Unveiling Strength: Efficient Recovery of Reflectivity From Coarsely Quantized Seismic Data

Future oil and gas exploration technology ought to be more modular, versatile, adaptable, scalable, and automated. However, a seismic survey generates massive amounts of data on a daily basis. Nevertheless, seismic data transmission via resource-constraint wireless medium remains a major challenge. Hence, a low-profile acquisition method is essential for both the geophone and the fusion center. This approach is required to minimize data congestion at the fusion center and alleviate storage demands. Seismic datasets are typically acquired in 32-bit floating point precision; however, after processing, the sparse reflectivity series is recovered with the objective to enhance the resolution. Hence, fine-scale quantization may be unnecessary, and a coarser scale with fewer bits might suffice. For this purpose, the study employs a segment-based deep neural network (DNN) coupled with low-bit uniform quantization to recover reflectivity, thereby effectively reducing the amount of seismic data collected in the field. The proposed technique has the potential of off-line DNN training, allowing it to be implemented in real-time. Since it does not rely on presuming the inherent statistical properties of noise or the seismic signal, the approach presented proves advantageous for a broad spectrum of seismic data. This approach, unlike other transform-domain methods, operates in time-domain, making it ideal for quickly diagnosing traces in fusion centers for quality. Finally, significant reconstruction gains is demonstrated when comparing the proposed method to the existing state-of-the-art method. Particularly, the proposed setup has the capability to reduce the 32-bits (4.3 billion levels) to 8-bits (256 levels) quantization while maintaining a normalized correlation metric of ~0.96.