# Earthquake Nowcasting with Deep Learning

arXiv: https://arxiv.org/abs/2201.01869 · HTML: https://mlsanity.com/paper/2201.01869/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 88 references that are in the map.
- First posted on arXiv 18 Dec 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting](https://mlsanity.com/paper/1905.03806/) (2019)
- [Attend and Diagnose: Clinical Time Series Analysis using Attention Models](https://mlsanity.com/paper/1711.03905/) (2017)
- [Attention in Natural Language Processing](https://mlsanity.com/paper/1902.02181/) (2019)
- [Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving](https://mlsanity.com/paper/1808.05819/) (2018)
- [Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets](https://mlsanity.com/paper/1907.08456/) (2019)
- [How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey](https://mlsanity.com/paper/2005.11691/) (2020)
- [A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources](https://mlsanity.com/paper/2007.12269/) (2020)
- [Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scales](https://mlsanity.com/paper/1912.08949/) (2019)
- [Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial prediction](https://mlsanity.com/paper/1908.07805/) (2019)
- [Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework](https://mlsanity.com/paper/1612.01022/) (2016)
- [Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretation](https://mlsanity.com/paper/2008.04882/) (2020)
- [Deep Contextual Clinical Prediction with Reverse Distillation](https://mlsanity.com/paper/2007.05611/) (2020)
- [AICov: An Integrative Deep Learning Framework for COVID-19 Forecasting with Population Covariates](https://mlsanity.com/paper/2010.03757/) (2020)
- [Leveraging Text Data Using Hybrid Transformer-LSTM Based End-to-End ASR in Transfer Learning](https://mlsanity.com/paper/2005.10407/) (2020)
- [Spatiotemporal Pattern Mining for Nowcasting Extreme Earthquakes in Southern California](https://mlsanity.com/paper/2012.14336/) (2020)

## Built on by (most cited first, 3 of 3)

- [Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting](https://mlsanity.com/paper/2408.11990/) (2024)
- [Deep Learning Foundation and Pattern Models: Challenges in Hydrological Time Series](https://mlsanity.com/paper/2410.15218/) (2024)
- [Leveraging Exogenous Signals for Hydrology Time Series Forecasting](https://mlsanity.com/paper/2511.11849/) (2025)

## 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.