利用神经网络拾取叠加速度
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摘要
鉴于三维地震和高密度二维地震的数据量很大,仍然采用人工方法拾取速度谱,不仅效率低,而且精度低。为此,人们提出许多新的方法。本文采用人工神经网络与模糊数学相结合的方法。首先对输入数据用模糊教学方法作边界搜索和模糊聚类预处理,然后通过人工神经网络误差反向传递算法(BP算法),学习结定的样本值,训练网络模型,输入经过预处理的待识别数据,完成识别工作,自动提取叠加速度。该方法肯有抗噪能力强、拾取速度精度高的特点,而且能够实现自动解释速度谱。
Manual vclocity spcctrum pickup results in low cfficiency and poor accuracy because both 3D seismic survey and high-density 2D seismic survey have very great data volume. So, people advance continuously many new mcthods to solve the problem. I put forward a new method which involves neural network and fuzzy mathematics. The method works in the following steps:Make boundary search and clustering processing of the lnput data with the aid of fuzzv mathematics.Learn thc given samples to train nctwork model by using BP algorithm for neural network error.Input the preproccssed data for recognition.Automatlcally pick up stack velocity after the data recognition.The rnethod has good noise-resistance and offers accurate velocity, so that ve-locity spectrum can be lnterpreted automatlcally.
引文
1王倩.利用模式识别、模糊判别和神经网络技术进行储层研究,石油物探专题情报成果集(第7~8集),1993
    2王倩.神经网络在地震勘探中的应用,石油物探专题情报成果集(第9集),1994
    3 Jurandyr Schmidt等.神经网络提取叠加速度,第 62届SEG年会论文集,石油工业出版社,1993
    4贺仲雄.模糊数学及其应用,天津出版社,1983
    5何樵登等著.地震勘探原理和方法,地质出版社,1986

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