Shaohang Zhu | Searcharxiv

arXiv2026

pDSurfTomo: A High-Performance Parallel Computing Package for Direct Surface Wave Tomography

Surface wave tomography is essential for investigating the shear-wave velocity structure of the crust and upper mantle. The direct surface wave tomography method, DSurfTomo, has become one of the most widely adopted packages due to its ability to account for ray path bending in complex media to increase subsurface characterization accuracy. However, its inherent serial architecture lacks effective support for multicore CPUs and GPUs. Furthermore, its built-in solver is computationally expensive when solving large-scale linear systems. Consequently, the software struggles to meet current demands for large-scale, high-resolution surface wave tomography. To address these limitations, we propose pDSurfTomo, a highly optimized package utilizing hybrid CPU-GPU acceleration. First, it overcomes the scalability bottleneck in sensitivity kernel computation through a refined parallel design; also, it uses vectorization techniques to accelerate the modeling of surface wave dispersion, achieving efficient computation of the sensitivity kernel. Second, it implements parallelization of the serial fast marching method using OpenMP, significantly reducing computation time for surface wave traveltimes. Finally, it incorporates GPU acceleration to efficiently solve large-scale sparse linear least-squares problems. To streamline the workflow, we provide a cross-platform GUI with remote server connectivity, allowing users to execute and visualize inversion tasks locally while seamlessly utilizing remote computing clusters. Application to an observed dispersion dataset from 229 stations in North China demonstrates that pDSurfTomo reduces computation time by more than an order of magnitude while maintaining a negligible discrepancy compared to the original DSurfTomo. It is expected that pDSurfTomo will provide a highly efficient and accessible solution for large-scale, high-resolution surface wave tomography.

physics.geo-ph↗

arXiv2026

OmniXtreme: Breaking the Generality Barrier in High-Dynamic Humanoid Control

High-fidelity motion tracking serves as the ultimate litmus test for generalizable, human-level motor skills. However, current policies often hit a "generality barrier": as motion libraries scale in diversity, tracking fidelity inevitably collapses - especially for real-world deployment of high-dynamic motions. We identify this failure as the result of two compounding factors: the learning bottleneck in scaling multi-motion optimization and the physical executability constraints that arise in real-world actuation. To overcome these challenges, we introduce OmniXtreme, a scalable framework that decouples general motor skill learning from sim-to-real physical skill refinement. Our approach uses a flow-matching policy with high-capacity architectures to scale representation capacity without interference-intensive multi-motion RL optimization, followed by an actuation-aware refinement phase that ensures robust performance on physical hardware. Extensive experiments demonstrate that OmniXtreme maintains high-fidelity tracking across diverse, high-difficulty datasets. On real robots, the unified policy successfully executes multiple extreme motions, effectively breaking the long-standing fidelity-scalability trade-off in high-dynamic humanoid control.

cs.RO↗