Marine controlled-source electromagnetic method as a new geophysical exploration method has been intensively studied and successfully applied into marine hydration and gas hydrate resource detection. Although the development of detection devices has got some process, there are still significant gaps for data process between China and Western countries. This paper presents a suit of 2.5D inversion program of marine controlled-source electromagnetic method and is based on the nonlinear conjugate gradient algorithm. Through analysis of influence of inversion parameters, such as regularizing operator, precondition matrix and refine factor of conjugate gradient direction, the best parameters combination is chosen and applied into the following inversion stimulation. Theoretical and measured data are used to prove the correction and effectiveness of this inversion program. The forward simulation is realized by adaptive finite element method and unstructured triangular mesh that helps to improve the program's simulation ability with parallel mechanism It has been proved that this algorithm could reflect resistive anomalies correctly and distinguish more subtle changing of electrical character than OCCAM. Moreover, it also breaks the limitation of 2.5D inversion that always needs server or large parallel clusters and make the 2.5D inversion could be realized on normal PC.

海洋可控源电磁法作为一种较新的地球物理勘探方法,在国外已被成功应用于海洋油气资源与天然气水合物的探测中,而我国在这方面的研究开始较晚.本文编写了基于非线性共轭梯度算法的海洋可控源电磁2.5维反演程序,结合理论数据对反演代码的正确性进行验证并讨论了反演参数对反演运算速度及效果的影响.研究结果表明,1)本文编写的2.5维反演程序正确可靠,计算结果与理论模型一致,2)在非线性共轭梯度反演计算中,正则化因子、预条件矩阵、线性搜索及共轭梯度更新因子等参数对实际的反演速度及精度都存在一定影响,可根据不同的勘探需求,调整反演参数以达到较好的反演结果.

We present a new gait identification method based on dynamic time warping (DTW), as video surveillance system requires high accuracy and precision. It could reduce computational cost of gait recognition, significantly improve the recognition rate for gait and meet the demand of video surveillance. The characters of human appearance have been utilized to extract entire binary image of human silhouette from video sequences, herein the centroid of silhouette is obtained. As the contour is unfolded clockwise, every video sequence could be converted to a normalized 1D (one-dimension) distance. The DTW-based distance between probe sequences and standard ones has been calculated to compare with the threshold. Finally, the similarity is induced to identify human gait by DTW distance. The average accuracy improves to 92.8% in gait recognition. Therefore, it is applicable for video surveillance in different scenarios.