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Since the century discovery of cosmic ray, the origin of cosmic ray is always a mystery. The study on the origin of high-energy cosmic ray is in an interdiscipline between the very high-energy (VHE) gamma-ray astronomy and the cosmic ray physics. The Large High Altitude Air Shower Observatory (LHAASO) is a unique and new generation cosmic-ray station with the advantages of high altitude, all-weather, and large-scale. It takes the function of hybrid technology to detect cosmic rays and to upgrade greatly the resolving power between gamma rays and cosmic rays. The LHAASO is expected to make the full-sky survey to find new gamma-ray sources, to obtain the highest sensitivity of gamma-ray detection at the high energy band of > 30 TeV, and to make the very high precision measurement on the component energy spectra of cosmic rays in a broad energy range of 5 orders of magnitude, in order to provide the evidence for revealing the mystery of the origin of cosmic ray. This paper describes the detector structure, performance superiority and scientific motivation of the LHAASO.

The flowback rate of a shale gas well is controlled by many factors, such as geological and engineering factors, which have a certain complexity. For the aforementioned reason, the knowledge-driven method often has difficulty effectively capturing the correlation rules among multiple datasets. As a data-driven algorithm, deep learning has strong advantages in data correlation analysis, nonlinear fitting and other applications. In this paper, an algorithm is set up to forecast the shale gas well flowback rate by using a deep learning algorithm based on the flowback characteristic factors and data from 286 shale gas wells in the Weiyuan field. An algorithm and prediction model of the flowback parameters of the Weiyuan shale gas wells is established to effectively capture their flowback characteristics. First, the general situation of the study area is briefly introduced, and the data used to predict the flowback rate in this area are analyzed. Second, based on the deep learning algorithm, two kinds of deep feedforward neural network structures and a neural network-based prediction method of shale gas well flowback rate are designed. Finally, according to the regional data, the prediction and analysis of the flowback rate of Weiyuan shale gas wells are carried out. The results show that the correlations between 17 factors, such as fracturing section length and shale gas well flowback rate, are not obvious, but that the deep learning method proposed in this paper can effectively and accurately capture the correlations between the data and predict the flowback rate. The results show high accuracy.