煤系变形介质条件研究在煤层小断层预测中的应用——以下峪口矿2号煤层为例

煤系介质条件作为构造形成和发育的的物质基础，必然影响断层的发育。以陕西下峪口煤矿2号煤层为对象，研究了煤层围岩介质参数与煤层小断层间的关系，确定了控制2号煤层小断层发育的4个主要因素，并据此建立了预测小断层发育程度的数学模型。     

Effective properties of a cemented or an injected granular material

In this paper the macroscopic elastic properties of injected or cemented sands are derived from the characteristics of the constituents and the analysis of the microstructure using a multi‐scale modelling approach. Particular interest is given to the choice of the representative elementary volume, by relying on existing microstructural data. The periodic homogenization is adopted and required numerical solutions are performed by the finite element method. An assessment of the validity of the multi‐scale approach is achieved through comparison with theoretical and experimental results on cemented and injected granular media reported in the literature. The capabilities of the model are also used to investigate the influence of geometrical and mechanical microscale parameters on the macroscopic behaviour of the treated materials. Copyright © 2004 John Wiley & Sons, Ltd.     

Reverse time migration with Gaussian beams using optimized ray tracing systems in transversely isotropic media

Prestack depth migration is a key technology for imaging complex reservoirs in media with strong lateral velocity variations. Prestack migrations are broadly separated into ray-based and wave-equation-based methods. Because of its efficiency and flexibility, ray-based Kirchhoff migration is popular in the industry. However, it has difficulties in dealing with the multi-arrivals, caustics and shadow zones. On the other hand, wave-equation-based methods produce images superior to that of the ray-based methods, but they are expensive numerically, especially methods based on two-way propagators in imaging large regions. Therefore, reverse time migration algorithms with Gaussian beams have recently been proposed to reduce the cost, as they combine the high computational efficiency of Gaussian beam migration and the high accuracy of reverse time migration. However, this method was based on the assumption that the subsurface is isotropic. As the acquired azimuth and maximum offsets increase, taking into account the influence of anisotropy on seismic migration is becoming more and more crucial. Using anisotropic ray tracing systems in terms of phase velocity, we proposed an anisotropic reverse time migration using the Gaussian beams method. We consider the influence of anisotropy on the propagation direction and calculate the amplitude of Gaussian beams with optimized correlation coefficients in dynamic ray tracing, which simplifies the calculations and improves the applicability of the proposed method. Numerical tests on anisotropic models demonstrate the efficiency and accuracy of the proposed method, which can be used to image complex structures in the presence of anisotropy in the overburden.     

大地电磁测深反演技术有效性对比试验

设计了一个简单的三维模型:三维低阻异常体和高阻异常体位于一维层状介质模型中,以"十"字型和"米"字型观测剖面方式作为模拟方式,开展模型一维、二维、三维反演技术有效性对比试验,其中一维反演计算采用自适应正则化(ARIA)反演,二维反演计算采用非线性共轭梯度(NLCG)反演,三维反演计算采用REBOCC三维反演,将剖面下方不同的反演结果与原始模型进行对比分析,发现:在该模型条件下,一维和二维反演都能得到反映模型真实信息的结果,REBOCC三维反演更偏向重建原始模型的宏观电性特征;在一维反演结果中,对于深部的电性结构特征,通常TE极化模式的反演结果好于TM极化模式,TE/TM几何平均值反演结果介于前两者之间;在二维反演结果中,通常TM极化模式的反演结果好于TE极化模式,TE/TM联合模式反演结果与TM极化模型相当,甚至更好;模型REBOCC三维反演相比一维、二维反演更易受反演结果多解性影响,REBOCC三维反演结果偏向重建原始模型的宏观电性特征。     

多孔黏弹介质平面波理论及波场数值模拟

本文将黏弹固体骨架与各向异性多孔弹性模型相结合,并将地层黏弹性的特性考虑其中,更好地刻画了快P波和横波在地震频带的衰减现象。将多孔黏弹模型模拟结果同传统弹性模型模拟结果对比显示,多孔黏弹模型在模拟真实储层方面更具优势。     

Visualizing spatiotemporal trajectories of mobile social media users using space–time cube

The implementation of social network applications on mobile platforms has significantly elevated the activity of mobile social networking. Mobile social networking offers a channel for recording an individual’s spatiotemporal behaviors when location-detecting capabilities of devices are enabled. It also facilitates the study of time geography on an individual level, which has previously suffered from a scarcity of georeferenced movement data. In this paper, we report on the use of georeferenced tweets to display and analyze the spatiotemporal patterns of daily user trajectories. For georeferenced tweets having both location information in longitude and latitude values and recorded creation time, we apply a space–time cube approach for visualization. Compared to the traditional methodologies for time geography studies such as the travel diary-based approach, the analytics using social media data present challenges broadly associated with those of Big Data, including the characteristics of high velocity, large volume, and heterogeneity. For this study, a batch processing system has been developed for extracting spatiotemporal information from each tweet and then creating trajectories of each individual mobile Twitter user. Using social media data in time geographic research has the benefits of study area flexibility, continuous observation and non-involvement with contributors. For example, during every 30-minute cycle, we collected tweets created by about 50,000 Twitter users living in a geographic region covering New York City to Washington, DC. Each tweet can indicate the exact location of its creator when the tweet was posted. Thus, the linked tweets show a Twitter users’ movement trajectory in space and time. This study explores using data intensive computing for processing Twitter data to generate spatiotemporal information that can recreate the space–time trajectories of their creators.     

Novel insights on intensity and typology of direct human-nature interactions in protected areas through passive crowdsourcing

Recent advances in geotagging, sharing and automatically analyzing online content from Social Networking Sites (SNS) offer unprecedented opportunities for the analysis of human-nature interactions. Previous studies in this field, however, offer limited insights regarding the benefits of automated content analysis especially at large scales, biases arising from the selection of SNS sources, and the predictive power of visitation models based on SNS data. We explore quantitative and qualitative aspects related to intensity, interests and sentiments associated with on-site experiences in 568 protected areas in Israel and the Palestinian Authority. We analyze counts and content of >100,000 photographs and tweets from four different SNSs, calibrate visitation models and predict visitation in unmonitored sites, cluster sites based on the typology of human-nature interactions reflected in online photographs, and characterize the polarity of sentiments associated with experiences in individual sites and clusters thereof. We find benefits in combining data from multiple sources and controlling for biases related to sites’ photogenicity and type of human-nature interactions. Our results suggest that current best estimates of visitation in unmonitored sites underestimate by 39% the actual number of visits. We discuss how the techniques and findings in this study are applicable in the broader context of the management and conservation of sites of environmental or cultural interest.