基于4000m深钻地球物理测井信息的岩石物理及地应力参数分析

山东胶东地区属于华北克拉通东南缘,地质构造复杂。区域内金矿资源丰富,是中国重要的黄金生产基地。为了揭示金成矿带的深部成矿规律,山东黄金地质矿产勘查有限公司采用金刚石小口径绳索取芯钻探完成了三山岛金矿床的深部控矿勘探,并由中石化胜利石油工程有限公司测井公司采用自主研发的 60mm小井眼测井仪器,实施了4000m探矿深钻综合数字地球物理测井工作,创下了小口径超深孔地球物理测井的新记录。本文基于地球物理测井信息,根据前人已建立的岩石物理和地应力参数的测井计算模型,并通过纵波时差以及密度曲线的合成预测横波时差,实现了对岩石物理及地应力参数的计算。结果表明： 利用测井资料计算的动态弹性参数较为连续,可以较好地反映钻孔一定厚度环状井壁地层剖面的变化;根据地球物理参数反演得出该区水平方向最小主应力梯度基本稳定在0.02MPam-1,水平方向最大主应力梯度主要分布在0.024～0.03MPam-1,垂向地应力梯度基本稳定在0.027MPam-1,3个地应力关系是垂向应力 最大水平主应力 最小水平主应力,地应力状态类型为正断型。研究结果对未来深部矿床的开采设计具有重要的指导意义。     

Parameterization of the Ångström–Prescott formula based on machine learning benefit estimation of reference crop evapotranspiration with missing solar radiation data

Accurately estimated reference evapotranspiration (ET₀) is essential to regional water management. The FAO recommends coupling the Penman–Monteith (P-M) model with the Ångström–Prescott (A-P) formula as the standard method for ET₀ estimation with missing R_s measurements. However, its application is usually restricted by the two fundamental coefficients (a and b) of the A-P formula. This paper proposes a new method for estimating ET₀ with missing R_s by combining machine learning with physical-based P-M models (PM-ET₀). The benchmark values of the A-P coefficients were first determined at the daily, monthly, and yearly scales, and further evaluated in R_s and ET₀ estimates at 80 national R_s measuring stations. Then, three empirical models and four machine-learning methods were evaluated in estimating the A-P coefficients. Machine learning methods were also used to estimate ET₀ (ML-ET₀) to compare with the PM-ET₀. Finally, the optimal estimation method was used to estimate the A-P coefficients for the 839 regular weather stations for ET₀ estimation without R_s measurement for China. The results demonstrated a descending trend for coefficient a from northwest to southeast China, with larger values in cold seasons. However, coefficient b showed the opposite distribution as the coefficient a. The FAO has recommended a larger a but a smaller b for southeast China, which produced the region's largest R_s and ET₀ estimation errors. Additionally, the A-P coefficients calibrated at the daily scale obtained the best estimation accuracy for both R_s and ET₀, and slightly outperformed the monthly and yearly coefficients without significant difference in most cases. The machine learning methods outperformed the empirical methods for estimating the A-P coefficients, especially for the sites with extreme values. Further, ML-ET₀ outperformed the PM-ET₀ with yearly A-P coefficients but underperformed those with daily and monthly ones. This study indicates an exciting potential for combining machine learning with physical models for estimating ET₀. However, we found that using the A-P coefficients with finer time scales is unnecessary to deal with the missing R_s measurements.     

三维插值方法在2 m温度评估中的应用

模式地形与实况站点地形高度间固有差异严重影响了2 m温度检验评估精度,传统二维插值方法仅满足预报要素与实况观测间经纬度二维空间上的一致性,而忽略了垂直方向的一致性问题,使得检验评估中预报与观测不在同一三维空间上进行检验,从而引起严重的评估误导.利用模式三维预报变量,结合近地面要素预报产品,建立新的近地面要素三维插值方法,以确保预报与观测在三维空间上保持一致性.利用2013年7月整月24 h预报产品与观测的对比分析发现,新插值方法有效地改进了由于地形误差引起的评估误导问题,在不同地形高度条件下均保持了较为一致的误差分布趋势,显示其评估结论不受地形因素影响,具有更好的实际应用价值.进一步关注新插值方法在复杂地形区不同模拟分辨率条件下的评估改进效果,采用GRAPES区域预报模式（GRAPES-MESO4.0）针对2012年7月个例,对青藏高原复杂地形区进行了不同分辨率（45、15、5和2 km）条件下的60 h模拟敏感分析,结果表明新插值方法在高分辨率条件下（2 km）依然显示出明显的改进优势.     

BCC_CSM1.1m模式对福建前汛期降水预测的误差订正

采用1991—2017年BCC_CSM1.1m季节预测模式的月降水预测数据及福建省前汛期（4—6月）66个国家气象站降水资料,利用距平相关系数(ACC)、时间相关系数(TCC)、平均方差技巧评分(MSSS)和趋势异常综合评分(Ps)等评估方法,检验评估了提前0、1、2和5个月模式对福建省前汛期降水的预测能力。采用系统偏差、一元线性回归和EOF-相似误差（EOFL和EOFNL）等4种统计方法对回报结果进行订正,并进行效果检验。BCC_CSM1.1m在不同起报时间对福建省前汛期降水的预测均能抓住降水的前两个主模态：全省一致和南北反向分布的空间特征,但预测的气候平均值较实况存在负偏差。模式在不同起报时间对前汛期降水预测的TCC高技巧区主要位于福建省北部,ACC技巧和Ps评分存在比较大的年际差异,负系统偏差的存在使得MSSS技巧不高。经订正后,模式的预测能力得到明显提升。系统偏差、线性回归、EOF相似误差线性和非线性订正方法提前2个月起报的2011—2017年平均Ps评分分别提高5.9、3.5、6.7和7.8分;不同起报时间线性回归订正的2011—2017年平均ACC技巧分别提高0.02、0.21、0.12和0.11;上述4种方法订正的MSSS评分均有了显著提高,其中系统偏差和线性回归订正后达正技巧。综合而言,线性回归订正较其他3种订正方法表现出更为稳定的订正技巧。     

甘肃红石山地区泥盆纪—石炭纪有限洋盆重建与蛇绿混杂岩深部结构

采用构造岩相学和构造-古地理单元恢复、AMT、地面高精度磁法等综合方法,对甘肃红石山和邻区泥盆纪—石炭纪有限洋盆进行重建,对红石山蛇绿混杂岩带进行构造岩相学解剖和深部探测。划分出5个构造层,前华里西期、华里西期、印支期、燕山期和喜山期。北部构造带为四顶黑山-雀儿山晚古生代岩浆弧相,中部构造带红石山蛇绿混杂岩带恢复为泥盆纪—石炭纪有限洋盆相+洋壳残片相,南部构造带为白山-狼娃山晚古生代岛弧相。蛇绿岩套从下到上的垂向单元为晚泥盆世—早石炭世变质超镁铁杂岩→堆晶超镁铁-镁铁岩→均质辉长岩和少量的辉绿岩墙→早石炭世扫子山期深水相硅质岩和含炭硅质细碎屑岩（有限洋盆相）。陆缘海相岛弧地体南北两侧为早石炭世扫子山期和白山期浅海相火山岛弧带。哈萨克斯坦板块南缘南向俯冲碰撞在敦煌地块北缘增生楔构造背景下,形成SSZ型蛇绿岩套。红石山构造-蛇绿混杂岩带与周缘地质体具有不同的构造岩相学边界,从北向南具有明显的4个构造岩相学水平分带,中心部位为超基性岩构造岩块单元,以糜棱岩相辉长岩和蛇纹石片岩为包络面的构造岩片（基质单元）,蛇绿混杂岩带总体为无根状大型透镜体。红石山构造-蛇绿混杂岩带将两侧地质体卷入混杂岩带内,这种复杂镶嵌结构的构造岩相体有利于形成大型-超大型斑岩型金铜钼矿床、浅成低温热液金矿床和造山型金矿床。     

Improving long-lead seasonal forecasts of precipitation over Southern China based on statistical downscaling using BCC_CSM1.1m

Long-lead precipitation forecasts for 1–4 seasons ahead are usually difficult in dynamical climate models due to the model deficiencies and the limited persistence of initial signals. But, these forecasts could be empirically improved by statistical approaches. In this study, to improve the seasonal precipitation forecast over the southern China (SC), the statistical downscaling (SD) models are built by using the predictors of atmospheric circulation and sea surface temperature (SST) simulated by the Beijing Climate Center Climate System Model version 1.1 m (BCC_CSM1.1 m). The different predictors involved in each SD model is selected based on both its close relationship with the target seasonal precipitation and its reasonable prediction skill in the BCC_CSM1.1 m. Cross and independent validations show the superior performance of the SD models, relative to the BCC_CSM1.1 m. The temporal correlation coefficient of SD models could reach > 0.4, exceeding the 95 % confidence level. The SC precipitation index can be much better forecasted by the SD models than by the BCC_CSM1.1 m in terms of the interannual variability. In addition, the errors of the precipitation forecast in all four seasons are significantly reduced over most of SC in the SD models. For the 2015/2016 strong El Niño event, the SD models outperform the dynamical BCC_CSM1.1 m model on the spatial and regional-average precipitation anomalies, mostly due to the effective SST predictor in the SD models and the weak response of the SC precipitation to El Niño-related SST anomalies in the BCC_CSM1.1 m.     

北京西北活动构造定量资料与未来强震地点的讨论

通过阐述北京西北的活动构造定量数据及其精度,用确定性方法、概率方法和综合评分方法,通过排队,比较出北京西北及邻近地区未来强震可能发生的地点,讨论预测结果中存在的一些问题．结果显示,未来强震最危险的断裂段为：阳原盆地南缘断裂B段和A段、延矾盆地的燕水段、宣化盆地南缘断裂、怀安镇南缘断裂东段和阳高——天镇北缘断裂东段等段落．亦即阳原-深井盆地为最危险区;天镇-怀安镇-宣化盆地为次危险区;万全-张家口和蔚县东北-矾山西南为更次危险区．     

盐碱地中华绒螯蟹育苗水质调配技术的研究

研究了地下卤水中Na^ ／K^ 对中华绒螫蟹(Eriocheir sinensis)幼体生长发育的影响以及幼体对人工海水盐度的适应性。结果表明：随着地下卤水中Na^ ／K^ 的降低，中华绒螫蟹幼体存活率、变态率和体质量增长率逐渐升高，当地下卤水中的Na^ ／K^ 低于18、6时，中华绒螯蟹幼体的存活率、变态率和体质量增长率与海水对照相比差异不显著；人工海水盐度梯度设置为18～26，Z1→Z2、Z3→Z4、Z5→M各阶段不同处理组幼体存活率和体质量增长率差异显著，而变态率差异基本不显著，各阶段的最适盐度分别为20，22和20。     

1米太阳望远镜液压监控系统

1m太阳望远镜的方位驱动控制系统采用液压轴承,液压系统正常工作时望远镜才能实现平稳旋转。可通过油压、油温监测液压系统的工作状态。针对原有液压系统不能提供其工作状态实时监测和自动化程度较低的缺陷,设计了液压监控系统并将其集成到望远镜控制系统中,可实现油压油温数据采集、油压异常报警、油泵自动启停等功能,保证液压轴承的安全运转。介绍了该监控系统的结构和工作原理,阐述了油压、油温数据采集系统的软硬件实现并给出了相应的实验数据和运行结果。理论分析和实验结果表明系统工作正常,数据采集速度快、分辨率高,精度可满足要求,能长期稳定运行。     

递减平均法与一元线性回归法对ECMWF温度预报订正能力对比

基于ECMWF细网格模式的定时最高(低)气温预报产品,针对2017年陕西99个国家级气象站的日最高(低)气温预报,检验和比较了递减平均法和一元线性回归法两种方法对气温预报误差的订正效果。结果表明,两种方法都显著地提高了日最高(低)气温的预报准确率,随着预报时效的延长,订正能力逐渐减弱。技巧评分与模式对气温的预报能力有显著的负相关关系,秦岭及其以南地区的日最高气温预报和秦岭以北地区的日最低气温预报的准确率偏低,其技巧评分一般超过40%,极大值超过70%。两种方法都有效降低了系统误差,较小误差范围的站次增多,较大误差范围的站次减少,对日最高气温在预报绝对误差≤2℃误差范围的订正能力较为突出,对日最低气温在预报绝对误差≥3℃误差范围的订正更有优势。一元线性回归法对日最高气温预报的订正能力略优于递减平均法,对日最低气温预报的订正能力不及递减平均法,利用这两种方法对气温预报进行混合订正的效果更佳。