Seismic Inference using Genetic Algorithms

被引:0
|
作者
Travis S. Metcalfe
机构
[1] Aarhus University,Theoretical Astrophysics Center
来源
关键词
numerical methods; stellar interiors; stellar oscillations; white dwarfs;
D O I
暂无
中图分类号
学科分类号
摘要
A flood of reliable seismic data will soon arrive. The migration to largertelescopes on the ground may free up 4-m class instruments for multi-sitecampaigns, and several forthcoming satellite missions promise to yieldnearly uninterrupted long-term coverage of many pulsating stars. We willthen face the challenge of determining the fundamental properties of thesestars from the data, by trying to match them with the output of ourcomputer models. The traditional approach to this task is to make informedguesses for each of the model parameters, and then adjust them iterativelyuntil an adequate match is found. The trouble is: how do we know that oursolution is unique, or that some other combination of parameters will notdo even better? Computers are now sufficiently powerful and inexpensivethat we can produce large grids of models and simply compare all ofthem to the observations. The question then becomes: what range ofparameters do we want to consider, and how many models do we want tocalculate? This can minimize the subjective nature of the process, but itmay not be the most efficient approach and it may give us a false sense ofsecurity that the final result is correct, when it is really justoptimal. I discuss these issues in the context of recent advances inthe asteroseismological analysis of white dwarf stars.
引用
收藏
页码:141 / 151
页数:10
相关论文
共 50 条
  • [1] Seismic inference using genetic algorithms
    Metcalfe, TS
    [J]. ASTROPHYSICS AND SPACE SCIENCE, 2003, 284 (01) : 141 - 151
  • [2] INVERSION FOR SEISMIC ANISOTROPY USING GENETIC ALGORITHMS
    HORNE, S
    MACBETH, C
    [J]. GEOPHYSICAL PROSPECTING, 1994, 42 (08) : 953 - 974
  • [3] Inference systems by using ordinal sums and genetic algorithms
    Ciaramella, A
    Tagliaferri, R
    Pedrycz, W
    [J]. NAFIPS 2004: ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY, VOLS 1AND 2: FUZZY SETS IN THE HEART OF THE CANADIAN ROCKIES, 2004, : 629 - 634
  • [4] Inversion of seismic refraction data using genetic algorithms
    Boschetti, F
    Dentith, MC
    List, RD
    [J]. GEOPHYSICS, 1996, 61 (06) : 1715 - 1727
  • [5] Inversion of Seismic Prospecting Parameters using Improved Genetic Algorithms
    Wang, Chao
    Li, Zhibin
    [J]. PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON MODELLING AND SIMULATION (ICMS2009), VOL 8, 2009, : 57 - 62
  • [6] Cooperation between the Inference System and the Rule Base by Using Multiobjective Genetic Algorithms
    Marquez, Antonio
    Alfredo Marquez, Francisco
    Peregrin, Antonio
    [J]. HYBRID ARTIFICIAL INTELLIGENCE SYSTEMS, 2008, 5271 : 739 - 746
  • [7] Performance improvement of the attitude estimation system using fuzzy inference and genetic algorithms
    Kim, Min-Soo
    [J]. ANALYSIS AND DESIGN OF INTELLIGENT SYSTEMS USING SOFT COMPUTING TECHNIQUES, 2007, 41 : 445 - 454
  • [8] Using Genetic Algorithms for the Inference of Motifs That Are Represented In Only a Subset of Sequences of Interest
    Thompson, Jeffrey A.
    Congdon, Clare Bates
    [J]. 2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS, 2011, : 1005 - 1005
  • [9] Inference of gene regulatory model by genetic algorithms
    Ando, S
    Iba, H
    [J]. PROCEEDINGS OF THE 2001 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2, 2001, : 712 - 719
  • [10] SEISMIC VELOCITY STRUCTURE OF OCEANIC-CRUST BY INVERSION USING GENETIC ALGORITHMS
    DRIJKONINGEN, GG
    WHITE, RS
    [J]. GEOPHYSICAL JOURNAL INTERNATIONAL, 1995, 123 (03) : 653 - 664