EML trees are proven to be universal approximators for W^{k,∞} functions by mimicking polynomial representations and invoking classical neural network approximation results, with a proposed learning algorithm demonstrated on optimization problems.
The Seismic Wavefield Common Task Framework
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abstract
Seismology faces fundamental challenges in state forecasting and reconstruction (e.g., earthquake early warning and ground motion prediction) and managing the parametric variability of source locations, mechanisms, and Earth models (e.g., subsurface structure and topography effects). Addressing these with simulations is hindered by their massive scale, both in synthetic data volumes and numerical complexity, while real-data efforts are constrained by models that inadequately reflect the Earth's complexity and by sparse sensor measurements from the field. Recent machine learning (ML) efforts offer promise, but progress is obscured by a lack of proper characterization, fair reporting, and rigorous comparisons. To address this, we introduce a Common Task Framework (CTF) for ML for seismic wavefields, demonstrated here on three distinct wavefield datasets. Our CTF features a curated set of datasets at various scales (global, crustal, and local) and task-specific metrics spanning forecasting, reconstruction, and generalization under realistic constraints such as noise and limited data. Inspired by CTFs in fields like natural language processing, this framework provides a structured and rigorous foundation for head-to-head algorithm evaluation. We evaluate various methods for reconstructing seismic wavefields from sparse sensor measurements, with results illustrating the CTF's utility in revealing strengths, limitations, and suitability for specific problem classes. Our vision is to replace ad hoc comparisons with standardized evaluations on hidden test sets, raising the bar for rigor and reproducibility in scientific ML.
years
2026 5verdicts
UNVERDICTED 5representative citing papers
A hybrid system using synthetic-pretrained denoisers for trajectory reconstruction, Lorenz ODE fitting for short forecasts, and histogram-tail substitution for long-time statistics achieved 83.83551 on the Lorenz challenge leaderboard.
Adaptive ESN framework with scenario-specific techniques like exact state synchronization and histogram-guided selection achieves 74.91 on the CTF-4-Science Lorenz benchmark.
Divide-and-conquer modeling using scenario-specific techniques reaches a public score of 79.63 on the CTF-4-Science Lorenz benchmark.
Bidirectional LSTM with Huber loss outperforms vanilla LSTM, attention-augmented LSTM, TCN, and CNN hybrids on Lorenz attractor forecasting in an empirical challenge study.
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