# Proposal for a System Model for Offline Seismic Event Detection in Colombia
## 4 Seismic Detection Model Proposal
### Classification Process
In these cases, additional machine learning approaches can be implemented, such as feature selection using forward or backward procedures [52]. With the selection of a machine learning algorithm as the classification algorithm, the classification process can be configured with a set of subprocesses depending on the specific machine learning algorithm requirements, which may be feature-based or time-series-based. The proposed set of subprocesses for the classification process is shown in the subprocesses diagram of Figure 6. The sequence of subprocesses can fulfill the classification capabilities for attribute-based or time series machine learning algorithms. To set up the subprocesses, it is necessary to determine the number of classification models desired to represent the dynamics perceived by the stations. A single classification model might represent the events linking all the stations throughout a centralized fusion, or multiple classification models might correspond to each station separately throughout decentralized functions (such as the ones proposed by bagging techniques). In the case of decentralized fusion, each station has its classification model; hence, the Pre-processing and the Model definition stages need to be done separately for each selected station. The results provided by the per-station models can be composed into a combined result in the Testing subprocess using logical functions or more complex integration functions, producing a single classification model based on individual station analysis. When the selected stations and the Seismic events dataset enter the classification process, the first subprocess that manipulates the datasets is the _Seismic dataset slicing_ subprocess. This subprocess oversees linking the events to the selected stations and splits the Seismic events dataset into datasets per station if a decentralized schema is going to be used. In the _Pre-processing_ stage, six subprocesses oversee formatting the dataset into a scheme ready to be used as input for a learning algorithm in the _Model definition_ stage. Each subprocess in the _Pre-processing_ stage is applied to every component that the signal may contain. After the _Seismic dataset slicing_, the seismic signals associated with each event in the dataset are filtered in the _Signals filtering_ subprocess. This subprocess considers the influence of noise sources that affect the seismic signals. The considered noise sources are the soil vibrations produced by natural causes and the instrumental noise associated with the measurement equipment hardware [41]. The filter  
Figure 6: Classification process. choice is crucial for the classification model performance, since the quality of the signal depends largely on the quality of the filter; therefore, the ability of the learning algorithm to generalize P-wave dynamics relies on the filter. It is recommended to make a frequency analysis to obtain the frequency components of the signals and the noise. The suggested frequency band is between 1 and 12 Hz [53], and different filter techniques can be applied to this subprocess [54]. When the _Signals filtering_ subprocess is performed successfully, the dataset is relocated to the _Signals normalization_ subprocess, whose objective is to standardize the scale of the signals to the [1, 1] interval according to the minimum and maximum value of each signal and remove the direct current component that is commonly added by the instrumentation. Then, the normalized signals are re-sampled in the _Signals re-sampling_ subprocess. The definition of a common sampling rate for the signals is necessary due to the variety of the sampling rates that the signals may have, which are the outcome of diverse sampling properties that the acquisition devices and the digitalization algorithms present in the seismological stations. The seismic signals associated with each record are commonly signals that contain information before the arrival of the P-wave and after the arrival of the S wave.