win德赢

Abstract: Passive source imaging can reconstruct body wave reflections similar to those of active sources through seismic interferometry (SI). It has become a low-cost, environmentally friendly alternative to active source seismic, showing great potential. However, this method faces many challenges in practical applications, including uneven distribution of underground sources and complex survey environments. These situations seriously affect the reconstruction quality of virtual shot records, resulting in unguaranteed imaging results and greatly limiting passive source seismic exploration applications. In addition, the quality of the reconstructed records is directly related to the time length of the noise records, but in practice it is often difficult to obtain long-term, high-quality noise segments containing body wave events. To solve the above problems, we propose a deep learning method for reconstructing passive source virtual shot records and apply it to passive source time-lapse monitoring. This method combines the UNet network and the BiLSTM (Bidirectional Long Short-Term Memory) network for extracting spatial features and temporal features respectively. It introduces the spatial attention mechanism to establish a hybrid SUNet-BiLSTM-Attention (SBA) network for supervised training. Through pre-training and fine-tuning training, the network can accurately reconstruct passive source virtual shot records directly from short-time noisy segments containing body wave events. The experimental results of theoretical data show that the virtual shot records reconstructed by the network have high resolution and signal to noise ratio (SNR), providing high-quality data for subsequent monitoring and imaging. Finally, to further validate the effectiveness of proposed method, we applied it to field data collected from gas storage in northwest China. The reconstruction results of field data effectively improve the quality of virtual records and obtain more reliable time-lapse imaging monitoring results, which have significant practical value.