Publication Date

8-2021

Date of Final Oral Examination (Defense)

5-24-2021

Type of Culminating Activity

Thesis

Degree Title

Master of Science in Computer Science

Department

Computer Science

Supervisory Committee Chair

Catherine Olschanowsky, Ph.D.

Supervisory Committee Member

Dylan Mikesell, Ph.D.

Supervisory Committee Member

Steven Cutchin, Ph.D.

Abstract

Tsunami detection and forecasting is a difficult problem that scientists are trying to tackle. Early path estimation and accurate prediction of the arrival time and size of a tsunami can save lives and help with impact assessment. Tsunami inducing earthquakes cause ground and sea-surface displacements that push up on the atmosphere. This atmospheric disturbance propagates upwards as an acoustic wave and eventually hits the ionosphere. IonoSeis is a software simulation package that leverages satellite-based ionospheric remote-sensing techniques to determine the epicenter of these earthquakes.

The execution time of the ray-tracing component of IonoSeis prevents its use as a real-time modeling tool. A proposed solution is to replace this component with a newer ray-tracing package developed by Los Alamos National Laboratory called GeoAc and parallelize it. This research is a case study that uses the sparse polyhedral framework (SPF) to represent the operational GeoAc code and thereby drive the requirements for a SPF optimization framework that is being actively developed.

DOI

https://doi.org/10.18122/td.1842.boisestate

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