Parallel Source Scanning Algorithm using GPUs

The use of methods using waveform stacking are nowadays more common in microseismic monitoring applications because they avoid manual or automatic phase picking. The Source Scanning Algorithm (SSA) is a widely known technique in which the source location is estimated using a brightness function obtained from stacking the normalized absolute amplitude seismograms recorded at several stations. The SSA has the advantage of the straightforwardness of its implementation but has the inconvenience of being computationally costly even for small-scale experiments. Our approach is then to parallelize the sequential SSA using graphics processing units (GPUs), and we named this parallel version pSSA. We have parallelized the Stacking step of the SSA Algorithm because this is by far the most computationally demanding Step. This can be done efficiently because of the spatial independence of the data. In our test cases we performed sequential and parallel computations of the SSA and pSSA in two different platforms. Additionally, we compared the performance of pSSA with a parallel implementation using OpenMP. We demonstrate that pSSA has produced speedups up to 125x as compared to the sequential version. We implemented a client-server architecture to receive and process the data. This architecture can treat with various simultaneous clients and also with out-of-order data packets. This allows for re-sending lost or corrupted data. We anticipate that pSSA has the impact of allowing SSA like algorithm to be used in microseismic experiment design and the use of on-site real-time denoising techniques, as well as the potential of being used in traffic light warning systems for fluid injection operations.