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Multiple problems, including high computational cost, spurious local minima, and solutions with no geologic sense, have prevented widespread application of full waveform inversion (FWI), especially FWI of seismic reflections. These problems are fundamentally related to a large number of model parameters and to the absence of low frequencies in recorded seismograms. Instead of inverting for all the parameters in a dense model, image-guided full waveform inversion inverts for a sparse model space that contains far fewer parameters. We represent a model with a sparse set of values, and from these values, we use image-guided interpolation (IGI) and its adjoint operator to compute finely and uniformly sampled models that can fit recorded data in FWI. Because of this sparse representation, image-guided FWI updates more blocky models, and this blockiness in the model space mitigates the absence of low frequencies in recorded data. Moreover, IGI honors imaged structures, so image-guided FWI built in this way yields models that are geologically sensible.

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The electronic structure and transport of graphdiyne nanoribbons are investigated theoretically by ab initio calculations. We find that some edge states of zigzag graphdiyne nanoribbons are confined in a narrow energy range. For non-magnetic zigzag graphdiyne nanoribbons, the edge states whose energy is near the valence band top form a special electronic transport channel and lead to current peaks (about several μA) at small bias below the conduction voltage. However, ferromagnetic graphdiyne nanoribbons do not have such current peaks because the edge states energy is much higher than the valence band top and the transport channel cannot be formed. Such special effect, which is not found in graphene nanoribbons, does not depend on the width of zigzag graphdiyne nanoribbons. According to the result, it is feasible to apply this novel property to design a magnetically controllable nanoscale switch.