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Earthquake Detection and Analysis
Research Guide
What is Earthquake Detection and Analysis?
Earthquake Detection and Analysis is the study of seismic phenomena, precursors such as ionospheric anomalies and electromagnetic radiation, and methods for monitoring seismic activity and developing prediction models.
This field encompasses 135,879 papers focused on earthquake precursors including ionospheric anomalies, seismic electromagnetics, pre-earthquake signals, thermal infrared emission, and lithosphere-atmosphere-ionosphere coupling. Researchers monitor seismic activity and build earthquake prediction models using tools like waveform analysis and empirical relations. Key works include frequency-magnitude relations and source parameter determination from global seismicity data.
Topic Hierarchy
Research Sub-Topics
Ionospheric Anomalies
Ionospheric anomalies research detects pre-earthquake disturbances in the ionosphere using GPS-TEC, satellite data, and radio signals. Studies correlate these with seismic activity for potential forecasting.
Lithosphere-Atmosphere-Ionosphere Coupling
Lithosphere-atmosphere-ionosphere coupling investigates physical mechanisms linking tectonic stress to atmospheric and ionospheric perturbations. Researchers model radon emanation, infrared anomalies, and electromagnetic coupling.
Seismic Electromagnetics
Seismic electromagnetics studies electromagnetic emissions generated by rock fracturing during earthquake preparation. Work includes ULF/ELF signals, source mechanisms, and ground-satellite observations.
Thermal Infrared Anomalies
Thermal infrared anomalies detect surface temperature rises before earthquakes using satellite imagery like MODIS and Landsat. Research validates correlations with stress-induced gas releases.
ULF Geomagnetic Variations
ULF geomagnetic variations analyze ultra-low frequency magnetic field changes as earthquake precursors from piezoelectric effects and currents. Studies deploy magnetometers for real-time detection.
Why It Matters
Earthquake detection and analysis enables early warning systems that deliver alerts faster than strong ground shaking, as shown in the February 2023 Kahramanmaraş, Türkiye sequence where dense sensor networks characterized earthquakes in real-time. Google utilized motion sensors on over two billion mobile phones from 2021 to 2024 to detect quakes and send warnings to millions in 98 countries. The USGS awarded more than $23 million for earthquake monitoring and research in high-risk areas, supporting seismic networks. Recent advances like the dEPIC framework integrate fiber-optic seismic arrays for offshore early warning, while WaveCastNet forecasts wavefields using deep sequence-to-sequence learning.
Reading Guide
Where to Start
"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis" by Huang et al. (1998), as it provides foundational signal processing techniques essential for analyzing seismic waveforms before advancing to source mechanics.
Key Papers Explained
Huang et al. (1998) establish empirical mode decomposition for non-stationary seismic time series, which supports Brune (1970) analysis of tectonic stress in shear wave spectra and Okada (1992) calculations of internal deformations from faults. Gutenberg and Richter (1944) quantify earthquake frequencies, building toward Dziewoński et al. (1981) waveform-based source parameters and Kanamori and Anderson (1975) empirical seismology relations. These connect signal processing, fault mechanics, frequency statistics, and source modeling.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Recent preprints advance deep learning for wavefield forecasting with WaveCastNet (2025) and MagEs for GNSS magnitude estimation (2025), alongside dEPIC for DAS-integrated EEW (2025). News highlights Google's use of billions of phones for global alerts and USGS $23 million funding for monitoring.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | The empirical mode decomposition and the Hilbert spectrum for ... | 1998 | Proceedings of the Roy... | 22.7K | ✓ |
| 2 | Tectonic stress and the spectra of seismic shear waves from ea... | 1970 | Journal of Geophysical... | 5.1K | ✕ |
| 3 | Frequency of earthquakes in California* | 1944 | Bulletin of the Seismo... | 4.2K | ✕ |
| 4 | The mechanics of earthquakes and faulting | 1990 | Choice Reviews Online | 3.6K | ✕ |
| 5 | Journal of Geophysical Research | 1949 | Nature | 3.5K | ✓ |
| 6 | Cosmic ray contributions to dose rates for luminescence and ES... | 1994 | Radiation Measurements | 3.5K | ✕ |
| 7 | Internal deformation due to shear and tensile faults in a half... | 1992 | Bulletin of the Seismo... | 3.4K | ✕ |
| 8 | Determination of earthquake source parameters from waveform da... | 1981 | Journal of Geophysical... | 3.1K | ✕ |
| 9 | Theoretical basis of some empirical relations in seismology | 1975 | Bulletin of the Seismo... | 2.9K | ✕ |
| 10 | Finite strain isotherm and velocities for single‐crystal and p... | 1978 | Journal of Geophysical... | 2.7K | ✕ |
In the News
This simple math trick could transform earthquake science
# This simple math trick could transform earthquake science ## Scientists can’t predict earthquakes—but a powerful new modeling breakthrough could help us understand their risks faster than ever.
Rapid wavefield forecasting for earthquake early warning via deep sequence to sequence learning
We propose a deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequence-to-sequence foreca...
Integrating fiber-optic seismic arrays into earthquake early warning systems with the dEPIC framework
Distributed Acoustic Sensing (DAS) can enhance earthquake early warning (EEW) by transforming existing fiber-optic cables into dense seismic arrays, including in offshore areas with sparse instrume...
Google tapped billions of mobile phones to detect quakes worldwide — and send alerts
Technology giant Google harnessed motion sensors on more than two billion mobile phones between 2021 and 2024 to detect earthquakes, and then sent automated warnings to millions of people in 98 cou...
USGS Announces Recipients of Recent Earthquake ...
_USGS recently awarded more than \\$23 million for earthquake monitoring and applied research._ _This funding supports earthquake research in high-risk areas, contributes to the maintenance and ope...
Code & Tools
**FAST**is an end-to-end and unsupervised earthquake detection pipeline. It is a useful tool for seismologists to extract more small earthquakes fr...
QuakeMigrate uses a waveform migration and stacking algorithm to search for coherent seismic phase arrivals across a network of instruments. It pro...
## Repository files navigation # EQcorrscan ## A python package for the detection and analysis of repeating and near-repeating earthquakes. ## C...
Qseek is a an automatic, data-driven earthquake detection and localisation tool designed for large seismic data sets. It combines neural network ph...
Backprojection and matched-filtering (BPMF) is a two-step earthquake detection workflow with 1) backprojection to build an initial earthquake catal...
Recent Preprints
Rapid wavefield forecasting for earthquake early warning via deep sequence to sequence learning
Earthquakes generate complex seismic wavefields as energy is released from the rupture and propagates through the Earth’s interior and surface, producing ground motions that can cause significant d...
Exploring end-to-end earthquake early warning performance in large earthquakes using the February 2023 Kahramanmaraş, Türkiye sequence
Earthquake early warning systems (EEWS) aim to alert users of impending strong shaking before it reaches their location 1 . Using dense, distributed sensor networks, EEWS can rapidly detect and cha...
A deep learning pipeline for large earthquake analysis using high-rate global navigation satellite system data
Deep learning techniques for processing large and complex datasets have unlocked new opportunities for fast and reliable earthquake analysis using Global Navigation Satellite System (GNSS) data. Th...
Title: Rapid wavefield forecasting for earthquake early warning via deep sequence to sequence learning
We propose a deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequence-to-sequence foreca...
Integrating fiber-optic seismic arrays into earthquake early warning systems with the dEPIC framework
Distributed Acoustic Sensing (DAS) can enhance earthquake early warning (EEW) by transforming existing fiber-optic cables into dense seismic arrays, including in offshore areas with sparse instrume...
Latest Developments
Recent developments in earthquake detection and analysis include the use of deep learning models for rapid wavefield forecasting to improve early warning systems, as demonstrated by a November 2025 study, and the utilization of elastogravity signals for real-time tracking of earthquake growth, particularly for large magnitude events, as of May 2022 (nature.com, nature.com).
Frequently Asked Questions
What are common earthquake precursors studied in this field?
Precursors include ionospheric anomalies, seismic electromagnetics, pre-earthquake signals, thermal infrared emission, lithosphere-atmosphere-ionosphere coupling, electromagnetic radiation, and geoelectric potential changes. These phenomena are monitored to identify potential seismic events. ULF geomagnetic variations also feature in research on pre-earthquake signals.
How do researchers determine earthquake source parameters?
Dziewoński et al. (1981) used waveform data to derive source mechanisms and hypocentral coordinates as the centroid of stress glut density. This combines classical seismology problems into one analysis. The method applies to global and regional seismicity studies.
What is the frequency-magnitude relation for earthquakes?
Gutenberg and Richter (1944) estimated destructive shock frequencies in California by statistical comparison with worldwide data. Their work revised historical estimates using imperfect records. The relation forms a basis for seismic hazard assessment.
How are seismic waveforms analyzed for non-stationary signals?
Huang et al. (1998) introduced empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. This method decomposes data into intrinsic mode functions. It applies to seismic signal processing.
What tools exist for automated earthquake detection?
FAST is an end-to-end unsupervised pipeline for detecting small earthquakes from continuous data, runnable on Google Colab. QuakeMigrate uses waveform migration and stacking for catalogues with locations and phase arrivals. EQcorrscan detects repeating earthquakes via template matching.
What recent methods improve earthquake early warning?
WaveCastNet forecasts wavefields using convolutional long expressive memory in a sequence-to-sequence framework for real-time ground motion prediction. The dEPIC framework integrates DAS from fiber-optic cables into EEW for dense offshore arrays. MagEs estimates magnitudes from high-rate GNSS data via deep learning.
Open Research Questions
- ? How can deep learning models like WaveCastNet accurately forecast high-dimensional seismic wavefields in real-time for early warning?
- ? What limits the performance of end-to-end EEWS in large earthquakes like the 2023 Kahramanmaraş sequence?
- ? How effectively can fiber-optic DAS arrays enhance offshore earthquake detection in operational EEW frameworks?
- ? Can high-rate GNSS data alone provide reliable magnitude estimates for large earthquakes using deep learning pipelines?
- ? What mathematical models best integrate mobile phone sensors with traditional seismic networks for global quake detection?
Recent Trends
Deep learning has surged in earthquake early warning, with preprints on WaveCastNet for wavefield forecasting , MagEs for GNSS analysis (2025), and dEPIC for fiber-optic DAS integration (2025).
2025Google's detection via over two billion phones (2021-2024) expanded global alerts to 98 countries.
USGS funded over $23 million for seismic networks in high-risk areas.
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