Two hundred km of 2D seismic survey was carried out at the Lesser Himalayan Thrust Belts in Dailekh district, western Nepal. The main motivation is to elucidate the geologic relationship between the known oil and gas ...
Two hundred km of 2D seismic survey was carried out at the Lesser Himalayan Thrust Belts in Dailekh district, western Nepal. The main motivation is to elucidate the geologic relationship between the known oil and gas seeps, subsurface structure, and stratigraphy in the area. This is a challenging task which is from its extreme structural and geological complexity such as thrust faulting, tight folding, steep dip layers, and strong lateral variations in seismic velocity. Seismic data were acquired with SERCEL 428XL system and processed by GEOEAST computer software. In order to increase signal-to-noise ratio (SNR), suppress interference, and search for optimum acquisition parameters, a series of comparative tests on the different charge depth and size, group interval, CDP fold, geophone array, and single high-sensitivity geophone were conducted. We also tested 2S3L (two lines shooting and three lines receiving) wide line profiling. The results indicate that single hole with charge depth of 12 m, 4-16 kg charge size (less charge size for the densely populated areas), single high-sensitivity geophone, and 1S2L wide line profiling with 132 folds are the optimum acquisition parameters. On the basis of comparative process experiment, data processing workflow consisting of data preparation, prestack denoising, amplitude compensation, deconvolution, tomography static correction, velocity analysis, residual static correction, CRS stack, poststack migration, prestack time migration (PSTM), and prestack depth migration (PSDM) was selected. Maybe affected by problem of conflicting dip in complex media, CRS stack section does not show satisfactory geological characteristics. PSTM profile has moderate signal-to-noise (S/N) ratio;the shallow, medium, and deep continuous reflections can be observed in section. More details of the geological structures can be observed in PSDM section, especially in medium and shallow layers (less than 3000 ms or 4000 m), but PSDM method is more expen
Short-term traffic flow prediction is of vital significance to traffic monitoring and induction. It is a nonlinear problem with considerable uncertainty. In this paper, a novel teaching-learning based optimization (NT...
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ISBN:
(数字)9789881563903
ISBN:
(纸本)9781728165233
Short-term traffic flow prediction is of vital significance to traffic monitoring and induction. It is a nonlinear problem with considerable uncertainty. In this paper, a novel teaching-learning based optimization (NTLBO) algorithm is proposed for wavelet neural network, whose parameters are optimized by NTLBO. Parameters of the network are divided into four parts to initialize the population of the novel algorithm. The four parts of parameters are optimized separately in teaching process. A self-learning process for teacher is attached as well to combine local search strategy and global search method in network training, which enhance the search ability of the algorithm. Finally, the model will be verified by time-series data, compared with other neural network methods. The final experimental results indicates that the new algorithm performs better in forecasting and nonlinear curve fitting.
In recent years, fires frequently occur. Property damage and casualties caused by fires are very serious. The scene of the fire is very complicated and rescue operations are difficult to carry out in time. In order to...
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Novel G.654.E has been large-scale deployed in optical communication network, so it has become urgent problems to reduce the splicing loss, improve the success probability of in one splicing and unsatisfactory splicin...
Novel G.654.E has been large-scale deployed in optical communication network, so it has become urgent problems to reduce the splicing loss, improve the success probability of in one splicing and unsatisfactory splicing loss from different manufacturers in practical engineering applications. Based on the theory of single-mode fiber splicing loss testing, we obtained a large number of laboratory experiment data and practical network engineering data to verify the splicing performance form the same and different manufacturers of G.654.E fiber in various aspects based on different splicing machines. And also we propose a targeted splicing optimization scheme for practical engineering applications. All of the research provides a guidance for engineering application of G.654.E optical fiber in practical network engineering.
Short-term traffic flow prediction is of vital significance to traffic monitoring and induction. It is a nonlinear problem with considerable uncertainty. In this paper, a novel teaching-learning based optimization(NTL...
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Short-term traffic flow prediction is of vital significance to traffic monitoring and induction. It is a nonlinear problem with considerable uncertainty. In this paper, a novel teaching-learning based optimization(NTLBO) algorithm is proposed for wavelet neural network, whose parameters are optimized by NTLBO. Parameters of the network are divided into four parts to initialize the population of the novel algorithm. The four parts of parameters are optimized separately in teaching process. A self-learning process for teacher is attached as well to combine local search strategy and global search method in network training, which enhance the search ability of the algorithm. Finally, the model will be verified by time-series data, compared with other neural network methods. The final experimental results indicates that the new algorithm performs better in forecasting and nonlinear curve fitting.
N 2 reduction reaction (NRR) by light is an energy-saving and sustainable ammonia (NH 3 ) synthesis technology. However, it faces significant challenges, including high energy barriers of N 2 activation and unclear ca...
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N 2 reduction reaction (NRR) by light is an energy-saving and sustainable ammonia (NH 3 ) synthesis technology. However, it faces significant challenges, including high energy barriers of N 2 activation and unclear catalytic active sites. Herein, we propose a strategy of photo-driven ammonia synthesis via a proton-mediated photoelectrochemical device. We used redox-catalysis covalent organic framework (COF), with a redox site (−C=O) for H + reversible storage and a catalytic site (porphyrin Au) for NRR. In the proton-mediated photoelectrochemical device, the COF can successfully store e − and H + generated by hydrogen oxidation reaction, forming COF−H. Then, these stored e − and H + can be used for photo-driven NRR (108.97 umol g −1 ) under low proton concentration promoted by the H-bond network formed between −OH in COF−H and N 2 on Au, which enabled N 2 hydrogenation and NH 3 production, establishing basis for advancing artificial photosynthesis and enhancing ammonia synthesis technology.
Dramatic global changes to the environment have wrought unprecedented reductions in biodiversity, with>28%species assessed by the International Union for Conservation of Nature(IUCN) under the threat of extinction[...
Dramatic global changes to the environment have wrought unprecedented reductions in biodiversity, with>28%species assessed by the International Union for Conservation of Nature(IUCN) under the threat of extinction[1]. China, as one of the world’s megadiverse countries, plays a critical role in global biodiversity conservation.
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