PhaseNet paper reading summary

PhaseNet paper reading summary

PhaseNet: a deep-neural-network-based seismic arrival-time pickingmethod

background

• Earthquake monitoring and location are the foundation of seismology

  • The quality of earthquake catalogs depends primarily on the quantity and precision of time-of-arrival measurements
  • Phase picking is typically performed by network analysts
  • However, there are more and more seismographs, and the data flow is increasing, making manual picking difficult

• S wave is the most difficult in phase pickup

  • S waves emerge from scattered waves of P waves
  • S-waves can reduce depth-origin trade-offs based on P-waves for earthquake locations
  • S-wave structure is important for strong ground motion predictions

past research

• Short-Term Average/Long-Term Average (STA/LTA) Method

  • This method records the ratio of the energy in the short-term window to the energy in the long-term window
  • The peak value higher than the threshold indicates the arrival of P and S
  • This method is easily affected by noise, and the accuracy is relatively low

• statistical model

  • A method based on higher-order statistics (kurtosis and skewness) identifies the transition from Gaussian to non-Gaussian, which coincides with the occurrence of seismic events

• shallow neural network

  • A traditional shallow neural network is tested against four manually defined features
  • Variance, absolute value of skewness, demeanor, and combination of skewness and kurtosis based on sliding window forecasts
  • Most phase selections are more focused on P waves

• Despite the above work, the accuracy of automatic picking is still not good

  • Because seismic wave travel is highly complex due to multiple effects
  • Traditional automatic picking algorithms manually define features and require careful data processing

paper idea

• Instead of using manually defined features, deep neural networks learn features from labeled data
• Input: unfiltered three-component seismic wave travel vertically north-south-east
• Output: Three probability distributions: PS noise
• The peaks of the P-wave and S-wave probability distributions were designed to correspond to the predicted arrival times of PS
• High precision and recall

data set

• Northern California
• 779514 records
• Divided into training set verification machine and test data set 623054 77866 78592
• Training set and validation set are used for training and fine-tuning parameter model selection
• The test set is used to evaluate performance
• The dataset covers a wide range of SNRs for various instruments

data preprocessing

• Randomly select a 30S time window containing PS arrival time as phaseNet input
  • 100Hz sampling, then this is the most common sampling rate of the original data set, then each component of the input waveform has 3001 data points
  • Normalize the waveform of each component by subtracting the mean and dividing by the standard deviation

Model

• The architecture of PhaseNet is obtained by modifying the U-Net network

• The U-Net network is a deep neural network method for biomedical image processing, aiming at locating attributes in images

• Locate properties of time series into three categories: P-wave S-wave noise

• The input is an earthquake three-component seismogram

• The output is the PS noise probability distribution

• Input and output sequence contains 3001 data points 30S 100HZ sampling

• The input seismic data goes through four downsampling stages and four upsampling stages

• Using 1D convolution and RELU in one stage

• Downsampling extracts useful information from seismic data shrinking it to fewer neurons

• Upsampling expands Qi to the probability distribution of PS noise at each time point

• The input dimension is 3 x 3001

• The output is the probability of 3 x 3001 ps noise at each sampling point

experiment

• Evaluation index: precision rate recall rate F1 score

• Distribution of time residuals for automatically and manually labeled P and s arrival times
• The residual distribution of P picks is easier to pick than the narrow P wave of S picks
• PhaseNet's P and S selections have narrower residual distributions compared to AR selectors

• Tests on different instruments
  

• different SNR

• The test set is divided into 10 different classes according to the value of log10(SNR). Calculate precision, recall and F1-score for each class.

• Even for low SNR data, the precision of PhaseNet is high, while the recall becomes relatively small.