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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. 

Abstract Volcanic summit craters are typically noted to form by roof collapse into a depressurized magma chamber or by explosive excavation. Recent examples of effusive activity (e.g., Kilauea Volcano, Hawai'i) allowed specifically for quantification of the collapse process. However, small spatiotemporal morphologic change related to background mass wasting and low‐level explosive activity has not been well quantified in volcanic craters. Telica volcano, Nicaragua, is a persistently restless basaltic‐andesite stratovolcano. Telica's persistent restlessness is caused by a long‐lived magmatic‐hydrothermal system with high‐temperature crater fumaroles and low‐frequency seismicity, punctuated by subdecadal, low‐explosivity (VEI 1–2) phreatic eruptions. We use photographic observations (1994 to 2017) and structure‐from‐motion point cloud construction and differencing (2011 to 2017) to analyze changes at Telica in the context of summit crater formation and eruptive precursors. Crater wall retreat (up to 40 m) spatially correlates with long‐lived high‐temperature fumaroles in the crater walls, whereas eruptions eject material (>5 m) from the crater floor through vent formation and/or clearing. These processes sustain a morphology similar to that of pit craters but without a shallow depressurized magma chamber. Our observations indicate system‐wide sealing prior to eruption by viscous magma in the conduit and eruption of a dome in 2017 and hydrothermal mineralization, not from vent covering talus; though, vent covering talus can redirect the shallow conduit. This study shows promise for photogrammetric techniques in correlating morphologic change with summit crater formation and volcanic activity and the power of long‐term visual observations in understanding active volcanic processes.