Earthquakes Meet AI: How Deep Learning Revealed 4.5x More Seismic Events in the Hindu Kush-Pamir
What if thousands of tiny earthquakes were hiding beneath one of the world’s most dangerous tectonic zones—completely missed by traditional methods?
That’s exactly what a team of geophysicists from the University of Sydney and IIT Roorkee uncovered in the Hindu Kush-Pamir region (HKPR), one of Earth’s most seismically complex zones. And their tool of choice? Not more instruments, not field deployments—but a powerful AI-based model that listens more closely than ever before.
When AI Listens to the Earth
The HKPR region lies at the collision boundary of the Indian and Eurasian plates and is known for its intermediate-depth seismicity, peculiar slab geometry, and poorly understood active faults. Traditional earthquake detection methods—manual inspection, STA/LTA triggers, or waveform matching—often miss faint events, especially those buried under noise.
The researchers built a transformer-based deep learning model that detects earthquakes, picks P and S phases, and estimates magnitudes. By analyzing ten months of waveform data from 83 seismic stations across the region, their model:
- Detected over 21,000 seismic events, more than 4.5 times what the ISC catalogue recorded for the same period.
- Captured thousands of very low-magnitude earthquakes (<3.0) that had previously gone unnoticed.
- Mapped active and neotectonic faults in glaciated, inaccessible areas with remarkable clarity.
(a) Observed seismicity from ISC; (b) model-predicted seismicity; (c) monthly earthquake counts comparing model and ISC data; (d) magnitude comparison showing model’s sensitivity to smaller events due to STEAD training data.
A Peek Into the AI Architecture
The deep learning model, based on the EQTransformer framework, uses a multi-level attention mechanism—similar to how the human eye focuses on important parts of an image. It processes waveform data to:
- Distinguish seismic signals from background noise.
- Accurately determine the arrival of P and S waves.
- Estimate magnitudes using amplitude information and gated recurrent neural networks (BiLSTM).
The network was trained on the STEAD (Stanford Earthquake Dataset), a global collection of over one million labeled seismic events. Once trained, the model could rapidly process large volumes of continuous data from the HKPR region with minimal manual intervention.
Why This Matters
1. Discovery of Hidden Faults
The AI model revealed seismicity concentrated along:
- The Vakhsh Thrust System
- The Darvaz-Karakul Fault
- North- and northeast-trending neotectonic faults in the western Pamir
These areas are poorly mapped due to glaciation and remoteness. The new data illuminates potential sources of future seismic hazards that had not been captured by conventional methods.
2. Imaging the Deep Earth
The model captured thousands of intermediate-depth earthquakes (70–300 km), providing insight into slab stretching and possible slab break-off beneath the Hindu Kush. This reveals an intricate dynamic between crustal deformation and mantle subduction that traditional catalogues failed to reflect in such detail.
3. Towards Real-Time Monitoring
The entire analysis, which would have taken weeks or months manually, was processed in under an hour using AI. This opens new doors for real-time seismic monitoring in other tectonically active or under-instrumented regions.
Model captures evolving seismic patterns, including crustal and deep events, with seasonal shifts in activity across major fault zones and consistent intermediate-depth deformation.
Key Findings in Numbers
| Metric | ISC Catalogue | AI-Based Model |
|---|---|---|
| Total Events (10 months) | 1,877 | 21,229 |
| Events Located (hypocenters) | – | 8,467 |
| Detected Low-Mag Events (<3) | Rare | Thousands |
| Processing Time | Manual Weeks | ~1 Hour |
Global Relevance
Because the model was trained on a global dataset (STEAD), it can be applied to any region with a reasonable density of seismic stations. This includes active or even intraplate regions across South Asia, making it a valuable tool for both research and risk management.
Conclusion
This study shows that attention-based deep learning models are not just promising—they’re transformative. By enabling the detection of thousands of previously undetected events and accurately locating seismicity across complex terrain, this AI tool represents a leap forward in our ability to monitor Earth’s tectonic heartbeat.
Far from replacing seismologists, this AI model acts as an extension of their senses—seeing through noise, mapping the unmapped, and opening a new era of automated, data-driven seismology.
Article Reference:
Singh, S. P., & Silwal, V. (2023). Enhanced crustal and intermediate seismicity in the Hindu Kush-Pamir region revealed by attentive deep learning model. Artificial Intelligence in Geosciences, 4, 150–163.
Available online at: https://doi.org/10.1016/j.aiig.2023.10.002
Authors:
- Dr. Satyam Pratap Singh, EarthByte Group, The University of Sydney
- Dr. Vipul Silwal, Department of Earth Sciences, IIT Roorkee