A study on small magnitude seismic phase identification using 1D deep residual neural network

Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio.With improved seismometers and better global coverage,a sharp increase in the volume of recorded seismic data has been achieved.This makes handling seismic data rather daunting by using traditional approaches and therefore fuels the need for more robust and reliable methods.In this study,we develop 1D deep Residual Neural Network(ResNet),for tackling the problem of seismic signal detection and phase identification.This method is trained and tested on the dataset recorded by the Southern California Seismic Network.Results demonstrate that the proposed method can achieve robust performance for the detection of seismic signals and the identification of seismic phases.Compared to previously proposed deep learning methods,the introduced framework achieves around 4%improvement in earthquake detection and a slightly better performance in seismic phase identification on the dataset recorded by Southern California Earthquake Data Center.The model generalizability is also tested further on the STanford EArthquake Dataset.In addition,the experimental result on the same subset of the STanford EArthquake Dataset,when masked by different noise levels,demonstrates the model’s robustness in identifying the seismic phases of small magnitude.

Identification method of seismic phase in three-component seismograms on the basis of wavelet transform

This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave locator functions are constructed by using eigenvalue analysis method to wavelet transform coefficient across several scales. Locator functions formed by wavelet transform have stated noise resistance capability, and is proved to be very effective in identifying the P and S arrivals of the test data and actual earthquake data.

DiTing:A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology

In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research.In this study,based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing”)with the largest known total time length.Data were recorded using broadband and short-period seismometers.The obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity labels.All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake.Each three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise ratios.The magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,respectively.The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion prediction.Such research will further promote the development and application of artificial intelligence in seismology.

Blockly earthquake transformer:A deep learning platform for custom phase picking

Deep-learning(DL)algorithms are increasingly used for routine seismic data processing tasks,including seismic event detection and phase arrival picking.Despite many examples of the remarkable performance of existing(i.e.,pre-trained)deep-learning detector/picker models,there are still some cases where the direct applications of such models do not generalize well.In such cases,substantial effort is required to improve the performance by either developing a new model or fine-tuning an existing one.To address this challenge,we present Blockly Earthquake Transformer(BET),a deep-learning platform for efficient customization of deep-learning phase pickers.BET implements Earthquake Transformer as its baseline model,and offers transfer learning and fine-tuning extensions.BET provides an interactive dashboard to customize a model based on a particular dataset.Once the parameters are specified,BET executes the corresponding phase-picking task without direct user interaction with the base code.Within the transfer-learning module,BET extends the application of a deep-learning P and S phase picker to more specific phases(e.g.,Pn,Pg,Sn and Sg phases).In the fine-tuning module,the model performance is enhanced by customizing the model architecture.This no-code platform is designed to quickly deploy reusable workflows,build customized models,visualize training processes,and produce publishable figures in a lightweight,interactive,and open-source Python toolbox.