Low-Cost Quantized Compressed Sensing and Transmission Method for Ultrasonic Imaging Logging

Given the limited computing, storage, and communication transmission abilities of downhole edge devices, it is awkward to transmit massive imaging data collected by an edge device to the ground server. The predicament results in long latency times and severely degrades the efficiency of downhole state monitoring. To reduce the cost of transmitting logging image data, a low-cost quantized compressed sensing (CS) and transmission framework is proposed in this work. The method exploits the inherent sparsity of the logging image data to compress and transmit the image as a 1-D vector. By significantly reducing the amount of data that needs to be transmitted, quantized CS and transmission (QCST) achieves efficient communication of information. Compared to existing compression reconstruction methods, a hardware-deployed implementation of a neural network (NN) is presented for image compression. Furthermore, a data quantization method is designed that convert the floating-point to binary data for reduced transmission costs. Finally, an image reconstruction network is adopted to improve the quality of compressed logging images using the powerful nonlinear modeling capabilities of NN. Comprehensive experiments demonstrate that the use of CS for compression and quantized transmission, combined with NN for reconstruction, could achieve a peak-signal-to-noise ratio (PSNR) of about 20 dB at extremely low sampling rates of 0.01 (1%) on a real-field dataset. It also outperforms existing methods in terms of maximizing compression gain and reconstruction quality, providing a new solution for logging data compression and transmission. Code available at
https://github.com/Qiang-Feng98/QCST
.