Rupture zones in the area of the 17.08.99 Izmit (NW Turkey) large earthquake (Mw 7.4) and stress changes caused by its generation

The M_w 7.4 Izmit earthquake of 17 August 1999 struck a part ofthe North Anatolian fault in the area of Izmit Bay (NW Turkey). Historicalinformation shows that the fault which moved during the generation of thisearthquake consists of two fault segments moved during the generation oflarge (M 7) earthquakes in 1719 and 1754, respectively. Since then onlythe central part (between Izmit and Lake Sapanca) of this fault ruptured bythe generation of a smaller shock (M = 6.6) in 1878.The spatial stress variations based on the calculation of changes in theCoulomb Failure Function (CFF) associated with this earthquake aresupported by the distribution of strong aftershock foci. Large positive valuesof CFF to the east and west of the mainshock epicenter are inagreement with the notion that secondary faults were triggered there by thegeneration of the main event. Large positive values of CFF are alsoobserved in the adjacent western fault segment where the 1766 event wasgenerated, evidencing the occurrence of the next strong earthquake in thissegment.     

小块体重的多元线性回归方程在锡铁山铅锌矿床储量计算中的应用

从锡铁山铅锌矿床小块体重样品原始测定数据入手，运用数学地质中的回归分析，建立了本矿床的小块体重多元线性回归方程，为矿床储量计算提供了科学参数。利用回归方程法计算小块体重进而可以推广到所有贱金属矿床的储量计算中。     

A partial least-squares regression approach to land use studies in the Suzhou-Wuxi-Changzhou region

In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares (PLS) regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and in-fluencing factors demonstrate the land use character of rural industrialization and urbaniza-tion in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.     

A spatial perspective for predicting enrollment in a regional pharmacy school

Having the ability to predict enrollment is an important task for any school’s recruiting team. The purpose of this study was to identify significant factors that can be used to predict the spatial distribution of enrollments. As a case study, we used East Tennessee State University (ETSU) pharmacy school, a regional pharmacy school located in the Appalachian Mountains. Through the application of a negative binomial regression model, we found that the most important indicators of enrollment volume for the ETSU pharmacy school were Euclidean distance, probability (based on competing pharmacy schools’ prestige, driving distance between schools and home and tuition costs), and the natural barrier of the Appalachian Mountains. Using these factors, together with other control variables, we successfully predicted the spatial distribution of enrollments for ETSU pharmacy school. Interestingly, gender also surfaced as a variable for predicting the pharmacy school’s enrollment. We found female students are more sensitive to the geographic proximity of home to school.     

Spatial Analysis Techniques as Successful Mineral-Potential Mapping Tools for Orogenic Gold Deposits in the Northern Fennoscandian Shield,Finland

Geoscientific Information Systems (GIS) provide tools to quantitatively analyze and integrate spatially referenced information from geological, geophysical, and geochemical surveys for decision-making processes. Excellent coverage of well-documented, precise and good quality data enables testing of variable exploration models in an efficient and cost effective way with GIS tools. Digital geoscientific data from the Geological Survey of Finland (GTK) are being used widely as spatial evidence in exploration targeting, that is ranking areas based on their exploration importance. In the last few years, spatial analysis techniques including weights-of-evidence, logistic regression, and fuzzy logic, have been increasingly used in GTK’s mineral exploration and geological mapping projects. Special emphasis has been put into the exploration for gold because of the excellent data coverage within the prospective volcanic belts and because of the increased activity in gold exploration in Finland during recent years. In this paper, we describe some successful case histories of using the weights-of-evidence method for the Au-potential mapping. These projects have shown that, by using spatial modeling techniques, exploration targets can be generated by quantitatively analyzing extensive amounts of data from various sources and to rank these target areas based on their exploration potential.     

The mean value concept in mono-linear regression of multi-variables and its application to trace studies in geosciences

The "mean value concept" in a mono-linear regression of multi-variables is clarified.Its applications to reconstruction of the past 90-year salinity of the sea surface water in Xisha Islands and to tracing the past 80 ka paleo-geomagnetic events from the Luochuan loess 10Be record are introduced in detail,which show the significance and the potential of the "mean value concept" in geoscience research.     

Non‐linear system identification of the versatile‐typed structures by a novel signal processing technique

Non‐linear structural identification problems have raised considerable research efforts since decades, in which the Bouc–Wen model is generally utilized to simulate non‐linear structural constitutive characteristic. Support vector regression (SVR), a promising data processing method, is studied for versatile‐typed structural identification. First, a model selection strategy is utilized to determine the unknown power parameter of the Bouc–Wen model. Meanwhile, optimum SVR parameters are selected automatically, instead of tuning manually. Consequently, the non‐linear structural equation is rewritten in linear form, and is solved by the SVR technique. A five‐floor versatile‐type structure is studied to show the effectiveness of the proposed method, in which both power parameter known and unknown cases are investigated. Copyright © 2007 John Wiley & Sons, Ltd.     

全站仪近距离测距精度检验方法的探讨

提出了室内基线法，给出了相应的数据处理方法，并用实测数据进行了计算，结果显示全站仪在室内、短边条件下的测距精度较标称精度有很大的提高。该法所得结果与现存两种方法的结果基本相当，从而证明了该法能够客观地反映出工业测量用全站仪的近距离测距精度。