Distributed Acoustic Sensing (DAS) that transforms city-wide fiber-optic cables into a large-scale strain sensing array has shown the potential to revolutionize urban traffic monitoring by providing a fine-grained, scalable, and low-maintenance monitoring solution. However, the real-world application of DAS is hindered by challenges such as noise contamination and interference among closely traveling cars. In response, we introduce a self-supervised U-Net model that can suppress background noise and compress car-induced DAS signals into high-resolution pulses through spatial deconvolution. Our work extends recent research by introducing three key advancements. Firstly, we perform a comprehensive resolution analysis of DAS-recorded traffic signals, laying a theoretical foundation for our approach. Secondly, we incorporate space-domain vehicle wavelets into our U-Net model, enabling consistent high-resolution outputs regardless of vehicle speed variations. Finally, we employ L-2 norm regularization in the loss function, enhancing our model’s sensitivity to weaker signals from vehicles in remote traffic lanes. We evaluate the effectiveness and robustness of our method through field recordings under different traffic conditions and various driving speeds. Our results show that our method can enhance the spatial-temporal resolution and better resolve closely traveling cars. The spatial deconvolution U-Net model also enables the characterization of large-size vehicles to identify axle numbers and estimate the vehicle length. Monitoring large-size vehicles also benefits imaging deep earth by leveraging the surface waves induced by the dynamic vehicle-road interaction.

Memory-related constraints (memory bandwidth, cache size) are nowadays the performance bottleneck of most computational applications. Especially in the scenario of multiple cores, the performance does not scale with the number of cores in many cases. In our work, we present our FPGA-based solution for the 3D Reverse Time Migration (RTM) algorithm. As the most computationally demanding imaging algorithm in current oil and gas exploration, RTM involves various computational challenges, such as a high demand for storage size and bandwidth, and a poor cache behavior. Combining optimizations from both the algorithmic and architectural perspectives, our FPGA-based solution manages to remove the memory constraints and provide a high performance that can scale well with the amount of computational resources available. Compared with an optimized CPU implementation using two quad-core Intel Nehalem CPUs, our solution achieves 4x speedup on two Virtex-5 FPGAs, and 8x speedup on two Virtex-6 FPGAs. Our projection demonstrates that the performance will continue to scale with the future increase of FPGA capacities.