A Deep CNN Model for Ringing Effect Attenuation of Vibroseis Data

Zhuang Jia, Wenkai Lu
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Abstract

In the field of exploration geophysics, seismic vibrator is one of the widely used seismic sources to acquire seismic data, which is usually named vibroseis. "Ringing effect" is a common problem in vibroseis data processing due to the limited frequency bandwidth of the vibrator, which degrades the performance of first-break picking. In this paper, we proposed a novel deringing model for vibroseis data using deep convolutional neural network (CNN). In this model we use end-to-end training strategy to obtain the deringed data directly, and skip connections to improve model training process and preserve the details of vibroseis data. For real vibroseis deringing task we synthesize training data and corresponding labels from real vibroseis data and utilize them to train the deep CNN model. Experiments are conducted both on synthetic data and real vibroseis data. The experiment results show that deep CNN model can attenuate the ringing effect effectively and expand the bandwidth of vibroseis data. The STA/LTA ratio method for first-break picking also shows improvement on deringed vibroseis data using deep CNN model.
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用于减弱震荡数据振铃效应的深度 CNN 模型
在勘探地球物理领域,地震震源是获取地震数据的广泛使用的震源之一,通常被命名为震源。由于震源的频率带宽有限,"振铃效应 "是震源数据处理中的一个常见问题,会降低初至拾取的性能。在本文中,我们利用深度卷积神经网络(CNN)为振动采样数据提出了一种新的振铃模型。在该模型中,我们采用端到端训练策略直接获取掺杂数据,并跳过连接以改进模型训练过程,保留振动信号数据的细节。对于真实的振动信号剔除任务,我们从真实的振动信号数据中合成训练数据和相应的标签,并利用它们来训练深度 CNN 模型。实验同时在合成数据和真实 vibroseis 数据上进行。实验结果表明,深度 CNN 模型可以有效地减弱振铃效应,并扩大振动信号数据的带宽。利用深度 CNN 模型,STA/LTA 比值法进行初至拾取也显示出对经掺杂的振动信号数据的改进。
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