Classification of natural and non-natural earthquake signals based on residual neural networks
Aiming to accurately differentiate between natural and non-natural earthquakes,a neural network model based on one-dimensional convolution and residual structures,named ResNet-1D,was constructed.This model automatically extracts features from three-component seismic records using convolutional layers with convolutional kernels of different lengths,pooling layers composed of max-pooling,and residual structures.The adaptive moment estimation method (Adams) is used to optimize parameters,and a linear discriminant function (Linear) is applied to distinguish between natural and non-natural earthquakes.Using 40000 velocity records of natural and non-natural earthquakes,compiled by the China Earthquake Networks Center from 2008 to 2020,the data was randomly divided into training,validation,and test datasets in a 6︰2︰2 ratio.The test results show that the classification accuracy for natural and non-natural earthquakes is 92.65％ and 94.30％,respectively.Compared with traditional machine learning methods,the ResNet-1D model significantly improves the test results in terms of accuracy,precision,recall,and F1 score,effectively enhancing the accuracy of identifying natural and non-natural earthquakes.Moreover,variations in magnitude and epicentral distance also affect the classification accuracy of the model,with higher magnitudes and greater distances resulting in lower accuracy.The model proposed in this paper offers higher accuracy and provides technical support for accurately distinguishing between natural and non-natural earthquakes in seismic monitoring.
万方数据
维普
