Seismic Data Quality Analysis Based on Image Recognition Using Convolutional Neural Network

Abstract

Seismometer monitoring and evaluation activities at the Indonesia Tsunami Early Warning System (InaTEWS) station can be carried out through a seismometer sensor calibration system with the use of the software of Seismic Data Quality Analysis. The software output is in the form of a spectrum image that represents the conditions of the seismometer following the spectrum results. The identification of the seismometer condition can be made by pattern recognition in the spectrum image. This study employed a neural network, specifically the Convolutional Neural Network (CNN), to analyse the pattern condition. The test results show that the performance of the system will be excellent if 1024 hidden layers are used. In addition, the epoch test shows that the system works well when given a maximum epoch value of 50. The test of image size gives the result that the system performance will result in good using input with a size of 30x20 pixels. The final results of the classification of spectrum images using CNN will exhibit the identification of seismometer. For the validation, the confusion matrix test shows that the corresponding findings are 80%, while the conflicting results are 20%.