In this paper, cagelike ZnO∕SiO2 nanocomposites were prepared and their microwave absorption properties were investigated in detail. Dielectric constants and losses of the pure cagelike ZnO nanostructures were measured in a frequency range of 8.2–12.4GHz. The measured results indicate that the cagelike ZnO nanostructures are low-loss material for microwave absorption in X band. However, the cagelike ZnO∕SiO2 nanocomposites exhibit a relatively strong attenuation to microwave in X band. Such strong absorption is related to the unique geometrical morphology of the cagelike ZnO nanostructures in the composites. The microcurrent network can be produced in the cagelike ZnO nanostructures, which contributes to the conductive loss.

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Full waveform inversion (FWI) aims to reconstruct high-resolution subsurface models from the full wavefield, which includes diving waves, post-critical reflections and short-spread reflections. Most successful applications of FWI are driven by the information carried by diving waves and post-critical reflections to build the long-to-intermediate wavelengths of the velocity structure. Alternative approaches, referred to as reflection waveform inversion (RWI), have been recently revisited to retrieve these long-to-intermediate wavelengths from shortspread reflections by using some prior knowledge of the reflectivity and a scale separation between the velocity macromodel and the reflectivity. This study presents a unified formalism of FWI, named as Joint FWI, whose aim is to efficiently combine the diving and reflected waves for velocity model building. The two key ingredients of Joint FWI are, on the data side, the explicit separation between the short-spread reflections and the wide-angle arrivals and, on the model side, the scale separation between the velocity macromodel and the shortscale impedance model. The velocity model and the impedance model are updated in an alternate way by Joint FWI and waveform inversion of the reflection data (least-squares migration), respectively. Starting from a crude velocity model, Joint FWI is applied to the streamer seismic data computed in the synthetic Valhall model. While the conventional FWI is stuck into a local minimum due to cycle skipping, Joint FWI succeeds in building a reliable velocity macromodel. Compared with RWI, the use of diving waves in Joint FWI improves the reconstruction of shallow velocities, which translates into an improved imaging at deeper depths. The smooth velocity model built by Joint FWI can be subsequently used as a reliable initial model for conventional FWI to increase the high-wavenumber content of the velocity model.

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