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This researcher from IIT Madras may have just found the answer to earthquake detection in nic- Edexlive

 


Can the Fukushima Daiichi nuclear disaster be avoided? The natural disaster in 2011 in Japan was the result of a magnitude 9.0 earthquake that subsequently caused a devastating tsunami in the Pacific Ocean. The thing about earthquakes is that the seismographs we have today can’t detect the non-destructive first wave, which can leave us an invaluable fraction of a minute to shut down nuclear power plants, turn off elevators, or pull the plug on public transit services like the metro. While the condition of the Fukushima nuclear reactor was complicated by the tsunami, it is an example of the far-reaching impact that early detection of an earthquake can have.

It was precisely this time period that impressed doctoral researcher Kanchan Agarwal when she joined IIT Madras as a project partner in 2015. Fast-forward six years later, and now, Kanchan and her team at IIT M have developed a new approach that can help detect the first benign wave of an earthquake. Destructive can save us time to minimize damage. The research was funded by the Bhabha Center for Atomic Research and published in the peer-reviewed scientific journal PLOS One.

Curiosity can transform mountains. And in Kanchan’s case, it turns out to be nothing less than a driving force. After receiving her Bachelor of Technology degree in Electronics and Instrumentation, Kanchan met Professor Aaron K. How do you detect an earthquake quickly enough to prevent damage?

Kanchan knew quite a bit about the field of seismology, but as a native of Meerut, Uttar Pradesh, she had some haircuts with minor earthquakes. “Understanding the physics behind the process was a daunting challenge, but it was also what piqued my interest in the project,” says the 28-year-old, adding, “It allowed me to research and see for myself how earthquake signals are recorded and the analysis that can be done, and how we can detect it.” .

In 2016, she joined IIT M for a dual degree of Postgraduate and Ph.D. and started working seriously on the project. She developed her ability to analyze and process seismic signals by taking courses at the institute, reading to gain a basic understanding of the signal, the fundamental nature of each wave, how it is created and participating in detailed discussions on the topic with her professors, including Professor Aaron.

Detecting earthquakes is not as simple as getting a reading from the Richter scale, which is the most common. There are different types of scales that measure different parts of the tectonic activity that causes earthquakes. These include the Richter Scale, the Mercalli Scale, the Moment Magnitude Scale, and a couple more. Kanchan’s team experimented with the idea of ​​combining time-frequency, or predictive tools, that can measure the interval of seismic activity, and signal detection, a multi-scale system, to arrive at a more accurate detection rate.

“This approach helps detect the P-wave seismic signal with a high accuracy rate,” Kanchan says. “The false alarm rate is also reduced due to the time-frequency positioning feature.” Predictive models are developed to detect seismic noise when there is no event or earthquake, after which a model is developed to maintain the noise prediction based on this pattern. And in the event of an earthquake, the deviation from the prediction and the actual noise will alert the user to the P wave or the first wave of tremor.

“To improve prediction and reduce the rate of fall alarm, we analyzed the predictions and actual data in the time-frequency domain using a tool called Wave Length Package Transformation. It separates the data into higher and lower frequency bands, so it becomes easier to pick out the outgoing frequency bands that we know the P-wave can. to occur in. The frequency of the P-wave cannot exceed two hertz. Because of this localization feature, we were able to detect an earthquake with a magnitude as low as 0.6. Kanchan who is currently at Honeywell, a US-based technology development company, explains that the current technology Failed to detect this slight earthquake.Hope to finish PhD in next two months.

The approach is only an algorithm at this point, and more experience and polishing are needed for it to take the form of a device at a seismic station. This is Kanchan’s dream, and she is confident that her creativity is outperforming the existing ones because she tested on real-time earthquake data, and this approach can be used in any process where anomalies need to be detected. The dynamic nature of the innovation is that it need not be restricted to the field of seismology, but can be used in any area that requires anomaly detection.

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