Remote Sensing, Vol. 10, Pages 968: The Use of Massive Deformation Datasets for the Analysis of Spatial and Temporal Evolution of Mauna Loa Volcano (Hawai`i) (Remote Sensing)

In this work, we exploited large DInSAR and GPS datasets to create a 4D image of the magma transfer processes at Mauna Loa Volcano (Island of Hawai’i) from 2005 to 2015. The datasets consist of 23 continuous GPS time series and 307 SAR images acquired from ascending and descending orbits by ENVISAT (ENV) and COSMO-SkyMed (CSK) satellites. Our results highlight how the joint use of SAR data acquired from different orbits (thus with different look angles and wavelengths), together with deformation data from GPS networks and geological information can significantly improve the constraints on the geometry and location of the sources responsible for the observed deformation. The analysis of these datasets has been performed by using an innovative method that allows building a complex source configuration. The results suggest that the deformation pattern observed from 2005 to 2015 has been controlled by three deformation sources: the ascent of magma along a conduit, the opening of a dike and the slip along the basal decollement. This confirms that the intrusion of the magma within a tabular system (rift dikes) may trigger the sliding of the SE portion of the volcanic edifice along the basal decollement. This case study confirms that it is now possible to exploit large geodetic datasets to improve our knowledge of volcano dynamics. The same approach could also be easily applied in other geodynamical contexts such as geothermal reservoirs and regions with complex tectonics.