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The study of crustal deformation fields caused by earthquakes is important for a better understanding of seismic hazard and growth of geological structures in tectonically active areas. In this study, we present, using interferometric measurements constructed from Sentinel-1 Terrain Observation with Progressive Scan (TOPS) data and ALOS-2 ScanSAR, coseismic deformation and source model of the Mw 7.3, 12 November 2017 earthquake that hit northwest of the Zagros Mountains in the region between Iran–Iraq border. This was one of the strongest seismic events to hit this region in the past century, and it resulted in an uplift area of about 3500 km2 between the High Zagros Fault (HZF) and Mountain Front Fault (MFF) with a maximum amount of 70 cm south of Miringe fault. A subsidence over an area of 1200 km2 with a maximum amount of 35 cm occurred near Vanisar village at the hanging wall of the HZF. Bayesian inversion of interferometric synthetic aperture radar (InSAR) observations suggests a source model at a depth between 14 and 20 km that is consistent with the existence of a decoupling horizon southwest edge of the northern portion of the Zagros Mountains near the MFF. Moreover, we present evidence for a number of coseismically induced rockslides and landslides, the majority of them which occurred along or close to pre-existing faults, causing decorrelation in differential interferograms. Exploiting the offset-tracking technique, we estimated surface motion by up to 34 and 10 m in horizontal and vertical directions, respectively, due to lateral spreading on a big coseismic-induced landslide near Mela-Kabod. Field observations also revealed several zones of en echelon fractures and crack zones developed along a pre-existing fault passing through Qasr-e Shirin City, which exhibited secondary surface slip by up to 14 cm along its strike.

Interferometric Synthetic Aperture Radar (InSAR) capability to detect slow deformation over terrain areas is limited by temporal decorrelation, geometric decorrelation and atmospheric artefacts. Multitemporal InSAR methods such as Persistent Scatterer (PS-InSAR) and Small Baseline Subset (SBAS) have been developed to deal with various aspects of decorrelation and atmospheric problems affecting InSAR observations. Nevertheless, the applicability of both PS-InSAR and SBAS in mountainous regions is still challenging. Correct phase unwrapping in both methods is hampered due to geometric decorrelation in particular when using C-band SAR data for deformation analysis. In this paper, we build upon the SBAS method implemented in StaMPS software and improved the technique, here called ISBAS, to assess tectonic and volcanic deformation in the center of the Alborz Mountains in Iran using both Envisat and ALOS SAR data. We modify several aspects within the chain of the processing including: filtering prior to phase unwrapping, topographic correction within three-dimensional phase unwrapping, reducing the atmospheric noise with the help of additional GPS data, and removing the ramp caused by ionosphere turbulence and/or orbit errors to better estimate crustal deformation in this tectonically active region. Topographic correction is done within the three-dimensional unwrapping in order to improve the phase unwrapping process, which is in contrast to OPEN ACCESS Remote Sens. 2015, 7 8324 previous methods in which DEM error is estimated before/after phase unwrapping. Our experiments show that our improved SBAS approach is able to better characterize the tectonic and volcanic deformation in the center of the Alborz region than the classical SBAS.In particular, Damavand volcano shows an average uplift rate of about 3 mm/year in the year 2003-2010. The Mosha fault illustrates left-lateral motion that could be explained with a fault that is locked up to 17-18 km depths and slips with 2-4 mm/year below that depth.

An earthquake swarm affected the Bransfield Strait, Antarctica, a unique rift basin in transition from intra-arc rifting to ocean spreading. The swarm, counting ~85,000 volcano-tectonic earthquakes since August 2020, is located close to the Orca submarine volcano, previously considered inactive. Simultaneously, geodetic data reported up to ~11 cm northwestward displacement over King George Island. We use a broad variety of geophysical data and methods to reveal the complex migration of seismicity, accompanying the intrusion of 0.26–0.56 km3 of magma. Strike-slip earthquakes mark the intrusion at depth, while shallower normal faulting the ~20 km long lateral growth of a dike. Seismicity abruptly decreased after a Mw 6.0 earthquake, suggesting the magmatic dike lost pressure with the slipping of a large fault. A seafloor eruption is likely, but not confirmed by sea surface temperature anomalies. The unrest documents episodic magmatic intrusion in the Bransfield Strait, providing unique insights into active continental rifting.

Dust storms are important phenomena over large regions of the arid and semi-arid areas of the Middle East. Due to the influences of dust aerosols on climate and human daily activities, dust detection plays a crucial role in environmental and climatic studies. Detection of dust storms is critical to accurately understand dust, their properties and distribution. Currently, remotely sensed data such as MODIS (Moderate Resolution Imaging Spectroradiometer) with appropriate temporal and spectral resolutions have been widely used for this purpose. This paper investigates the capability of two physical-based methods, and random forests (RF) classifier, for the first time, to detect dust storms using MODIS imagery. Since the physical-based approaches are empirical, they suffer from certain drawbacks such as high variability of thresholds depending on the underlying surface. Therefore, classification-based approaches could be deployed as an alternative. In this paper, the most relevant bands are chosen based on the physical effects of the major classes, particularly dust, cloud and snow, on both emissive infrared and reflective bands. In order to verify the capability of the methods, OMAERUV AAOD (aerosol absorption optical depth) product from OMI (Ozone Monitoring Instrument) sensor is exploited. In addition, some small regions are selected manually to be considered as ground truth for measuring the probability of false detection (POFD) and probability of missing detection (POMD). The dust class generated by RF is consistent qualitatively with the location and extent of dust observed in OMERAUV and MODIS true colour images. Quantitatively, the dust classes generated for eight dust outbreaks in the Middle East are found to be accurate from 7% and 6% of POFD and POMD respectively. Moreover, results demonstrate the sound capability of RF in classifying dust plumes over both water and land simultaneously. The performance of the physical-based approaches is found weaker than RF due to empirical thresholds that are not always true.