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

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

Oct 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008georl..3519306b&link_type=abstract

Geophysical Research Letters, Volume 35, Issue 19, CiteID L19306

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Computation

2

Computational Geophysics: Neural Networks, Fuzzy Logic, Machine Learning, Marine Geology And Geophysics: Gas And Hydrate Systems, Seismology: Tomography (6982, 8180), Physical Properties Of Rocks: Acoustic Properties, Nonlinear Geophysics: Self-Organization

Scientific paper

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.

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