基于独立分量分析的地震属性分析方法研究

Conventional seismic attribute analysis is not only time consuming, but also has several possible results. Therefore, seismic attribute optimization and multi-attribute analysis are needed. In this paper, Fuyu oil layer in Daqing oil field is our main studying object. And there is much difference between seismic attributes and well logs. So under this condition, Independent Component Analysis (ICA) and Kohonen neural net are introduced to seismic attribute optimization and multi-attribute analysis. The main contents are as follows: (1) Now the method of seismic attribute compression is mainly principal component analysis (PCA). In this article, independent component analysis (ICA), which is superficially related to PCA, but much more powerful, is used to seismic reservoir characterizeation. The fundamental, algorithms and applications of ICA are surveyed. And comparation of ICA with PCA is stydied. On basis of the ne-entropy measurement of independence, the FastICA algorithm is implemented. (2) Two parts of ICA application are included in this article: First, ICA is used directly to identify sedimentary characters. Combined with geology and well data, ICA results can be used to predict sedimentary characters. Second, ICA treats many attributes as multi-dimension random vectors. Through ICA transform, a few good new attributes can be got from a lot of seismic attributes. Attributes got from ICA optimization are independent. (3) In this paper, Kohonen self-organizing neural network is studied. First, the characteristics of neural network’s structure and algorithm is analyzed in detail, and the traditional algorithm is achieved which has been used in seism. From experimental results, we know that the Kohonen self-organizing neural network converges fast and classifies accurately. Second, the self-organizing feature map algorithm needs to be improved because the result of classification is not very exact, the boundary is not quite clear and the velocity is not fast enough, and so on. Here frequency sensitive principle is introduced. Combine it with the self-organizing feature map algorithm, then get frequency sensitive self-organizing feature map algorithm. Experimental results show that it is really better. (4) Kohonen self-organizing neural network is used to classify seismic attributes. And it can be avoided drawing confusing conclusions because the algorithm’s characteristics integrate many kinds of seismic features. The result can be used in the division of sand group’s seismic faces, and so on. And when attributes are extracted from seismic data, some useful information is lost because of difference and deriveative. But multiattributes can make this lost information compensated in a certain degree.

基于信息重建技术的地震信号处理方法研究

The dissertation addressed the problems of signals reconstruction and data restoration in seismic data processing, which takes the representation methods of signal as the main clue, and take the seismic information reconstruction (signals separation and trace interpolation) as the core. On the natural bases signal representation, I present the ICA fundamentals, algorithms and its original applications to nature earth quake signals separation and survey seismic signals separation. On determinative bases signal representation, the paper proposed seismic dada reconstruction least square inversion regularization methods, sparseness constraints, pre-conditioned conjugate gradient methods, and their applications to seismic de-convolution, Radon transformation, et. al. The core contents are about de-alias uneven seismic data reconstruction algorithm and its application to seismic interpolation. Although the dissertation discussed two cases of signal representation, they can be integrated into one frame, because they both deal with the signals or information restoration, the former reconstructing original signals from mixed signals, the later reconstructing whole data from sparse or irregular data. The goal of them is same to provide pre-processing methods and post-processing method for seismic pre-stack depth migration. ICA can separate the original signals from mixed signals by them, or abstract the basic structure from analyzed data. I surveyed the fundamental, algorithms and applications of ICA. Compared with KL transformation, I proposed the independent components transformation concept (ICT). On basis of the ne-entropy measurement of independence, I implemented the FastICA and improved it by covariance matrix. By analyzing the characteristics of the seismic signals, I introduced ICA into seismic signal processing firstly in Geophysical community, and implemented the noise separation from seismic signal. Synthetic and real data examples show the usability of ICA to seismic signal processing and initial effects are achieved. The application of ICA to separation quake conversion wave from multiple in sedimentary area is made, which demonstrates good effects, so more reasonable interpretation of underground un-continuity is got. The results show the perspective of application of ICA to Geophysical signal processing. By virtue of the relationship between ICA and Blind Deconvolution , I surveyed the seismic blind deconvolution, and discussed the perspective of applying ICA to seismic blind deconvolution with two possible solutions. The relationship of PC A, ICA and wavelet transform is claimed. It is proved that reconstruction of wavelet prototype functions is Lie group representation. By the way, over-sampled wavelet transform is proposed to enhance the seismic data resolution, which is validated by numerical examples. The key of pre-stack depth migration is the regularization of pre-stack seismic data. As a main procedure, seismic interpolation and missing data reconstruction are necessary. Firstly, I review the seismic imaging methods in order to argue the critical effect of regularization. By review of the seismic interpolation algorithms, I acclaim that de-alias uneven data reconstruction is still a challenge. The fundamental of seismic reconstruction is discussed firstly. Then sparseness constraint on least square inversion and preconditioned conjugate gradient solver are studied and implemented. Choosing constraint item with Cauchy distribution, I programmed PCG algorithm and implement sparse seismic deconvolution, high resolution Radon Transformation by PCG, which is prepared for seismic data reconstruction. About seismic interpolation, dealias even data interpolation and uneven data reconstruction are very good respectively, however they can not be combined each other. In this paper, a novel Fourier transform based method and a algorithm have been proposed, which could reconstruct both uneven and alias seismic data. I formulated band-limited data reconstruction as minimum norm least squares inversion problem where an adaptive DFT-weighted norm regularization term is used. The inverse problem is solved by pre-conditional conjugate gradient method, which makes the solutions stable and convergent quickly. Based on the assumption that seismic data are consisted of finite linear events, from sampling theorem, alias events can be attenuated via LS weight predicted linearly from low frequency. Three application issues are discussed on even gap trace interpolation, uneven gap filling, high frequency trace reconstruction from low frequency data trace constrained by few high frequency traces. Both synthetic and real data numerical examples show the proposed method is valid, efficient and applicable. The research is valuable to seismic data regularization and cross well seismic. To meet 3D shot profile depth migration request for data, schemes must be taken to make the data even and fitting the velocity dataset. The methods of this paper are used to interpolate and extrapolate the shot gathers instead of simply embedding zero traces. So, the aperture of migration is enlarged and the migration effect is improved. The results show the effectiveness and the practicability.

Design of Photonic Crystal Semiconductor Optical Amplifier With Polarization Independence

Slow-light effects in photonic crystal (PC) waveguides can enhance light-mater interaction near the photonic band edge, which can be used to design a short cavity length semiconductor optical amplifier (SOA). In this paper, a novel SOA based on slow-light effects in PC waveguides (PCSOA) is presented. To realize the amplification of the optical signal with polarization independence, a PCSOA is designed with a compensated structure. The cascaded structure leads to a balanced amplification to the TE and TM polarized light.

Independence of the brittle-ductile transition from the rubber particle size for impact-modified poly(vinyl chloride)

A series of acrylic impact modifiers (AIMS) with different particle sizes ranging from 55.2 to 927.0 nm were synthesized by seeded emulsion polymerization, and the effect of the particle size on the brittle-ductile transition of impact-modified poly(vinyl chloride) (PVC) was investigated. For each AIM, a series of PVC/AIM blends with compositions of 6, 8, 10, 12, and 15 phr AIM in 100 phr PVC were prepared, and the Izod impact strengths of these blends were tested at 23 degrees C. For AIMs with particle sizes of 55.2, 59.8, 125.2, 243.2, and 341.1 nm, the blends fractured in the brittle mode when the concentration of AIM was lower than 10 phr, whereas the blends showed ductile fracture when the AIM concentration reached 10 phr. It was concluded that the brittle-ductile transition of the PVC/AIM blends was independent of the particle size in the range of 55.2-341.1 nm. When the particle size was greater than 341.1 nm, however, the brittle-ductile transition shifted to a higher AIM concentration with an increase in the particle size. Furthermore, the critical interparticle distance was found not to be the criterion of the brittle-ductile transition for the PVC/AIM blends.

Independence of alpine meadow plants from soil organic matter for nitrogen supply: evidence from stable nitrogen isotopes

Stable nitrogen isotope signatures of major sources of mineral nitrogen ( mineralization of soil organic nitrogen, biological N-2 fixation by legumes, annual precipitation and plant litter decomposition) were measured to relatively define their individual contribution to grass assimilation at the Haibei Alpine Meadow Ecosystem, Qinghai, China. The results indicated that delta N-15 values (- 2.40 parts per thousand to 0.97 parts per thousand) of all grasses were much lower than those of soil organic matter (3.4 +/- 0.18 parts per thousand) and mineral nitrogen ( ammonium and nitrate together,7.8 +/- 0.57 parts per thousand). Based on the patterns of stable nitrogen isotopes, soil organic matter (3.4 +/- 0.18 parts per thousand), biological N-2 fixation (0 parts per thousand), and precipitation (- 6.34 +/- 0.24 parts per thousand) only contributed to a small fraction of nitrogen requirements of grasses, but plant litter decomposition (- 1.31 +/- 1.01 parts per thousand) accounted for 67%.