Traditional unsupervised seismic facies analysis techniques need to assume that seismic data obey mixed Gaussian distribution.However,fi eld seismic data may not meet this condition,thereby leading to wrong classifi c...Traditional unsupervised seismic facies analysis techniques need to assume that seismic data obey mixed Gaussian distribution.However,fi eld seismic data may not meet this condition,thereby leading to wrong classifi cation in the application of this technology.This paper introduces a spectral clustering technique for unsupervised seismic facies analysis.This algorithm is based on on the idea of a graph to cluster the data.Its kem is that seismic data are regarded as points in space,points can be connected with the edge and construct to graphs.When the graphs are divided,the weights of the edges between the different subgraphs are as low as possible,whereas the weights of the inner edges of the subgraph should be as high as possible.That has high computational complexity and entails large memory consumption for spectral clustering algorithm.To solve the problem this paper introduces the idea of sparse representation into spectral clustering.Through the selection of a small number of local sparse representation points,the spectral clustering matrix of all sample points is approximately represented to reduce the cost of spectral clustering operation.Verifi cation of physical model and fi eld data shows that the proposed approach can obtain more accurate seismic facies classification results without considering the data meet any hypothesis.The computing efficiency of this new method is better than that of the conventional spectral clustering method,thereby meeting the application needs of fi eld seismic data.展开更多
In this study, density functional theory calculations reveal how boron group ions M^+(M = B, Al, Ga, In, and Tl) directly convert carbon and methane into ethylene at room temperature. M^+ reacts with the carbon atom t...In this study, density functional theory calculations reveal how boron group ions M^+(M = B, Al, Ga, In, and Tl) directly convert carbon and methane into ethylene at room temperature. M^+ reacts with the carbon atom to form the cation MC^+. Then, the reaction of MC^+ with methane leads to the cleavage of metal-carbon bond and the formation of CH2CH2 through C-C coupling, with the ion M^+ serving as a leaving group. The cycle then begins again. The mechanism of C/CH4 system catalyzed by five ion types is investigated herein, and the reasons for the different reactivity of five ion types are determined. The moderate strength of the Al^+-C bond results in Al^+ being the only appropriate catalyst of M^+(M?=?B, Al, Ga, In, and Tl) that can catalyze methane and carbon into ethylene.展开更多
基金This work was supported by National Natural Science Foundation of China(Nos.U1562218,41604107,and 41804126).
文摘Traditional unsupervised seismic facies analysis techniques need to assume that seismic data obey mixed Gaussian distribution.However,fi eld seismic data may not meet this condition,thereby leading to wrong classifi cation in the application of this technology.This paper introduces a spectral clustering technique for unsupervised seismic facies analysis.This algorithm is based on on the idea of a graph to cluster the data.Its kem is that seismic data are regarded as points in space,points can be connected with the edge and construct to graphs.When the graphs are divided,the weights of the edges between the different subgraphs are as low as possible,whereas the weights of the inner edges of the subgraph should be as high as possible.That has high computational complexity and entails large memory consumption for spectral clustering algorithm.To solve the problem this paper introduces the idea of sparse representation into spectral clustering.Through the selection of a small number of local sparse representation points,the spectral clustering matrix of all sample points is approximately represented to reduce the cost of spectral clustering operation.Verifi cation of physical model and fi eld data shows that the proposed approach can obtain more accurate seismic facies classification results without considering the data meet any hypothesis.The computing efficiency of this new method is better than that of the conventional spectral clustering method,thereby meeting the application needs of fi eld seismic data.
基金Supported by the special steady growth science and technology foundation of Yanan Science and Technology Bureau(2017WZZ-08)the doctoral research program(YDBK2017-09)+1 种基金the research program(YD2016-09)(D2018009)of Yan’an University。
文摘In this study, density functional theory calculations reveal how boron group ions M^+(M = B, Al, Ga, In, and Tl) directly convert carbon and methane into ethylene at room temperature. M^+ reacts with the carbon atom to form the cation MC^+. Then, the reaction of MC^+ with methane leads to the cleavage of metal-carbon bond and the formation of CH2CH2 through C-C coupling, with the ion M^+ serving as a leaving group. The cycle then begins again. The mechanism of C/CH4 system catalyzed by five ion types is investigated herein, and the reasons for the different reactivity of five ion types are determined. The moderate strength of the Al^+-C bond results in Al^+ being the only appropriate catalyst of M^+(M?=?B, Al, Ga, In, and Tl) that can catalyze methane and carbon into ethylene.
