新疆精河M_W6.3地震产生的静态应力变化研究

利用精河M_W6.3地震有限断层破裂模型,计算了精河地震产生的位移场、应力场、周围主要断层上的静态库仑应力变化以及主震对余震的触发作用。结果表明:(1)精河地震产生的地表隆升最大值约为6.6cm,沉降最大值约为1.8cm;水平位移方向呈现震中南北侧向震中汇聚、震中东西侧向外"流出"的特点。(2)精河地震产生的水平面应力场展布南北侧物质主要受到指向震中的拉张力作用,东西两侧物质主要受到因震中过剩物质东西向排出而导致的东西向挤压力作用。(3)震中西侧距震中约20km的库松木契克山前断裂中段和震中东北部距震中约50km的四棵树-古尔图南断裂西段的库仑应力加载均大于0.01MPa,即2处为地震危险区。(4)在震源深度为8~12km的余震事件中,约有85.5%处于库仑应力加载区,即受到主震的的触发作用;在深度为4~8km的余震事件中,约有87%受到主震的应力触发作用。     

若干西边界潜流的气候态分布特征

"西边界潜流(WBUC)"是海洋环流中的重要现象,与表层环流相比,对次表层潜流的结构认识不足。本文利用SODA、OFES和ARGO资料,分析了北太平洋中的棉兰老潜流(MUC)和吕宋潜流(LUC)、南太平洋中的大堡礁潜流(GBRUC)和东澳大利亚潜流(EAUC)及南印度洋中的阿加勒斯潜流(AUC)的气候态空间分布特征,并且根据地转流反向的判据,分析WBUC的发生条件。     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

Geometric form of Haiyuan fault zone in the crustal interior and dynamics implications

The deep seismic reflection data on profile HY2 are reprocessed by the method of simultaneous inversion of velocity distribution and interface position. By the travel-time inversion with the data of the diving wave Pg and fault plane reflection wave, we determine the geometric form and velocity of Haiyuan fault zone interior and surrounding rock down to 10 km depth. The measured data show that the amplitudes have strong attenuation in the range of stake number 37–39 km, suggesting the fault zone has considerable width in the crustal interior. The results of this paper indicate that to the north of the fault zone the crystalline basement interface upheaves gradually from southwest to northeast and becomes shallow gradually towards northeast, and that to the south of the fault zone, within the basin between Xihua and Nanhua mountains, the folded basement becomes shallow gradually towards southwest. The obliquity of the fault zone is about 70° above the 3 km depth, about 60° in the range of the 3–10 km depths. From the results of this paper and other various citations, we believe that Haiyuan fault zone is in steep state from the Earth’s surface to the depth of 10 km. Foundation item: Joint Seismological Science Foundation of China (201001) and State Key Basic Research Development and Programming Project (95-13-02-02). Contribution No. RCEG200308, Exploration Geophysical Center, China Earthquake Administration.     

鄂尔多斯盆地延川南区块目的煤层含气性分析

为计算鄂尔多斯盆地延川南区块煤层气资源量、甄选有利区带及确定先导性试验井组的部署,对全区探井的煤层含气性进行综合分析。结果显示:区块内煤层气含量为5.54~19.56m3/t;兰氏体积为13.59~33.78m3/t,兰氏压力为2.06~3.72MPa,含气饱和度为41.48%~92.19%;临储压力比为19.18%~84.28%。区块整体含气性显示较好,开发较为有利,其中以包括Y3、Y6等井在内的万宝山构造带为煤层气开发的最有利区带。     

塔河油田巴楚组底部砂泥岩段沉积特征

塔河油田下石炭统巴楚组底部砂泥岩段岩性复杂,沉积相类型及分布规律不清楚。利用岩心观察、连井剖面对比、薄片分析、地震属性分析等方法,对巴楚组底部砂泥岩段沉积特征进行系统研究。结果表明:巴楚组底部砂泥岩段主要为冲积扇、潟湖、障壁型滨岸相沉积。冲积扇沉积发育于工区北部S85井附近,为北东-南西向展布的朵状体,岩性以大套细-中砾岩为主,物性差,不具储集能力;潟湖沉积分布于工区南部古鼻凸西南轴部,岩性主要为钙质泥岩、泥灰岩;障壁岛沉积位于工区南缘,沿北东东向平行于阿克库勒古鼻隆轴线分布;滨岸沉积位于工区西南部,主要发育临滨亚相,以细砂岩沉积为主。障壁沙坝和滨岸临滨砂岩的储集能力良好。     

顾及噪声密度函数差异的自适应回归算法及其在人工标靶提取中的应用

仪器、周围环境和人为操作等往往会造成点云中包含大量的噪声,导致模型回归精度低等问题.RANSAC算法凭借其简单实现、稳健的优势广泛应用于解决模型回归的问题.但是,针对不同的场景,RANSAC算法需要不断地调整参数来估计最优模型解.本文考虑到RANSAC及其现有改进算法的不足,以及内群点与噪声之间往往存在密度分布差异性,...     

FY-1C可见光通道衰减分析及订正试验

利用FY-1C连续两年的遥感资料,以敦煌辐射校正场为分析区,系统分析了FY-1C可见光、近红外通道的衰减特征,开展了通道衰减订正试验。分析结果表明,FY-1C除可见光通道1衰减强烈外,其它各可见光、近红外通道衰减性能稳定。利用统计分析法,可以建立定标系数衰减订正量随时间的变化关系。订正后的定标系数与2000年辐射校正场外定标实验结果一致性很好。更新后的定标系数可以有效克服通道衰减带来的通道反射率测值误差,提高FY-1C可见光、近红外通道遥感资料定量应用的精度。     

庐山变质核杂岩的变质变形及构造意义

庐山出露的变质核杂岩的构造层次具有典型的三层结构,即最上面的未变质沉积盖层、中间韧性流变层、核部的变质基底,其间分别以上剥离断层和拆离断层为分界线。上部沉积盖层以脆性变形为主,中间韧性流变层变形程度不一,但远离拆离带变质变形逐渐减弱,而核部的变质基底以强塑性变形为特征。对变质核杂岩南北两条地质剖面的分析,得到三个构造层次的岩石变质变形及岩性变化特征。区域动力学分析反映变质核杂岩东部原拆离带在五里正断层附近。拆离带中多硅白云母压力计得出的变质压力为0.6~1.1 GPa;锆石U-Pb年龄测试得到变质核杂岩的隆升时代为早白垩世。     

Sea Level Prediction in the Yellow Sea From Satellite Altimetry With a Combined Least Squares-Neural Network Approach

Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast.