The application of the Lucas-Kanade (LK) optical flow technique has seen a huge success in a wide variety of fields. The goal of this paper is to apply the Lucas-Kanade technique in measuring the deformation of flexible birdlike airfoil due to steady aerodynamic loads at transitional low Reynolds-numbers with a single pixel resolution. A pyramidal scheme is used to implement a coarse-to-fine warping strategy to allow large displacements. A nonlinear structure tensor is employed to diffuse local data anisotropically to preserve discontinuities in the optical flow field. Median filtering is introduced after each iteration to remove outliers. The upper surface of the airfoil is sprayed with stochastic ink dot pattern for easy capture by two cameras observed from two different angles above the airfoil to create a pattern on the airfoil for the deformation measurement. Finally, a general result of wind tunnel experiments is selected, two optical flow fields are calculated on two images generated from each camera respectively, and the optical flow results are compared with the image correlation results.
Due to the high exploration cost, limited number of wells for source rocks drilling and scarce test samples for the Total Organic Carbon Content (TOC) in the Huizhou sag, the TOC prediction of source rocks in this area and the assessment of resource potentials of the basin are faced with great challenges. In the study of TOC prediction, predecessors usually adopted the logging assessment method, since the data is only confined to a “point” and the regional prediction of the source bed in the seismic profile largely depends on the recognition of seismic facies, making it difficult to quantify TOC. In this study, we combined source rock geological characteristics, logging and seismic response and built the mathematical relation between quasi TOC curve and seismic data based on the TOC logging date of a single well and its internal seismic attribute. The result suggested that it was not purely a linear relationship that was adhered to by predecessors, but was shown as a complicated non-linear relationship. Therefore, the neural network algorithm and SVMs were introduced to obtain the optimum relationship between the quasi TOC curve and the seismic attribute. Then the goal of TOC prediction can be realized with the method of seismic inversion.