Seismic Data Interpretation

In this project, we are trying to present that how in the last 30 days earth’s structure has been acting through the details provided to us by the United States Geological Surveying department. The data at our disposal is in csv (comma separated values) format, and thus the use of R language was imminent. At a longer viewpoint we decided to divide the project into two parts, firstly being the achievement of seismic data interpretation for which we studied the data and applied a few basic techniques, including cluster analysis to identify behavior of that data. Secondly we did a bit of research and formulated a couple of case studies that highlight the impact these earthquakes had on the economy and corporate workflow of the country. Since Nepal and Japan were the most recent nations to face these natural disasters, it was mandatory to classify them as our focal points of these case studies.

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. Usage of R clearly helps us in the process as the predefined packages like “scatterplot3d” and “rgeos” were of direct usage.

This is of prime importance to clarify that we attempt to present the details of the earthquake data of the entire world for last thirty days. Prediction of results through this analytical report for the future origins of such situations is not being complied as such.

Skills: Data Mining, Data Science, R Programming Language, Research, Statistical Analysis

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