Deep Seismic CS: A Deep Learning Assisted Compressive Sensing for Seismic Data

For large-scale seismic exploration in areas that lack even basic infrastructure, wired geophones are impractical because of the huge effort involved and their high deployment and operating costs. A network of wireless geophones capable of recording and transmitting data could be an inexpensive solution. However, a typical seismic survey can generate hundreds of terabytes of raw seismic data per day. It takes a huge amount of energy to transmit this massive amount of data from geophones to the on-site data collection center, thus making the transformation from pre-wired to wireless geophones a significant challenge. To reduce data traffic to the data center without putting additional strain on the geophone, a standalone and lightweight compressive sensing (CS) method is proposed in this work. The method takes advantage of the inherent sparsity in the seismic data to enable the geophone to sense data in a compressed manner. This significantly reduces the amount of data that needs to be recorded/transmitted by the geophone, making it energy-efficient. However, instead of employing conventional optimization-based CS reconstruction methods, we propose an efficient implementation of a deep convolutional neural network (DCNN). This network processes the compressed data received at the collection center without any a priori assumptions about the underlying seismic signal statistics, making it appropriate for a wide range of seismic data. The use of CS for energy-efficient sensing and transmission combined with powerful DCNN for reconstruction yields a system that could achieve signal-to-noise ratio (SNR) of around 30 dB with a compression gain of 16 on a field dataset. Finally, when compared with existing methods, the proposed approach demonstrates significant superiority in maximizing compression gain and reconstruction quality for both synthetic and real-field datasets.