# Predicting the Magnitude of Earthquakes Using Grammatical Evolution
## 1 Introduction
The connection was established in 2016, highlighting that the careful and precise monitoring of slow-slip events may yield valuable insights into the probability of forthcoming large-magnitude earthquakes [8]. An alternative approach involves the systematic monitoring of potential precursory signals preceding the onset of a major seismic event [9]. Several researchers have suggested alternative mechanisms, including electromagnetic anomalies and ionospheric disturbances, although investigations in this area remain in their preliminary stages [5]. In addition to these approaches, \begin{table}
\begin{tabular}{c c c c} \hline
**2010–2025** & **Deaths** & **Injured People** & **Homeless People** \\ \hline Floods & 82,644 & 111,954 & 7,552,142 \\ \hline Storms & 51,091 & 125,266 & 3,671,100 \\ \hline Wildfires & 1622 & 13,810 & 94,925 \\ \hline Earthquakes & 337,372 & 698,085 & 1,547,581 \\ \hline \end{tabular}
\end{table} 
Table 1: Natural disasters from EM-DAT. certain institutions have also explored the study of animal behavior as a potential means of anticipating seismic activity [10]. The following section presents researchers' efforts to forecast imminent seismic events through the application of machine learning techniques. One study proposed an efficient and accurate framework for estimating earthquake magnitudes directly from unprocessed single-station waveform data, yielding minimal average error and a standard deviation close to 0.2, without requiring instrument-response adjustments, as validated using seismic records from Southern California [11]. Another study demonstrated that machine learning-driven approaches enhanced the accuracy of aftershock location forecasts and shed light on key physical parameters that may govern earthquake triggering during the most dynamic phase of the seismic cycle [12]. In another study, researchers trained convolutional neural networks (CNNs) on over 18 million manually picked seismographs from Southern California, and estimated earthquake parameters directly from raw waveform data, without the need for feature extraction. The model demonstrated high precision, achieving a standard deviation of just 0.023 s in arrival times and 95% accuracy in polarity classification [13]. Building on recent advancements, the authors of [14] employed machine learning techniques on datasets derived from shear laboratory experiments, aiming to uncover previously undetected signals that might precede seismic events. The same researchers subsequently conducted an analysis using a machine learning-based method initially developed in the laboratory. They processed extensive raw seismic data from Vancouver Island to distinguish relevant signals from background seismic noise, an approach that may prove valuable in assessing whether and how a slow slip event could be coupled with or evolve into a major earthquake [15]. Subsequently, a high-resolution earthquake catalog was developed through machine learning techniques, providing new insights into the complexity and duration of earthquake sequences, as well as their interrelation with recent neighboring seismic events [16]. Following this, another study introduced a global deep learning model capable of concurrently detecting earthquakes and identifying seismic phases. The proposed model demonstrated superior performance compared to existing deep learning approaches and conventional detection and phase-picking methods. Notably, it enabled the detection, and localization of approximately twice as many earthquakes, while utilizing less than one third of available seismic stations [17]. A subsequent study focused on California, and demonstrated that nearest-neighbor diagrams offer a straightforward and effective method for distinguishing between various seismic patterns and evaluating the reliability of earthquake catalogs [18]. Another research team concluded that the Weibull model provided a superior fit to seismic data for California, exhibiting well-behaved tail characteristics.