# Earthquake Phase Association with Graph Neural Networks arXiv: https://arxiv.org/abs/2209.07086 ยท HTML: https://mlsanity.com/paper/2209.07086/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 75 references that are in the map. - First posted on arXiv 15 Sep 2022. ## Builds on (most cited first, 21 of 21) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017) - [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017) - [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018) - [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019) - [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016) - [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018) - [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018) - [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020) - [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015) - [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017) - [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017) - [GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models](https://mlsanity.com/paper/1802.08773/) (2018) - [Position-aware Graph Neural Networks](https://mlsanity.com/paper/1906.04817/) (2019) - [Identity-aware Graph Neural Networks](https://mlsanity.com/paper/2101.10320/) (2021) - [Graph Information Bottleneck](https://mlsanity.com/paper/2010.12811/) (2020) - [PhaseLink: A Deep Learning Approach to Seismic Phase Association](https://mlsanity.com/paper/1809.02880/) (2018) - [A multi-channel approach for automatic microseismic event association using RANSAC-based arrival time event clustering(RATEC)](https://mlsanity.com/paper/1702.01856/) (2017) - [MAF-GNN: Multi-adaptive Spatiotemporal-flow Graph Neural Network for Traffic Speed Forecasting](https://mlsanity.com/paper/2108.03594/) (2021) - [Beyond Correlation: A Path-Invariant Measure for Seismogram Similarity](https://mlsanity.com/paper/1904.07936/) (2019) ## Built on by (most cited first, 11 of 11) - [SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction](https://mlsanity.com/paper/2309.02320/) (2023) - [Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting](https://mlsanity.com/paper/2408.11990/) (2024) - [Full-waveform earthquake source inversion using simulation-based inference](https://mlsanity.com/paper/2410.23238/) (2024) - [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) - [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) - [Deep Learning Forecasts Caldera Collapse Events at Kilauea Volcano](https://mlsanity.com/paper/2404.19351/) (2024) - [HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity](https://mlsanity.com/paper/2507.10850/) (2025) - [Improving moment tensor solutions under Earth structure uncertainty with simulation-based inference](https://mlsanity.com/paper/2603.18925/) (2026) - [Event-Centric Earthquake Monitoring with a Multi-Station Transformer](https://mlsanity.com/paper/2610.17404/) (2026) ## Abstract (arXiv) Seismic phase association connects earthquake arrival time measurements to their causative sources. Effective association must determine the number of discrete events, their location and origin times, and it must differentiate real arrivals from measurement artifacts. The advent of deep learning pickers, which provide high rates of picks from closely overlapping small magnitude earthquakes, motivates revisiting the phase association problem and approaching it using the methods of deep learning. We have developed a Graph Neural Network associator that simultaneously predicts both source space-time localization, and discrete source-arrival association likelihoods. The method is applicable to arbitrary geometry, time-varying seismic networks of hundreds of stations, and is robust to high rates of sources and input picks with variable noise and quality. Our Graph Earthquake Neural Interpretation Engine (GENIE) uses one graph to represent the station set and another to represent the spatial source region. GENIE learns relationships from data in this combined representation that enable it to determine robust source and source-arrival associations. We train on synthetic data, and test our method on real data from the Northern California (NC) seismic network using input generated by the PhaseNet deep learning phase picker. We successfully re-detect ~96% of all events M>1 reported by the USGS during 500 random days between 2000unicodex20132022. Over a 100-day continuous interval of processing in 2017unicodex20132018, we detect ~4.2x the number of events reported by the USGS. Our new events have small magnitude estimates below the magnitude of completeness of the USGS catalog, and are located close to the active faults and quarries in the region. Our results demonstrate that GENIE can effectively solve the association problem under complex seismic monitoring conditions.