Efficient Algorithms for Data Analytics in Geophysical Imaging
| dc.contributor.author | Kump, Joseph Lee | en |
| dc.contributor.committeechair | Martin, Eileen R. | en |
| dc.contributor.committeemember | Embree, Mark P. | en |
| dc.contributor.committeemember | Hewett, Russell Joseph | en |
| dc.contributor.department | Mathematics | en |
| dc.date.accessioned | 2021-06-15T08:01:16Z | en |
| dc.date.available | 2021-06-15T08:01:16Z | en |
| dc.date.issued | 2021-06-14 | en |
| dc.description.abstract | Modern sensing systems such as distributed acoustic sensing (DAS) can produce massive quantities of geophysical data, often in remote locations. This presents significant challenges with regards to data storage and performing efficient analysis. To address this, we have designed and implemented efficient algorithms for two commonly utilized techniques in geophysical imaging: cross-correlations, and multichannel analysis of surface waves (MASW). Our cross-correlation algorithms operate directly in the wavelet domain on compressed data without requiring a reconstruction of the original signal, reducing memory costs and improving scalabiliy. Meanwhile, our MASW implementations make use of MPI parallelism and GPUs, and present a novel problem for the GPU. | en |
| dc.description.abstractgeneral | Modern sensor designs make it easier to collect large quantities of seismic vibration data. While this data can provide valuable insight, it is difficult to effectively store and perform analysis on such a high data volume. We propose a few new, general-purpose algorithms that enable speedy use of two common methods in geophysical modeling and data analytics: crosscorrelation, which provides a measure of similarity between signals; and multichannel analysis of surface waves, which is a seismic imaging technique. Our algorithms take advantage of hardware and software typically available on modern computers, and the mathematical properties of these two methods. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:31151 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/103864 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Algorithms | en |
| dc.subject | Wavelets | en |
| dc.subject | Cross-correlations | en |
| dc.subject | MASW | en |
| dc.subject | GPU | en |
| dc.title | Efficient Algorithms for Data Analytics in Geophysical Imaging | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Mathematics | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |