IIT Madras Researchers develop alternative method to detect earthquakes
IIT Madras: Researchers at the Indian Institute of Technology Madras have developed an alternative method for accurate detection and picking of the first waves in earthquake signals, which provides a small lead time and enables measures to save lives. The research can help in developing a robust Early Warning System which can give a lead time of approximately 30 seconds to 2 minutes till the destructive surface waves hit the ground.
The research was undertaken by Ms. Kanchan Aggarwal, under the guidance of Prof. Arun K Tangirala, Department of Chemical Engineering, IIT Madras.
How is the new approach different from existing methods?
- The new approach is commensurate with the noise characteristics, resulting in minimal sensitivity to outliers or robust detection.
- The research also offers a more flexible frequency band selection, by decomposing both lower and higher frequencies at each level, resulting in accurate detection, and
- The new approach will allow the user to discard the noise in undesired time-frequency bands, resulting in an improved signal-to-noise ratio (SNR) which results in accurate picking of P-wave onset.
Practical aspects of the research
- The solution provided by the researchers of IIT Madras results in accurate detection and picking of the first waves in earthquake signals, which will provide a small lead time of approximately 30 seconds to 2 minutes and enable measures to save lives.
- Even though the lead time appears small, it is sufficient to shut down the nuclear reactors, transportation such as the metro and to park the elevators in high-rise buildings at the nearest floor, among numerous other measures that will ultimately save lives.
- Regarding the practical aspects of the research, Professor Arun K Tangirala said, “The proposed framework is not necessarily limited to the detection of seismic events but is generic and can be used for fault detection and isolation in other domains as well. “
- He then added, “Furthermore, the framework can incorporate any predictive models, including the Machine Learning and Deep Learning models, which will reduce the human intervention in the detection.”
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