Application Of Surface Wave In Near-surface Structure Of Paralic Zone

This project is supported by the son project of high precision seismic exploration technology in paralic zone. In the paralic zone .there are ample oil and gas resources, moreover, it is the important mothball area and reliver of oil and gas industry to increase reserves and enhance output. However, people'cognition about paralic zone is far not enough, and the near surface structure of paralic zone is not simple as people imagined. By exploring the paralic zone in recent years, people find there is not flat but fluctuant and the underground geology structure also is not all horizontal layer upon layer. Furthermore, in the structure, there are high velocity layer among the low velocity layer and it increases difficulty of, exploration in paralic zone. In addition, the situation of every paralic zone are not same as each other, and it brings serious handicap to exploration and exploitation of oil and gas resources in paralic zone, especially, the static correction in seismic data processing.By analyzing the actuality of study in home and international, we know there is most work of surveying near-surface structure to aim at the land, while there is few work about the near-surface structure of paralic zone. Because of complexity of the near-surface structure of paralic zone , the routine method to survey near-surface structure adapt to it no longer. As traditional disturbance in the seismic profile, surface wave has very high value in use to survey near-surface structure. Not only we should take advantage of it to survey and know the various near-surface structure of,paralic zone, offering exact data of low and degressive velocity region for exploration of oil and gas, but also the method exempts us from collecting surface wave again. To read and analyze the literature, the writer learned people have already applied many methods in surface wave inversion, though these methods have worked effectively, they have limitation themselves. So the writer summarizes the merit and demerit of these methods, and put forward a way to apply Artificial Neural Network technology to inverse dispersion curve of surface wave and achieve good effect.Firstly, aiming at the characteristic of near-surface structure in paralic zone, designing the model of horizontal layer, transverseisotropy geological structure and near-surface structure of paralic zone, the writer simulates the dispersion curve of surface wave based on theory of surface wave exploration, analyzes character of dispersion and relation between frequency and wave number. Then, writer uses two dimension all wave field simulation software to model the seismic record of near-surface structure of paralic zone and linearizes surface wave based on surface wave velocity scaning method. After distilling surface wave applying eigenvalue decomposition and rebuild theory, he calculates the dispersion curve using F-V method and inverses the dispersion curve making use of Artificial BP Neural Network technology to obtain the parameters of layer. At last, the write contrasts the result of inversion with near-surface structure parameters, analyzes the difference between them, concludes the last result, finds the reason of problem, finally, puts forward measure and advice.The way to use Artificial BP Neural Network technology to inverse the dispersion curve and predict parameter of layer is the innovation of this paper. Because of Neural Network's five merit: high parallelism, high nonlinearity global function, good ability of distinguishing fault and good associational-memory function, very strong adaptive and learning ability itself, the ability of processing the problem characterized with complex information, unclear underground and ambiguous deducing regulation, so it's ability of inversion and prediction is very mighty, it:can predict target data requested accurately. Contrasted with others inversion methods, Neural Network has more strong flexibility, it is important that aiming at the problem about low velocity constringency and local minimal extremum in BP algorithm. After using the way to add momentum item and conjugate-grads measure, we can overcome the two problem, and make Neural Network technology to work more effectively in the course of inversing surface wave.