Research Article Influence of Error in Estimating Anisotropy Parameters on VTI Depth Imaging S. Y. Moussavi Alashloo, D. P. Ghosh, Y. Bashir, and W. Y. Wan Ismail Center of Seismic Imaging, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Malaysia Correspondence should be addressed to S. Y. Moussavi Alashloo; y.alashloo@gmail.com Received 11 March 2016; Revised 5 April 2016; Accepted 20 April 2016 Academic Editor: Alexey Stovas Copyright © 2016 S. Y. Moussavi Alashloo et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. hin layers in sedimentary rocks lead to seismic anisotropy which makes the wave velocity dependent on the propagation angle. his aspect causes errors in seismic imaging such as mispositioning of migrated events if anisotropy is not accounted for. One of the challenging issues in seismic imaging is the estimation of anisotropy parameters which usually has error due to dependency on several elements such as sparse data acquisition and erroneous data with low signal-to-noise ratio. In this study, an isotropic and anelliptic VTI fast marching eikonal solvers are employed to obtain seismic travel times required for Kirchhof depth migration algorithm. he algorithm solely uses compressional wave. Another objective is to study the inluence of anisotropic errors on the imaging. Comparing the isotropic and VTI travel times demonstrates a considerable lateral diference of wavefronts. Ater Kirchhof imaging with true anisotropy, as a reference, and with a model including error, results show that the VTI algorithm with error in anisotropic models produces images with minor mispositioning which is considerable for isotropic one speciically in deeper parts. Furthermore, over- or underestimating anisotropy parameters up to 30 percent are acceptable for imaging and beyond that cause considerable mispositioning. 1. Introduction It is well-known that hydrocarbon reservoirs and overlying strata are commonly anisotropic [1, 2]. In reality, it is rare to have media with elliptical or weak anisotropy properties. However, anellipticity (deviation of wavefield from ellipse) has been commonly observed in the Earth’s subsurface, and it is a signiicant characteristic of elastic wave propagation [3, 4]. Another challenging issue in depth imaging is the compu- tation of the travel time taken by a seismic wave from source to receiver. An eicient method to compute travel times is solving the eikonal equation by employing inite diferences [5, 6]. Diferent techniques have been introduced to solve the eikonal equation, such as embedding methods, single- pass methods, sweeping methods, and iterative methods [7]. he main diference of these techniques is in how they cope with the complication of multivalued solutions and in inding solutions in the vicinity of cusps and discontinuities [8]. Anisotropy was initially added to an eikonal solver algorithm by Dellinger [9]. he embedding and iterative methods are both time consuming, particularly in heterogeneous and anisotropic conditions [7]. Fast sweeping methods are originally proposed for isotropic media [10]; however, a modiication is executed to handle the anisotropic condition [11]. Single-pass or fast marching method (FMM) is another tool for computing travel times but is not generally applicable for anisotropic medium [5]. his algorithm has since been modiied to work for anisotropy [12, 13]. In this study, a prestack depth migration algorithm is developed based on an anelliptic VTI compressional wave equation. Fomel’s anelliptic approximation [14] for both phase and group velocity of P-wave are employed to derive the eikonal equation. he fast marching inite diference approach is used as our eikonal solver since it is fast and stable for travel time computation. In anisotropic study, four anisotropic models are used: a true model which is exactly similar to the model employed for forward modelling, a model with values 30 percent less than the true model, a model with values 40 percent less than the true model, and a model with values 30 percent more than the true model. Hindawi Publishing Corporation International Journal of Geophysics Volume 2016, Article ID 2848750, 6 pages http://dx.doi.org/10.1155/2016/2848750