基于对称性的委内瑞拉及周边地区MW≥6.0地震趋势判断及与地球自转关系分析

在全球化的大背景下,重大自然灾害发生的频率不断加快,其危害对人们的生产生活造成了严重的威胁。地震灾害是重大自然灾害之一,发生的频率虽然不高,但造成的人员伤亡和经济损失十分巨大。对地震进行趋势判断,其理论和现实意义重大。文章运用三元、四元、五元可公度计算来预测委内瑞拉及周边地区近30多年M_W ≥ 6.0地震信号的强弱,利用蝴蝶结构图和可公度结构系加强对时间对称性的分析,再通过震源的经纬度迁移和空间迁移规律,本文创造性地分析了该地地震与地球自转加速度的关系。结果得出：（1）委内瑞拉及周边地区发生M_W ≥ 6.0地震的时间上在 2018年信号最强,2017和2019年较强。（2）空间上在10°N—11°N,63°W—65°W之间地震可能性最强。（3）委内瑞拉及其周边地区M_W ≥ 6.0地震与地球自转速度的转折变化有密切关系,未来可通过其变化规律帮助分析地震发生趋势。本文可为地震灾害的趋势判断研究提供一定的参考,为后续研究工作带来一些启示。     

Research on the time changes of transfer functions at Heze observatory before and after Heze earthquake of magnitude 5.9 A discussion with Qi Wang

In this paper, the coefficients of Parkinson vectora, b and the in-phase partA _r, B_r and quadrature-phase partA _i, B_i of transter functions are calculated by using two kinds of data processing methods, respectively. The results of both methods are close to each other. All these six parameters above are low in value, showing that the electric structure in Heze area is rather uniform. It seems that the anomalous changes appeared in both results, and the values of parameters decrease before and after Heze earthquake. The Chinese version of this paper appeared in the Chinese edition ofActa Seismologica Sinica,13, 113–120, 1991.     

复数矢量球函数的理论研究

给出了正交归一化的复数矢量球函数的定义和性质。复数矢量球面调和函数是球体位移的研究基础,采用复数矢量球面调和函数可把位移场的球形场和环形场一同表示出来,为地球各种动力学效应的统一研究提供了理论依据。同时还研究了在球坐标系下弹性应变张量与复数矢量球函数的一些关系,为地球弹性动力学方程的解算提供参考。     

利用混合光谱分解与SVM估算不透水面覆盖率

对比了线性混合光谱分解模型(SMA)与支持向量机(SVM)在TM影像上估算不透水面覆盖率(ISP)的精度,通过SVM模型拟合TM像元光谱特征与样本ISP间的关联而获得对未知像元ISP的估算能力。对于天津市主城区的TM影像,选择学校区、工矿区和住宅区的高分辨率影像分类结果作为训练样本(7020个)和验证样本(1500个),SVM模型的ISP估算均方差(15.4%)优于SMA估算结果(19.4%);在增加缨帽变化“绿度分量”及混合光谱分解“高反射率分量”作为SVM特征变量后,ISP估算精度提高为12%。研究结果表明：SVM模型能够拟合各像元光谱组分间非线性关系且具有较好小样本泛化的性能,适用于地面样本较少的大区域ISP制图;增加与ISP相关性大的光谱特征向量作为SVM输入能提供更多的区域地物空间分布信息,能够调整无样本的地表类型的ISP估算值,提高区域ISP估算的整体精度。     

基于多源地球物理数据的机器学习方法在地质体分类中的应用——以黑龙江多宝山矿集区为例

为适应多源地球物理大数据地质解释的需要,同时也为了在地质体物性存在交叠情况下快速有效地实现多源地球物理数据的地质解释,本文提出应用机器学习的支持向量机方法对多源地球物理数据进行地质解释的新思路,并给出了利用物性三体（密度、磁化率、电阻率）进行地质体圈定与分类方法.阐明了参数归一化、参数寻优对模型训练与学习及分类结果的影响与作用.本文将黑龙江多宝山矿集区物性三体与矿集区的区域地质、矿床地质及钻井资料相结合,利用所提出的方法对地下地质体进行了圈定与分类,对分类结果经过交叉检验,正确率达81.6%,表明了训练模型具有较高的可信度.经对预测模型填充已知物性参数正演的重磁异常与实测重磁异常对比,证明两者在整体和细节上均有高度的相似性,间接说明对地质体圈定与分类结果的可靠性,进一步表明利用多源地球物理数据,采用支持向量机方法圈定地质体及对地质体进行岩性识别方面的合理性与有效性.多源地球物理数据机器学习的支持向量机方法在多宝山矿集区地质体圈定与分类所取得较好的应用效果,为多源综合地球物理的地质解释提供了可借鉴的成功经验,也提供了多源地球物理资料地质解释的一种新型的技术手段,开辟了应用人工智能方法进行多源地球物理资料地质解释的新途径.

     

2011年梅汛期一次暴雨过程的对流涡度矢量方程诊断分析

利用NCEP FNL资料、FY-2E云顶亮温、常规观测及加密自动站降水量等资料,以及高分辨率中尺度模式WRF V3.3的模拟结果,结合对流涡度矢量方程对2011年6月一次江淮梅雨锋暴雨过程进行了诊断分析。结果表明,模拟结果再现了该次暴雨过程的降水特征,该过程主要受地面低压及梅雨锋锋面系统的控制和影响,其中6月14-15日为所选暴雨个例最旺盛的阶段,且该时段伴随梅雨锋上中尺度对流系统的移动发展和梅雨锋锋生。对流涡度矢量及其垂直分量的倾向方程的诊断分析表明,对流涡度矢量垂直分量的局地变化主要受非绝热加热项的影响;而非绝热加热与次级环流和梅雨锋锋生的关系说明中尺度对流活动与梅雨锋锋生存在类CISK机制的反馈关系。因此,对流涡度矢量,特别是其垂直分量可以用来诊断和揭示伴随非绝热加热的中尺度对流系统与梅雨锋锋生之间的关系。     

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A Review of Potential Vorticity and its Possible Application in the moist Atmospheric Flow

Potential vorticity (PV) has been served as a powerful and useful dynamic tracer for the understanding of the large-scale dynamics and synoptic variations in the atmosphere and oceans. Significant progress has been made on the application of PV. In recent decades there has been a substantial amount of work done on PV in a general moist atmosphere. In this paper PV and the generalized moist potential vorticity (GMPV) and their application in the tropical cyclones and mesoscale meteorological field are reviewed. The GMPV is derived for a real atmosphere (neither totally dry nor saturated) by introducing a generalized potential temperature instead of the potential temperature or equivalent potential temperature. Such a generalization can depict the moist effect on PV anomaly in the non-uniformly saturated atmosphere. A new convective vorticity vector (CVV) is introduced in connection with GMPV in order to diagnose the development of tropical deep convections.     

基于两种机器学习方法分析东海北部海域三疣梭子蟹(Portunus trituberculatus)时空分布

为了解东海北部海域三疣梭子蟹(Portunustrituberculatus)资源时空分布规律,探索更适合三疣梭子蟹资源量预测的模型方法,根据2006—2007年共四个季度在东海北部海域的底拖网调查数据,运用梯度提升回归树(gradientboostingregressiontree,GBRT)和支持向量机(supportvector machine,SVM)这两种机器学习方法,分析了三疣梭子蟹时空分布与环境因子之间的关系,同时使用方差解释率(VE)、相对均方根误差(RMSE)以及决定系数R2等指标对不同模型的拟合效果、预测性能以及稳定性等进行了比较,选择其中最佳模型对东海北部海域三疣梭子蟹资源分布进行预测。结果显示, GBRT模型的拟合效果相对优于SVM模型,两种模型的拟合结果均显示底层海水盐度(SBS)为影响三疣梭子蟹资源分布最为显著的环境因子。GBRT模型的预测性能较高且模型较为稳定,其预测结果显示夏季的资源量高于其他三个季节,且各季节所研究海域的东南部均存在一个资源分布的低值区。研究结果预期可为三疣梭子蟹资源分布及资源量预测新方法的探索和分析提供技术指导。     

An Intelligent Method for Geochemical Discrimination of Tectonic Settings of Basalt Based on Olivine Composition: GWO-SVM Method and its VerificationEI北大核心CSCD

Geochemical discrimination of tectonic settings of basalts has been an important research direction of geochemistry for decades. Olivine is one of the earliest crystallized minerals of basaltic magma, which records a lot of hidden information of the formation and evolution of the magma. Therefore, basic elements in olivine are used to discriminate three tectonic settings, including the mid-ocean ridge basalt (MORB), ocean island basalt (OIB) and island arc basalt (IAB). However, it is still difficult to accurately discriminate the tectonic settings by using these diagrams. The machine learning algorithm is introduced to solve the aforementioned problem. The classification performance of the machine learning discrimination method largely depends on the rationality of parameter determination. To this end, the paper proposes a coupling intelligent method for geochemical discrimination of tectonic settings using olivine composition of the basalts based on the grey wolf optimizer (GWO)-optimized support vector machine (SVM), or GWO-SVM for short. GWO is used to seek the optimal parameter combination of SVM to form the optimal mapping relationship between basic elements in olivine and basalt tectonic settings, so as to realize the accurate discrimination of MORB, OIB and IAB. In addition, according to the published geochemical data of basalt samples, the discrimination performance of GWO-SVM is evaluated by means of the simulation experiment, hold-out validation and k-fold cross-validation. The evaluation results are represented by the confusion matrix and its derived evaluation indicators. The results show that GWO-SVM can discriminate the tectonic settings of the basalts based on olivine compositions with overall classification accuracy of up to 85%. Thus, in comparison with the traditional discrimination diagram method, the machine learning discrimination method based on multi-algorithm fusion can significantly improve the discrimination accuracy of basalt tectonic settings. © 2020, Science Press. All right reserved.