Application Study of Bit Selection, Bit Weight and Rotary Speed in SUDAN

Data on all drilled wells were analyzed for comparison of the effect of different bit types, weight on bit, and rotary speeds on the penetration rate in the various formations and depth intervals. This was initiated in an attempt to improve penetration rates and reduce per well drilling times. Based on this study, an optimum bit selection, bit weight and rotary speed program was incorporated into drilling plans for subsequent wells drilled in the area. Initiation of this study resulted in significant improvement in penetration rates, in spite of the fact that some adverse conditions existed, such as formations, which have been pressure depleted by production, and drilling of high angle holes.

A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction

The accurate prediction of landslide susceptibility shortly after a violent earthquake is quite vital to the emergency rescue in the 72-h‘‘golden window”.However,the limited quantity of interpreted landslides shortly after a massive earthquake makes landslide susceptibility prediction become a challenge.To address this gap,this work suggests an integrated method of Crossing Graph attention network and xgBoost(CGBoost).This method contains three branches,which extract the interrelations among pixels within a slope unit,the interrelations among various slope units,and the relevance between influencing factors and landslide probability,respectively,and obtain rich and discriminative features by an adaptive fusion mechanism.Thus,the difficulty of susceptibility modeling under a small number of coseismic landslides can be reduced.As a basic module of CGBoost,the proposed Crossing graph attention network(Crossgat)could characterize the spatial heterogeneity within and among slope units to reduce the false alarm in the susceptibility results.Moreover,the rainfall dynamic factors are utilized as prediction indices to improve the susceptibility performance,and the prediction index set is established by terrain,geology,human activity,environment,meteorology,and earthquake factors.CGBoost is applied to predict landslide susceptibility in the Gorkha meizoseismal area.3.43%of coseismic landslides are randomly selected,of which 70%are used for training,and the others for testing.In the testing set,the values of Overall Accuracy,Precision,Recall,F1-score,and Kappa coefficient of CGBoost attain 0.9800,0.9577,0.9999,0.9784,and 0.9598,respectively.Validated by all the coseismic landslides,CGBoost outperforms the current major landslide susceptibility assessment methods.The suggested CGBoost can be also applied to landslide susceptibility prediction in new earthquakes in the future.

Paleontology Knowledge Graph for Data-Driven Discovery

A knowledge graph(KG)is a knowledge base that integrates and represents data based on a graph-structured data model or topology.Geoscientists have made efforts to construct geosciencerelated KGs to overcome semantic heterogeneity and facilitate knowledge representation,data integration,and text analysis.However,there is currently no comprehensive paleontology KG or data-driven discovery based on it.In this study,we constructed a two-layer model to represent the ordinal hierarchical structure of the paleontology KG following a top-down construction process.An ontology containing 19365 concepts has been defined up to 2023.On this basis,we derived the synonymy list based on the paleontology KG and designed corresponding online functions in the OneStratigraphy database to showcase the use of the KG in paleontological research.

Dynamics of bacterial communities during a seasonal hypoxia at the Bohai Sea:Coupling and response between abundant and rare populations

Marine bacterial community plays a vital role in the formation of the hypoxia zone in coastal oceans.Yet,their dynamics in the seasonal hypoxia zone of the Bohai Sea(BHS)are barely studied.Here,the 16S r RNA gene-based high-throughput sequencing was used to explore the dynamics of their diversity,structure,and function as well as driving factors during the gradual deoxygenation process in the BHS.Our results evinced that the bacterial community was dominated by Proteobacteria,followed by Bacteroidetes,Firmicutes,Actinobacteria,and Cyanobacteria,etc.The abundant subcommunity dominated in the number of sequences(49%)while the rare subcommunity dominated in the number of species(99.61%).Although abundant subcommunity accounted for most sequences,rare subcommunity possessed higher diversity,richness and their population dramatically changed(higher turnover)during the hypoxia transition.Further,co-occurrence network analysis proved the vital role of rare subcommunity in the process of community assembly.Additionally,beta diversity partition revealed that both subcommunities possessed a higher turnover component than nestedness and/or richness component,implying species replacement could explain a considerable percentage of community variation.This variation might be governed by both environmental selection and stochastic processes,and further,it influenced the nitrogen cycle(PICRUSt-based prediction)of the hypoxia zone.Overall,this study provides insight into the spatial-temporal heterogeneity of bacterial and their vital role in biogeochemical cycles in the hypoxia zone of the BHS.These findings will extend our horizons about the stabilization mechanism,feedback regulation,and interactive model inside the bacterial community under oxygen-depleted ecosystems.

过去20年中国海水养殖空间分布变化

China’s mariculture provides more than 60%of the world’s mariculture products and plays an important role in the world’s aquaculture and food supply.Research on changes in the spatial distribution pattern of China’s mariculture,however,remains lacking.To accurately reflect the changes in the spatial pattern of mariculture in China,in this study,we used multitemporal optical and synthetic aperture radar remote sensing images to enhance the characteristics of mariculture and extracted the spatial distribution data for mariculture in China in 2000,2010,and 2020.Accordingly,we explored the distribution pattern and changes in mariculture in China.We found that,in 2020,China’s mariculture exhibited a distribution pattern of more in the north and less in the south.With the Yangtze River estuary as the boundary,the proportion of mariculture in northern China was 70.9%,and that in southern China was only 29.1%.This difference did not exist in 2000,but it emerged because of the rapid development of mariculture in northern China from 2010 to 2020.In addition,by superimposing the mariculture data with shoreline and water depth data,we found that more than 90%of China’s mariculture area was located in the sea area within 20 km of the shoreline and that more than 80%of the mariculture area was located in the sea area with water depths of less than 20 m.In addition,the spatial distribution of mariculture in China developed from near the shore and moved outward from shallow to deep water areas.We examined the driving factors that influence changes in the spatial distribution of mariculture in China.We argue that technological advancements in mariculture,as well as the intensive concentration of mariculture near the shore,policy constraints and incentives,and economic development,collaborate to shape the current pattern of mariculture development in China.