# PhaseLink: A Deep Learning Approach to Seismic Phase Association

arXiv: https://arxiv.org/abs/1809.02880 · HTML: https://mlsanity.com/paper/1809.02880/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2018.

## Built on by (most cited first, 11 of 11)

- [Earthquake Phase Association with Graph Neural Networks](https://mlsanity.com/paper/2209.07086/) (2022)
- [Deep Learning-based Damage Mapping with InSAR Coherence Time Series](https://mlsanity.com/paper/2105.11544/) (2021)
- [SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction](https://mlsanity.com/paper/2309.02320/) (2023)
- [Random Noise vs State-of-the-Art Probabilistic Forecasting Methods : A Case Study on CRPS-Sum Discrimination Ability](https://mlsanity.com/paper/2201.08671/) (2022)
- [Multi-task multi-station earthquake monitoring: An all-in-one seismic Phase picking, Location, and Association Network (PLAN)](https://mlsanity.com/paper/2306.13918/) (2023)
- [VAEneu: A New Avenue for VAE Application on Probabilistic Forecasting](https://mlsanity.com/paper/2405.04252/) (2024)
- [Double Difference Earthquake Location with Graph Neural Networks](https://mlsanity.com/paper/2410.19323/) (2024)
- [High-Rate Phase Association with Travel Time Neural Fields](https://mlsanity.com/paper/2307.07572/) (2023)
- [Generalized Neural Networks for Real-Time Earthquake Early Warning](https://mlsanity.com/paper/2312.15218/) (2023)
- [Physics-Aware Machine Learning for Seismic and Volcanic Signal Interpretation](https://mlsanity.com/paper/2603.17855/) (2026)
- [Event-Centric Earthquake Monitoring with a Multi-Station Transformer](https://mlsanity.com/paper/2610.17404/) (2026)

## Abstract (arXiv)

Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is widely employed to detect earthquakes on permanent and temporary seismic networks, and underlies most seismicity catalogs produced around the world. This task can be challenging because the number of sources is unknown, events frequently overlap in time, or can occur simultaneously in different parts of a network. We present PhaseLink, a framework based on recent advances in deep learning for grid-free earthquake phase association. Our approach learns to link phases together that share a common origin, and is trained entirely on tens of millions of synthetic sequences of P- and S-wave arrival times generated using a simple 1D velocity model. Our approach is simple to implement for any tectonic regime, suitable for real-time processing, and can naturally incorporate errors in arrival time picks. Rather than tuning a set of ad hoc hyperparameters to improve performance, PhaseLink can be improved by simply adding examples of problematic cases to the training dataset. We demonstrate the state-of-the-art performance of PhaseLink on a challenging recent sequence from southern California, and synthesized sequences from Japan designed to test the point at which the method fails. For the examined datasets, PhaseLink can precisely associate P- and S-picks to events that are separated by ~12 seconds in origin time. This approach is expected to improve the resolution of seismicity catalogs, add stability to real-time seismic monitoring, and streamline automated processing of large seismic datasets.