# Scanning Inside Volcanoes with Synthetic Aperture Radar Echography Tomographic Doppler Imaging
## 1 Introduction
The authors used data to construct tomographic images of the principal geological units of California, with low-speed anomalies corresponding to the main sedimentary basins and high-speed anomalies corresponding to the igneous cores of the major mountain ranges. In [17], ambient seismic noise signal processing demonstrated the feasibility of measuring very small relative seismic velocity perturbations in the order of 0.05%. The ability to record volcanic edifice inflation in this way should improve the ability to predict eruptions, their intensity, and the potential environmental impact. The foundations of diffraction tomography for offset vertical seismic profiling and well-to-well tomography are presented in [18]. Computer simulations are used for underground vertical seismic profiling. The influence of quality in tomographic reconstructions obtained via the filtered back-propagation algorithm is investigated in [19]. To better understand the volcanic phenomena acting on Montserrat, a subset of the data, recorded at several land stations located from the southeast to the northwest line, was analyzed in [20]. The resulting velocity model reveals the presence of high-velocity underground body movements at the volcanic edifice cores. The development and assessment of three novel muon tracing methods and two scattering angle projection methods for muon-tomography are provided in [21]. The reconstructed images shows an expected improvement in image quality when compared with conventional techniques. Three-dimensional models of the P-wave velocity (\(Vp\)), the ratio of P- to S-wave velocity (\(Vs\)), \(\left(\frac{Vp}{Vs}\right)\), and the P-wave quality factor (\(Qp\)) are determined [22]. \(Vp\) and \(\left(\frac{Vp}{Vs}\right)\) models were determined by jointly inverting P travel times and S-P travel-time intervals, and a \(Qp\) model by inverting \(t*\frac{t}{Qp}\) observations derived from modeling the velocity amplitude spectrum of P wave arrivals. The upper crustal isotropic and radial anisotropic structures of the Jeju Island offshore Volcano were imaged in [23]. Results of an ambient seismic noise tomography study of the Merapi-Merbabu complex are presented in [24]. A seismic survey for the characterization of the main subsurface features of the Solfatara was developed in [25]. Using the complete dataset, the authors carried out surface wave inversion with high spatial resolution. A classical minimization of a least-squares objective function was computed to retrieve the dispersion curves of the surface waves. The recognition and localization of magmatic fluids are prerequisites for evaluating the volcano hazard of the highly urbanized area of Mt Vesuvius. Evidence and constraints for the volumetric estimation of magmatic fluids underneath this sleeping volcano were studied in [26]. Experimental measurements revealed the presence of magma at relatively shallow depths. The volume of fluids (approx. 30 km\({}^{3}\)) is sufficient to contribute to future explosive eruptions. Finally, controlled source audio-magnetotelluric sounding tomography performed in the past in the volcanic area of Mt. Vesuvius by [27] allowed a reliable electrical structure to be recovered down to a few km of depth. In the field of vibration estimation by processing SAR images, there are other relevant works. A new critical infrastructure monitoring procedure was developed in [28]. The technique was applied by processing COSMO-SkyMed data to detect and monitor the destabilization of the Mosul dam, which is the largest hydraulic structure in Iraq. The procedure consists of an in-depth modal evaluation based on micro-motion (m-m) estimation through Doppler tracing of sub-apertures and multi-chromatic analysis (MCA). On the other hand, a comprehensive damage detection procedure was designed using micromotion estimation of critical sites based on modal property analysis developed in [29]. Specifically, the m-m is processed to extract modal features such as natural frequencies and mode shapes generated by large infrastructure vibrations.