We demonstrate an optical parametric oscillator (OPO) based on random phase matching in a polycrystalline χ (2) material, ZnSe. The subharmonic OPO utilized a 1.5-mm-long polished ZnSe ceramic sample placed at the Brewster's angle and was synchronously pumped by a Kerr-lens mode-locked Cr:ZnS laser with a central wavelength of 2.35 µm, a pulse duration of 62 fs, and a repetition frequency of 79 MHz. The OPO had a 90-mW pump threshold, and produced an ultrabroadband spectrum spanning 3-7.5 µm. The observed pump depletion was as high as 79%. The key to success in achieving the OPO action was choosing the average grain size of the ZnSe ceramic to be close to the coherence length (~ 100 µm) for our 3-wave interaction. This is the first OPO that uses random polycrystalline material with quadratic nonlinearity and the first OPO based on ZnSe. Very likely, random phase matching in ZnSe and similar random polycrystalline materials (ZnS, CdS, CdSe, GaP) represents a viable route for generating few-cycle pulses and multi-octave frequency combs, thanks to a very broadband nonlinear response.

No abstract

No abstract

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model's recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.

Volcanic activity was quite frequent during the deposition of the Late Carboniferous Ha’erjiawu Formation in the Santanghu Basin. The petrology and organic and inorganic geochemical indicators were used to investigate hydrocarbon potential, paleoenvironmental conditions and organic matter enrichment of the mudstones. The results show that the oil generation capacity of the Ha’erjiawu Formation mudstones, which has abundant oil-prone organic matter (Type II kerogen with hydrogen index values mainly ranging from 250 to 550 mg HC/g TOC) in mature stage (Tmax values mainly ranging from 435 to 450 °C), is considerable. The Ha’erjiawu Formation was deposited in a dysoxic, freshwater-mildly brackish, and warm-humid environment. During its deposition, the Ha’erjiawu Formation received hydrothermal inputs. The volcanic hydrothermal activities played an important role in the organic matter enrichment. In addition, the total organic carbon (TOC) is significantly positively correlated with the felsic mineral content, but it is negatively correlated with the carbonate mineral content and C27/C29 ratios, indicating that terrigenous organic matter input also contributed to the primary productivity in the surface water. Therefore, the formation of the high-quality source rocks in the Ha’erjiawu Formation was jointly affected by the hydrothermal activity and the terrigenous organic matter input.

No abstract

No abstract

No abstract