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The interaction between volcanic activity and flank instability during the Christmas Eve eruption at Mount Etna in 2018 is explored, using a mechanically consistent inverse model fitting high spatial resolution SAR data. Inversions search for fractures that may be curved and can accommodate co‐eval pressure and shear stress changes. Displacements associated with the eruption result from the interaction between two intrusion sources: a buried dyke and a curved sheared intrusion that fed the eruption. Moreover, we identify that the sheared magmatic intrusion induced the observed eastward slip on the Pernicana fault, while the Fiandaca fault was undergoing stress accumulation, which was suddenly released during a M5.0 seismic event. The Fiandaca fault is determined to be listric, rooting beneath the mobile eastern flank of the volcano. This study highlights the role of curved fractures, acting as sheared intrusions or as faults, in volcanoes exhibiting flank instabilities. Plain Language Summary: In this study, we thoroughly examine how volcanic activity and flank dynamics interacted during the 2018 eruption of Mount Etna. We use high‐resolution satellite SAR data and a model considering complex mechanical aspects of the volcano's behavior. Formal inversions reveal that the eruption is triggered by the interplay between two intrusion sources: a buried dyke and a curved sheared intrusion, consistent with the earthquake pattern. This study emphasizes the significant role of curved sheared intrusions and faults at volcanoes prone to flank instabilities. Furthermore, we quantify how summit magmatic intrusions promote fault slip in the eastern flank of Etna. These findings differ from previous research that used simpler approaches. In summary, our study provides a new insight into how volcanoes like Etna can erupt and trigger flank slip, shedding light on the complex interactions between magma and tectonics. Key Points: Modeling shows that the magma intrusion feeding the 2018 eruption accommodated coeval opening and shear displacementsBoth the summit intrusions and the Fiandaca fault are curved, probably connected to the detachment at the base of the mobile eastern sectorThe Fiandaca fault was previously locked. It released accumulated stress, while the Pernicana fault passively responded to the intrusion [ABSTRACT FROM AUTHOR]

Copyright of Revue Francaise de Photogrammetrie et de Teledetection is the property of Societe Francaise de Photogrammetrie et de Teledetection and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In volcanic domains, magma transport and pressure build-up induce high stress–strain perturbations in the surrounding volcanic edifice that may lead to volcanic flank movements and possible instability. In this study, we focus on the 2007 March–April episode of volcanic activity at Piton de la Fournaise (PdF) Volcano, La Réunion Island. This episode was associated with a large volume of emitted lava (240 × 106 m3) and a 340-m caldera collapse. We present observations of continuous seismic velocity changes measured using cross-correlations of ambient seismic noise over 10 yr at PdF. Overall, we observe a large velocity reduction starting a few days prior to the major 2007 April 2 eruption. Comparison of seismic velocity change measurements with observations of deformation from InSAR and GPS shows that the seismic velocity reduction coincided with a widespread flank movement starting at the time of injection of magma to feed an initial eruption, a few days before the 2007 April 2 eruption. We emphasize the potential of noise-based seismic velocity change measurements, together with geodetic observations, to detect and monitor possibly hazardous slope instabilities. [ABSTRACT FROM AUTHOR]

The assessment of the spatial probability of future vent opening is one of the key factors in quantifying volcanic hazard, especially for active volcanoes where eruptions can occur at different locations and altitudes over distributed areas. Mount Etna (Italy), one of the most active volcanoes in the world, exhibits such variability, and its flank eruptions can harm people, properties and services over the volcano's slopes. In this paper, we quantify the spatial probability of future vent opening for Etna's flank eruptions, adopting a kernel analysis and testing different functions (exponential, Cauchy, uniform and Gaussian). Starting from the assumption that the location of past fissures is indicative of where future events will occur, we consider the flank eruptions of the last 4000 years, thus accounting for a much longer and complete record than in previous studies. The large dataset of eruptive fissures enables splitting the data into training and testing subsets. This allows selecting the best kernel model, testing the completeness of the fissure dataset and investigating a possible migration through time in fissure location. The results show that neither under-recording nor possible migration over time significantly affects the informative value of previous flank fissures in forecasting the location of future ones. The resulting map highlights that the most likely opening area follows a northeast-to-south trend, corresponding to the location of the most active rifts. It also shows that the southern flank of the volcano, which is the most urbanized one, sits downhill of the largest cumulated probability area for flank eruption. We also run sensitivity analyses to test the effect of (i) restricting the data to the most recent 400 years and (ii) including the information on the stress induced on the mapped fissures by sources of deformation proposed in the literature for recent eruptions of Etna. The sensitivity analyses confirm the main features of the proposed map and add information on the epistemic uncertainty attached to it. [ABSTRACT FROM AUTHOR]

The Central Volcanic Zone of the Andes (CVZA) extends from southern Peru, through the Altiplano of Bolivia, to the Puna of northern Chile and Argentina, between latitudes 14–28° S of the Andean cordillera, with altitudes rising up to more than 4000 m above sea level. Given the large number of active volcanoes in this area, which are often located close to both urban areas and critical infrastructure, prioritization of volcanic risk reduction strategies is crucial. The identification of hazardous active volcanoes is challenging due to the limited accessibility, the scarce historical record, and the difficulty in identifying relative or absolute ages due to the extreme arid climate. Here, we identify the highest-risk volcanoes combining complementary strategies: (i) a regional mapping based on volcanic hazard parameters and surrounding density of elements at risk and (ii) the application of the recently developed volcanic risk ranking (VRR) methodology that integrates hazard, exposure, and vulnerability as factors that increase risk and resilience as a factor that reduces risk. We identified 59 active and potentially active volcanoes that not only include the volcanic centres with the most intense and frequent volcanic eruptions (e.g. the El Misti and Ubinas volcanoes, Peru) but also the highest density of exposed elements (e.g. the cities of Arequipa and Moquegua, Peru). VRR was carried out for 19 out of the 59 volcanoes, active within the last 1000 years or with unrest signs, highlighting those with the highest potential impact (i.e. Cerro Blanco in Argentina and Yucamane, Huaynaputina, Tutupaca, and Ticsani in Peru) and requiring risk mitigation actions to improve the capacity to face or overcome a disaster (e.g. volcanic hazard and risk/impact assessments, monitoring systems, educational activities, and implementation of early warning systems). [ABSTRACT FROM AUTHOR]

On 22 December 2018, parts of the Anak Krakatau edifice collapsed, triggering a deadly tsunami. To investigate pre‐collapse surface displacements, we analyzed Interferometric Synthetic Aperture Radar satellite geodetic data from 2006 to 2018, acquired from ALOS‐1 (2006–2011), COSMO‐SkyMED (2012–2018), and Sentinel‐1 (2014–2018). We identified line‐of‐sight displacements on the southwestern flank throughout the study period. Inversion of COSMO‐SkyMED data revealed a rectangular dislocation with a cumulative slip of 12 m from April 2012 to December 2018. Fixing the fault geometry, we found the optimal slip for time periods corresponding to slip rate changes, ranging from 1.2 to 3.1 m/yr. The slip estimates for ALOS‐1 and Sentinel‐1 data were 0.88 m/yr and 1.1 m/yr, respectively, over their individual time periods. Overall, the detachment fault experienced approximately 15 m of slip from 2006 to 2018 with acceleration and deceleration periods, and a notable acceleration prior to the 2018 collapse. Plain Language Summary: In this study, we investigated the changes that occurred on Anak Krakatau volcano before its collapse on 22 December 2018. We used satellite data from three different sources; ALOS‐1, COSMO‐SkyMED, and Sentinel‐1, covering the period from 2006 to 2018. Our analysis revealed that the southwestern side of the volcano was moving over time from 2006 to before the collapse in 2018. By examining data from COSMO‐SkyMED, we found that there was a rectangular area where the ground shifted by about 12 m. We investigated detailed movement and calculated that the ground slipped at different rates over time, ranging from 1.2 to 3.1 m per year. Data from ALOS‐1 and Sentinel‐1 gave us slightly different slip estimates and rates. Overall, our findings show that the deeper part of the ground moved around 15 m in total from 2006 to 2018 with varying speed and a significant acceleration before the 2018 collapse occurred. Key Points: InSAR time series show ground motion away from the satellite on the southwest flank of Anak Krakatau for the period 2006–2018The signal is likely induced by long‐term normal slip on a preexisting detachment fault which slipped rapidly in 2018 causing a collapseThe surface displacements and slip rate involve accelerations and decelerations, with a major acceleration right before the collapse [ABSTRACT FROM AUTHOR]

In this study, the geomorphological evolution of an inner flank of the Cratère Dolomieu at Piton de La Fournaise/La Réunion was investigated with the help of terrestrial laser scanning (TLS) data, terrestrial photogrammetric images, and historical webcam photographs. While TLS data and the terrestrial images were recorded during three field surveys, the study was also able to use historical webcam images that were installed for the monitoring of the volcanic activity inside the crater. Although the webcams were originally intended to be used only for visual monitoring of the area, at certain times they captured image pairs that could be analyzed using structure from motion (SfM) and subsequently processed to create digital terrain models (DTMs). With the help of all the data, the geomorphological evolution of selected areas of the crater was investigated in high temporal and spatial resolution. Surface changes were detected and quantified on scree slopes in the upper area of the crater as well as on scree slopes at the transition from the slope to the crater floor. In addition to their quantification, these changes could be assigned to individual geomorphological processes over time. The webcam photographs were a very important additional source of information here, as they allowed the observation period to be extended further into the past. Besides this, the webcam images made it possible to determine the exact dates at which geomorphological processes were active. [ABSTRACT FROM AUTHOR]

The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method's performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning. [ABSTRACT FROM AUTHOR]

The article addresses anthropogenic and geological conditions related to the development of soil subsidence in the western zone of Recife (Brazil). Over the past 50 years, human activity has intensified in areas previously affected by soft soils (clay, silt, and sandstone) resulting in subsidence due to additional loads (landfills and constructions). The duration of the settlement process can be significantly influenced by the specific characteristics of the soil composition and geological conditions of the location. This work presents, for the first time, accurate InSAR time series maps that describe the spatial pattern and temporal evolution of the settlement, as well as the correlation with the geological profile, and validation with Global Navigation Satellite System (GNSS) data. Persistent Scatterer Interferometry (PS-InSAR) was employed in the analysis of Single Look Complex (SLC) images generated by 100 ascending COSMO-SkyMed (CSK) and 65 PAZ (32 ascending, and 33 descending) from the X-band, along with 135 descending Sentinel-1 (S1) acquisitions from the C-band. These data were acquired over the period from 2011 to 2023. The occurrence of subsidence was identified in several locations within the western region, with the most significant displacement rates observed in the northern, central, and southern areas. In the northern region, the displacement rates were estimated to be approximately −20 mm/year, with the Várzea and Caxangá neighborhoods exhibiting the highest rates. In the central region, the displacement rates were estimated to be approximately −15 mm/year, with the Engenho do Meio, Cordeiro, Torrões, and San Martin neighborhoods exhibiting the highest rates. Finally, in the southern region, the displacement rates were estimated to be up to −25 mm/year, with the Caçote, Ibura, and Ipsep neighborhoods exhibiting the highest rates. Additionally, east–west movements were observed, with velocities reaching up to −7 mm/year toward the west. These movements are related to the lowering of the land. The study highlights that anthropogenic effects in the western zone of Recife contribute to the region's vulnerability to soil subsidence. [ABSTRACT FROM AUTHOR]

Copyright of Online Journal of Art & Design is the property of Online Journal of Art & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the evolution of interferometric synthetic aperture radar into a tool for active hazard monitoring, new methods are sought to quickly and automatically interpret the large number of interferograms that are created. We present a convolutional neural network (CNN) that is able to both classify the type of deformation, and to locate the deformation within an interferogram in a single step. We achieve this through building a "two headed model," which returns both outputs after one forward pass of an interferogram through the network. We train our model by first creating a data set of synthetic interferograms, but find that our model's performance is improved through the inclusion of real Sentinel‐1 data. We also investigate how model performance can be improved by best organizing interferograms such that they can exploit the three channel nature of computer vision models trained on very large databases of labeled color images, but find that using different data in each of the three input channels degrades performance when compared to the simple case of repeating wrapped or unwrapped phase across each channel. We also release our labeled Sentinel‐1 interferograms as a database named VolcNet, which consists of ∼500,000 labeled interferograms. VolcNet comprises of time series of unwrapped phase and labels of the magnitude, location, and duration of deformation, which allows for the automatic creation of interferograms between any two acquisitions, and greatly increases the amount of data available compared to other labeling strategies. Plain Language Summary: The latest radar satellites provide unprecedented volumes of measurements of ground movement around subaerial volcanoes (referred to as interferograms). These data can be used to detect signs of volcanic unrest, but the number of interferograms produced when monitoring all ∼1,500 subaerial volcanoes requires automation of the required analysis. We have trained a neural network to perform this analysis, and it enhances the state‐of‐the‐art by both differentiating between different styles of volcanic deformation, and by determining the spatial size of a deformation signal. Models of this type are commonly adapted from models that were trained in a domain in which there is far more labeled data. We explore if data that complements our radar derived measurements (such as a digital elevation model of a volcano) can be used with the three inputs that are available in the best performing neural networks from other domains (interpreting color images of everyday life). However, we find that the best performance is achieved when this data is omitted. In adapting an existing model to our problem, we created a novel database of interferograms for multiple volcanoes which we term VolcNet, and make available to the community. Key Points: Passing interferometric synthetic aperture radar (InSAR) data (e.g., phase and digital elevation model) as channels to a pretrained Convolutional neural network (CNN) degrades performanceOur database of InSAR time series contains up to ∼500,000 labeled interferogramsOur CNN uses unwrapped data to differentiate between deformation patterns, and determines their size [ABSTRACT FROM AUTHOR]

An increase in volcanic thermal emissions can indicate subsurface and surface processes that precede, or coincide with, volcanic eruptions. Space-borne infrared sensors can detect hotspots -- defined here as localized volcanic thermal emissions -- in near-real-time. However, automatic hotspot detection systems are needed to efficiently analyze the large quantities of data produced. While hotspots have been automatically detected for over 20 years with simple thresholding algorithms, new computer vision technologies, such as convolutional neural networks (CNNs), can enable improved detection capabilities. Here we introduce HotLINK: the Hotspot Learning and Identification Network, a CNN trained to detect hotspots with a dataset of -3,800 satellite-based, Visible Infrared Imaging Radiometer Suite (VIIRS) images from Mount Veniaminof and Mount Cleveland volcanoes, Alaska. We find that our model achieves an accuracy of 96% (Fl-score 0.92) when evaluated on -1,700 unseen images from the same volcanoes, and 95% (Fl-score 0.67) when evaluated on -3,000 images from six additional Alaska volcanoes (Augustine Volcano, Bogoslof Island, Okmok Caldera, Pavlof Volcano, Redoubt Volcano, Shishaldin Volcano). In comparison with an existing threshold-based hotspot detection algorithm, MIROVA (Coppola et al., Geological Society, London, Special Publications, 2016, 426, 181-205), our model detects 22% more hotspots and produces l2% fewer false positives. Additional testing on -700 labeled Moderate Resolution Imaging Spectroradiometer (MODIS) images from Mount Veniaminof demonstrates that our model is applicable to this sensor's data as well, achieving an accuracy of 98% (F1-score 0.95). We apply HotLINK to 10 years of VIIRS data and 22 years of MODIS data for the eight aforementioned Alaska volcanoes and calculate the radiative power of detected hotspots. From these time series we find that HotLINK accurately characterizes background and eruptive periods, similar to MIROVA, but also detects more subtle warming signals, potentially related to volcanic unrest. We identify three advantages to our model over its predecessors: 1) the ability to detect more subtle volcanic hotspots and produce fewer false positives, especially in daytime images; 2) probabilistic predictions provide a measure of detection confidence; and 3) its transferability, i.e., the successful application to multiple sensors and multiple volcanoes without the need for threshold tuning, suggesting the potential for global application. [ABSTRACT FROM AUTHOR]

On 21 May 2023, a hidden eruption occurred at the Southeast Crater (SEC) of Etna (Italy); indeed, bad weather prevented its direct and remote observation. Tephra fell toward the southwest, and two lava flows propagated along the SEC's southern and eastern flanks. The monitoring system of the Istituto Nazionale di Geofisica e Vulcanologia testified to its occurrence. We analyzed the seismic and infrasound signals to constrain the temporal evolution of the fountain, which lasted about 5 h. We finally reached Etna's summit two weeks later and found an unexpected pyroclastic density current (PDC) deposit covering the southern lava flow at its middle portion. We performed unoccupied aerial system and field surveys to reconstruct in 3D the SEC, lava flows, and PDC deposits and to collect some samples. The data allowed for detailed mapping, quantification, and characterization of the products. The resulting lava flows and PDC deposit volumes were (1.54 ± 0.47) × 106 m3 and (1.30 ± 0.26) × 105 m3, respectively. We also analyzed ground-radar and satellite data to evaluate that the plume height ranges between 10 and 15 km. This work is a comprehensive analysis of the fieldwork, UAS, volcanic tremor, infrasound, radar, and satellite data. Our results increase awareness of the volcanic activity and potential dangers for visitors to Etna's summit area. [ABSTRACT FROM AUTHOR]

Earthquake source parameters can be estimated using seismological observations, but the identification of the fault responsible is often complicated by location uncertainties and the inherent ambiguity between nodal planes. Satellite Interferometric Synthetic Aperture Radar (InSAR) can be used to observe ground deformation and model fault geometry but is limited by climate conditions (water vapour) and ground coverage (dense vegetation). In the tropics, the atmosphere is dynamic and most regions are densely vegetated, making detecting coseismic deformation challenging. Here, we perform a systematic inspection of coseismic interferograms from Sentinel-1 SAR images, to assess their suitability for detecting coseismic deformation in Costa Rica. Using data from the seismological network, we target seven earthquakes between 2016 and 2020 with depths |$\le \, 20$| km and magnitudes M w 5.3–6.2. For each event, we use the seismic parameters to compute line-of-sight displacements for ascending and descending geometries and for both nodal planes and generate 12- and 24-d coseismic interferograms where available. We obtain interferograms with coseismic displacement signals for three of the seven earthquakes. We invert the geodetic data to retrieve the earthquake source parameters but the lack of offshore geodetic coverage causes trade-offs between parameters and large uncertainties. The Jacó and Golfito earthquakes likely occurred on the subduction interface and the geodetic locations were 6–9 km closer to the coast than previous seismic estimates. The Burica earthquake occurred on a shallow steeply dipping thrust fault in the outer forearc. For the other earthquakes, no coseismic deformation was detected due to atmospheric noise or poor coherence. These results demonstrate the suitability of 12-d Sentinel-1 interferograms for monitoring shallow earthquakes of magnitude > M w 5.7 in Central America. This approach can be used to begin a surface deformation catalogue for the region, which will ultimately help improve the understanding of active deformation processes and improve hazard maps. [ABSTRACT FROM AUTHOR]

Since 1971, remote sensing techniques have been used to map and monitor phenomena and parameters of the coastal zone. However, updated reviews have only considered one phenomenon, parameter, remote data source, platform, or geographic region. No review has offered an updated overview of coastal phenomena and parameters that can be accurately mapped and monitored with remote data. This systematic review was performed to achieve this purpose. A total of 15,141 papers published from January 2021 to June 2023 were identified. The 1475 most cited papers were screened, and 502 eligible papers were included. The Web of Science and Scopus databases were searched using all possible combinations between two groups of keywords: all geographical names in coastal areas and all remote data and platforms. The systematic review demonstrated that, to date, many coastal phenomena (103) and parameters (39) can be mapped and monitored using remote data (e.g., coastline and land use and land cover changes, climate change, and coastal urban sprawl). Moreover, the authors validated 91% of the retrieved parameters, retrieved from remote data 39 parameters that were mapped or monitored 1158 times (88% of the parameters were combined together with other parameters), monitored 75% of the parameters over time, and retrieved 69% of the parameters from several remote data and compared the results with each other and with available products. They obtained 48% of the parameters using different methods, and their results were compared with each other and with available products. They combined 17% of the parameters that were retrieved with GIS and model techniques. In conclusion, the authors addressed the requirements needed to more effectively analyze coastal phenomena and parameters employing integrated approaches: they retrieved the parameters from different remote data, merged different data and parameters, compared different methods, and combined different techniques. [ABSTRACT FROM AUTHOR]

Volcanic activity can induce flank failure, sometimes generating large earthquakes and tsunamis. However, the failure structures have never been fully characterized and the failure mechanism is still debated. Magmatic activity is a possible trigger, either through fault slip, which might be induced by dyke intrusions, or through sill intrusions, which might be undergoing coeval normal displacements and slip. At the Piton de la Fournaise volcano, satellite imagery combined with inverse modeling highlights the pathways of 57 magmatic intrusions that took place between 1998 and 2020. We show that a major arcuate dyke intrusion zone is connected at depth to a sill intrusion zone, which becomes a fault zone towards the sea, forming a spoon-shaped structure. Some sills are affected by coeval normal displacement and seaward slip. Overall, the structure is characterized by a continuum of displacement from no slip, to sheared sills and finally pure slip. Repeated intrusions into this spoon-shaped structure could trigger catastrophic collapses., (© 2022. The Author(s).)

We studied the Vallesian records from Türkiye and Iran, and uncovered a diverse record of Cormohipparion species from Yeniyaylacık and Sinap, Türkiye, and their apparent descendant, Hipparion gettyi from Maragheh, Iran. This prompted us to examine the early evolutionary diversification, biogeography and paleoclimatology of western Eurasian hipparions. Our study also included the early hipparion records from Austria, Germany and the Iberian Peninsula. Whereas the Vienna Basin, Austria, has the oldest recorded occurrence of Hippotherium sp. at 11.4–11.0 Ma, our investigation revealed that the most primitive Old World hipparions belonged to the North American genus Cormohipparion. We integrated the data on crania, dentitions, 3rd metacarpals and 3rd metatarsals to further evaluate the systematics of Vallesian hipparions (11.2–9.6 Ma). Sinap, Türkiye, has the most primitive Cormohipparion in Eurasia, Co. sinapensis, but based on postcranial anatomy, we found that also Co. kecigibi is a second Cormohipparion species of Sinap's early Vallesian. The locality of Yeniyaylacık, Türkiye, dated ca. 9.1 Ma, has the latest occurrence of Eurasian Cormohipparion, Co. cappadocium. Our analyses also revealed that the genus Hipparion sensu stricto was probably directly derived from Subparatethyan (including Türkiye) Cormohipparion, with the most primitive taxa being Hipparion gettyi and Hipparion prostylum. Hipparion gettyi first occurred at Maragheh, Iran, ca. 9.0 Ma at the end of MN 10. Hipparion sensu stricto includes H. gettyi, H. prostylum, H. dietrichi and H. hippidiodus and had a range that extended from France through Greece and Türkiye to China. [ABSTRACT FROM AUTHOR]

Volcano geodesy often involves the use of models to explain observed surface deformation. A variety of forward models are used, from analytical point sources to numerical simulations that consider complex magma system geometries, topography, and material properties. Various inversion methods can then be used to relate observed volcano data to models. Ideally, forward models should be verified through intercomparison, to check for implementation errors and quantify the error induced by any approximations used. Additionally, forward models and inversion methods should be validated through tests with synthetic and/or real data, to determine their ability to match data and estimate parameter values within uncertainty. However, to date, there have not been comprehensive verification and validation efforts in volcano geodesy. Here, we report on the first phase of the Drivers of Volcano Deformation (DVD) exercises, which were designed to build community involvement through web-based exercises involving calculations of static elastic displacement around pressurized magma reservoirs. The forward model exercises begin with a spherical reservoir in a homogeneous half space, then introduce topography, heterogeneous elastic properties, and spheroidal geometries. The inversion exercises provide synthetic noisy surface displacement data for a spherical reservoir in a homogeneous half space and assess consistency in estimates of reservoir location and volume/pressure change. There is variability in the results from both forward modeling and inversions, which highlights the strengths and limitations of different forward models, as well as the importance of inversion method choice and uncertainty quantification. This first phase of the DVD exercises serves as a community resource and will facilitate further efforts to develop standards of reproducibility. [ABSTRACT FROM AUTHOR]

In various regions worldwide, people rely heavily on groundwater as a significant water source for daily usage. The resulting large-scale depletion of groundwater has triggered surface deformation in densely populated urban areas. This paper aims to employ Persistent Scattered Interferometry Synthetic Aperture Radar (PS-InSAR) techniques to monitor and quantify the land surface deformation (LSD), assess the relationships between LSD and groundwater levels (GWL), and provide insights for urban planning in Lahore, Pakistan, as the research area. A series of Sentinel-1 images from the ascending track between 2017 and 2020 were analyzed. Moreover, the Mann–Kendall (MK) test and coefficient of determination were computed to analyze the long-term trends and spatial relationships between GWL depletion and line of sight (LOS) displacement. Our findings reveal significant increases in land subsidence (LS) and GWL from 2017 to 2020, particularly in the city center of Lahore. Notably, the annual mean subsidence during this period rose from −27 mm/year to −106 mm/year, indicating an accelerating trend with an average subsidence of −20 mm/year. Furthermore, the MK test indicated a declining trend in GWL, averaging 0.49 m/year from 2003 to 2020, exacerbating LS. Regions with significant groundwater discharge are particularly susceptible to subsidence rates up to −100 mm. The LS variation was positively correlated with the GWL at a significant level (p < 0.05) and accounted for a high positive correlation at the center of the city, where the urban load was high. Overall, the adopted methodology effectively detects, maps, and monitors land surfaces vulnerable to subsidence, offering valuable insights into efficient sustainable urban planning, surface infrastructure design, and subsidence-induced hazard mitigation in large urban areas. [ABSTRACT FROM AUTHOR]

The 1980 eruption of Mount St. Helens was instrumental in advancing understanding of how volcanoes work. Lateral edifice collapses and the generation of volcanic debris avalanches were not widely recognized prior to that eruption, making assessment of their hazards and risks challenging. The proliferation of studies since 1980 on resulting deposits and evaluation of processes leading to their generation has built on the insights from the 1980 eruption. Volcano-related destabilizing phenomena, such as strength reduction by hydrothermal alteration, deformation and structural modifications from shallow magma intrusion, and thermal pressurization of pore fluids supplement those factors also affecting nonvolcanic slopes and can lead to larger failures. Remote and ground-based monitoring techniques can aid in detecting potentially destabilizing dynamic processes and in forecasting the size and location of future large lateral collapses, although forecasting remains a topic of investigation. More than a thousand large lateral collapse events likely ≥ 0.01 km3 in volume have now been identified from deposits or inferred from source area morphology, leading to a recognition of their importance in the evolution of volcanoes and the hazards they pose. Criteria for recognition of debris-avalanche deposits include morphological factors and textural characteristics from outcrop to microscopic scale, allowing discrimination from other volcaniclastic deposits. Lateral edifice failure impacts a broad spectrum of volcanic structures in diverse tectonic settings and can occur multiple times during the evolution of individual volcanoes. Globally, collapses ≥ 0.1 km3 in volume have been documented 5–6 times per century since 1500 CE, with about one per century having a volume ≥ 1 km3. Smaller events < 0.1 km3 are underrepresented in the earlier record but also have high hazard impact. [ABSTRACT FROM AUTHOR]

Ship target detection is an important application of synthetic aperture radar (SAR) imaging remote sensing in ocean monitoring and management. However, SAR imaging is a form of coherence imaging, meaning that there is a large amount of speckle noise in each SAR image. This seriously affects the detection of an SAR image ship target when the fuzzy C-means (FCM) clustering method is used, resulting in numerous errors and incomplete detection. It is also associated with a slow detection speed, which easily falls into the local minima. To overcome these issues, a new method based on block thumbnail particle swarm optimization clustering (BTPSOC) was proposed for SAR image ship target detection. The BTPSOC algorithm uses block thumbnails to segment the main pixels, which improves the resistance to noise interference and segmentation accuracy, enhances the ability to process different types of SAR images, and reduces the runtime. When particle swarm optimization (PSO) technology is used to optimize the FCM clustering center, global optimization is achieved, the clustering performance is improved, the risk of falling into the local minima is overcome, and the stability is improved. The SAR images from two datasets containing ship targets were used in verification experiments. The experimental results show that the BTPSOC algorithm can effectively detect the ship target in SAR images and that it maintains good integrity with regard to the detailed information from the target region. At the same time, experiments comparing the deep convolution neural network (CNN) and constant false alarm rate (CFAR) were conducted. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) remote sensing generally lacks deformation sensitivity in the along-track direction. In this proposed approach, across-track observations from conventional InSAR, using both ascending and descending passes, were superimposed with the along-track movement derived from multi-aperture InSAR (MAI) to determine the full three-dimensional (3-D) velocity of the Siachen Glacier in the Karakoram range of the Himalayas. The along-track velocity signal is essential for estimating the movement component in the north/south direction, which is needed for a complete delineation of 3-D deformation. The velocity observed was improved using the MAI technique in comparison to the conventional ascending/descending 3-D velocity estimation approach, and substantial differences were noticed between these two methods, particularly in the lower part of the glacier, which is moving almost in an along-track (north/south) direction. Glacier velocity varied from 0.3 md−1 in the accumulation zone to 0.60 md−1 in the terminus zone of the Siachen Glacier using this newly proposed approach. This study presents a 3-D velocity estimation without any preconceived assumptions regarding the flow conditions of glaciers and without any azimuth ambiguity. [ABSTRACT FROM AUTHOR]

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The 6-month-long effusive eruption at the Fagradalsfjall volcano in 2021 is the most visited eruption site in Iceland to date (June 2023), and it needed intense lava flow hazard assessment. In this study we document how strategies for lava flow modeling were implemented using the stochastic model MrLavaLoba to evaluate hazards during this effusive event. Overall, the purposes were threefold: (a) pre-eruption simulations to investigate potential lava inundation of critical infrastructure, (b) syn-eruption simulations for short-term (2-week time frame) lava flow hazard assessment and (c) syn-eruption simulations for long-term (months to years) hazard assessments. Additionally, strategies for lava barrier testing were developed, and syn-eruption topographic models were incorporated into simulations in near real time. The model provided promising results that were shared regularly at stakeholder meetings with the monitoring personnel, scientists and civil-protection representatives helping to identify potential short-term and long-term lava hazards. This included evaluation of the timing of barrier overflow and the filling and spilling of lava from one valley to another. During the crisis the MrLavaLoba model was updated to increase functionality such as by considering multiple active vents. Following the eruption, the model was optimized substantially, decreasing the computational time required for the simulations and speeding up the delivery of final products. [ABSTRACT FROM AUTHOR]

Spaceborne interferometric synthetic aperture radar (InSAR) techniques are important for landslide detection and monitoring; however, several limitations and uncertainties, such as the unique north–south flying direction and side-look radar observing geometry, currently limit the ability of InSAR to credibly detect landslides, especially those related to high and steep slopes. Here, we conducted experimental and statistical analysis on the feasibility of time-series InSAR monitoring for steep slopes using ascending and descending SAR images. First, the theoretical (TGNSS), practical (PGNSS), and terrain (Hterrain) (T-P-H) indices for sensitivity evaluations of the slope displacement monitoring results from time-series InSAR were proposed for slope monitoring. Subsequently, two experimental and statistical studies were conducted for the cases with and without Global Navigation Satellite System (GNSS) monitoring data. Our experimental results of two high and steep open-pit mines showed that the defined theoretical and practical sensitivity indices can quantitatively evaluate the feasibility of ascending and descending InSAR observations in steep-slope deformation monitoring with GNSS data, and the terrain sensitivity index can qualitatively evaluate the feasibility of landslide monitoring results from ascending and descending Sentinel-1 satellite data without GNSS data. We further demonstrate the generalizability of these proposed indices using four landslide cases with both public GNSS and InSAR monitoring data and 119 landslide cases with only InSAR monitoring data. The statistical results indicated that greater indices correlated with higher reliability of the monitoring results, suggesting that these novel indices have wide suitability and applicability. This study can help to improve the practice of slope deformation monitoring using spaceborne InSAR, especially for high and steep slopes. [ABSTRACT FROM AUTHOR]