## 1 Introduction  
Earthquakes are among the natural disasters that have caused the greatest harm to humanity. They are one of the main types of natural disasters that can occur without warning, resulting in devastating effects and even thousands of deaths in a matter of seconds. Nowadays, considerable efforts have been made to study and investigate the possible causes and mechanisms that trigger an earthquake, but no method has been able to predict the occurrence of an earthquake. Although the short-term forecast of an earthquake is currently an unresolved problem, we can still use the distribution of the maximum to study the extreme values and thus calculate the long-term risks of intense earthquakes. The occurrence of an earthquake is a problem involving multiple geophysical processes, such as the movement of magma, the rotation of the Earth, the resistance of materials in the subduction zones and many other causes and factors. Different models have been proposed to study the dynamics of the movements produced during an earthquake using statistical physical approaches [1]. Some research indicates that there are some precursor phenomena such as changes in seismic activity, electromagnetic signals, variations in the ionosphere and chemical emissions [2; 3; 4; 5]. Ultra-low frequency (ULF) variations in Earth's electrical and magnetic fields have often been observed before earthquakes [6], however, the chances of false positives and negatives imply the need for a more rigorous approach to obtain sufficient credibility in the scientific community [3]. Despite occasional evidence, no physical quantity related to electromagnetic waves has been completely accepted as a trigger or warning at the pre-seismic stage [3]. Even for large earthquakes, no precursors have been detected that provide a reliable forecast and no proven method has been able to predict earthquakes in the short term [1; 3]. More optimistic approaches consider that one of the reasons for the skepticism of short-term prediction is because the observations of the precursors have not yet been perfected [7]. Although accurate earthquake forecasts are not available, it is possible to establish risks of future events using statistical inference [3]. One of the most reliable and robust methods for the study of earthquakes, based on long-term studies, is the statistical technique known as the extreme value theory (EVT). The EVT is used to model extreme events in a wide variety of environmental, economic and engineering processes [8]. In environmental sciences, it is used to study the long-term risks of extreme events such as rainfall [9], winds [10], heat waves [11] and earthquakes [12; 13]. The generalized extreme value (GEV) distribution is an asymptotic distribution built on the assumptions of independence and stationarity of a suitably long sample. However, although extreme value analysis was developed for the study of stationary phenomena, it has been adapted for the study of non-stationary observations with spatiotemporal trends [14]. One of the most common problems related with the information obtained by sensors is the loss of information caused by the failures of the system responsible for measuring and collecting the data. This loss can be due to several reasons, e.g. a measuring device suddenly suffering a breakdown and obtaining an incomplete measurement, or instruments not being calibrated for measurements above or below a certain level [15]. In such cases, the likelihood must be adjusted to consider the effect of censored observations, depending on the type of censorship presented in the data [16]. Statistically, a sample is either left-censoring if its measurement cannot be observed but it is known that it is to the left or below a certain value, or it is right-censoring if its measurement is above or to the right of a given value. Three types of censorship are considered. Type I occurs when the observation is below a fixed number. In Type II censored data, a fixed number of observations is censored.