Development Of Unified Integrated Model To Characterize Geophysical Data Using Image Processing Technique
| dc.contributor.author | SAHEED, ISHOLA KEHINDE | |
| dc.date.accessioned | 2016-06-14T07:40:17Z | |
| dc.date.available | 2016-06-14T07:40:17Z | |
| dc.date.issued | 2015-03 | |
| dc.description.abstract | Conducting geophysical surveys using electrical resistivity imaging where different electrode array configurations are used or multiple geophysical techniques are employed in order to obtain comprehensive information about the layered Earth model. The purpose of this research was to combine these techniques into an integrated unified image that increases the overall quality and reliability of the geophysical images for subsurface characterization. To this end, unsupervised classification technique via clustering algorithm was employed. To meet this need, resistivity imaging based on data sets from different standard electrode arrays was conducted for both synthetic and field data examples. For the synthetic case, basic statistical parameters (i.e. minimum, maximum, median, and average) were introduced to merge the different images to a single unified image. Also, the 2-D post inversion images were combined to a single unified image using k-means clustering technique with some initial parameters defined before implementing the clustering and classification procedures. All the inverse geophysical images were pre-processed, manipulated, and image analysis carried out using an image processing package, PCI Geomatica. The performance of the geophysical images was carried out in the synthetic examples using mean absolute error, mean absolute percentage error and construction of an error matrix table while for the field data unified images available borehole lithologic logs were used. Results show that the best images representing the true models comparable to those from individual electrode configurations were obtained from maximum approach. Additionally, the overall accuracy and kappa coefficients show good agreement between the unified and true models’ images. Furthermore, the unified images obtained by the post-classification merging of some clusters show that there are two to four groups representing features of the models in the synthetic examples while in the field examples, the unified models contain between three to six groups. Each of these groups is characterized by the measured geophysical parameter(s) (i.e., electrical resistivity, chargeability or seismic refraction velocity) together with inferred lithological units. Overall, the subsurface local geology of the study areas is characterized by clay, clayey sand, silt, sand and gravelly sand. The cases considered in this study can be viewed as a successful application of how image processing technique can be a promising additional tool where combination of different geophysical data is the key to a comprehensive subsurface characterization. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/2104 | |
| dc.subject | Development Of Unified Integrated Model To Characterize Geophysical Data | en_US |
| dc.subject | Using Image Processing Technique | en_US |
| dc.title | Development Of Unified Integrated Model To Characterize Geophysical Data Using Image Processing Technique | en_US |
| dc.type | Thesis | en_US |
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