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Earthquake is one of the devastating events in natural hazards that causes great casualties and property damage every day in the world since it is hard to predict. With the increasing amount of earthquake datasets collected, many researchers try to solve the task of predicting the earthquake in future time. Earthquake prediction is to estimate the time, location, and magnitude of the future earthquake. In this project, we will use precursory pattern-based feature extraction method for earthquake prediction, which can predict both the magnitude range of future earthquakes and obtain the effective time range of prediction results.

Earthquake precursor refers to a part of seismic records before the mainshock, which is represented as the precursory pattern of earthquake.

Keywords: Earthquake, seismic activity, Precursory pattern, CART, Timeseries.

Earthquake prediction is a branch of the science of seismology concerned with the specification of the time, location, and magnitude of future earthquakes within stated limits, [1] and particularly “”the determination of parameters for the next strong earthquake to occur in a region.

[2] Earthquake prediction is sometimes distinguished from earthquake forecasting, which can be defined as the probabilistic assessment of general earthquake hazards, including the frequency and magnitude of damaging earthquakes in a given area over the years or decades.[3] Prediction can be further distinguished from earthquake warning systems, which upon detection of an earthquake, provide a real-time warning of seconds to neighboring regions that might be affected.

Predictions are deemed significant if they can be shown to be successful beyond random chance. Therefore, methods of statistical hypothesis testing are used to determine the probability that an earthquake such as is predicted would happen anyway (the null hypothesis).

The predictions are then evaluated by testing whether they correlate with actual earthquakes better than the null hypothesis.

In many instances, however, the statistical nature of earthquake occurrence is not simply homogeneous. Clustering occurs in both space and time. In southern California, about 6% of M3.0 earthquakes are “”followed by an earthquake of larger magnitude within 5 days and 10 km.”” In central Italy, 9.5% of M3.0 earthquakes are followed by a larger event within 48 hours and 30 km. While such statistics are not satisfactory for purposes of prediction (giving ten to twenty false alarms for each successful prediction) they will skew the results of any analysis that assumes that earthquakes occur randomly in time, for example, as realized from a Poisson process. It has been shown that a “”naive”” method based solely on clustering can successfully predict about 5% of earthquakes; “”far better than ’chance’””.

Earthquake prediction is an immature science it has not yet led to a successful prediction of an earthquake from first physical principles. Research into methods of prediction, therefore, focus on empirical analysis, with two general approaches: either identifying distinctive precursors to earthquakes or identifying some kind of geophysical trend or pattern in seismicity that might precede a large earthquake.[21] Precursor methods are pursued largely because of their potential utility for short-term earthquake prediction or forecasting, while ’trend’ methods are generally thought to be useful for forecasting, long term prediction (10 to 100 years time scale) or intermediate-term prediction (1 to 10 years time scale).

- Feature Extraction( precursory pattern-based) to better capture the characteristics of earthquake, thus can be used to enhance earthquake prediction.
- Applying machine learning algorithm — CART (Classification And Regression Trees) algorithm on the extracted features to predict the magnitude of Earthquake on the Richter Scale.

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