TY - JOUR

T1 - Earthquake Magnitude Estimation Based on Machine Learning

T2 - 1st International Symposium on Physics and Applications, ISPA 2020

AU - Apriani, M.

AU - Wijaya, S. K.

AU - Daryono,

N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.

PY - 2021/7/12

Y1 - 2021/7/12

N2 - Indonesia has high level of seismic activity, so determining magnitude of an earthquake is important in the Earthquake Early Warning System. In the Earthquake Early Warning System, the parameter magnitude must be estimated earlier, so that warnings can be disseminated before the S and surface waves arrive. In previous studies machine learning technology can be used to recognized earthquake events and extract hidden information with massive datasets. This study was a preliminary, proposed the alternative methods to calculate the earthquake magnitude as fast as possible, the data was 1s before and 3 seconds after the P wave from the 3-component single station raw seismogram historical data and developed with a classification deep neural network (DNN) model, classical machine learning random forest (RF) algorithm and the regression deep neural network (DNN). Results from the statistical analysis show that the waveform can be modelled by deep neural network (DNN) models. Classification DNN Model that we constructed reaches good pattern which final loss of 0.63. If it benchmarked to another model such as Random forest (RF), Classification DNN was a better model than RF which is determined by final loss of RF. Our recommendation related to estimate the magnitude from seismic raw modelling are better using Classification DNN with larger dataset. In our study, with relatively small dataset, modelling using RF algorithm can be another option. Another suggestion related this work was utilizing the Regression DNN, that resulting best alternative related to estimation of magnitude.

AB - Indonesia has high level of seismic activity, so determining magnitude of an earthquake is important in the Earthquake Early Warning System. In the Earthquake Early Warning System, the parameter magnitude must be estimated earlier, so that warnings can be disseminated before the S and surface waves arrive. In previous studies machine learning technology can be used to recognized earthquake events and extract hidden information with massive datasets. This study was a preliminary, proposed the alternative methods to calculate the earthquake magnitude as fast as possible, the data was 1s before and 3 seconds after the P wave from the 3-component single station raw seismogram historical data and developed with a classification deep neural network (DNN) model, classical machine learning random forest (RF) algorithm and the regression deep neural network (DNN). Results from the statistical analysis show that the waveform can be modelled by deep neural network (DNN) models. Classification DNN Model that we constructed reaches good pattern which final loss of 0.63. If it benchmarked to another model such as Random forest (RF), Classification DNN was a better model than RF which is determined by final loss of RF. Our recommendation related to estimate the magnitude from seismic raw modelling are better using Classification DNN with larger dataset. In our study, with relatively small dataset, modelling using RF algorithm can be another option. Another suggestion related this work was utilizing the Regression DNN, that resulting best alternative related to estimation of magnitude.

UR - http://www.scopus.com/inward/record.url?scp=85110815932&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/1951/1/012057

DO - 10.1088/1742-6596/1951/1/012057

M3 - Conference article

AN - SCOPUS:85110815932

SN - 1742-6588

VL - 1951

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

IS - 1

M1 - 012057

Y2 - 17 December 2020 through 18 December 2020

ER -