The use of unmanned aerial vehicles (UAVs) for Earth and environmental sensing has increased significantly in recent years. This is particularly true for multi- and hyperspectral sensing, with a variety of both push-broom and snap-shot systems becoming available. However, information on their radiometric performance and stability over time is often lacking. The authors propose the use of a general protocol for sensor evaluation to characterize the data retrieval and radiometric performance of push-broom hyperspectral cameras, and illustrate the workflow with the Nano-Hyperspec (Headwall Photonics, Boston USA) sensor. The objectives of this analysis were to: (1) assess dark current and white reference consistency, both temporally and spatially; (2) evaluate spectral fidelity; and (3) determine the relationship between sensor-recorded radiance and spectroradiometer-derived reflectance. Both the laboratory-based dark current and white reference evaluations showed an insignificant increase over time (99.5% of pixel–waveband combinations. Using a mercury/argon (Hg/Ar) lamp, the hyperspectral wavelength bands exhibited a slight shift of 1-3 nm against 29 Hg/Ar wavelength emission lines. The relationship between the Nano-Hyperspec radiance values and spectroradiometer-derived reflectance was found to be highly linear for all spectral bands. The developed protocol for assessing UAV-based radiometric performance of hyperspectral push-broom sensors showed that the Nano-Hyperspec data were both time-stable and spectrally sound.

SUMMARY: The Tjörnes Fracture Zone in North Iceland is one of two transform zones in Iceland capable of generating earthquakes of magnitude $\sim$7. More than 150 years have passed since the last two major earthquakes occurred on the Húsavík–Flatey fault, one of the two main transform structures within the Tjörnes Fracture Zone. Given the seismic hazard posed to Húsavík and adjacent coastal communities, accurately determining the slip rate and locking depth of this fault is crucial for a robust earthquake hazard and risk assessment. In this study, we significantly expand the existing global navigation satellite system data set for the Tjörnes Fracture Zone by incorporating more than a decade of additional data and doubling the number of stations. This expansion not only improves the spatial coverage of the network, but also refines the station velocities. We present an updated interseismic velocity field for North Iceland and implement a backslip model with nine dislocation segments to describe the plate boundary deformation. Additionally, we include a point pressure source for the ongoing broad uplift signal in the study area. Our findings indicate a locking depth of $7.3\substack{+0.9 \\ -0.7}$ km and an average slip rate of $6.9\pm 0.2$ mm yr−1 for the Húsavík–Flatey fault. With our updated approach, we can narrow down model parameter constraints from previous studies and thereby provide an enhanced understanding of the earthquake potential of this region.

Recent studies of the Reykjanes Peninsula in Iceland have shown that Interferometric Synthetic Aperture Radar (InSAR) data can reveal surface movements across new and pre-existing fractures associated with stress changes during volcanic dyke intrusions. These data have revealed many fractures in areas where optical imagery or field observations are obscured by vegetation, infrastructure, or young lava flows. Mapping active faults and fractures in geologically dynamic regions is essential for assessing tectonic and volcanic hazards, as pre-existing fractures and crustal weaknesses can control magma pathways, dyke propagation, and the location of eruptive activity. However, systematic fracture mapping from wrapped interferograms remains a time-consuming manual task. Deep learning approaches have been widely utilized successfully for mapping faults in seismic data and optical images, and similarly, to detect glacier crevasses in SAR backscatter images. Here, we investigate the feasibility of automatic fracture mapping directly from wrapped interferograms using deep learning, focusing on the current volcanic unrest on the Reykjanes Peninsula. We address the task as a binary classification problem, and implement a convolutional neural network with a U-Net architecture trained using a Dice loss to address strong class imbalance. We initially trained our model on a rather small, highly imbalanced dataset consisting of Sentinel-1 and TerraSAR-X interferograms of the area from September 2023 to February 2024. Despite relatively modest F1-Score (~56%), the model successfully identifies all major fracture movements in the test data and is able to detect features absent from the original labels, providing a fairly robust fracture map that can be easily refined. These results demonstrate that deep learning can be used to extract meaningful structural information from wrapped interferograms, even with limited data and imperfect training labels, and constitute to the best our knowledge the first application of deep learning to fracture mapping in wrapped interferograms. Current work is aimed at improving the model performance by including the latest fractures dataset of the Reykjanes Peninsula, consisting of fractures mapped on TerraSAR-X interferograms from September 2021 to July 2024. Additionally, ongoing efforts are focused on generating physically realistic synthetic interferograms that capture the complexity of fracturing and fracture reactivation due to dyke emplacement and propagation and other sources of deformation. By addressing the current limitations, this approach has the potential to enable transferable fracture-mapping workflows applicable across diverse tectonic settings and InSAR datasets, contributing to more efficient geohazard monitoring.

Oceanic transform faults (OTFs) accommodate a significant portion of global plate motions, yet the physical mechanisms governing their characteristically low seismic coupling and the initiation of earthquake swarms remain poorly understood. Here, we provide the first direct geodetic evidence of shallow slow slip events (SSEs) on an OTF. By leveraging the increase of seismic activity during earthquake swarms as temporal constraints on high-resolution, land-based continuous GNSS data from near the just-offshore Húsavík-Flatey Fault (HFF) in North Iceland, we utilized a signal-stacking strategy to isolate ultra-slow transients from stochastic noise. This approach detected SSEs that are several weeks in duration and with an average moment magnitude of Mw 5.34, systematically preceding the seismic swarms. The marked spatial complementarity between SSEs and swarms, combined with their temporal synchronicity, seem to indicate that the aseismic transients act as a mechanical trigger for the swarm-like activity along the western portion of HFF. The pronounced contrast between the weakly coupled western segment of the HFF—characterized by concurrent SSEs and earthquake swarms—and the strongly coupled eastern segment, which lacks moderate to large earthquake clusters, reveals a fundamental along-strike heterogeneity in fault behavior. The evidence of systematic aseismic slip release along the HFF accompanying swarm activity indicates that the seismic moment deficit of OTFs can be reconciled by aseismic slip transients. Our results corroborate that OTFs are complex faults where rheological and geometrical segmentation result in a complex interplay of slow and fast slip release.Acknowledgements: We gratefully acknowledge Baptiste Rousset and Estelle Neyrinck for generously sharing their geodetic matched filter code for this analysis.

The Tjörnes Fracture Zone poses significant seismic hazard to the town of Húsavík and other nearby coastal communities in North Iceland as it is capable of generating earthquakes of magnitude ~7. The 120 km long offset connects the offshore Kolbeinsey Ridge to the onshore Northern Volcanic Zone and accommodates approximately 18 mm/yr of transform motion between the North American and Eurasian plates. Most of the deformation is taken up by the two main structures of the fracture zone. The Grímsey Oblique Rift exhibits bookshelf faulting and consists of steeply dipping faults, arranged en-echelon and striking roughly N-S, bounding a series of left-stepping basins. The Húsavík-Flatey Fault is a ~100 km-long right-lateral strike-slip fault. It is mostly offshore, except for its easternmost ~25 km that comes onshore just north of Húsavík. To assess how the deformation is partitioned within the Tjörnes Fracture Zone and to calculate the rate of seismic moment accumulation on the Húsavík-Flatey Fault we use geodetic data from our North Iceland GNSS network. The network covers an area of roughly 200 km by 130 km in size and includes 21 continuous and 92 campaign-style GNSS stations. The continuous data now span up to ~21 years from 2001 to 2022. The first campaign measurements that focused on the HFF were carried out in 1995 and since then we have expanded the campaign-station network to the West towards Tröllaskagi and Skagafjörður and remeasured the network on several occasions. Data from the 2002, 2007, 2009, 2010, 2011, 2013, 2016, 2019, and 2022 campaigns are included in our study. In addition, several stations from the nationwide ISNET reference station network within our study area also included. The GNSS data is used to produce the most up to date velocity field from North Iceland. Relative to the North American plate, our results show a gradual increase of East velocities towards the Northeast across the two main transform structures that reach roughly 18 mm/yr on the Eurasian plate. At the northern tip of the Tjörnes peninsula, between the two transform structures, the velocities are roughly at half the total rate seen at the easternmost stations on the Eurasian plate. Limited deformation is found Southwest of the Húsavík-Flatey Fault in Tröllaskagi, within the so-called Dalvík zone, located on the North American plate. These results are used to study the present day-kinematics of the Tjörnes Fracture Zone and to further improve the locking depth and slip-rate estimates of the main lineaments.

The Tjörnes Fracture Zone (TFZ) in North Iceland links the offshore Kolbeinsey Ridge to the onshore Northern Volcanic Zone. About 98% of the seismicity in the TFZ concentrates along the Húsavík-Flatey Fault (HFF), a ~120-km-long right-lateral strike-slip fault, and the Grímsey Oblique Rift (GOR), an oblique rift with associated volcanism. Many earthquake swarms have occurred in the TFZ during the past decades. A notable swarm began on June 19, 2020, on the HFF and lasted until October 2020. It included three earthquakes of magnitude 5.4, 5.7 and 6.0, making it the most energetic earthquake sequence in North Iceland since the 1976 M6.2 Kópasker earthquake. The seismicity began on the western HFF and propagated to the northwest and southeast along the HFF. The first large event (M5.4) occurred on June 20 at 15:05h and was followed by the M5.7 earthquake four hours later at 19:26h. While the earthquake activity on the western HFF increased significantly, these events and their aftershocks also show a distinct N-S lineation, roughly perpendicular to the HFF, indicating that a conjugate fault may have been activated. The third large event and the largest of the sequence (M6.0) struck on the following day at 19:07h. It occurred at the eastern shoulder of the N-S oriented Eyjafjarðaráll Rift (ER), which is the southern continuation of the Kolbeinsey Ridge. This M6.0 event is the largest normal faulting earthquake recorded in Iceland, and thus constraining the parameters of this sequence is crucial for seismic hazard assessment in the region. However, the offshore location of these events makes this task challenging. Here, we attempt to overcome this challenge by combining waveform data from broadband stations up to 4,000 km away from the earthquakes and co-seismic GNSS offsets from 20 continuous and 10 campaign stations in North Iceland to estimate the earthquake source parameters, utilizing Bayesian inference. An initial double couple source was estimated from waveform data only as a prior for the joint geodetic and waveform data inversion. The M5.4 and M5.7 events correspond to strike-slip faulting on a WNW-ESE right-lateral fault or a N-S left-lateral fault, while the M6.0 event indicates normal faulting on a N-S fault. Our preliminary results show that incorporating GNSS data yields shallower depths than with waveform data alone (7.9 km for the first event, 5.1 km for the second event, and 10.2 km for the third event). However, previous results from moment tensor inversions of local broadband data indicate shallower depths for the event in the ER than for the strike-slip earthquakes, emphasizing the difficulty in constraining the depths of these offshore events. For their magnitudes, the first event was estimated as M5.3, with and without geodetic data, and the latter two events were found slightly larger in the joint inversion (M5.8 and M6.1) than in the seismic data solution, indicating limited aseismic moment release. These findings highlight the importance of combining diverse datasets to improve our understanding of complex tectonic settings and to constrain the proportion of seismic/aseismic moment release in earthquake sequences.