Improved Anomalous Amplitude Attenuation Method Based on Deep Neural Networks

Abstract

In seismic exploration, seismic data usually contain anomalous amplitude noise whose high energy may affect the results of subsequent processing steps. In industry, this kind of noise is generally suppressed using the anomalous amplitude attenuation (AAA) method. The AAA method essentially suppresses abnormal amplitude noise using a median filter in the time-frequency domain. This makes its performance heavily dependent on the parameters, especially the window width of the median filter. Thus, we propose an improved anomalous amplitude attenuation (IAAA) method based on deep neural networks. The IAAA method contains two steps. In the first step, deep neural networks are used to detect the locations and the widths of noise regions. In the second step, the noise information (locations and widths) obtained at the previous step is exploited to apply the AAA method with more appropriate parameters to each noisy region. Compared with the conventional AAA method, the IAAA method can suppress the noise more effectively and preserve signals better. The experiments on both synthetic data and field data demonstrate that our method outperforms the conventional AAA method.