Slip rate of the seismogenic fault of the 2021 Maduo earthquake in western China inferred from GPS observations

Seismic and field observations indicate that the Mw7.4 Maduo earthquake ruptured the Jiangcuo fault,which is a secondary fault~85 km south of the northern boundary of the Bayan Hor block in western China.The kinematic characteristics of the Jiangcuo fault can shed lights on the seismogenic mechanism of this earthquake.Slip rate is one of the key parameters to describe the kinematic features of a fault,which can also provide quantitative evidences for regional seismic hazard assessments.However,due to lack of effective observations,the slip rate of the Jiangcuo fault has not been studied quantitatively.In this study,we consider the interaction between the Jiangcuo fault and the eastern Kunlun fault,and estimate the slip rates of the two faults using the interseismic GPS observations across the seismogenic region.The inferred results show that the slip rates of the Jiangcuo fault and the Tuosuo Lake segment of the Kunlun fault are 1.2±0.8 and 5.4±0.3 mm a^(-1),respectively.Combining the slip rate with the average slip inferred from the coseismic slip model,the earthquake recurrence interval of the Jiangcuo fault is estimated to be 1800700+3700 years(1100–5500 years).Based on the results derived from previous studies,as well as calculations in this study,we infer that the slip rate of the Kunlun fault may decrease gradually from the Tuosuo Lake segment to the eastern tip.The Jiangcuo fault and its adjacent parallel secondary faults may have absorbed the relative motion of blocks together with the Kunlun fault.

Rapid classification of copper concentrate by portable laser-induced breakdown spectroscopy combined with transfer learning and deep convolutional neural network

This paper investigates the combination of laser-induced breakdown spectroscopy〔LIBS〕and deep convolutional neural networks〔CNNs〕to classify copper concentrate samples using pretrained CNN models through transfer learning.Four pretrained CNN models were compared.The LIBS profiles were augmented into 2D matrices.Three transfer learning methods were tried.All the models got a high classification accuracy of>92%,with the highest at 96.2%for VGG16.These results suggested that the knowledge learned from machine vision by the CNN models can accelerate the training process and reduce the risk of overfitting.The results showed that deep CNN and transfer learning have great potential for the classification of copper concentrates by portable LIBS.