Distributed acoustic sensing (DAS) is an emerging technique that uses fiber-optic cables to detect ground vibrations. DAS can record vibrations similarly to a dense network of seismic stations, aiding earthquake detection and the analysis of subsurface structures. Although terrestrial DAS setups are relatively accessible, there are few submarine optical cables available for use. As a result, most DAS data currently come from terrestrial cables. We started monitoring earthquakes using a communication cable that crosses the North Anatolian fault in the Marmara Sea for the first time. This cable crosses a bay that contains a trench that is related to deformation along the North Anatolian fault, creating a unique and complex observational environment. We present overviews of seismic data from local, regional, and teleseismic events retrieved using the new DAS system and make these data publicly available. The analysis of waveforms from nearby seismic stations and DAS instruments highlights the potential seismic applications of DAS data in the Marmara Sea.

Ionospheric delays dominate the long-wavelength deformation measurement errors in interferometric synthetic aperture radar (InSAR) time series. Most existing correction techniques are SAR data-based and rely on interferometric coherence, which restricts their applicability in vegetated areas and operational systems. Here, we investigate a rapid ionospheric correction approach that uses external total electron content (TEC) products derived from global navigation satellite system (GNSS) observations with a focus on the long-wavelength deformation mapping. We tested five products, including global ionospheric maps (GIMs), Madrigal TEC products, and local ionospheric maps. The method is fully automatic and reduces processing time from days to minutes, desirable for operational data systems as well as existing InSAR databases. We validate the method using C-band Sentinel-1 and L-band ALOS-2 data in the western United States, northern Chile, and southwestern Japan. Using independent GNSS displacements as reference, our method reduces the RMSE from 1.7-3.8 to 0.9-2.9 mm/yr for Sentinel-1 ascending tracks in mid-latitude regions and all descending tracks, achieving accuracy comparable to the split-spectrum method (1.0-2.8 mm/yr). However, for Sentinel-1 ascending track in low-latitude regions and ALOS-2 descending tracks, the split-spectrum method still remains much more precise. We demonstrate the effectiveness of the GNSS-based TEC approach using OPERA displacement products over the western USA. The global analysis of ionospheric delays using multiyear GIM products could serve as a useful reference for potential ionospheric impact on InSAR velocities for most common InSAR missions.

Understanding the shallow rupture mechanisms on coseismic faults and assessing the influence of fault area propagation is essential for disaster prevention. Since 2000, Hualien and nearby areas in eastern Taiwan have experienced frequent earthquakes, making it a good area to study the evolution of fault rupture. This study proposes a two-dimensional dynamic discrete element model to simulate the shallow rupture behavior of the Milun Fault. Results indicate that the rupture process proceeds through multiple evolutionary stages, with fractures propagating upward from depth but failing to fully break through to the surface, resulting instead in surface cracking without complete rupture. The second deviatoric stress invariant serves as an effective indicator of stress accumulation and release during rupture progression. For the preferred model, the modeled vertical uplift near the fault reached 0.6 m, consistent with field observations reporting a maximum coseismic uplift of approximately 0.585 m along the Milun Fault. Given the scarcity of near-fault observational constraints, the simulation represents a physically plausible scenario rather than a unique reconstruction. The integration of stress evolution, crack propagation, and near-field displacement provides new insight into the mechanical processes governing shallow thrust fault rupture and can be applied to similar fault systems exhibiting near-surface deformation.

Abstract This study investigates whether hydraulic fracturing contributed to the 2021 Ms 6.0 Luxian earthquake in the Sichuan Basin. Time‐series InSAR analysis of multi‐temporal Sentinel‐1 data reveals coseismic deformation associated with the 2021 Luxian earthquake and localized uplift near hydraulic fracturing platforms. We then developed a three‐dimensional poroelastic model to simulate pore‐pressure changes and stress perturbations caused by water injection from both nearby and distant platforms. The results show that both water injection from the single platform above the coseismic fault and cumulative injection from multiple distal platforms increase Coulomb stress on the sedimentary‐layer fault. However, for the basement fault, the induced Coulomb stress remains below 0.01 MPa in all cases. The optimal fault‐slip mechanism derived from the injection model aligns with the inverted coseismic slip pattern in the maximum‐slip zone. These results suggest that hydraulic fracturing brought the sedimentary‐layer fault closer to instability and may have exacerbated rupture during the Luxian earthquake.

The probability distribution of inter-event time (IET) between two consecutive earthquakes is a measure for the uncertainty in the occurrence time of earthquakes in a region of interest. It is well known that the IET distribution for regular earthquakes is commonly characterized by a power law with the exponent of 0.3. However, less is known about other classes of earthquakes, such as volcanic earthquakes, which do not manifest mainshock-aftershocks sequences. Since volcanic earthquakes are caused by the movement of magmas, their IET distribution may be closely related to the volcanic activities and therefore of particular interest. Nevertheless, the general form of IET distribution for volcanic earthquakes and its dependence on volcanic activity are still unknown. Here we show that the power-law exponent characterizing the IET distribution exhibits a few common values depending on the stage of volcanic activity. Volcanoes with steady seismicity exhibit the lowest exponent ranging from 0.6 to 0.7. During the burst period, when the earthquake rate is highest, the exponent reaches its peak at approximately 1.3. In the preburst phase, the exponent takes on the intermediate value of 1.0. These values are common to several different volcanoes. Since the preburst phase is characterized by the distinct exponent value, it may serve as an indicator of imminent volcanic activity that is accompanied by a surge in seismic events.

SAR and GNSS are two dominant techniques to measure the Earth's deformation. They have different characteristics in that InSAR has superior spatial resolution, and GNSS has superior temporal resolution. Also, GNSS has better precision than InSAR, and InSAR measurements have significant spatial correlation mainly because of the atmospheric disturbance. Therefore, if available, InSAR measurements will be more precise when combined with GNSS measurements. This study investigates the temporal evolution of land subsidence and slow slip transients in the Boso Peninsula, Japan, from InSAR and GNSS measurements. First, we generated interferograms of available ALOS-2 images. The generated interferograms are corrected to be consistent with GNSS measurements every 20 km or so. The correction assumes that the spatial variation of the noise in InSAR measurements is represented as a polynomial function, the degree of which is constrained adaptively. Then, the corrected interferograms are fed to the time-series analysis. The time series generated allows us to separate continuing subsidence of up to 20 mm/yr with a shorter wavelength and slow slip transients with a longer wavelength.

Catastrophic earthquakes have struck along the East Anatolian Fault (EAF), including the 2020 Elaziğ earthquake and the 2023 Kahramanmaraş earthquake doublet. We integrated ground velocity from 2015-2020, together with co- and post-seismic deformation from the 2020 and 2023 earthquakes to investigate the fault slip evolution of the EAF, aiming to reveal the potential future seismic hazards. The spatiotemporally continuous deformation field allows us to derive complete slip distribution models throughout the earthquake cycle, including an interseismic period, two distinct postseismic periods, and three coseismic events. The results demonstrate that the slip patterns exhibit complementary characteristics across the interseismic, coseismic, and postseismic phases. Additionally, by integrating historical earthquake records with the strain accumulated and released during the observation period and quantifying the corresponding slip deficits, we evaluated the seismic potential of the EAF. Among these, the Palu segment emerged as having particularly high seismic risk, emphasizing the need for close monitoring. Geodetic observations suggest segments of the East Anatolian Fault which display interseismic slip deficits correlate with the rupture zones of the 2020 Elaziğ earthquake and the 2023 Kahramanmaraş earthquake doublet and may be useful to identify high risk regions.

Mount Fuji volcano, located 100~km away from Tokyo, directly threatens over 30~million people. It last erupted in 1707, and has remained dormant since then. Seismicity --and particularly Low-Frequency Earthquakes (LFE)-- is to now the primary indicator of processes occurring beneath the volcano and is usually linked to fluid movement. Yet, these signals are usually manually picked and classified as such, without the ability to formally define them for automatic detection systems. Our goal is to develop an automatic method to detect and classify LFEs, among other seismic events at Mount Fuji using the continuous seismic records from 2008 at 11 stations. First, we use the CovSeisNet software to detect events by analyzing the wavefield coherence, derived from the network covariance matrix width. Over one year of continuous data, the wavefield coherence shows distinct patterns that correspond to various event types, including LFEs and tectonic earthquakes. To enable interpretation, we apply a manifold learning algorithm (UMAP) to reduce the dimension of the coherence patterns into two dimensions to ease the interpretation. We name this low-dimensional representation a "coherence atlas" where each point represents a time window of seismic data, grouped by similarity. This automatic approach enables not only the detection but also the classification of seismic events, as compared with the Japan Meteorological Agency catalog. Moreover, the atlas helps identify previously unrecorded events and facilitates the definition of new event classes. By autonomously mapping and classifying seismic activity beneath Mount Fuji, this method offers unprecedented insights into its activity and allows us to detect new events that had been hidden in the manually prepared catalog.

Rupture characteristics and heterogeneity of large earthquakes are essential for seismic hazard assessments. We use relocated aftershocks, geodetic measurements, and seismic waveform data to distinguish contributions from closely separated fault structures of the 2024 Mw 7.5 Noto earthquake. We find that the initial rupture triggered slip on a complex fault of a preceding swarm and led to bilateral slow rupture there. The earthquake ruptured two fault segments with contrasting dip angles along the eastern and western segments. Aftershocks continued to rupture the preexisting swarm faults. A delayed rupture occurred southwest of the hypocenter, implying that substantial resistance caused by a barrier temporally hindered rupture propagation. Additional stress from surrounding slip eventually overcame the strength of the barrier fault section, leading to a compound rupture. The mainshock triggered a small earthquake swarm, in which the relatively larger events were not followed by abundant aftershocks. Our findings demonstrate the influence of strong asperities and complex geometry in the progression of cascading ruptures.

The moment‐duration ( M 0 ‐ T ) scaling law reveals fundamental earthquake physics across various sizes and tectonic settings. However, the validity of the cubic relation ( M 0 ∝ T 3 ) inferred for large (Mw ≥ 7) megathrust events has been recently questioned due to the scarcity of observations and similarities to slow earthquakes. Here, by compiling events over the past 500 years from global subduction zones, we double the number of earthquakes studied (>260) compared to previous studies. A possible scale change is observed, at moment‐magnitude and duration of ∼7.6 and ∼38.1 s, respectively. The new catalog reveals an accelerated decrease of the scaling exponent as a function of magnitude from 2.5 (Mw ≥ 7) to below 1 (Mw > 8.7), indicating increasingly longer durations than expected for larger events. The rapid increase in duration with earthquake size is interpreted as the interplay of seismogenic bounds, trench‐breaching, and subevents, which delays lateral rupture propagation. Our study aids in understanding slow and fast earthquakes.

Multi-looking, aimed at reducing data size and improving the signal-to-noise ratio, is indispensable for largescale InSAR data processing. However, the resulting "Fading Signal" caused by multi-looking breaks the phase consistency among triplet interferograms and introduces bias into the estimated displacements. This inconsistency challenges the assumption that only unwrapping errors are involved in triplet phase closure. Therefore, untangling phase unwrapping errors and fading signals from triplet phase closure is critical to achieving more precise InSAR measurements. To address this challenge, we propose a new method that mitigates phase unwrapping errors and fading signals. This new method consists of two key steps. The first step is triplet phase closure-based stacking, which allows for the direct estimation of fading signals in each interferogram. The second step is Basis Pursuit Denoising-based unwrapping error correction, which transforms unwrapping error correction into sparse signal recovery. Through these two procedures, the new method can be seamlessly integrated into the traditional InSAR workflow. Additionally, the estimated fading signal can be directly used to derive soil moisture as a by-product of our method. Experimental results on the San Francisco Bay area demonstrate that the new method reduces velocity estimation errors by approximately 9 %-19 %, effectively addressing phase unwrapping errors and fading signals. This performance outperforms both ILP and Lasso methods, which only account for unwrapping errors in the triplet closure. Additionally, the derived by-product, soil moisture, shows strong consistency with most external soil moisture products.

The geometric complexity of strike-slip faults, such as stepovers, bends and branches, is a pivotal indicator of segmented fault rupture. These features act as barriers to the activity of strike-slip faults, leading to uneven stress distribution along the fault zone, thereby influencing the initiation, propagation, and termination of earthquake ruptures.The East Anatolian Fault (EAF) is a significant sinistral strike-slip fault, connecting with the North Anatolian Fault (NAF) to the north and the Dead Sea Fault (DSF) to the south. Located in southeastern Turkey, it plays a crucial role in accommodating the relative motion between the northward-moving Arabian Plate and the westward-moving Anatolian Block. Despite the relative quietude of the EAF since the beginning of the 20th century, historical seismic activity indicates its potential to generate devastating earthquakes, as evidenced by a series of relatively large earthquakes occurring between 1822 and 1905. On January 24, 2020, the Pütürge segment at the northeastern end of the EAF experienced the Mw6.8 Elaziğ earthquake (2020 event). Subsequently, on February 6, 2023, the southwestern segment of the EAF witnessed the Turkey–Syria Earthquake Sequence (2023 event). The consecutive occurrence of these two seismic events has provided an opportunity to investigate the tectonic activity characteristics and seismic triggering relationships of the EAF.In this study, we take the two events that occurred on the EAF in 2020 and 2023 as the time nodes and take the EAF as the research object. Utilizing InSAR technology, the research investigated the deformation during seismic cycles (interseismic, coseismic, and postseismic) based on Sentinel-1 radar data. We computed interseismic deformation velocities from 2015 to 2020 and displacement time series from February 2020 to February 2023, as well as from February 2023 to September 2023. Subsequently, this study extensively considered the geometric complexity of faults and established an elastic triangular dislocation model. Based on this model, we derived the interseismic fault slip distribution for the EAF from 2015 to 2020, as well as coseismic and postseismic fault slip distributions for the 2020 and 2023 events. The results indicate that: 1) The slip rate along the EAF exhibits a decreasing trend from the northeastern end (5 mm/yr) to the southwestern end (2 mm/yr); 2) The interseismic slip deficits of the EAF correlate well with the coseismic fault slip distribution of the 2020 and 2023 events; 3) The postseismic fault slip following the 2020 and 2023 events primarily occurs at coseismic slip deficit areas.

Phase decorrelation hampers the accuracy of distributed scatterer (DS) interferometry (DSI) in high-precision deformation monitoring. While several advanced phase linking (PL) techniques built upon the sample coherence matrix (SCM) have shown effectiveness in enhancing the signal-to-noise ratio (SNR), their performance significantly degrades under suboptimal SCM estimation, particularly in scenarios of fast decorrelation and near-zero coherence levels. This article presents an enhanced sequential phase estimator motivated by the degradation of theoretical accuracy due to pure noise-bearing interferograms in full-stack-exploiting PL schemes. In the proposed estimator, Bayesian ensemble theory is first employed to divide the full-stack data adaptively into ministacks based on the coherence pattern, rather than constant-size ministacks usually determined empirically. Building on this, the estimator introduces a nonconvex regularization-based sparse SCM by constraining the coherence matrix to have potential sparsity and low-rank (LR), which suppresses the influence of noisy interferometric pairs and improves SCM estimation. Moreover, we incorporate the deformation elasticity theory to provide additional spatial constraints on the reconstructed phase time series across adjacent pixels, realized by using the strain model to reduce phase discontinuity and further enhance the SNR. Experiments on the simulated and real Sentinel-1 images over a landslide-prone area in western Guizhou, China, demonstrate the effectiveness and superior performance of the new method.

The Virunga Volcanoes is the first Supersite established on the African continent in a highly populated Multi-hazards region. This permanent Supersite was established in a critical context as little was known about the Virunga hazards sources and their dynamics, and little done as measures to evaluate, mitigate and reduce their impacts. Similarly, the active volcanoes are poorly studied and monitored, because of the lack of both qualified human resources and appropriate infrastructures. Therefore, the establishment of the “Virunga Volcanoes Supersite” aimed mostly at helping put together local and international scientists and agencies; support them to access to Earth Observatory (EO) data and potentially to equipment for ground-based data collection, as well as the building of a pool of collaboration. The expectation was that the above-mentioned collaborations and supports would improve the early warning capacity of the local scientists and agencies involved in Natural Hazards Assessment and volcano monitoring, risk reduction and management. After two years of existence, the Virunga Volcanoes Supersite has allowed the building of institutional collaboration between the Goma Volcano Observatory and some of the world leading institutes specialized in the study and monitoring of active volcanoes, and the assessment of related hazards (e.g. Italian INGV, United Stated USGS-VDAP, discussions are ongoing for others). The supersite has also allowed the access-free of charge- to a variety of EO data, e.g. COSMO-SkyMed SAR data from the Agenzia Spaziale Italiana, Pleiades from the Centre National d’Etudes Spaciales. More importantly, the Virunga Volcanoes Supersite has brought a large number of local and international scientists and agencies work together to and study Nyiragongo and Nyamulagira active volcanoes in order to better understand their eruptive mechanism, and hence better assess the related hazards and manage risks. Through this pool of collaboration, numerus activities have been successfully conducted, going from field works to assistance ordering and processing EO data, which yielded the production of useful hazards maps. Thus, the Virunga has requested and successfully obtained the activation of the Copernicus EMS services for the Virunga volcanoes for risk analyses with a focus on volcanic hazard. The later has yielded the production of hazards maps for future eruptions management, the collection of ground-based data used to produce Risk and Recovery mapping of Virunga volcanoes, as well as the production of thorough natural hazards assessment in the Virunga region and Lake Kivu. However, similar works have to be conducted in other parts of the Area of Interest of the Virunga Volcanoes Supersite, and hence the future works that use EO data (InSAR and Pleiades, principally) will aim at (1) continuing the ongoing study and monitoring of ground deformation of Nyiragongo and Nyamulagira volcanoes; (2) mapping and monitoring earthquakes and earthquake-triggered landslides in Lake Kivu Basin; (3) mapping of normal and non-normal faults in the Lake Kivu Basin, (4) along with geohazards assessment and forecast in Virunga and Lake Kivu Basin regions. Most of these results were obtained while the Virunga Volcanoes Supersite has any budget, as they were conducted through voluntary international partnership, as are all the GEO Geohazard Supersites and Natural Laboratory initiative (GSNL). On the other hand, this prevented the Virunga Supersite from realizing some key activities such as long-term trainings abroad or acquiring infrastructures and equipment useful for ground-based data collection, and data processing in general. However, some of the activities that required funding have benefited from the support from INGV, the USGS-VDAP, the Volcano Active foundation, the International Association of Volcanology and Chemistry of the Earth's Interior (IAVCEI) which for instance supported short trainings, the donation of some equipment for some ground-based data collection, the processing of EO data and the attendance to meetings and workshops. Therefore, the Virunga Volcanoes Supersite is lacking of funds to support its mid to long-term objectives which are to permit local scientists access the strong trainings, skills and experience for both ground based and EO data continuous acquisition and analysis for volcano monitoring, to continuously assess natural hazards, vulnerability and risks in the region and regularly produce the related updated maps.

The study of volcanic ash and its different components provides key information that can help understand the likely evolution of volcanic activity during early stages of a crisis and possible transitions towards different eruptive styles. However, classifying ash particles into components such as juvenile or lithic is not straightforward. Diagnostic observations may vary depending on the style of eruption, and there is no standardized methodology, which may lead to ambiguities in assigning a given particle to a given class. To address this problem, we created the web-based Volcanic Ash DataBase (VolcAshDB) which is made of > 6,300 multi-focused binocular images of particles from a range of magma compositions and types of volcanic activity (https://volcash.wovodat.org/). For each particle image, we quantitatively extracted 33 features of shape, texture, and color, and visually classified each particle into one of the four main components: free crystal, altered material, lithic, and juvenile. We used the data in VolcAshDB to setup a variety of machine learning-based models aimed at improving ash particle classification. We identified the features that are discriminant of a given particle type through explanatory AI and the Shapley values from the predictions made by an XGBoost model. We have also developed an accurate Vision Transformer model (93% accuracy) that could be potentially used by volcano observatories to obtain a relatively rapid and objective score on a particle-by-particle basis. Such models could be used for petrologic monitoring in a reproducible and systematic manner aiding in making more informed decisions for hazard mitigation.

Ionospheric delays are the dominant error source for large-scale interferometric synthetic aperture radar (InSAR) deformation mapping. Current ionospheric correction methods are mostly InSAR data-based, thus depend on coherence and can be challenging for vegetated areas with strong decorrelation or operational systems. In this work, we evaluate the feasibility of a rapid ionospheric delay correction method for large-scale deformation velocity based on various external total electron content (TEC) products derived from the global navigation satellite system (GNSS), including global ionospheric maps, MIT TEC products and local ionospheric maps. This method is automatic and could reduce the processing time from several days to minutes, thus desiring for operational systems and rapid responses. We test this method using C-band Sentinel-1, L-band ALOS-2 and LuTan-1 time-series acquired over western USA, Japan, China (mid-latitude), and northern Chile (low-latitude), using independent GNSS displacement and curated range split-spectrum estimations as references. Preliminary results from C-band Sentinel-1 data show improvements in 4 out of 5 tracks and in those 4 tracks the RMSE is reduced from 1.7-3.8 mm/yr to 1.0-3.0 mm/year, comparable with the split-spectrum method of 1.1-2.8 mm/year. The current effect of the L-band is still not very satisfactory. The estimated ionospheric delay shows consistent ramps along the azimuth direction between the proposed method and split-spectrum method, therefore, is promising for rapid large-scale deformation mapping.

Estimating deformation at the upstream dam slope from Interferometric Synthetic Aperture Radar (InSAR) is challenging due to the complete loss of coherence in seasonally inundated upstream slope. Here, we present an improved Distributed Scatterer-InSAR method that accounts for the seasonal decorrelation of upstream dam slopes and optimizes the interferogram pair selection with inter- and multi-annual baselines. We term this novel method Seasonally Inundated Distributed Scatterer InSAR (SIDS-InSAR). We apply the method with multi-sensor InSAR observations during 2007-2023 at the Xiaolangdi Reservoir (XLD), China, including Sentinel-1, ALOS-1, and ALOS-2. The results show that a new deformation map on a 1540 x 50 m2 upstream slope in XLD, and a decaying settlement of 4.7 cm/yr (2007-2010) and 2.5 cm/yr (2015-2023), with an RMSE of 0.62 cm/yr compared to the leveling measurement. Additionally, the deformation rates are heterogeneous across the dam body as 3.7, 4.2, and 3.2 cm/yr for upstream, crest, and downstream, respectively. This study demonstrates that the SIDS-InSAR method has potential to provide a more comprehensive deformation time series of dam body, especially for the leading-edge upstream slope part.