mlsanityData to 7 Oct 2026

Earthquake Nowcasting with Deep Learning

arXiv 2201.01869 ↗First posted 18 Dec 2021physics.geo-ph cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

We review previous approaches to nowcasting earthquakes and introduce new approaches based on deep learning using three distinct models based on recurrent neural networks and transformers. We discuss different choices for observables and measures presenting promising initial results for a region of Southern California from 1950-2020. Earthquake activity is predicted as a function of 0.1-degree spatial bins for time periods varying from two weeks to four years. The overall quality is measured by the Nash Sutcliffe Efficiency comparing the deviation of nowcast and observation with the variance over time in each spatial region. The software is available as open-source together with the preprocessed data from the USGS.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 88 references that are in the map. First posted on arXiv 18 Dec 2021.

Builds on

18 of 88 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
  3. 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
  4. 2019Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecastingcited by 161
  5. 2017Attend and Diagnose: Clinical Time Series Analysis using Attention Modelscited by 105
  6. 2019Attention in Natural Language Processingcited by 100
  7. 2018Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Drivingcited by 90
  8. 2019Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasetscited by 68
  9. 2020How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Surveycited by 38
  10. 2020A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resourcescited by 30
  11. 2019Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scalescited by 27
  12. 2019Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial predictioncited by 24
6 more
  1. 2016Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning frameworkcited by 23
  2. 2020Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretationcited by 18
  3. 2020Deep Contextual Clinical Prediction with Reverse Distillationcited by 16
  4. 2020AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariatescited by 3
  5. 2020Leveraging Text Data Using Hybrid Transformer-LSTM Based End-to-End ASR in Transfer Learningcited by 2
  6. 2020Spatiotemporal Pattern Mining for Nowcasting Extreme Earthquakes in Southern Californiacited by 1

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcastingcited by 3
  2. 2024Deep Learning Foundation and Pattern Models: Challenges in Hydrological Time Seriescited by 3
  3. 2025Leveraging Exogenous Signals for Hydrology Time Series Forecastingcited by 0