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Table 1 Root Mean Square Errors (RMSE) of hypocentral parameters estimated by various networks

From: Application of deep learning-based neural networks using theoretical seismograms as training data for locating earthquakes in the Hakone volcanic region, Japan

Method Training Estimation Root Mean Square Error (RMSE)
Longitude (degree) Latitude (degree) Depth (km) Time (s) Magnitude
1DCNN (ConvNetQuakea) Simulation data (80%) Simulation data (20%) 0.0406 0.0365 1.679 0.0930
2DCNN Simulation data (80%) Simulation data (20%) 0.0213 0.0184 1.3267 0.5002 0.1018
3DCNN Simulation data (80%) Simulation data (20%) 0.0100 0.0078 0.6208 0.2095 0.0371
   Observed data 0.0301 0.0291 1.3062 1.3816 0.5830
3DCNN + CUTOUT Simulation data (80%) Simulation data (20%) 0.0075 0.0091 0.5298 0.2444 0.0311
   Observed data 0.0167 0.0313 1.7504 0.9427 0.6063
  1. aConvNetQuake (Perol et al. 2018) is a task to classify earthquake location into specific regions. The results show in this table for ConvNetQuake is obtained by added our task to perform regression to locate hypocentral parameters during the learning stage