Imaging on the Edge: GPU-Accelerated Traveltime Tomography on a Jetson Nano

We present a GPU-accelerated, computationally efficient seismic imaging approach implemented in CUDA for use on NVIDIA's edge device, the Jetson Nano. The presented implementation in-tends to enable fast seismic imaging in autonomous exploration tasks on robotic platforms where electrical power and weight are limiting factors. For the imaging method we consider traveltime tomography, a seismic imaging technique based on first-arrival trav-eltimes. To this end, we rely on the fast iterative method that solves the eikonal equation in a parallel fashion and provides first-arrival traveltime maps. Furthermore, we employ a gradient-based ray tracer to reconstruct wave paths through the subsurface. We im-plement ray tracer and parts of the tomography update in a parallel fashion for efficient computation on the GPU of the Jetson Nano. We demonstrate the imaging capability of our implementation for synthetic as well as real seismic data. We also show that our edge device implementation achieves imaging results in a comparable time range as a state-of-the-art geophysical inversion tool running on a powerful desktop CPU.