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Abstract Surface deformation and seismicity provide critical information to understand the dynamics of volcanic unrest. During 2006–2007, >80 mm/yr uplift was observed by interferometric synthetic aperture radar (InSAR) at the central Atka volcanic center, Alaska, coinciding with an increasing seismicity rate. On November 25, 2006, a phreatic eruption occurred at the Korovin volcanic vent, 5‐km north of the central Atka, following the drainage of its crater lake a month prior to the eruption. The InSAR data are assimilated into three‐dimensional finite element models using the Ensemble Kalman Filter to investigate: (1) the pressure source creating the surface deformation; (2) the triggering of the volcano‐tectonic (VT) earthquakes in the Atka volcanic center; and (3) the triggering of the phreatic eruption at Korovin. The models show that the pressure source required to create the surface deformation is a NE‐tilted, oblate ellipsoid, which rotated from steep to gentle dipping from June to November 2006 before the eruption. The modeled dilatancy in a pre‐existing weak zone, coinciding with the Amlia‐Amukta fault, driven by the pressure source has a spatial and temporal correlation with the evolution of the VT earthquakes during the unrest. The fault dilatancy may have increased the connected porosity and permeability of the fault zone allowing fluid injection which triggered the observed seismicity. In addition, the dilatated fault may have increased the fluid capacity of the fault zone by ∼105 m3, causing the discharge of the crater lake at Korovin. Consequently, the phreatic eruption of the Korovin volcano may have been triggered.
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Automatic Detection of Volcanic Surface Deformation Using Deep Learning
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.
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- Award ID(s):
- 2018280
- PAR ID:
- 10476819
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Solid Earth
- Volume:
- 125
- Issue:
- 9
- ISSN:
- 2169-9313
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1029/2020JB019840
