Earthquake Prediction Using QSVM

Earthquake detection is a critical area of study with profound implications for public safety and disaster preparedness. In this research, we explore the application of Quantum Support Vector Machines (QSVM) to predict the timing of laboratory earthquakes using seismic signal data. The dataset utilized for this study is the LANL Earthquake Prediction dataset from a Kaggle competition. Our methodology encompasses data pre- processing, feature engineering, and the application of QSVM for earthquake prediction. Additionally, we have developed a user-friendly Python graphical user interface (GUI) using PyQt5 to facilitate earthquake prediction. This GUI allows users to upload seismic data and obtain real-time predictions of the time remaining before the next laboratory earthquake. The results of our research demonstrate the effectiveness of QSVM in earthquake prediction, offering a promising approach for improving early warning systems. Through extensive evaluation, we have measured the model’s accuracy and discussed its potential in comparison to traditional methods. While acknowledging certain limitations, this study underscores the potential of QSVM as a valuable tool in earthquake prediction. In conclusion, this research contributes to the field of earthquake prediction by introducing QSVM as an innovative machine learning approach and providing a user interface for real-time earthquake forecasting. Further exploration and refinement of this approach hold promise for enhancing our ability to mitigate earthquake related risks and protect lives and infrastructure.