地质调查智能空间框架构建与实践

正通过构建以信息空间和物理空间相融合的地质调查智能空间,为构建以"需求+数据+知识+智能驱动"为一体的数据管理、数据处理、数据服务等地质调查工作环境,提供空间数据自适应感知、非结构化数据挖掘服务、地质知识的流程化和智能化应用等,探索地质调查智能空间工作模式,取得有效应用效果。     

The multiperiodic δ Scuti star 4 Canum Venaticorum: amplitude variability

Recent multisite campaigns of the Delta Scuti Network have revealed 34 frequencies of pulsation for the star 4 CVn. Our present knowledge of the frequencies makes it possible to reanalyse the shorter data sets in the literature, photometric observations from 1966 to 1997.
4 CVn shows strong amplitude variability with time-scales of ten years or longer, although for neighbouring years the amplitudes usually are similar. Seven of the eight dominant modes show annual variability of ∼12 per cent. The variability increases to ∼40 per cent over a decade. The formally derived time-scale of variation of 30 years can only be a rough estimate, since this is also the length of the available data span. The variability is compared with that of FG Vir, which shows lower amplitude variability.
The cyclic behaviour of the amplitude variations excludes an evolutionary origin. There exists some evidence that a mode at 6.12 d⁻¹, which appeared during 1996 and 1997, may have been present with small amplitudes in the 1976–1978 time period.
The pulsation mode at 7.375 d⁻¹ exhibited the most rapid decrease found so far: the V amplitude dropped from the highest known value of 15 mmag in 1974 to 4 mmag in 1976 and 1 mmag in 1977. After that the mode has been increasing in amplitude. There exists a phase jump between 1976 and 1977, suggesting the growth of a new mode. It is interesting to note that this mode also has the strongest coupling with other modes with combination frequencies, f _i ± f _j . The amplitudes of these combination frequencies are also strongly variable from year to year. We speculate that power is transferred between the modes through mode-coupling.     

华北地区中强震前中小地震活动的动态演化特征研究

应用地震活动加速指数AI算法对华北地区1970年以来M_L≥6.0地震进行了回溯性研究。 利用该方法统计中强震前的中小地震活动频度, 分析结果显示在空间上能够反应出中小震活动频度的异常。 另外, 应用有关地震活动的频度面积S值方法, 对M_L6.0级以上强震前后的中小地震活动在时间分布上进行了研究, 此方法对AI算法的计算结果在时间上给予了支持。 结果表明: AI指数能够量化地给出中强震前中小地震活动的相对增强或减弱程度, 中强震的S值在震前表现为高值或低值异常, 震后下降或上升。 S值的异常点与AI扫描值的异常结果在时空分布上相辅相成。 在地震目录完整的情况下, 中强震震中区及其附近地区震前1 a的中小地震活动与之前3 a、 5 a的活动背景相比, 出现明显的“加速”或“减速”现象。 利用这两个参量的变化, 可为该地区中强震的预测分析提供一定的判定指标。     

伊拉克艾哈代布油田中-下白垩统碳酸盐岩储层孔隙结构及控制因素

伊拉克艾哈代布油田中-下白垩统碳酸盐岩储层孔隙结构类型划分及其差异性的受控因素和成因的认识是制约该类储层分类评价的一个关键问题。本文综合运用了常规薄片、铸体薄片、扫描电镜、电子探针、常规物性、压汞分析、阴极发光、稳定同位素分析等手段及统计分析方法对储层的主要孔隙类型、喉道类型、喉道分布类型进行了研究,并在此基础上确定了高孔中渗细喉型、中高孔低渗细喉型、中高孔特低渗微喉型、低孔特低渗极微喉型四种孔隙结构类型及其划分依据和方案,同时指出了在该类储层中好的孔隙结构具有网络状、偏细型、分选较差的孔喉特征。通过分析明确了孔隙结构差异性的主要控制因素为岩石组构和溶蚀作用,认为该套储层的孔喉空间及结构是以沉积形成的原生孔隙受同生期或准同生期的溶蚀改造为基础,并在后期表生岩溶作用下的改造而成;而先期受岩石组构控制下的溶蚀作用控制了储层孔隙结构的差异性。通过本文研究将对中东地区该类储层的形成及分类评价研究具有较好的指导意义。     

智慧遥感制图(iMap)

本文基于地图制图发展趋势,提出智慧遥感制图的概念(iMap).旨在梳理遥感制图未来的努力方向.iMap应从制图目的灵活性、制图知识可迁移性、数据多源性、多算法集成性和制图系统易用性等方面深入利用大数据和人工智能技术,创新遥感制图理论和技术.     

A high-resolution seismic catalog for the 2021 MS6.4/MW6.1 Yangbi earthquake sequence,Yunnan, China: Application of AI picker and matched filter

We present a high-resolution seismic catalog for the 2021 M_S6.4/M_W6.1 Yangbi sequence. The catalog has a time range of 2021-05-01 to 2021-05-28, and contains ~8,000 well located events. It captures the features of the whole foreshock sequence and the early aftershocks. We designed a detection strategy incorporating both an artificial intelligent (AI) picker and a matched filter algorithm. Here, we adopt a hybrid AI method incorporating convolutional and recurrent neural network (CNN & RNN) for event detection and phase picking respectively (i.e. CERP), a light-weight AI picker that can be trained with small volume of data. CERP is first trained with detections from a STA/LTA and Kurtosis-based method called PAL, and then construct a rather complete template set of ~4,000 events. Finally, the matched filter algorithm MESS augments the initial detections and measures differential travel times with cross-correlation, which finally results in precise relocation. This process gives 9,026 detections, among which 7,943 events can be well relocated. The catalog shows as expected power-law distribution of frequency magnitude and reveals detailed pattern of seismicity evolution. The main features are: (1) the foreshock sequence images simple fault geometry with consistent strike, but also show a variable event depth along strike; (2) the mainshock ruptures the same fault of the foreshock sequence and activate conjugate faults further to the southeast; (3) complex seismicity are developed in the post-seismic period, indicating complex triggering mechanisms. Thus, our catalog provides a reliable basis for further investigations, such as b-value studies, rupture process, and triggering relations.     

基于百度AI卷积神经网络的茶树结霜智能识别北大核心

基于采集的近20000张茶园图像,分别筛选出1000张茶园结霜和1000张未结霜图像作为训练样本,利用百度AI开放平台的EasyDL经典版图像识别功能,应用卷积神经网络算法,建立茶园结霜识别模型,进行茶树结霜图像智能判别。茶树结霜智能识别模型的准确率、F1评分、精确率、召回率分别为99.2%、99.2%、99.3%、99.1%。模型检验结果为:样本地20张结霜图像的平均识别置信度为95.51%,20张非结霜图像的平均识别置信度为99.99%;非样本地(江南茶区)80张结霜图像的平均识别置信度为97.53%,80张非结霜图像的平均识别置信度为95.11%。