Use of
Artificial Neural Network to Identify Turbidite
Deposit Gradation Generated in Laboratory
Manica, Rafael1,
Ana Luiza de Oliveira Borges Borges1 (1) Universidade Federal do
Artificial Neural Networks (ANN) are intelligent computational systems inspired by the human
biological system capable of reproduce the human brain ability in solving
problem tasks. Based this, the present study aims to introduce this
complementary non-subjective tool in order to identify turbidity depositional
patterns inferred from physical simulation. In this case, three different types
of bed gradation patterns which are usually found in natural turbidity systems
(normal grading, inverse grading and massive grading). The procedure employed
digital images of the deposits generated from physical simulation. These images
were also used to training (pattern learning), validating (efficiency checking)
and simulating the ANN. Thus, after 18 turbidity currents experiments have been
performed with all the three proposed types of bed grading patterns, it was
observed that the ANN's results demonstrated accuracy
factors ranged between 53-92% (mean of 76%) for normal grading, 52-99% (mean of
73%) for massive grading and 38-76% (mean of 53%) for inverse grading. The
methodology introduced in this study, although quite simple, presented very
concise results, which closely match up the results obtained through
conventional analyzing tools (grain size analysis and qualitative observations)
being an alternative tool to reduce uncertainty and subjectivity in physical
simulation analysis. Furthermore, this non-intrusive methodology (based in
digital images only) can be extrapolated to field observations allowing the
classification of several turbidity facies and/or sedimentological process in natural sites
AAPG Search and Discover Article #90063©2007 AAPG Annual Convention, Long Beach, California