CSCNN: A Compressive Sensing Method Based on Convolutional Neural Networks for Seismic Data

The efficient collection and transmission of large-scale seismic data are crucial aspects of seismic monitoring and large seismic surveys in exploration seismology, which is becoming increasingly important due to the proliferation of sensor networks and seismic monitoring equipment. Compressed sensing (CS) theory enhances the efficiency of seismic data acquisition and transmission by reducing the volume of data required, thereby circumventing the limitations imposed by the Nyquist sampling theorem. However, traditional CS methods have limitations in accurately reconstructing data and overlook the correlation between the measurement matrix and the signal itself. Herein, a seismic data acquisition method based on CS and deep learning has been proposed in this study, namely, the convolutional neural network-based compressed sensing method (CSCNN). The CSCNN framework comprises sampling and reconstruction blocks. The sampling block employs a convolutional layer to perform an observational function in CS systems. The reconstruction block comprises a deconvolution layer used for the preliminary reconstruction of seismic data and employs alternating cascaded residual blocks (RBs) and introduced attention mechanism (AM) to enhance the accuracy of seismic data reconstruction. Results demonstrate that, the CSCNN method outperforms traditional reconstruction methods in effectively reconstructing data at equivalent compression ratios, yielding a higher signal-to-noise ratio (SNR) and reduced error.