# Proposal for a System Model for Offline Seismic Event Detection in Colombia
## 4 Seismic Detection Model Proposal
### Classification Process  
The analysis of seismological stations and sample selection processes provide the input datasets to the classification process, regarding seismological stations and seismic record datasets, respectively. With these inputs, the classification process implements supervised learning strategies using the selected seismological stations to detect the seismic event. The outputs of the classification process are (a) the average performance metrics of the classification approach and (b) the classification model, which is trained and validated, as described in Figure 5. The performance metrics are related to the ability of the classifier to differentiate between a seismic event and a non-seismic event, i.e., a binary classification, and the classification model corresponds to the implementation of the classifier, and it is able to classify new signals and provide the event detection output. The identification and classification of seismic events can be done using different techniques, as described in Section 3. For instance, the phase picking of seismic waves is widely used for real-time monitoring, detection, and localization with the P-wave picking is the main method for detection in early warning systems. Several algorithms for P-wave picking have been proposed in the time-domain [42], whereas the STA/LTA and its variations are the most implemented algorithm in observation and detection networks [43]. In Colombia, the SeisComP3 software is currently used by the RSNC for the acquisition of seismic data from stations located throughout the national territory. With the STA/LTA-based AutoPick module, the P-wave is detected by a SeisComP3 implementation [44; 45]. Traditional approaches such as STA/LTA are suitable options for the classification algorithm. Nevertheless, these approaches have limitations regarding their adaptability to the behavior of seismic waves [5; 46; 47]. As [48] state, the automatic picking of seismic waves can remove the ambiguity derived from the lack of synchronization between channels and signals proceeding from the seismological stations. Furthermore, [48; 49; 50] have shown that STA/LTA and cross-correlation approaches have a high rate of Types I and II errors (namely false negative and false positive) due to excessive noise that cannot be removed from the source and very low-frequency components that might not be enhanced. These factors can be handled accurately (as far as possible) using machine learning techniques. Considering that the dynamic behavior of seismic signals recorded by sensors is subject to many factors that influence the signal, as denoted in Section 4.2, machine learning algorithms represent an appropriate alternative for the development of classification models, since they enable the abstraction of attributes associated with the signals, based on the modeling of large training datasets. Machine learning algorithms rely on the quantity and quality of data and, as described in Sections 3.2 and 4.1, current seismological services can provide huge amounts of data that can be processed to obtain datasets to classify seismic records such as the ones provided as output in the Sample Selection process. As [51] state, machine learning algorithms are particularly well-used in seismology due to their facility to model complex relationships of a wide range of variables. Since the majority of tasks in  
Figure 5: Block diagram of the classification process. this context are normally targeting classification problems, machine learning drives a well-structured solution, since it can build a predictive environment in which a model is trained over sample data and tested over unseen data, guaranteeing the generalization of the solution against the data and the context. Sometimes, this procedure can be harmful if there is not enough data to proceed with the training procedure or when the representative descriptors (features or covariates) are almost the same as the number of samples.