Sensor Placement for Tsunami Early Warning via Large-Scale Bayesian Optimal Experimental Design

2026-04-09

AI Summary

This study addresses parameter uncertainty in high-dimensional hyperbolic inverse problems for tsunami early warning by reformulating Bayesian optimal experimental design (OED) as a dense submatrix selection problem in data space. The authors propose a pipelined multi-GPU greedy algorithm based on Schur complement updates, which fully overlaps computation and I/O and overcomes the limitations of conventional low-rank approximations in hyperbolic systems. Demonstrating near-perfect weak and strong scaling on the Perlmutter and Frontier supercomputers, the method efficiently optimizes the placement of 175 sensors in the Cascadia Subduction Zone digital twin, handling parameter fields with over one billion degrees of freedom.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Real-time tsunami early warning relies on distributed sensor networks to infer seismic sources and seafloor motion. Optimizing these networks via Bayesian optimal experimental design (OED) is exceptionally challenging for systems governed by hyperbolic partial differential equations, which lack the spectral decay required by standard low-rank approximations. We present a scalable Bayesian OED framework for linear time-invariant systems. By reformulating the inverse problem in the data space, we transform OED into dense matrix subset selection. We propose a multi-GPU, Schur-complement-update-based, greedy algorithm that solves the OED problem using a pipelined approach that fully overlaps I/O with GPU computations. Our framework achieves near-perfect weak and strong scaling across hundreds of GPUs on Perlmutter and Frontier. Applied to the 2025 Gordon Bell Prize-winning digital twin for tsunami forecasting in the Cascadia Subduction Zone, we optimize a 175-sensor network, minimizing the uncertainty of a parameter field with over one billion degrees of freedom.

Problem

• Sensor Placement
• Tsunami Early Warning
• Bayesian Optimal Experimental Design
• Hyperbolic PDEs
• Uncertainty Quantification

Innovation

• Bayesian optimal experimental design
• sensor placement
• multi-GPU algorithm
• Schur complement
• digital twin