Automatic Picking Of P-wave First Arrival From Active Sources Using Deep Learning

In seismic data processing,the picking of arrivals takes plenty of time and labor,and its accuracy plays a key role in imaging seismic structures.In the recent decades,seismologists have developed a variety of methods to pick up arrival times and achieved some improvement on seismic data processing.However,the traditional automatic picking methods have limitations.With the exponential increase of seismic data,new methods with high resolution for automatically picking arrivals of seismic phases are urgently needed.Recently,artificial intelligence,especially deep learning technology,has made remarkable achievements in data mining,image recognition,speech recognition,target detection,and other fields.Because deep learning is good at extracting features from data,it has been widly applied in the seismological studies.In this study,we propose two methods for the active-source P wave first-arrival picking based on deep learning by waveform classification and image segmentation.First,we construct and train the Convolutionl Neural Networks(CNN)based on the difference between noise and seismic signal.Emitted from MINI28 vibroseis in the Jingdezhen seismic experiment,the vertical components of seismic waveforms recorded by EPS 32-bit portable seismometers are used for manually first-arrivals picking(a total of 7242).Based on these arrivals,we establish the training and testing sets,including 25,290 event samples and 710,616 noise samples(length of each sample: 2s).After 3,000 steps of training,we obtain a convergent CNN model,which can automatically classify seismic events and noise samples with high accuracy(> 99%).With the trained CNN model,we scan continuous seismic records and take the maximum output(probability of a seismic event)as the P-wave first arrival time.Compared with STA / LTA(short time average / long time average),our method shows higher precision and stronger anti-noise ability,especially with the low SNR seismic data.Second,taking common-shot record as a whole image,we construct the U-Net to extract the image boundary and pick P-wave first arrivals.We train the U-Net with first arrivals from 1450 common-shot gather records provided by Bureau of Geophysical Prospecting INC.With the advantage of the U-Net in image segmentation,the segments before and after the first arrivals of common-shot gather records are separated,and the first arrivals are picked up by extracting the boundary.Our results present a small error less than 0.02 s for 63% of test samples.According to the characteristics of seismic observation system,we can adopt the CNN or U-Net for automatically picking-up the P-wave first arrivals from active sources.