塔北地区寒武系白云岩储层裂缝充填特征与有效性

塔里木盆地寒武系白云岩储层具有年代老、埋深大、成岩作用强烈的特征,原生孔隙几乎被破坏殆尽,有效裂缝的发育程度是决定储层优劣的关键因素。根据岩芯描述、薄片鉴定、FMI成像测井、扫描电镜、阴极发光、电子探针以及流体包裹体分析等研究手段,开展了塔里木盆地北部地区(简称"塔北地区")寒武系白云岩储层的裂缝充填特征及有效性分析。塔北地区整体裂缝发育,天山南地区与塔河地区裂缝在产状、线密度、形态以及充填特征上存在差异。天山南地区裂缝线密度与有效性均低于塔河地区,充填矿物主要为自形白云石与石英,其次为方解石、石膏与黄铁矿。充填白云石分为3期,对应的温度区间分别为70℃～90℃、90℃～110℃及110℃以上,均为地层内的成岩流体成因;充填石英分两期,第一期为黏土矿物转化蚀变形成,第二期可能为热液成因。塔河地区裂缝线密度高,有效性较好,溶蚀作用发育,是有利的勘探地区,其充填物以热液矿物组合形式出现,主要为方解石,其次为石膏、鞍状白云石、黏土矿物、沥青等,重晶石以交代白云石的形式出现。充填方解石分为4期,对应的温度区间分别为70℃～100℃、100℃～130℃、130℃～180℃及180℃以上,前3期充填为不同埋深时期地层内成岩流体成因,最后一期充填为自下部运移而来的高温热液流体成因。     

改进的最小曲率迭代法在重力剖面数据插值中的应用

基于将约束点也作为待解算结点的思想,对传统最小曲率迭代法进行改进。改进后的方法在待解算结点周围存在约束点时,避免了根据约束点与待解算结点之间的不同关系需要进行分类推导的复杂过程,同时也避免了因使用泰勒展开式进行分解造成的精度损失,提高了解算精度。     

基于傅里叶级数的中国沿海地区Tm模型精化研究

本文采用中国沿海地区13个探空站2010~2014年实测地表温度T_s与平均温度T_m数据,利用傅里叶级数分析法精化中国沿海地区T_m模型,并将2015年探空站实测T_m数据与精化模型进行对比检验。结果表明,精化模型在T_m探测方面具有更高的计算精度,其计算大气可降水量的误差概率分布趋近于正态分布,具有较强的稳定性。     

Time-Domain Full Waveform Inversion Using the Gradient Preconditioning Based on Transmitted Wave Energy

The gradient preconditioning approach based on seismic wave energy can effectively avoid the huge memory consumption of the gradient preconditioning algorithms based on the Hessian matrix. However, the accuracy of this approach is prone to be influenced by the energy of reflected waves. To tackle this problem, the paper proposes a new gradient preconditioning method based on the energy of transmitted waves. The approach scales the gradient through a precondition factor, which is calculated by the ‘approximate transmission wavefield' simulation based on the nonreflecting acoustic wave equation. The method requires no computing nor storing of the Hessian matrix and its inverse matrix. Furthermore, the proposed method can effectively eliminate the effects of geometric spreading and disproportionality in the gradient illumination. The results of model experiments show that the time-domain full waveform inversion(FWI) using the gradient preconditioning based on transmitted wave energy can achieve higher inversion accuracy for deep high-velocity bodies and their underlying strata in comparison with the one using the gradient preconditioning based on seismic wave energy. The field marine seismic data test shows that our proposed method is also highly applicable to the FWI of field marine seismic data.     

Laboratory Experiments on an Internal Solitary Wave over a Triangular Barrier

Laboratory experiments were conducted to investigate the evolution of interfacial internal solitary waves(ISWs) incident on a triangular barrier. ISWs with different amplitudes were generated by gravitational collapse. The ISW energy dissipation and turbulence processes were calculated as waves passed over the triangular barrier. Experimental results showed that ISWs were reflecting back off the triangular barrier, and shoaling ISWs led to wave breaking and mixing when waves propagated over the obstacle. Wave instability created the dissipation of energy as it was transmitted from waves to turbulence. The rate of ISW energy dissipation, the maximum turbulent dissipation, and the buoyancy diffusivity linearly increased with the increase in the incident wave energy.     

Reverse-Time Migration of Converted S-Waves of Varying Densities

Ocean-Bottom Node(OBN) acquisitions provide both non-converted and converted reflection energy. There is a clear advantage to independently imaging both P-and S-waves, as they provide more information collectively than either does alone. In many conventional converted-wave pre-stack migration algorithms, density is treated as a constant, which is not the real-life case on earth. S-wave velocity and density information is crucial for hydrocarbon detection because it helps in the identification of porefilling fluids. In this paper, we focused on the effect of density on imaging, and developed a method of reverse-time migration(RTM) on converted s-waves of varying densities(VD-RTMCS). Phase correction was required prior to pre-stack migration to avoid constructive interference between data from adjacent sources. Synthetic data examples showed that when density variations were included, image profiles showed advantages in signal-to-noise ratio, vertical resolution and imaging of complex structures.     

Isotopic evidence for the moisture origin and influencing factors at Urumqi Glacier No.1 in upstream Urumqi River Basin,eastern Tianshan Mountains

The stable isotope has been extensively applied as an effective tracer especially in precipitation. In glacierized area of arid northwest China, temperature is widely considered to be a major factor affecting isotopes in precipitation, while the influences of precipitation amount, relative humidity and other meteorological parameters are still not clear. Based on analyses on stable isotope values of water samples and NCEP/NCAR(National Centers of Environmental Prediction/National Center for Atmospheric Research, USA) re-analysis data, the moisture source and characteristics of isotopes in the precipitation, meltwater and river water isotopes at Urumqi Glacier No.1 of the upstream Urumqi River Basin, eastern Tianshan Mountains from spring to autumn during four years(from 2008 to 2011) was studied. Results indicated that meltwater are the main source of water for the upper Urumqi River. Seasonal variation of δ~(18) O in precipitation demonstrated that δ18 O was more enriched in summer and depleted in spring and autumn. Temperature was positively correlated with isotopes, while precipitation amount and relative humidity was negatively correlated with isotopes. The water vapor was affected by westerly air mass and regional water vapor cycle. Meanwhile, back trajectory clustering analyses showed that the moisture mainly from Europe and central Asia. The moisture was more likely to be locally sourced with the ratio was 46.8%～52.1%.     

基于卷积神经网络与条件随机场方法提取乡镇非正规固体废弃物

随着村镇经济建设发展,生活垃圾和工业固体废弃物造成的污染问题日益突出,已经成为制约新农村建设发展和生态文明建设的关键问题,而目前针对乡镇非正规固体废弃物的调查与统计主要依赖全国各乡镇相关部门逐级调查上报,工作量较大。本文基于高分辨率遥感影像,将深度学习模型和条件随机场模型相结合引入到乡镇固体废弃物的提取研究中,探索一种基于深度卷积神经网络的乡镇固体废弃物提取模型。由于固体废弃物在影像上表现为面积小,分布破碎等特点,为了提高工作效率,将模型特分为识别和提取2个部分：① 通过全连接卷积网络（CNN）对固体废弃物进行快速识别判断,筛选感兴趣区域影像块;② 在传统的全卷积神经网络(FCN)的基础上加入条件随机场模型(CRF)提取固体废弃物边界,提高整体分割精度。根据安徽、山西等地区相关部门上报固体废弃物堆放点以及住房与城乡建设部城乡规划管理中心进行野外检查的结果,实验最终识别精度达到86.87%以上;形状提取精度为89.84%,Kappa系数为0.7851,识别与提取精度均优于传统分类方法。同时,该方法已经逐步应用于住房和城乡建设部有关成都、兰州、河北等部分乡镇非正规固体废弃物的核查工作,取得了较为满意的结果。     

基于深度学习的钨钼找矿靶区预测方法研究

随着矿产勘查工作由浅部矿向深部隐伏矿、由易识别矿向难识别矿发展,找矿难度日益增大,地质专家越来越重视新理论、新方法、新技术的应用。深度学习作为人工智能的前沿领域/技术,对于实现矿产资源预测“智能化预测评价”具有得天独厚的优势。本文以陕西省镇安县西部钨钼矿集区单元素化探异常原始数据为基础,提出了基于深度学习的钨钼矿产评价方法。该方法以归一化地球化学数据作为模型训练数据,通过深度学习中深度自编码网络方法实现异常值提取进而识别重点成矿有利地段,实现矿产资源找矿远景区定性预测。研究结果表明,在对957条单元素化探异常原始数据分类且做好模型标签后,整个过程在计算机的“黑盒子”中自动完成学习和预测,相较于传统预测研究方法,本文方法具有自动化程度高和客观性强的特征。此外,本文利用已知矿点构建训练数据集,采用随机森林方法对预测区进行矿产资源找矿靶区预测圈定,为进一步缩小找矿靶区范围提供科学依据。