January 2022: John Kuttai

With distributed array 3D DCIP aquisition and DIAS’s Common Voltage Reference method, the number of possible dipole collection grows signficantly compared to traditional 2D. Data sets can easily grow into a million or more data points. Being distributed, aquisition over extreme topography is done frequently. This often leads to large mesh cell counts for the fine discretization of the topography. The combination of numerous transmits and large cell counts heavily consume computational resources and take significant time for inversion. To improve performance, parallelizing the inversion is required. Configuring a cluster with the right combination of hardware and software can provide significant gains. SimPEG code is then modified to divide the inversion problem into smaller simulations which are assigned to each cluster node. Here I explore inverting datasets from small to large data and cell counts. The largest requiring more than 1TB of RAM, which is larger than most workstation’s capacity. The results and the work are based on the SimPEG framework parallelized for effective cluster usage.