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In this investigation, the laser marker ablation technique was employed on Cu-coated glass to fabricate micro-nanostructured antifog glass. The resulting surfaces exhibited a quasi-periodic micron hillock-hollow structure with dispersed nanoparticles distributed throughout, which played a role in the antifog property and superhydrophilicity. However, airborne organic pollutant deposition degraded the superhydrophilicity of ablated glass surfaces and, therefore, their antifog performance, which cannot be circumvented. Conventionally, furnace annealing for at least 1 h was used to decompose the organic pollutants and restore the superhydrophilicity, limiting the throughput and application scenario. Remarkably, the rapid regeneration of this property was achieved through either a 5 min rapid thermal treatment at 400 °C or a 1 s flame treatment. These are interventions that are hitherto unreported. Such short and simple treatment methods underscore the potential of laser-ablated glass for diverse practical applications. [ABSTRACT FROM AUTHOR]

High‐precision P‐wave and S‐wave velocity models are very important for seabed exploration. Elastic full‐waveform inversion is an effective way to obtain the velocity models. Full‐waveform inversion is strongly dependent on source wavelet, and inaccurate wavelet estimation will severely affect the inversion results. Furthermore, elastic full‐waveform inversion is a highly non‐linear problem and has larger calculation and memory cost. Based on the deconvolution method, we develop a multi‐scale source‐independent elastic full‐waveform inversion method in a hybrid domain to alleviate the non‐linearity, to improve computation efficiency and decrease memory cost and to reduce the influence of source wavelet. We reconstruct the deconvoluted objective function and further derive the adjoint source and gradient formulas for elastic full‐waveform inversion with the adjoint‐state method. We can obtain good inversion results by using this objective function, adjoint source and gradient even if the source wavelet is unknown. In order to illustrate the influence of different reference traces on the objective function, we provide the theoretical formula of the objective function changing with the variation of reference traces. Experiments show that the deconvolution objective function cannot completely eliminate the influence of inaccurate source wavelet on the inversion, and the inversion effect depends on the selection of reference trace. Different inversion results of the Marmousi2 model indicate that the minimum offset trace is the best reference trace. Comparison of the inverted Marmousi2 model by the proposed method and the conventional elastic full‐waveform inversion method using correct wavelet shows that the two inversion results are very close, which proves the effectiveness of the proposed method and indicates a potential application of the method in seabed exploration. [ABSTRACT FROM AUTHOR]

Due to the limitations of the actual acquisition environment in the field, especially in ocean bottom seismometer (OBS) acquisition, the acquired seismic data are often irregular and incomplete, which affects the subsequent data imaging, interpretation and hydrocarbon‐bearing reservoir prediction. The interpolation reconstruction algorithm based on the compressive sensing theory can reconstruct the data without the limitation of Nyquist sampling interval. However, the reconstruction accuracy and effect are different for different sparse representations of the data. On the basis of compressed sensing theory, we propose a Bregman iterative seismic data reconstruction method based on the sparse decomposition of discrete orthonormal Coiflets and Symlets wavelet transforms. First, the discrete orthonormal matrix is constructed by using the above two wavelet functions to make the original seismic data sparse, then the Bregman iterative algorithm is used to reconstruct the sparse coefficients in the discrete wavelet domain, and finally the recovery matrix is used to reconstruct the seismic data. The discrete orthonormal Coiflets and Symlets wavelet transforms have good sparse representation ability and can compensate for the problem that discrete Fourier transform cannot well sparse representation of the data. After numerical experiments on horizontal‐layered model, Marmousi2 model and actual data, it is verified that the proposed method can reconstruct the missing seismic data under the regular observation system and under the irregular observation system with non‐uniform OBS distribution. Furthermore, this method can hardly bring the interference of random noise, and the reconstruction result is of high accuracy. [ABSTRACT FROM AUTHOR]