SVM方法在扇体砂岩厚度预测中的应用

基于小样本学习理论的支持向量机(SVM)方法可用于建立非线性函数预测模型。利用支持向量机方法,根据样本数据采用自动拟合的方法构造核函数,使得建立的关系不仅具有较高的拟合精度,而且具有较好的推广性。地震波的频谱与其波形的关系是互为正、反傅立叶变换的关系,所以地震波的波形及其频谱是同一物理现象的两种不同的表达形式。波形特征沿纵横方向上的变化反映了地层介质在纵横方向上的差异;反射波频谱上的差异则反映了岩性和流体成分的不同以及地层厚度的变化等。直接由地震波波形预测扇体砂岩厚度,不仅充分利用了地震波信息,而且极大地提高了预测模型的准确性。模型及实例验证了该方法的适用性。

SVM method for predicting the thickness of sandstone

The Support Vector Machine (SVM) method can be used to set up a nonlinear function prediction model.It is based on the small sample learning theory.The kernel function can be constructed automatically based on the actual sample data by using the SVM method.As a result,the function not only gets a higher fit precision but is also better generalized.The frequency spectrum and seismic waveform are related by Fourier transform, so they are two different forms of the same physical phenomenon.The variety of waveform character reflects stratigraphic differences and frequency spectrum differences reflect the variation of lithology,fluid composition,and formation thickness.It directly predicts sandstone thickness using the seismic waveform.This not only fully utilizes the seismic information but also greatly increases the accuracy of the prediction.Model examples and actual applications show the applicability of this method.