Earthquake detection is a vital part of seismology, which allows for the establishment of early warning systems and prompt response measures to mitigate potential harm and protect lives. This research presents an innovative method for earthquake detection by analyzing P-waves using machine learning techniques. P-waves, also known as primary waves, provide vital information on the initial stages of earthquakes, which makes them an appropriate signal for detection algorithms.
Our model uses a dataset of seismic waveforms obtained from several seismic stations in Nepal. We perform data preprocessing to extract relevant properties from P-wave signals, such as amplitude, frequency, and temporal characteristics. The provided attributes are used as inputs for our machine learning models, which are specifically trained to differentiate seismic events from background noise.
We test the efficacy of various machine learning techniques, such as support vector machines, random forests, and neural networks, in classifying P-wave signals. The experimental results substantiate the efficacy of the suggested method, attaining a notable level of precision in earthquake detection while minimizing the occurrence of false positives.
In addition, we investigate how various ways of representing features and model architectures affect the overall performance of the detection system. In the future, we will analyse the capacity of the suggested model for expansion and its ability to be used in real-time situations in seismic monitoring networks.
To summarize, the proposed earthquake detection model presents a hopeful resolution for improving early warning systems and seismic monitoring efforts. By using machine learning methods to analyse P-wave signals, we can enhance the precision and effectiveness of earthquake detection, thereby aiding in disaster preparedness and response measures.