Structural Knowledge Discovery Used to Analyze Earthquake Ac(5)

The Subdue structural discovery system is being used as the Data Mining tool to study the "Orizaba Fault " located in Mexico, as part of a research project of the geologist Dr. Burke Burkart. We analyze the information of the Earthquake Database

depth of “33 Km.” This is a very interesting pattern,because it might give us information about the cause ofthose earthquakes. If the earthquake is not caused bysubduction (a force caused by the Pacific plate, whicheffects depth based on the closeness to the Pacific Ocean),then there is more possibility that it is related to the fault.However, we first have to evaluate and determine the depthof earthquakes caused by subduction in that zone.

Substructure 1, 19 instances.Substructure 2, 8 instances.

Figure 7: Substructures Found in Sub-Area 26 from Table 1.

As we see in this study, Subdue is capable of finding notonly the shared characteristics of the events, but also spacerelations between them. In the case of the identification ofshared characteristics, we used the pattern containing theregion number specification to recognize the area beingstudied. The pattern containing the depth node at 33 km.gave us information that the Geology specialist Dr. Burkartis studying so that he can use it to give direction to thisresearch. In the case of the space relations, we expect tofind patterns that represent parts of the paths of theinvolved fault. The time relations (“near_in_time” edges)were not considered by Subdue, because the earthquakes inthe area are not close in time. However, there are otherareas with different characteristics where “near_in_time”connections provide important information, and we hope touncover these relations in future studies.

Conclusions

In this research, we showed that Subdue was able tosuccessfully analyze the real-world earthquake databasewhen applied as the Data Mining tool of the KnowledgeDiscovery process. It was found that Subdue can be used tofind interesting patterns that might represent newknowledge or that might lead to new knowledge.

It was also shown how Subdue used prior knowledge toguide the search with temporal and spatial relationsprovided by the “near_in_time” and “near_in_distance”edges. Subdue was able to find substructures that includedthose edges. Using this knowledge representation, thesystem not only found repetitive patterns in the data, butalso provided temporal and distance relations that madepossible the discovery of more interesting patterns. As anexample in the Earthquake database, spatial relations wereincorporated through the “near_in_distance” edges. Subduewas able to find substructures containing these edges, andthese substructures are being used to help study the“Orizaba Fault” in Mexico.

Something very important about the temporal andspatial relations is the definition of the “near_in_time” and“near_in_distance” edges. We need to establish themeaning of “near” in both cases. This is not a simple task,because it depends directly on the domain and thesemantics of the relation to be represented.

In our future work we will be working on a concept

learning approach that will learn substructuresdistinguishing two sets of sub-areas so that we can studytheir geological behavior based on earthquake activity. Wewill continue the analysis of earthquake activity incollaboration with Dr. Burkart. We have also used thespatio-temporal relation annotations to study the AviationSafety Reporting System Database (Chittimoori, Gonzalezand Holder 1999), and we plan to work with other domainsincluding a graph representation of program source code.We are also working on a theoretical analysis of Subduebased on the PAC learning theory (Jappy and Nock 1998)and conceptual graphs (Sowa 1984).

References

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Cook, Diane J.; Holder, Lawrence B.; and Djoko, Surnjani1996. Scalable Discovery of Informative StructuralConcepts Using Domain Knowledge, IEEE Expert vol. 11number 5, pp. 59-68, October.

Sowa, J. F. 1984. Conceptual Structures – InformationProcessing in Mind and Machine, Addison-Wesley.Jappy, Pascal and Nock, Richard 1998, PAC LearningConceptual Graphs, Proceedings of the 6th InternationalConference on Conceptual Structures, pp. 303-315.Chittimoori, Ravindra N.; Gonzalez, Jesus A.; and Holder,Lawrence B. 1999. Structural Knowledge Discovery inChemical and Spatio-Temporal Databases, Proceedings ofthe Sixteenth National Conference on ArtificialIntelligence, pp. 959.

Djoko, Surnjani; Cook, Diane J.; and Holder, Lawrence B.1995. Analyzing the Benefits of Domain Knowledge inSubstructure Discovery, Proceedings of the first Int. Conf.on Knowledge Discovery and Data Mining, pp. 75-80.

Fayyad, Usama M.; Piatetsky-Shapiro, Gregory; Smyth,Padhraic; and Uthurusamy, Ramasamy 1996. Advances inKnowledge Discovery and Data Mining, AAAI Press/TheMIT Press, Menlo Park, California.

Hamblin, W. Kenneth and Christianses, Eric H. 1998.Earth’s Dynamic Systems, Prentice Hall.

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