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Properties of multi-gaussian Schell-model beams carrying an edge dislocation propagating in oceanic turbulence

Chinese Physics B 2019年第10期28卷 360-366页

作者： Da-Jun Liu Yao-Chuan Wang Gui-Qiu Wang Hong-Ming Yin Hai-Yang Zhong Department of Physics College of Science Dalian Maritime University

Based on the theory of coherence, the model of multi-gaussian Schell-model(MGSM) beams carrying an edge dislocation generated by the MGSM source is introduced. The analytical cross-spectral density of MGSM beams carrying an edge dislocation propagating in oceanic turbulence is derived, and used to study the evolution properties of the MGSM beams carrying an edge dislocation. The results indicate that the MGSM beam carrying an edge dislocation propagating in oceanic turbulence will evolve from the profile with two intensity peaks into a flat-topped beam caused by the MGSM source, and the beam will evolve into the gaussian-like beam due to the influences of oceanic turbulence in the far *** the propagation distance increases, the MGSM beam carrying an edge dislocation propagating in oceanic turbulence with the larger rate of dissipation of mean-squared temperature(χT) and ratio of temperature to salinity contribution to the refractive index spectrum(?) or the smaller rate of dissipation of kinetic energy per unit mass of fluid(ε) evolves into the flat-topped beam or a gaussian beam faster.

关键词： oceanic turbulence multi-gaussian Schell-model source edge dislocation laser propagation average intensity

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Exact Conditioning of Regression Random Forest for Spatial Prediction

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Artificial Intelligence in Geosciences 2020年第1期1卷 11-23页

作者： Francky Fouedjio AngloGold Ashanti Australia Ltd. Growth and Exploration140 St.Georges TerracePerthWA6000Australia

Regression random forest is becoming a widely-used machine learning technique for spatial prediction that shows competitive prediction performance in various geoscience *** other popular machine learning methods for spatial prediction,regression random forest does not exactly honor the response variable’s measured values at sampled ***,competitor methods such as regression-kriging perfectly fit the response variable’s observed values at sampled locations by *** matching the response variable’s measured values at sampled locations is often desirable in many geoscience *** paper presents a new approach ensuring that regression random forest perfectly matches the response variable’s observed values at sampled *** main idea consists of using the principal component analysis to create an orthogonal representation of the ensemble of regression tree predictors resulting from the traditional regression random ***,the exact conditioning problem is reformulated as a Bayes-linear-Gauss problem on principal component *** problem has an analytical solution making it easy to perform Monte Carlo sampling of new principal component scores and then reconstruct regression tree predictors that perfectly match the response variable’s observed values at sampled *** reconstructed regression tree predictors’average also precisely matches the response variable’s measured values at sampled locations by *** proposed method’s effectiveness is illustrated on the one hand using a synthetic dataset where the ground-truth is available everywhere within the study region,and on the other hand,using a real dataset comprising southwest England’s geochemical concentration *** is compared with the regression-kriging and the traditional regression random *** appears that the proposed method can perfectly fit the response variable’s measured values at sampled locations while achieving good out

关键词： Exact conditioning Monte Carlo sampling multi-gaussian Spatial prediction Principal component analysis Random forest

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