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Partner selection is the primary aspect of the formation of knowledge collaboration teams (KCTs). We propose a method of partner selection for KCTs based on a weighted social network analysis (SNA) method in which the individual knowledge competence and the collaboration performance of candidates are both considered. To select the desired partners, a biobjective 0-1 model is built, integrating the knowledge competence and collaboration performance, which is an NP-hard problem. Then, a multiobjective genetic algorithm (MOGA) is developed to solve the proposed model. Finally, a real-world example is provided to illustrate the applicability of the model, and the MOGA is implemented to search for Pareto solutions of partner selection for KCT in this case. Moreover, some simulation examples are used to test the efficiency of the algorithm. The results suggest that the proposed method can support effective and practical partner selection.

Sparkers are the sound source widely used in marine seismic exploration to provide high-resolution vertical sections. Sparkers are relatively simple, inexpensive, high-frequency sources. In this study, the types of noise occurring in sparker source data were analyzed and attenuated by a processing technique. Frequency-wave number (f-k) filtering is used for attenuating the linear noise. Predictive deconvolution is used for attenuating the ghost waves and bubble effects. A complete processing workflow was designed for processing the data, and the migration section was obtained. The results show that the sparker source data are capable of achieving vertical sections with very high resolution. It is suggested as a necessary technique for high-accuracy gas hydrate exploration in the South China Sea.