# Bayesian-Deep-Learning Estimation of Earthquake Location from Single-Station Observations

arXiv: https://arxiv.org/abs/1912.01144 · HTML: https://mlsanity.com/paper/1912.01144/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 16 of 16)

- [SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction](https://mlsanity.com/paper/2309.02320/) (2023)
- [SeisT: A foundational deep learning model for earthquake monitoring tasks](https://mlsanity.com/paper/2310.01037/) (2023)
- [Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis](https://mlsanity.com/paper/2408.12396/) (2024)
- [Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting](https://mlsanity.com/paper/2509.25017/) (2025)
- [SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Model](https://mlsanity.com/paper/2502.19960/) (2025)
- [Exploring Challenges in Deep Learning of Single-Station Ground Motion Records](https://mlsanity.com/paper/2403.07569/) (2024)
- [Deep Learning-based Average Shear Wave Velocity Prediction using Accelerometer Records](https://mlsanity.com/paper/2408.14962/) (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)
- [Fast Information Streaming Handler (FisH): A Unified Seismic Neural Network for Single Station Real-Time Earthquake Early Warning](https://mlsanity.com/paper/2408.06629/) (2024)
- [Double Difference Earthquake Location with Graph Neural Networks](https://mlsanity.com/paper/2410.19323/) (2024)
- [Generalized Neural Networks for Real-Time Earthquake Early Warning](https://mlsanity.com/paper/2312.15218/) (2023)
- [An Attention-based Framework with Multistation Information for Earthquake Early Warnings](https://mlsanity.com/paper/2412.18099/) (2024)
- [Deep Sequence Models for Predicting Average Shear Wave Velocity from Strong Motion Records](https://mlsanity.com/paper/2503.05224/) (2025)
- [Spatio-Temporal Graph Structure Learning for Earthquake Detection](https://mlsanity.com/paper/2503.11215/) (2025)
- [TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation](https://mlsanity.com/paper/2512.04694/) (2025)
- [Event-Centric Earthquake Monitoring with a Multi-Station Transformer](https://mlsanity.com/paper/2610.17404/) (2026)

## Abstract (arXiv)

We present a deep learning method for single-station earthquake location, which we approach as a regression problem using two separate Bayesian neural networks. We use a multi-task temporal-convolutional neural network to learn epicentral distance and P travel time from 1-minute seismograms. The network estimates epicentral distance and P travel time with absolute mean errors of 0.23 km and 0.03 s respectively, along with their epistemic and aleatory uncertainties. We design a separate multi-input network using standard convolutional layers to estimate the back-azimuth angle, and its epistemic uncertainty. This network estimates the direction from which seismic waves arrive to the station with a mean error of 1 degree. Using this information, we estimate the epicenter, origin time, and depth along with their confidence intervals. We use a global dataset of earthquake signals recorded within 1 degree (~112 km) from the event to build the model and to demonstrate its performance. Our model can predict epicenter, origin time, and depth with mean errors of 7.3 km, 0.4 second, and 6.7 km respectively, at different locations around the world. Our approach can be used for fast earthquake source characterization with a limited number of observations, and also for estimating location of earthquakes that are sparsely recorded -- either because they are small or because stations are widely separated.