Abstract：A seismic first-break picking method based on BP neural network integrated with momentum method and adaptive learning rate method was proposed in this paper.It improves the network weight updating process.If mean square error is not within the given error range,the weight update is cancelled.Otherwise,the weight is updated,and the learning rate changes accordingly.Through the analysis on the feasibility of first-break identification using different seismic attributes,four typical attributes,including RMS amplitude ratio,curve length ratio,amplitude and frequency,were chosen for model test.Model test results indicated that the improved method performs better than conventional BP neural network method.The application in real data demonstrated that the improved BP neural network algorithm has simple network structure,few parameters,fast convergence speed,good performance in stability and anti-noising and high first-break picking precision.
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