# A Machine-Learning Approach for Earthquake Magnitude Estimation

arXiv: https://arxiv.org/abs/1911.05975 · HTML: https://mlsanity.com/paper/1911.05975/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2019.

## Built on by (most cited first, 10 of 10)

- [Intra-domain and cross-domain transfer learning for time series data -- How transferable are the features?](https://mlsanity.com/paper/2201.04449/) (2022)
- [SeisT: A foundational deep learning model for earthquake monitoring tasks](https://mlsanity.com/paper/2310.01037/) (2023)
- [SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Model](https://mlsanity.com/paper/2502.19960/) (2025)
- [A CNN-BiLSTM Model with Attention Mechanism for Earthquake Prediction](https://mlsanity.com/paper/2112.13444/) (2021)
- [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)
- [Real-Time Earthquake Magnitude Classification from Initial P-Waves: Models, Dataset, and Comparative Analysis for South Asia](https://mlsanity.com/paper/2605.22836/) (2026)
- [SeisMamba: Low-Latency Single-Station Seismic Magnitude Estimation for Spatially Distributed Earthquake Early Warning](https://mlsanity.com/paper/2608.24561/) (2026)

## Abstract (arXiv)

In this study we develop a single-station deep-learning approach for fast and reliable estimation of earthquake magnitude directly from raw waveforms. We design a regressor composed of convolutional and recurrent neural networks that is not sensitive to the data normalization, hence waveform amplitude information can be utilized during the training. Our network can predict earthquake magnitudes with an average error close to zero and standard deviation of ~0.2 based on single-station waveforms without instrument response correction. We test the network for both local and duration magnitude scales and show a station-based learning can be an effective approach for improving the performance. The proposed approach has a variety of potential applications from routine earthquake monitoring to early warning systems.