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The sandstone reservoir of the Pinghu Formation in the Xihu Depression, East China Sea is characterized by great depth, small thickness, radical facies change and a widespread coal bed. It is difficult to describe the reservoir accurately using conventional reservoir prediction methods. In order to analyze the influence of coal-bearing strata on the prediction of the mid-low thickness sandstone reservoir, the seismic response of different sandstone–coal stratigraphic assemblages was simulated by seismic forward modeling. The modeling result indicates that the post-stack seismic response is dominated by coal bed, whereas the response of sandstone can hardly be recognized. In contrast, the difference between the pre-stack AVO (amplitude versus offset) response characteristics of coal seams and gas-bearing sandstones has been clarified based on the statistics pertaining to AVO characteristics of drilled wells. Therefore, we propose a method to reduce the interference of coal beds in sandstone reservoir prediction using far-gather seismic information. This method has significantly improved the accuracy of reservoir prediction and sand description in sand–coal coupled environments and has been applied successfully in the exploration of coal-rich strata in the Pingbei slope belt, Xihu Depression.

Self-monitoring is an important mechanism for the security of wireless sensor networks. In this work we propose a novel distributed node self-monitoring mechanism for wireless sensor networks. Assuming a sensor node can detect the communications (sending message) of its neighboring nodes, we design a one-to-many self-monitoring mechanism, in which the selected nodes, named Monitor Nodes (MNs), can monitor their neighboring nodes. A distributed dominating set approximation algorithm is design to obtain the MN set. The cooperation among MNs guarantees all regular nodes (RNs) are monitored. However, the MNs may be compromised by external attackers and produces false alarms. To improve the one-to-many selfmonitoring mechanism, we design a many-to-one dynamical monitoring approach to take the MNs under surveillance. The neighboring nodes of a MN are dynamically selected to monitor their MN. In the proposed mechanism, a node produces an alert when it detects abnormal behaviors of the node under surveillance. The sink node collects the alert messages to make judgment to the potential abnormal nodes. The proposed selfmonitoring mechanism can be used to detect and position multiple inter-network attacks, including selective forwarding, black-hole and DoS. Simulations demonstrate the effectiveness of the proposed node self-monitoring mechanism.978-1-4244-7618-3 /10/$26.00 ©2010 IEEE