Classification of Earthquake Observation Stations Using Multiple Input Convolutional Neural Network Method

Analysis of the data quality of earthquake observation stations becomes very important as quality control. However, determining the quality of earthquake observation stations is done manually by analyzing several existing parameters by an expert. This study proposes a deep learning method approach to classify the quality of earthquake observation stations based on the expert's ability to analyze the quality of earthquake observation station data. The method used is Multiple Input Convolutional Neural Network (MICNN). The proposed MICNN architecture development is a combination of previous research. The dataset used in this study results from recording 411 seismometers for 12 months with a data set containing three horizontal components (East and North) and the vertical component (Z) with a recording length of 30 days. Each component is transformed from the time domain into the frequency domain form to obtain a spectrogram image used as input in the MICNN model. In addition, each component has a CNN block that functions as an extraction feature on the spectrogram image. Classification in this study used three classes: Classified, Usable, and Unusable. Tests and experiments on the model were carried out to obtain the best accuracy by tuning hyperparameters for model validation and the accuracy of this MICNN model was 99%.