Isoscape of δ18O in Precipitation of the Qinghai-Tibet Plateau: Assessment and Improvement

The spatial distribution of stable water isotopes (also known as an isoscape) in precipitation has drawn increasing attention during the recent years. In this study, based on the observations at 32 stations, we assessed two widely applied global isoscape products (Regionalized Cluster-based Water Isotope Prediction (RCWIP) and Online Isotopes in Precipitation Calculator (OIPC)) at the Qinghai-Tibet Plateau (QTP) and then established an improved isoscape of oxygen isotopes in precipitation on a monthly basis using a regionalized fuzzy cluster method. Two fuzzy clusters can be determined, which is consistent using three meteorological data. The monthly isoscapes show the seasonal movement of high and low isotopic value regions across the QTP and reveal the influences of monsoon and westerly moisture. According to the cross validation, the δ18O in precipitation in the new monthly isoscapes for the QTP we propose performs better compared to the existing global products. To create a regional isoscape in many other regions, the regionalized fuzzy cluster method can be considered especially for regions with complex controlling regimes of precipitation isotopes.

Gray Fuzzy Cluster Method and its Application in Development Scheme of Fault Block Reservoirs

Fault-block oil and Gas Field is one of special complicated reservoirs. For its development, No matter at home or aboard, the instances are not too many, and most of them are terrestrial oil reservoir. For ocean fault-block oil reservoirs, there are no instances that can be consulted. This paper aims at some ocean fault block oil reservoir .Gray relation model and fuzzy cluster analysis are combined to set gray fuzzy cluster analysis .Through a case analysis ,gray fuzzy cluster method is used to clustering of reservoirs. with the experiences that has been obtained, so that the foundation are provided for the development of some ocean fault block oil reservoir.

Classification of Wear Debris Using Weighted Fuzzy Cluster Method

The analysis and identification of wear particles for machine condition monitoring is usually very time-consuming by experienced inspectors. In order to remedy the limitation, automation of the analysis procedure appears to be necessary. A novel weighted fuzzy c-means algorithm for wear particle classification is proposed in this paper. The algorithm uses the variation of the pixel intensities of a region to choose strong resembling area. Then, the spatial relationships of the membership function are constructed to regulate the pixel membership obtained from the FCM object function. Finally, wear debris are classified based on the fuzzy membership. The example shows that the method is briefly and effectively.