The Differential Evolution Inversion Method Based on Bayesian Theory for Micro-Seismic Data
详细信息
- 责任者：Song Weiqi (Earth Science and Technology ; China University of Petroleum ; Qingdao 266555 ; China) ; Gao Yanke
- 刊名：Chinese Journal of Geophysics
- 出版年：2013
- 卷期：56(4)
- 关键词：微震法 ; microseismic methods ; 贝叶斯分析 ; Bayesian analysis
- 页码：p.1331-1339
- 图表：illus., 1 table, 33 refs.
- 分类号：1600
- issnNo：ISSN0001-5733
- isbnNo：CN11-2074/P
摘要
Micro-seismic monitoring is difficult to pick up an accurate first wave.In order to improve the positioning accuracy of the inversion and reduce the problems of multiple solutions,this paper studies micro-seismic inversion method based on Bayesian theory.We discuss and analyze the statistical distribution of the theoretical model inversion residuals and covariance.We combine and compare the minimum point position and distribution gradients of residual covariance without noise and adding different levels of noise.We propose the priori information estimation method of the solution.In posteriori estimation,variance estimation of the solution space is difficult to obtain,that makes it impossible to calculate the weighting coefficients.By analyzing residual variation and the changes relationship of the solution,reconciliation of changes in the relationship by analyzing the residual variation,we try to study the method of using the residuals to obtain weighted coefficient.In order to accelerate the speed of optimization,we discuss the differential evolution inversion method.We use differential strategy in the mutation operation.The difference vector between individuals in the population disturbances individual is used to achieve individual variability.We take advantage of the mass distribution characteristics,improve the search capabilities of the algorithm,and avoid the shortcomings of variation manner in the genetic algorithm.We test the inversion effect by the theoretical model and compare the results of the search method at the same time.The test results prove that the inversion method works well.For different degrees of first arrival to interference,the approximation degree of inversion results to the exact solution is much larger than the search method.The inversion results of actual data are also better than the search method.
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