# Proposal for a System Model for Offline Seismic Event Detection in Colombia
## 2 Problem Statement  
Countries over the Pacific coast of Southern America have a long history of catastrophic earthquake events. According to the US Geological Survey, five of the top 20 largest earthquake events occurred since the earlier 1900s, including the largest, have occurred along the fault line traced by the borders of the Nazca Plate that subducts below the South American Plate [6]. The tectonic environment in Colombia can be described by its two main fault zones: (a) Romeral zone (intraplate seismic zone that runs from north to south of the country's Pacific coast with an approximate length of 1200 km) and (b) the frontal fault of Eastern Cordillera (fault system that divides the Colombian Andean territory from its eastern great plains, most likely a southern border of the Caribbean Plate) [7]. One local seismic zone on the Colombian northeast of great activity is the Bucaramanga Seismic Nest, where at least eight events with a magnitude Mw \(>\) 4.7 occur each year [8].  
Typical geological and seismic observation services such as SGC in Colombia provide seismic analysis in a two-stage fashion: first, by acquiring and storing seismic records (which can include up to three spatial components of accelerations, velocities, and ground displacements) and second, by performing seismic event recognition by looking for particular seismic characteristics within the stored data. Then, the geological service within a short time frame reports the occurrence of a seismic event, which is usually information that commonly contains the event magnitude and its approximate geolocation. [9; 10; 11]. This two-step procedure is complex, since it involves algorithms to read, synchronize, and process seismic data information. Geological services usually rely on black-box software that performs these tasks, closing the door to monitor sub-stages and therefore not letting the user integrate alternative algorithms that could eventually improve and/or fit specific site characteristics to the seismic data analysis sub-stage [12; 13].  
To establish methods for the detection and analysis of seismic events, the disposition of a set of historical seismic records that can be stored, read, and processed is essential to develop an accurate detection of future events (classic approach of learning from data). However, it is difficult to find a seismic dataset that fits the requirements for later processing stages, and when retrieved, the seismic files are not easy to interpret, as they include specific seismic parameters contained in legible formats that only specialized software such as SEISAN and SeisComP can process [14; 15].  
Moreover, several factors make an integral analysis of these Colombian historical records unfeasible: (a) limitations on the storage capacity, (b) limitations on the compatibility between current software resources, (c) limitations on the processing power required, (d) use of techniques that are not integratedwithin the detection models, and (e) low flexibility of the existing tools for modifications and adequations of storage and processing algorithms [16; 17].  
Furthermore, the online detection of coming earthquakes can be done by picking the seismic phases manually or by establishing a fixed-threshold approach; these techniques are statistically earthquake-proven for significant earthquakes and signals with low sampling rates and few numbers of components, given a higher signal-to-noise ratio. When high samples rates are considered from multiple three-dimensional seismological stations, the phases may be picked differently, introducing bias into the detection [18].  
In this sense, a system model for offline seismic event detection for the Colombian region is proposed, which allows the identification of patterns and dynamics in historical records, using machine learning techniques.  
The following sections present a theoretical basis and the description of the model for seismic event detection.