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Using JMP® 11 to Predict Occurrence of Seismic Activity to Prevent Hazards During Underground Mining Shifts


Mining activity ,often is associated with a high risk due to inherent ‘mining hazards’. Among these hazards, a special case of threat called a seismic hazard occurs frequently in many underground mines. Seismic hazard is hard to detect and is comparable to an earthquake. Existing techniques such as the Seismic method and the Seismo-acoustic monitoring allow a better understanding of rock mass processes but the accuracy of these methods is not precise when it comes to the prediction of seismic occurrence. The complexity of understanding the process involved and the disproportionate ratio of low energy seismic events to that of high energy seismic events make these techniques insufficient in predicting a seismic event accurately. To ensure safety at mining workplaces, it is important to predict these events accurately. Our objective to predict occurrence of seismic activity to prevent hazards during underground mining shifts


Zabiulla Mohammed is a Masters’ student in Management Information Systems at Spears School of Business, Oklahoma State University. He holds SAS Statistical Business Analyst and Base Programmer for SAS 9 Credentials. He has 5 years of experience working with two Fortune 100 companies. Currently he is enrolled in the SAS and OSU Data Mining Certificate program and works as Graduate Assistant in the Economics department. He has an undergraduate degree in Computer Science and Engineering.

Sai Vijay Kishore Movva is a graduate student in Management Information Systems at Spears School of Business, Oklahoma State University. He works as Research and Teaching assistant for the department of Marketing at OSU. Before joining OSU, he worked as Service Support Representative and as Content Manager. He holds the SAS and OSU Data Mining Certificate, Base SAS 9 and SAS Predictive Modeler certifications. He presented 2 posters at the SAS Analytics Conference 2013.