock. The postseismic observations favor a ramp‐flat structure in which the flat angle should be lower than 10°. The postseismic deformation is dominated by afterslip, while the viscoelastic response is negligible. A multisegment, stress‐driven afterslip model (hereafter called the SA‐2 model) with depth‐varying frictional properties better explains the spatiotemporal evolution of the postseismic deformation than a two‐segment, stress‐driven afterslip model (hereafter called the SA‐1 model). Although the SA‐2 model does not improve the misfit significantly, this multisegment fault with depth‐varying friction is more physically plausible given the depth‐varying mechanical stratigraphy in the region. Compared to the kinematic afterslip model, the mechanical afterslip models with friction variation tend to underestimate early postseismic deformation to the west, which may indicate more complex fault friction than we expected. Both the kinematic and stress‐driven models can resolve downdip afterslip, although it could be affected by data noise and model resolution. The transition depth of the sedimentary cover basement interface inferred by afterslip models is ∼12 km in the seismogenic zone, which coincides with the regional stratigraphic profile. Because the coseismic rupture propagated along a basement‐involved fault while the postseismic slip may activate the frontal structures and/or shallower detachments in the sedimentary cover, the 2017 Sarpol‐e Zahab earthquake may have acted as a typical event that contributed to both thick‐ and thin‐skinned shortening of the Zagros in both seismic and aseismic ways.