优化人工神经网络在折射波静校正中的应用

详细信息

The application of optimized artificial neural network to refraction static correction

Institute of Geophysics & Geomatics, China University of Geosciences, Wuhan 430074, China

摘要: 应用优化人工神经网络进行折射波初至拾取, 结合微测井和小折射资料反演风化层以及折射层速度, 并利用Taner全三维静校正原理进行折射波静校正。实际资料计算结果表明: 该方法有效提高了折射波初至拾取的效率, 很好地实现了CMP的“同相叠加”, 较好地改善了叠加剖面的信噪比和剖面的横向分辨率。
- 优化人工神经网络 /
- 折射波静校正 /
- 实际资料
Abstract: Optimized artificial neural network is used to pick refraction first arrival times, log and field refraction data are combined to inverse weathering layer velocity and refraction layer velocity, and Taner’s 3-D refraction static correction is applied in this paper. The result of real data shows that the work efficiency is enhanced by using Optimized artificial neural network to pick refraction first arrival times, the CMP gathers are stacked in phase and the ratio of S/N and transversal resolution of the stacked profile are improved.
- optimized artificial neural network /
- refraction static correction /
- real data