ArticleProceedings of the National Academy of Sciences of the United States of America2023
Earthquake alerting based on spatial geodetic data by spatiotemporal information transformation learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Explainable data generation from observed small samples by matrix tri-factorization.Nature communications · 2026Article
- Artificial neural manifolds.Nature communications · 2026Article
- Analysis of the transcriptomic and metabolomic landscape of prostate cancer with different anatomical origins using snFLARE-seq and mxFRIZNGRND.Nature communications · 2026Article
- Delayformer: Spatiotemporal Transformation for Predicting High-Dimensional Dynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- TransMarker: Unveiling dynamic network biomarkers in cancer progression through cross-state graph alignment and optimal transport.PLoS computational biology · 2025Article
- Action functional as an early warning indicator in the space of probability measures via Schrödinger bridge.Quantitative biology (Beijing, China) · 2025Article
- Deep learning for predicting the occurrence of tipping points.Royal Society open science · 2025Article
- Ultralow-Dimensionality Reduction for Identifying Critical Transitions by Spatial-Temporal PCA.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- One-core neuron deep learning for time series prediction.National science review · 2025Article
- Predicting multiple observations in complex systems through low-dimensional embeddings.Nature communications · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
Alerting for imminent earthquakes is particularly challenging due to the high nonlinearity and nonstationarity of geodynamical phenomena. In this study, based on spatiotemporal information (STI) transformation for high-dimensional real-time data, we developed a model-free framework, i.e., real-time spatiotemporal information transformation learning (RSIT), for extending the nonlinear and nonstationary time series. Specifically, by transforming high-dimensional information of the global navigation satellite system into one-dimensional dynamics via the STI strategy, RSIT efficiently utilizes two criteria of the transformed one-dimensional dynamics, i.e., unpredictability and instability. Such two criteria contemporaneously signal a potential critical transition of the geodynamical system, thereby providing early-warning signals of possible upcoming earthquakes. RSIT explores both the spatial and temporal dynamics of real-world data on the basis of a solid theoretical background in nonlinear dynamics and delay-embedding theory. The effectiveness of RSIT was demonstrated on geodynamical data of recent earthquakes from a number of regions across at least 4 y and through further comparison with existing methods.
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