Estimation of P-wave velocity from other borehole data
P-wave velocities are a key parameter for seismic processing and the absence of this parameter reduces the robustness of the images from very expensive seismic surveys. The P-wave velocities in an area are particular to the area, as the P-wave velocity depends on many factors and varies with geologi...
| Published in: | Proceedings, Near Surface Geoscience 2016 - First Conference on Geophysics for Mineral Exploration and Mining |
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| Main Authors: | , , |
| Format: | Conference Object |
| Language: | unknown |
| Published: |
2016
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| Subjects: | |
| Online Access: | https://hdl.handle.net/20.500.11937/6887 https://doi.org/10.3997/2214-4609.201602122 |
| Summary: | P-wave velocities are a key parameter for seismic processing and the absence of this parameter reduces the robustness of the images from very expensive seismic surveys. The P-wave velocities in an area are particular to the area, as the P-wave velocity depends on many factors and varies with geological conditions. Hence, using a localized model predicts P-wave velocity better than the application of a generic model for the entire dataset. In this work, we utilized fuzzy c-means (FCM) clustering to build a "fuzzy" relationship that estimates Vp. Our method was tested on a dataset from the Kevitsa Ni-Cu-PGE deposit in northern Finland. The borehole data comprises P-wave velocity, density, natural gamma, magnetic susceptibility, resistivity and assay data of Ni of six boreholes. In this area, there are many boreholes, but very few have P-wave velocity logged or the data is corrupted by tool limitations. Therefore, it is beneficial to predict the velocity from other data to help seismic processing. In order to demonstrate the robustness of our program, we used the data from five holes for training and one hole for Vp testing. The results show that our method can reasonably estimate P-wave velocity from other borehole data. |
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