Editorial on the Research Topic Multidisciplinary Geophysical Imaging of Volcanoes Great strides have been made in the geophysical imaging of volcanic systems on a wide range of spatial scales. Progress has included data and instrumentation advances, application of new techniques, and improvement in computational capabilities. The goal of this Research Topic is to highlight the imaging capabilities of geophysical methods in characterizing the internal structure and the dynamics of volcanoes,

more »

... ard an understanding of ongoing and potential eruptions, and to improve time-dependent estimation of related hazards. The majority of the contributions focused on Solfatara Volcano, the most active area of Campi Flegrei, which is experiencing an unrest in the recent years and that is becoming a natural laboratory for testing new methods and applications. Indeed, three contributions analyse active time-lapse seismic data recorded in a 2D and 3D geometry during the first Repeated Induced Earthquake and Noise experiment (RICEN: Festa et al., 2015), conducted from September 2013 to November 2014. De Landro et al., for instance describe a methodology based on the estimation of the quality factor Q from the RICEN active seismic data to obtain a high-resolution attenuation model of the very shallow subsurface. They found low P-wave quality factor (Q P 5-40 in the near-surface layer 30 m thick) in shallowest subsoil of Solfatara, which globally increases with depth in the explored volume and shows a strong lateral heterogeneity. Within the well-resolved central portion of the explored volume the Q P model shows features consistent with the hydrothermal fluid distribution within Solfatara. Scala et al., using the 3D active seismic data from the RICEN experiment analyzed instead the scattering properties of the Solfatara crater to evaluate the ratio between coherent and incoherent intensities. The scatterers are interpreted as regions richer in water with respect to the background and eventually due to the condensed steam running below the investigated area. The connection between the scattering mean free path and the type, and content of fluids retrieved here is of fundamental importance to image the volcanic structure: upscaling this technique to a kilometric size area could allow to provide constraints about the magma chamber and related feeding mechanism. Finally, Bernardinetti and Bruno, using two machine learning algorithms merged active 2D seismic data from the RICEN experiment (Bruno et al., 2017) with some geophysical anomalies recorded at the surface of the crater (Bruno et al., 2007) . The use of unsupervised learning techniques can reduce interpretation uncertainties while interpreting multivariate data, therefore improving the understanding of the complex dynamics occurring in volcanoes. This allowed to associate surface and subsurface anomalies to different hydrothermal features such as shallow gas-saturated and water-saturated zones and their underlying fractures/faults feeding system.