About Us
World Earthquakes Live™ (WEL) is a non-profit independent organization established in March 2010, one year prior to the catastrophic 9.1 magnitude Tōhoku earthquake[1] in Japan. Its primary aim is to provide real-time information and data about earthquakes that occur around the world.
In addition to providing earthquake and tsunami information, WEL also conducts research and development activities to improve its monitoring systems and data analysis methods using AI techinques[2][3][4][5][6].
The organization collaborates with various academic and research institutions to achieve its research goals.
WEL has developed an AI model that has the capability to predict earthquakes. This new model is based on the idea of utilizing precursors to tectonic activity, which are small changes that occur before an earthquake that could serve as an early warning sign.
One of the major advantages of this AI model is its ability to predict earthquakes, using data from precursor activity, much earlier than traditional methods. This early warning can provide valuable time for authorities to implement measures to minimize the impact of the earthquake. Furthermore, the WEL AI model can also help to better understand the underlying causes of earthquakes, which can aid in the development of more effective prevention and mitigation strategies.
WEL's mission is to reduce the impact of earthquakes on communities around the world by providing timely and accurate information and promoting preparedness. With its dedicated team of professionals, state-of-the-art monitoring systems, and ongoing research efforts, WEL is playing a crucial role in achieving this mission.
In conclusion, the WEL is a valuable organization that provides important information about earthquakes and plays a significant role in reducing the impact of earthquakes on communities worldwide.
It is important to keep in mind that the outcomes displayed in our AI techniques are in a testing environment and you should NOT make any decisions based on these results as they may contain errors or inaccuracies.
References
- Dielforder, A., Bocchini, G. M., Kemna, K., Hampel, A., Harrington, R. M., & Oncken, O. (2023). Megathrust stress drop as trigger of aftershock seismicity: Insights from the 2011 Tohoku earthquake, Japan. Geophysical Research Letters, 50, e2022GL101320. https://doi.org/10.1029/2022GL101320
- Johnson, P. A., Rouet-Leduc, B., Pyrak-Nolte, L. J., Beroza, G. C., Marone, C. J., Hulbert, C., et al. (2021). Laboratory earthquake forecasting: A machine learning competition. Proceedings of the National Academy of Sciences, 118(5), e2011362118. https://doi.org/10.1073/pnas.2011362118
- Tehseen, R.; Farooq, M.S.; Abid, A. Earthquake Prediction Using Expert Systems: A Systematic Mapping Study. Sustainability 2020, 12, 2420. https://doi.org/10.3390/su12062420
- Mohsen Yousefzadeh, Seyyed Ahmad Hosseini, Mahdi Farnaghi, Spatiotemporally explicit earthquake prediction using deep neural network, Soil Dynamics and Earthquake Engineering, Volume 144, 2021, 106663, ISSN 0267-7261, https://doi.org/10.1016/j.soildyn.2021.106663.
- R. Li, X. Lu, S. Li, H. Yang, J. Qiu and L. Zhang, "DLEP: A Deep Learning Model for Earthquake Prediction," 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9207621.
- M. H. A. Banna et al., "Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges," in IEEE Access, vol. 8, pp. 192880-192923, 2020, doi: 10.1109/ACCESS.2020.3029859.
Contact Us
office@world-earthquakes.com