欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)全球确定性预报模式(EC模式)产品自2015年起将空间分辨率从0.25°×0.25°提高到0.125°×0.125°。针对地形较为复杂的区域...欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)全球确定性预报模式(EC模式)产品自2015年起将空间分辨率从0.25°×0.25°提高到0.125°×0.125°。针对地形较为复杂的区域如贵州省,检验评估空间分辨率提升后的降水预报以及暴雨的预报效果。结果表明,中雨及以上等级降水的预报效果较之前有明显优化,TS评分均提高,空报率和漏报率也基本降低,各个站点雨量的相关系数有一定的提高。展开更多
Rock strength evaluation is critical in oil and gas exploration,but traditional methods,such as empirical formulas,laboratory tests,and numerical simulations,often struggle with accuracy,generalizability,and alignment...Rock strength evaluation is critical in oil and gas exploration,but traditional methods,such as empirical formulas,laboratory tests,and numerical simulations,often struggle with accuracy,generalizability,and alignment with field conditions.This study proposes the use of Random Forest and Transformer algorithms to predict rock strength from Elemental Capture Spectroscopy(ECS)logs.By utilizing the dry weight of minerals as input,the model predicts key mechanical properties,including Young's modulus,Poisson's ratio,bulk modulus,shear modulus,and uniaxial compressive strength.The findings demonstrate that mineral compositions,such as clay,quartz-feldspar-mica,carbonate,anhydrite,and pyrite,significantly influence rock strength.Specifically,clay content impacts Young's modulus,bulk modulus,and shear modulus,while quartz-feldspar-mica affects Poisson's ratio,and anhydrite is the primary factor influencing compressive strength.Positive correlations were observed between rock strength and the dry weight of anhydrite and carbonate minerals,while negative correlations emerged with clay,pyrite,and quartz-feldspar-mica.The Random Forest model outperformed the Transformer model in terms of predictive accuracy and computational efficiency.Its training time is only one three hundredth of the latter and its prediction time is just one tenth of the later,making it highly suitable for welllogging interpretation.Although the Transformer model was less computationally efficient,it exhibited strengths in predicting subsurface strength parameters,particularly in capturing spatial variations and forecasting these parameters across different spatial locations.This study introduces a novel AI-driven approach to rock strength evaluation,bridging the gap between mineral composition and mechanical properties,with significant implications for resource extraction and reservoir management.展开更多
以50%乙草胺EC为对照药剂,研究了96%精异丙甲草胺EC对蔗地杂草的防效,并评价了其安全性,以期为其在生产上的推广应用提供科学依据。研究结果表明,施药后60 d,96%精异丙甲草胺EC的3个剂量处理(100、150 mL/667m^(2)和200 m L/667m^(2))...以50%乙草胺EC为对照药剂,研究了96%精异丙甲草胺EC对蔗地杂草的防效,并评价了其安全性,以期为其在生产上的推广应用提供科学依据。研究结果表明,施药后60 d,96%精异丙甲草胺EC的3个剂量处理(100、150 mL/667m^(2)和200 m L/667m^(2))对蔗地双子叶杂草株防效分别达(75.0±9.1)%、(100±0.0)%和(100±0.0)%;对单子叶杂草株防效分别达(65.6±6.7)%、(75.9±2.9)%和(85.2±5.1)%,各剂量处理的杂草防效均显著高于50%乙草胺EC(200 mL/667m^(2))的防效。此外,与对照组相比,各药剂处理组对甘蔗的萌芽率、苗高和叶长无显著性影响。本研究结果说明,96%精异丙甲草胺EC在施用量为100~150 m L/667m^(2)时,对蔗地杂草有较好防效,且不影响甘蔗的生长发育。展开更多
文摘欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)全球确定性预报模式(EC模式)产品自2015年起将空间分辨率从0.25°×0.25°提高到0.125°×0.125°。针对地形较为复杂的区域如贵州省,检验评估空间分辨率提升后的降水预报以及暴雨的预报效果。结果表明,中雨及以上等级降水的预报效果较之前有明显优化,TS评分均提高,空报率和漏报率也基本降低,各个站点雨量的相关系数有一定的提高。
基金funded by General Program of National Natural Science Foundation of China(No.52274016,52374016)the Foundation of State Key Laboratory of Petroleum Resources and Prospecting(PRE/DX-2402)。
文摘Rock strength evaluation is critical in oil and gas exploration,but traditional methods,such as empirical formulas,laboratory tests,and numerical simulations,often struggle with accuracy,generalizability,and alignment with field conditions.This study proposes the use of Random Forest and Transformer algorithms to predict rock strength from Elemental Capture Spectroscopy(ECS)logs.By utilizing the dry weight of minerals as input,the model predicts key mechanical properties,including Young's modulus,Poisson's ratio,bulk modulus,shear modulus,and uniaxial compressive strength.The findings demonstrate that mineral compositions,such as clay,quartz-feldspar-mica,carbonate,anhydrite,and pyrite,significantly influence rock strength.Specifically,clay content impacts Young's modulus,bulk modulus,and shear modulus,while quartz-feldspar-mica affects Poisson's ratio,and anhydrite is the primary factor influencing compressive strength.Positive correlations were observed between rock strength and the dry weight of anhydrite and carbonate minerals,while negative correlations emerged with clay,pyrite,and quartz-feldspar-mica.The Random Forest model outperformed the Transformer model in terms of predictive accuracy and computational efficiency.Its training time is only one three hundredth of the latter and its prediction time is just one tenth of the later,making it highly suitable for welllogging interpretation.Although the Transformer model was less computationally efficient,it exhibited strengths in predicting subsurface strength parameters,particularly in capturing spatial variations and forecasting these parameters across different spatial locations.This study introduces a novel AI-driven approach to rock strength evaluation,bridging the gap between mineral composition and mechanical properties,with significant implications for resource extraction and reservoir management.
文摘以50%乙草胺EC为对照药剂,研究了96%精异丙甲草胺EC对蔗地杂草的防效,并评价了其安全性,以期为其在生产上的推广应用提供科学依据。研究结果表明,施药后60 d,96%精异丙甲草胺EC的3个剂量处理(100、150 mL/667m^(2)和200 m L/667m^(2))对蔗地双子叶杂草株防效分别达(75.0±9.1)%、(100±0.0)%和(100±0.0)%;对单子叶杂草株防效分别达(65.6±6.7)%、(75.9±2.9)%和(85.2±5.1)%,各剂量处理的杂草防效均显著高于50%乙草胺EC(200 mL/667m^(2))的防效。此外,与对照组相比,各药剂处理组对甘蔗的萌芽率、苗高和叶长无显著性影响。本研究结果说明,96%精异丙甲草胺EC在施用量为100~150 m L/667m^(2)时,对蔗地杂草有较好防效,且不影响甘蔗的生长发育。
