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| 1 | Key technologies and practice for gas field storage facility construction of complex geological conditions in China显示文摘In view of complex geological characteristics and alternating loading conditions associated with cyclic large amount of gas injection and withdrawal in underground gas storage(UGS) of China, a series of key gas storage construction technologies were established, mainly including UGS site selection and evaluation, key index design, well drilling and completion, surface engineering and operational risk warning and assessment, etc. The effect of field application was discussed and summarized. Firstly, trap dynamic sealing capacity evaluation technology for conversion of UGS from the fault depleted or partially depleted gas reservoirs. A key index design method mainly based on the effective gas storage capacity design for water flooded heterogeneous gas reservoirs was proposed. To effectively guide the engineering construction of UGS, the safe well drilling, high quality cementing and high pressure and large flow surface injection and production engineering optimization suitable for long-term alternate loading condition and ultra-deep and ultra-low temperature formation were developed. The core surface equipment like high pressure gas injection compressor can be manufactured by our own. Last, the full-system operational risk warning and assessment technology for UGS was set up. The above 5 key technologies have been utilized in site selection, development scheme design, engineering construction and annual operations of 6 UGS groups, e.g. the Hutubi UGS in Xinjiang. To date, designed main indexes are highly consistent with actural performance, the 6 UGS groups have the load capacity of over 7.5 billion cubic meters of working gas volume and all the storage facilities have been running efficiently and safely. | MA Xinhua ZHENG Dewen SHEN Ruichen WANG Chunyan LUO Jinheng SUN Junchang | 2018 | Petroleum Exploration and Development2018，45，3： | 2 |
| 2 | Can urban forests provide acoustic refuges for birds?Investigating the influence of vegetation structure and anthropogenic noise on bird sound diversity显示文摘As a crucial component of terrestrial ecosystems,urban forests play a pivotal role in protecting urban biodiversity by providing suitable habitats for acoustic spaces.Previous studies note that vegetation structure is a key factor influencing bird sounds in urban forests;hence,adjusting the frequency composition may be a strategy for birds to avoid anthropogenic noise to mask their songs.However,it is unknown whether the response mechanisms of bird vocalizations to vegetation structure remain consistent despite being impacted by anthropogenic noise.It was hypothesized that anthropogenic noise in urban forests occupies the low-frequency space of bird songs,leading to a possible reshaping of the acoustic niches of forests,and the vegetation structure of urban forests is the critical factor that shapes the acoustic space for bird vocalization.Passive acoustic monitoring in various urban forests was used to monitor natural and anthropogenic noises,and sounds were classified into three acoustic scenes(bird sounds,human sounds,and bird-human sounds)to determine interconnections between bird sounds,anthropogenic noise,and vegetation structure.Anthropogenic noise altered the acoustic niche of urban forests by intruding into the low-frequency space used by birds,and vegetation structures related to volume(trunk volume and branch volume)and density(number of branches and leaf area index)significantly impact the diversity of bird sounds.Our findings indicate that the response to low and high frequency signals to vegetation structure is distinct.By clarifying this relationship,our results contribute to understanding of how vegetation structure influences bird sounds in urban forests impacted by anthropogenic noise. | Zezhou Hao Chengyun Zhang Le Li Bing Sun Shuixing Luo Juyang Liao Qingfei Wang Ruichen Wu Xinhui Xu Christopher A.Lepczyk Nancai Pei | 2024 | Journal of Forestry Research2024，35，2： | 0 |
| 3 | Spectral imaging with deep learning显示文摘The goal of spectral imaging is to capture the spectral signature of a target.Traditional scanning method for spectral imaging suffers from large system volume and low image acquisition speed for large scenes.In contrast,computational spectral imaging methods have resorted to computation power for reduced system volume,but still endure long computation time for iterative spectral reconstructions.Recently,deep learning techniques are introduced into computational spectral imaging,witnessing fast reconstruction speed,great reconstruction quality,and the potential to drastically reduce the system volume.In this article,we review state-of-the-art deep-learning-empowered computational spectral imaging methods.They are further divided into amplitude-coded,phase-coded,and wavelength-coded methods,based on different light properties used for encoding.To boost future researches,we've also organized publicly available spectral datasets. | Longqian Huang Ruichen Luo Xu Liu Xiang Hao | 2022 | Light(Science & Applications)2022，11，6： | 0 |
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