Seismic attribute optimization based on support vector machine and coalbed methane prediction

In order to take full advantages of seismic attribute analysis technology, this paper explained the principle of support vector machine (SVM) based on event of small samples; SVM method for nonlinear optimization of seismic attributes in coal field was applied in prediction of coalbed methane (CBM) content and produced good effect. The study results indicate that the prediction of CMB based on SVM attribute optimization is more precise than using the drilling interpolations method, can better solve the learning problems of small samples and can be used as an effective method for the prediction of CMB.