# Proposal for a System Model for Offline Seismic Event Detection in Colombia
## 4 Seismic Detection Model Proposal
### Classification Process
The produced model is suitable for performing the offline detection of seismic signals through the identification of P-waves. The mentioned metrics indicate the performance of the classification algorithm with the best set of hyperparameters scored on the test set records. The classification model, as denoted in the graph of the resulting classification model (Figure 7), can be interpreted as a system with testing preprocessed signals as the input to a function that contains the resultant algorithm responsible for the event detection. The output of this algorithm is a value that indicates whether the input signal contains a P-wave or not, in case of being a binary classification process. The classification model is the result of the System Model for Offline detection. It is recommended to generate several classification models by testing different approaches. In this sense, depending on the performance of the classification model reflected on the metrics, a change in the selection of classification process parameters, such as the filter type and length of the window, among others, may be considered to improve the classification performance. In the same way, other parameters that belong to macro processes, such as the number of stations in the Analysis of seismological stations or filtering the seismic records by magnitude range in Seismic data seeking and gathering, may have a huge influence on the classification performance. Some studies have been carried out using the proposed System Model to perform the identification of P-waves in Colombia. In [41], a dataset of seismological records of events with epicenter in the department of Santander, Colombia between 2010 and 2017 was selected in the Seismic data seeking and gathering process. In the Reading and interpretation of the seismic records process, the records were described in the Nordic format. Then, in the Analysis of seismological stations, four stations were selected according to the epicenter distance. In the Sample Selection process, 20% of the downloaded records gathered were discarded due to inconsistencies in seismic attributes. Finally, in the Classification Process, the selected classification algorithm was logistic regression. The classification model was composed of four logistic regressors, one per each station, and a decentralized voting function that applied a logical function to the output of each regressor, to produce a binary output (P-wave or not P-wave). The obtained classification model produced an accuracy of 98.26% for the detection of the P-wave. Similarly, in [41], the use of the System Model was applied to a dataset of events with an epicenter in Santander, using four stations. In the binary classification process, the selected classification algorithm was a feed-forward back-propagation Artificial Neural Network (ANN) after being cleaned and the missing values handled appropriately. Unlike the voting function applied in [53], the classification algorithm relied on the behavior associated with all the stations, which were analyzed as a group in a  
Figure 7: Classification model. centralized function represented by the ANN. The degree of polarization, the ratio of vertical power to total power, skewness, and kurtosis of the three-component seismic data for each station were used as the selected features to feed the training process of the named ANN binary classifier. All the input features were extracted from observations whose classes were balanced and equally distributed in the datasets. With the described settings, the obtained classification model produces an accuracy of 99.24% for the detection of the P-wave.