考虑地震测网密度预测小断层的改进图解法

地震剖面所能识别的断层断距通常大于１５ｍ，小断层的识别是油田地质研究中的一大难题。若将在同一地质作用下形成、产于同一地质体内的具有相同性质的所有断层视为一个总体，用帕雷托（Ｐａｒｅｔｏ）模型可以表示断层的总体分布。用图解法对未检测到的小断层进行预测时，要考虑地震测网密度对断层识别的影响。提出改进的图解法，计算给定地震测网密度下一特定规模的断层被检测到的概率（简称为检测概率），并以此检测概率为指导，用最小二乘法来估计断层的分布参数，以提高小断层预测的精度。在此基础上，可以根据断层断距和长度之间的统计关系。

用MT资料解释苏北地区海相残留地层展布

在江苏苏北地区开展了以中、古生界为主要对象的大地电磁测深勘探 (MT) ,并进行了综合地质解释。以二维剖面解释为依据 ,探讨了苏北盆地建湖隆起及以北地区的现今构造区划和海相残留地层展布。资料表明 ,海相中、上古生界地层主要残存在该区的东部 ,西部仅残存下古生界地层 ,印支 -燕山中期发育于阜宁凹陷南界的NE向逆冲断层和一系列NW向正断层控制了海相地层残存。

一种估计未发现构造圈闭个数及圈闭面积大小分布的概率模型

在石油地震勘探中，地震测网密度为勘探程度的函数。勘探程度越高，测网的密度越大，地下地质构造四闭被检测到的概率也越大。本文根据顺序取样模型矩法中的Ｈ－Ｔ估计子介绍一种概率模型。在给定地震勘探测网密度的情况下，利用该模型可预测出探区中未被发现构造圈闭的个数及其构造规模分布的概率。此方法对优化测线布局和工作量测算均具有重要意义，文中以实例介绍了该方法的应用效果。

开发阶段海上复杂断块油田断层解释技术组合及应用

测井资料较少、气云区成像质量差等问题常导致传统断层解释技术难以满足海上复杂断块油田开发阶段断层精细刻画的需求。集成边缘保护结构增强滤波、方差蚂蚁体融合属性、变解释测网密度快速断层解释、构造样式分析、三维可视化质控等技术,研究形成了适用于海上复杂断块油田开发阶段的断层解释技术组合。具体流程为:首先开展地震数据解释性处理;随后提取平面导航属性;接着开展剖面平面互动断层解释;然后基于构造样式指导剖面断层组合;最后对成果进行校验并输出。应用该流程完成了老油田A的断裂系统精细解释,在满足解释精度的同时较大程度提升了解释效率,并通过地震剖面与压力测试结果验证了解释成果的合理性,为后续油田滚动挖潜提供了技术支持。

An Aircraft Trajectory Anomaly Detection Method Based on Deep Mixture Density Network

The timely and accurately detection of abnormal aircraft trajectory is critical to improving flight safety.However,the existing anomaly detection methods based on machine learning cannot well characterize the features of aircraft trajectories.Low anomaly detection accuracy still exists due to the high-dimensionality,heterogeneity and temporality of flight trajectory data.To this end,this paper proposes an abnormal trajectory detection method based on the deep mixture density network(DMDN)to detect flights with unusual data patterns and evaluate flight trajectory safety.The technique consists of two components:Utilization of the deep long short-term memory(LSTM)network to encode features of flight trajectories effectively,and parameterization of the statistical properties of flight trajectory using the Gaussian mixture model(GMM).Experiment results on Guangzhou Baiyun International Airport terminal airspace show that the proposed method can effectively capture the statistical patterns of aircraft trajectories.The model can detect abnormal flights with elevated risks and its performance is superior to two mainstream methods.The proposed model can be used as an assistant decision-making tool for air traffic controllers.

Anomalous Cell Detection with Kernel Density-Based Local Outlier Factor

Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.

Decision Cost Feature Weighting and Its Application in Intrusion Detection

This paper introduces the cost-sensitive feature weighting strategy and its application in intrusion detection. Cost factors and cost matrix are proposed to demonstrate the misclassification cost for IDS. How to get the whole minimal risk, is mainly discussed in this paper in detail. From experiments, it shows that although decision cost based weight learning exists somewhat attack misclassification, it can achieve relatively low misclassification costs on the basis of keeping relatively high rate of recognition precision. Key words decision cost - feature weighting - intrusion detection CLC number TP 393. 08 Foundation item: Supported by the National Natural Science Foundation Key Research Plan of China (90104030) and “20 Century Education Development Plan”Biography: QIAN Quan(1972-), male, Ph. D. research direction: computer network, network security and artificial intelligence

Fine Measurements and Analysis of Temperature Gradients in the Wells of the Jinsha River Groundwater Observational Network

Fine measurements have been conducted to temperatures and their gradients of six wells of the Jinsha River Groundwater Observational Network.The results show that the influence depths of sun radiation heat are 50m to 125m,average temperature gradients in the wells range from 0.11 to 2.81℃/hm and most are 1～2℃/hm,and the temperature gradients on varied depth sections of one well are highly changeable.Lithology of strata and their integrity,particularly high-angle crashed fault zones,have imposed major effects on the influence depths of sun radiation heat and temperature gradients of the wells.The micro dynamic characteristics of water temperature,such as coseismic effects,tidal effects and anomalies of the wells prior to earthquakes,probably depend,to a large degree,on the temperature gradients of the depths at which the water temperature sensors are settled.

Road Density Analysis Based on Skeleton Partitioning for Road Generalization

This paper proposes an algorithm for road density analysis based on skeleton partitioning. Road density provides metric and statistical information about overall road distribution at the macro level. Existing measurements of road density based on grid method, fractal geometry and mesh density are reviewed, and a new method for computing road density based on skeleton partitioning is proposed. Experiments illustrate that road density based on skeleton partitioning may reveal the overall road distribution. The proposed measurement is further tested against road maps at 1:10k scale and their generalized version at 1:50k scale. By comparing the deletion percentage within different density interval, a road density threshold can be found, which indicate the need for further operations during generalization. Proposed road density may be used to examine the quality of road generalization, to explore the variation of road network through temporal and spatial changes, and it also has future usage in urban planning, transportation and estates evaluation practice.