In this study, we extract earthquake co-occurrence patterns for investigating mechanical interactions in the affected areas. To extract seismic patterns, both co-occurrence among seismic events in the event sequence and distances between the hypocenters to find hot spots must be considered. Most previous researches, however, have considered only one of these aspects. In contrast, we utilized co-occurring cluster mining to extract seismic patterns by considering both co-occurrence in a sequence and distance between hypocenters. Then, we acquired affected areas and relationships between the co-occurrence patterns and focal mechanisms from the 2011--2012 hypocenter catalog. Some results were consistent with seismological literature. The results include highly affected areas that may indicate asperity, and change of focal mechanisms before and after the Tohoku Earthquake.

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Open Crowdsourcing platforms like Amazon Mechanical Turk provide an attractive solution for process of high volume tasks with low costs. However problems of quality control is still of major interest. In this paper, we design a private crowdsourcing system, where we can devise methods for the quality control. For the quality control, we introduce four worker selection methods, each of which we call preprocessing filtering, real-time filtering, post processing filtering, and guess processing filtering. These methods include a novel approach, which utilizes a collaborative filtering technique in addition to a basic approach of initial training or gold standard data. For an use case, we have built a very large dictionary, which is necessary for Large Vocabulary Continuous Speech Recognition and Text-to-Speech. We show how the system yields high quality results for some difficult tasks of word extraction, part-of-speech tagging, and pronunciation prediction to build a large dictionary.

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