English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

Neural network analysis of crosshole tomographic images: The seismic signature of gas hydrate bearing sediments in the Mackenzie Delta (NW Canada)

Authors
/persons/resource/klaus

Bauer,  Klaus
2.2 Geophysical Deep Sounding, 2.0 Physics of the Earth, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Pratt,  R. G.
External Organizations;

/persons/resource/haber

Haberland,  Christian
2.2 Geophysical Deep Sounding, 2.0 Physics of the Earth, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/mhw

Weber,  Michael
2.2 Geophysical Deep Sounding, 2.0 Physics of the Earth, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

External Ressource
No external resources are shared
Fulltext (public)

12000.pdf
(Any fulltext), 788KB

Supplementary Material (public)
There is no public supplementary material available
Citation

Bauer, K., Pratt, R. G., Haberland, C., Weber, M. (2008): Neural network analysis of crosshole tomographic images: The seismic signature of gas hydrate bearing sediments in the Mackenzie Delta (NW Canada). - Geophysical Research Letters, 35, L19306.
https://doi.org/10.1029/2008GL035263


https://gfzpublic.gfz-potsdam.de/pubman/item/item_237585
Abstract
Crosshole seismic experiments were conducted to study the in-situ properties of gas hydrate bearing sediments (GHBS) in the Mackenzie Delta (NW Canada). Seismic tomography provided images of P velocity, anisotropy, and attenuation. Self-organizing maps (SOM) are powerful neural network techniques to classify and interpret multi-attribute data sets. The coincident tomographic images are translated to a set of data vectors in order to train a Kohonen layer. The total gradient of the model vectors is determined for the trained SOM and a watershed segmentation algorithm is used to visualize and map the lithological clusters with well-defined seismic signatures. Application to the Mallik data reveals four major litho-types: (1) GHBS, (2) sands, (3) shale/coal interlayering, and (4) silt. The signature of seismic P wave characteristics distinguished for the GHBS (high velocities, strong anisotropy and attenuation) is new and can be used for new exploration strategies to map and quantify gas hydrates.