'DiTing' and 'DiTingtools':a large multi-label dataset and algorithm set for intelligent seismic data processing established based on the China Seismological Network

As the amount of seismic data increasing drastically worldwide, there are ever-growing needs for high-performance automatic seismic data processing  methods and high-quality, standardized professional datasets. To address this issue, we recently updated the 'DiTing' dataset, one of the world's largest seismological AI datasets with ~2.7 million traces and corresponding labels,  with 1,089,920 three-component waveforms from 264,298 natural earthquakes in mainland China and adjacent areas, and 958,076 Pg, 780603 Sg, 152752 Pn, 25956 Sn earthquake phase arrival tags, in addition to 249,477 Pg, 41610 Pn first motion polarity tags from 2020 to 2023. We also collected 15375 non-natural earthquake waveforms in mainland China from 2009 to 2023 and a manually labeled noise dataset containing various typical noise signals from the China Seismological Network. With the support of the 'DiTing' dataset, we developed and trained several deep learning models referred as 'DiTingTools' for automatic seismic data processing. In the continuous waveform detection and evaluation of more than 1,000 stations over a year across China, 'DiTingTools' has achieved an average recall rate of 80% for event detection, mean square error ±0.2s for P phase picking, and ±0.4s for S, the average identification accuracy rate of Pg first motion polarity reached 86.7% (U) and 87.9% (D), and 75.1% (U) and 73.1% (D) for Pn first motion polarity, the average magnitude prediction error of a single station is mainly concentrated at ±0.5. The remarkable generalization capabilities of 'DiTingTools' were demonstrated through its application on the China Seismic Network. Specifically, 'DiTingPicker', a model within 'DiTingTools' designed for earthquake detection and phase picking, was employed to analyze the M 6.8  earthquake that struck Luding County, Sichuan Province, in 2022. This tool was instrumental in automatically processing data to examine the main shock and intricate fault structures of the aftershocks. The effectiveness of 'DiTingTools' in earthquake prevention and disaster reduction was further validated through these practical applications.