# Predicting the Magnitude of Earthquakes Using Grammatical Evolution
## 1 Introduction  
Seismology, like most scientific disciplines, has matured over the centuries through the rational evolution of human reasoning. For many centuries, the interpretation of earthquakes was rooted in mythology, and it was only during the era of the profound Industrial Revolution that it became widely accepted as a geological phenomenon rather than an act of divine punishment. Despite this, one of the earliest seismic instruments, known as the seismoscope, did not record the timing or duration of ground motion, but merely signaled that seismic activity had taken place. This type of device was first developed by the Chinese scholar [PERSON] as early as 132 CE [1]. [PERSON] (1810-1881), often referred to as the \"father of seismology\" due to his pioneering contributions to the study of earthquakes, was an Irish geophysicist, civil engineer, and scientific researcher [2]. In today's era, at the threshold between the Fourth and Fifth Industrial Revolutions, the science of seismology continues its quest for the \"Holy Grail\" of accurately predicting an impending earthquake. According to the United States Geological Survey (USGS), neither the USGS nor any otherscientific institution has ever successfully predicted a major earthquake. A valid earthquake prediction must specify three critical elements: (1) the exact date and time, (2) the precise location, and (3) the anticipated magnitude [3]. Nevertheless, earthquake prediction was long considered an unattainable goal prior to the advent of artificial intelligence technologies. In recent years, however, annual conferences of leading scientific bodies such as the Seismological Society of America and the American Geophysical Union have included dedicated sessions on the application of machine learning (ML) in geophysical sciences. These scientific advancements, driven by the integration of AI-based methods, have increasingly captured the attention of seismologists and researchers in the field [4]. The necessity of accurate prediction is also evidenced by the following alarming data from the International Disaster Database EM-DAT [[https://www.emdat.be/](https://www.emdat.be/)]([https://www.emdat.be/](https://www.emdat.be/)) (accessed on 30 June 2025): between 2010 and 2025, earthquakes have resulted in 337,372 deaths and 698,085 injuries, and have left 1,547,581 individuals homeless. These data are depicted in Table 1. It is important to note that EM-DAT records only those events that meet at least one of the following criteria: ten or more fatalities, one hundred or more individuals affected, the declaration of a state of emergency, or a call for international assistance. These figures underscore the significance of seismic disasters and lead us to emphasize the urgent need for scientific advances in earthquake prediction. In order to contextualize this necessity further, it is worth examining comparable human losses caused by other natural disasters. These stark figures reveal how vulnerable humanity remains even in our modern era, in the face of natural disaster events that are further intensified by the effects of climate change. Nevertheless, there is room for optimism, as researchers are increasingly employing artificial intelligence and other advanced methodologies in the pursuit of forecasting earthquakes (and other natural disasters) in a timely manner, with the ultimate goal of enabling populations to seek safety and reduce potential losses [5]. Consequently, emphasis has been placed on various scientific approaches that investigate events that may potentially influence or trigger the release of seismic energy. The following are some examples that illustrate these approaches. In the state of Oklahoma, researchers have developed a sophisticated Bayesian approach to assess the relationship between wastewater injection practices and the occurrence of seismic events [6]. In early 2002, a study was conducted examining the correlation between long-period seismic tremors occurring at a depth of approximately 30 km, and underlying tectonic processes [7].