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Abstract 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. 

Abstract The processing of hundreds of Synthetic Aperture Radar (SAR) images acquired by two satellite systems: Sentinel‐1 and COSMO‐SkyMed reveals a decade of ground deformation for a ∼0.5 km diameter area around the summit crater of the only active carbonatitic volcano on Earth: Ol Doinyo Lengai in Tanzania. Further decomposing ascending and descending orbits when the appropriate SAR data sets overlap allow us to interpret the imaged deformation as ground subsidence with a significant rate of ∼3.6 cm/yr for the pixels located just north of the summit crater. Using geodetic modeling and inverting the highest spatial resolution COSMO‐SkyMed data set, we show that the mechanism explaining this subsidence is most likely a deflating very shallow (≤1 km depth below the summit crater at the 95% confidence level) magma reservoir, consistent with geochemical‐petrological and seismo‐acoustic studies. 

When volcanic unrest occurs, the scientific community can advance fundamental understanding of volcanic systems, but only with coordination before, during, and after the event across academic and governmental agencies. To develop a coordinated response plan, the Community Network for Volcanic Eruption Response (CONVERSE) orchestrated a scenario exercise centered around a hypothetical volcanic crisis in Arizona’s San Francisco Volcanic Field (SFVF). The exercise ran virtually from February 4 to March 4, 2022. Over 60 scientists from both academic and governmental spheres participated. The scenario exercise was assessed for its effectiveness in supporting collaborative production of knowledge, catalyzing transdisciplinary collaboration, supporting researcher confidence, and fostering a culture of inclusion within the volcanology community. This identified a need to support early career researchers through community and allyship. Overall, the 2022 CONVERSE exercise demonstrated how a fully remote, extended scenario can be authentically implemented and help broaden participation within the volcano science community. 

Abstract Interferometric Synthetic Aperture Radar (InSAR) provides subcentimetric measurements of surface displacements, which are key for characterizing and monitoring magmatic processes in volcanic regions. The abundant measurements of surface displacements in multitemporal InSAR data routinely acquired by SAR satellites can facilitate near real‐time volcano monitoring on a global basis. However, the presence of atmospheric signals in interferograms complicates the interpretation of those InSAR measurements, which can even lead to a misinterpretation of InSAR signals and volcanic unrest. Given the vast quantities of SAR data available, an automatic InSAR data processing and denoising approach is required to separate volcanic signals that are cause of concern from atmospheric signals and noise. In this study, we employ a deep learning strategy that directly removes atmospheric and other noise signals from time‐consecutive unwrapped surface displacements obtained through an InSAR time series approach using an end‐to‐end convolutional neural network (CNN) with an encoder‐decoder architecture, modified U‐net. The CNN is trained with simulated synthetic unwrapped surface displacement maps and is then applied to real InSAR data. Our proposed architecture is capable of detecting dynamic spatio‐temporal patterns of volcanic surface displacements. We find that an ensemble‐average strategy is recommended to stabilize detected results for varying deformation rates and signal‐to‐noise ratios (SNRs). A case study is also presented where this method is applied to InSAR data covering Masaya volcano, Nicaragua and the results are validated using continuous GPS data. The results confirm that our network can indeed efficiently suppress atmospheric and other noise to reveal the noise‐free surface deformation.