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A piled-up neutron-gamma discrimination system is designed to discriminate single and piled-up events under high counting rate. The data acquired by a Cs2LiLaBr6:Ce (CLLB) detector and an Am-Be neutron source are used to train and test the model in the n-γ discrimination system. The charge comparison method is applied to discriminate the non-piled-up events in the experimental data and label the dataset of single events. As a result of the method, the figure-of-merit (FOM) value is 1.10, which indicates that the wrong labeling ratio is about 0.248%. A dataset of piled-up events is created by adding up waveforms and labels of the events in the single-pulse dataset. The discrimination system consists of three convolutional models, called Model_PulseNum, Model_OnePulse and Model_TwoPulses. All the models are trained and tested by the created dataset. Model_PulseNum is created and trained to define the number of pulses in the waveform of the event, with an accuracy of 99.94%. The other two models (Model_OnePulse and Model_TwoPulses) are created and trained to discriminate the particle types for non-piled-up and two-fold piled-up events with the accuracy of 99.5% and 98.6%, respectively. For the whole discrimination system, the accurcy for the particle identification is over 97% for each class (γ, n, γ + γ, γ + n, n + γ and n + n). These results indicate that CNN model can improve the performance of particle detection systems by effectively discriminate neutron and gamma for both piled-up and non-piled-up events under high counting rates.

This paper will discuss the design and construction of BESIII, which is designed to study physics in the tau-charm energy region utilizing the new high luminosity BEPCII double ring e+e- collider. The expected performance will be given based on Monte Carlo simulations and results of cosmic ray and beam tests. In BESIII, tracking and momentum measurements for charged particles are made by a cylindrical multilayer drift chamber in a 1 T superconducting solenoid. Charged particles are identified with a time-of-flight system based on plastic scintillators in conjunction with dE/dx (energy loss per unit pathlength) measurements in the drift chamber. Energies of electromagnetic showers are measured by a CsI(Tl) crystal calorimeter located inside the solenoid magnet. Muons are identified by arrays of resistive plate chambers in the steel magnetic flux return. The level 1 trigger system, Data Acquisition system and the event filter system based on networked computers will also be described., 57 pages, 75 figures

Aimed at the disadvantages of Fuzzy C Means Clustering (FCM) algorithm which is sensitive to initial values and easily falls into local convergence, a new FCM algorithm named as CQPSO-FCM is proposed in this paper, which makes a combination of FCM algorithm and Chaotic Quantum Particle Swarm Optimization (CQPSO) algorithm. In order to solve the problems mentioned above and improve the ability of fuzzy classification, CQPSO algorithm is used to initialize the membership matrix, which significantly improves the global optimum search ability of the proposed algorithm. What’s more, this method could be used to predict accurately the fluid of complex fracture-cavity carbonate reservoir. Because the common fluid director of AVO inversion would not be available enough in fluid identification of carbonate reservoir, this method is then employed to establish a relation of fluid and reservoir between well logs and elastic properties of AVO inversion. By this accurate relation, the fluid enrichment and fluid type are both identified. The case study results not only prove a practical applicability of the method in predicting the fluid distribution in carbonate reservoir, but also provide a new way for fluid prediction in carbonate reservoir by taking full advantage of pre-stack seismic data.

A new fracture parameters inversion method based on azimuth anisotropic elastic impedance in HTI media is introduced in this paper. The azimuth anisotropic elastic impedance formula derived from Connolly’s elastic impedance theory and Ruger’s equation is deduced. The explicit relation of elastic impedance and fracture parameters (fracture density and direction) is built. The formula is further used to form an integral workflow of fracture parameters inversion based on azimuth anisotropic elastic impedance. Particularly, the EI inversion for each azimuth is constrained by the well data of anisotropic elastic impedance. The proposed method is successfully applied in both numerical data with different SNR and field data in the Tarim Basin. It is proved that the fracture parameters can be efficiently inverted, which shows an excellent stability in fracture density extraction even for low SNR data. This method provides a new idea and approach for the fractured reservoir prediction.

Seismic anisotropy is relevant to frequency. When fractures in reservoirs are saturated with different fluid types (oil, gas and brine), the variation rates of anisotropy versus seismic frequency (which is called anisotropy dispersion) are different. Thus, anisotropy dispersion can be used to distinguish different fluid types in fracture. In this paper, P-wave frequency-dependent azimuthal AVO inversion algorithm is proposed and applied to real seismic data. The studies focus on the analysis of P-wave anisotropic dispersion response for different fluid saturation. Continuous wavelet transform is employed for spectral decomposition, aiming at well time-frequency localization. Real data application indicates that the magnitude of P-wave anisotropy dispersion of brine is far stronger than that of oil and gas in fractures. Our study firmly proves that the method aimed at calculating the P-wave anisotropic dispersion for different fluids saturation is valid and reliable, and this brings us the confidence to discriminate fluid by the calculated anisotropic dispersion values in fractured reservoir.