基于深度学习的地震子波分类识别

针对地震勘探中噪声压制的问题,构建了一种适合分类和识别地震子波的卷积神经网络模型.首先对卷积神经网络模型的激活函数、卷积核大小以及归一化层等进行了设计,然后利用已搭建好的卷积神经网络对地震信号的时频谱图进行特征提取,最后实现了不同类型的含噪地震信号的分类和识别.实验结果表明,该模型有高分类率和识别率及较好的抗干扰能力,具有实际可行性,为地震勘探后续的一系列工作奠定了基础.

Classification and Recognition of Seismic Wavelets Based on Deep Learning

To realize noise suppression in seismic exploration,this paper constructs a convolutional neural network model suitable for classifying and identifying seismic wavelets.First,the activation function,convolution kernel size,and normalization layer of the convolutional neural network model are designed.Then,the constructed convolutional neural network is used to extract features from the time-frequency spectrum of the seismic signal.Classification and identification of noisy seismic signals.Experimental results show that the model has a high classification and recognition rate,better anti-interference ability and practicality,which lays a foundation for future work of seismic exploration.