Classification of clustered microseismic events in a coal mine using machine learning

Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines.Although machine learning has been widely applied in seismic data processing,feasibility and reliability of applying this technique to classify spatially clustered seismic events in underground mines are yet to be investigated.In this research,two groups of seismic events with a minimum local magnitude(ML) of-3 were observed in an underground coal mine.They were respectively located around a dyke and the longwall face.Additionally,two types of undesired signals were also recorded.Four machine learning methods,i.e.random forest(RF),support vector machine(SVM),deep convolutional neural network(DCNN),and residual neural network(ResNN),were used for classifying these signals.The results obtained based on a primary dataset showed that these seismic events could be classified with at least 91% accuracy.The DCNN using seismogram images as the inputs reached the best performance with more than 94% accuracy.As mining is a dynamic progress which could change the characteristics of seismic signals,the temporal variance in the prediction performance of DCNN was also investigated to assess the reliability of this classifier during mining.A cascaded workflow consisting of database update,model training,signal prediction,and results review was established.By progressively calibrating the DCNN model,it achieved up to 99% prediction accuracy.The results demonstrated that machine learning is a reliable tool for the automatic discrimination of spatially clustered seismicity in underground mining.

Spatiotemporal distribution characteristics and attribution of extreme regional low temperature event

Based on an objective identification technique for regional low temperature event（OITRLTE）, the daily minimum temperature in China has been detected from 1960 to 2013. During this period, there were 60 regional extreme low temperature events（ERLTEs）, which are included in the 690 regional low temperature events（RLTEs）. The 60 ERLTEs are analyzed in this paper. The results show that in the last 50 years, the intensity of the ERLTEs has become weak; the number of lasted days has decreased; and, the affected area has become small. However, that situation has changed in this century.In terms of spatial distribution, the high intensity regions are mainly in Northern China while the high frequency regions concentrate in Central and Eastern China. According to the affected area of each event, the 60 ERLTEs are classified into six types. The atmospheric circulation background fields which correspond to these types are also analyzed. The results show that, influenced by stronger blocking highs of Ural and Lake Baikal, as well as stronger southward polar vortex and East Asia major trough at 500-h Pa geopotential height, cold air from high latitudes is guided to move southward and abnormal northerly winds at 850 h Pa makes the cold air blow into China along diverse paths, thereby forming different types of regional extreme low temperatures in winter.

Classify the Higgs decays with the PFN and ParticleNet at electron-positron colliders

Various Higgs factories are proposed to study the Higgs boson precisely and systematically in a modelindependent way.In this study,the Particle Flow Network and ParticleNet techniques are used to classify the Higgs decays into multicategories,and the ultimate goal is to realize an"end-to-end"analysis.A Monte Carlo simulation study is performed to demonstrate the feasibility,and the performance looks rather promising.This result could be the basis of a"one-stop"analysis to measure all the branching fractions of the Higgs decays simultaneously.