表面相关多次波衰减方法与应用研究

The function of seismic data in prospecting and exploring oil and gas has exceeded ascertaining structural configuration early. In order to determine the advantageous target area more exactly, we need exactly image the subsurface media. So prestack migration imaging especially prestack depth migration has been used increasingly widely. Currently, seismic migration imaging methods are mainly based on primary energy and most of migration methods use one-way wave equation. Multiple will mask primary and sometimes will be regarded as primary and interferes with the imaging of primary, so multiple elimination is still a very important research subject. At present there are three different wavefield prediction and subtraction methods: wavefield extrapolation; feedback loop; and inverse-scattering series. I mainly do research on feedback loop method in this paper. Feedback loop method includs prediction and subtraction.Currently this method has some problems as follows. Firstly, feedback loop method requires the seismic data used to predict multiple is full wavefield data, but usually the original seismic data don’t meet this assumption, so seismic data must be regularized. Secondly, Multiple predicted through feedback loop method usually can’t match the real multiple in seismic data and they are different in amplitude, phase and arrrival time. So we need match the predicted multiple and that in seismic data through estimating filtering factors and subtract multiple from seismic data. It is the key for multiple elimination how to select a correct matching filtering method. There are many matching filtering methods and I put emphasis on Least-square adaptive matching filtering and L1-norm minimizing adaptive matching filtering methods. Least-square adaptive matching filtering method is computationally very fast, but it has two assumptions: the signal has minimum energy and is orthogonal to the noise. When seismic data don’t meet the two assumptions, this method can’t get good matching results and then can’t attenuate multiple correctly. L1-norm adaptive matching filtering methods can avoid these two assumptions and then get good matching results, but this method is computationally a little slow. The results of my research are as follows: 1. Proposed a method that interpolates seismic traces based on F-K migration and demigration. The main advantage of this method is that it can interpolate seismic traces in any offsets. It shows this method is valid through a simple model. 2. Comparing different Least-square adaptive matching filtering methods. The results show that equipose multi-channel adaptive matching filtering methods can get better results of multiple elimination than other matcing methods through three model data and two field data. 3. Proposed equipose multi-channel L1-norm adaptive matching filtering method. Because L1-norm is robust to large amplitude differences, there are no assumption on the signal has minimum energy and orthogonality, this method can get better results of multiple elimination. 4. Research on multiple elimination in inverse data space. The method is a new multiple elimination method and it is different from those methods mentioned above.The advantages of this method is that it is simple in theory and no need for the adaptive subtraction and computationally very fast. The disadvantage of this method is that it is not stabilized in its solution. The results show that equipose multi-channel and equipose pesudo-multi-channel least-square matching filtering and equipose multi-channel and equipose pesudo-multi-channel L1-norm matching filtering methods can get better results of multiple elimination than other matcing methods through three model data and many field data.