ENHANCING SEA BED LOGGING DATA PROCESSING USING SPLINE INTERPOLATION FOR DEEP WATER ENVIRONMENT

At present there are many techniques being used in processing data acquired from sea
bed logging (SBL) applications. Some of the techniques are Finite Element Method
(FEM), Finite Difference Method (FDM), Method of Moment (MOM), and Boundary
Element Method (BEM). These techniques involve complicated mathematical modeling
that requires high computer specification and performance to apply them. Thus, cubic
spline interpolation technique and normalized mean square error (NMSE) are proposed as
new tools to process these data. Cubic spline interpolation is using differential equations
of order 3 and it has the ability to fit SBL data well and NMSE are calculated between
original and interpolated data. These NMSE are used to distinguish data that have
hydrocarbon (HC) to data that has no HC because data with HC is known to have higher
NMSE than data without HC.
This was proven from data collected from scaled tank experiments on which HC
positions, transmitted frequency and amplitude and spline step size were varied and
NMSE were calculated for set up with and without HC. Maximum percentage increased
on NMSE obtained from environment with hydrocarbon to without hydrocarbon when oil
positions were varied, was 1300%, transmitted EM waves were varied, was 693% at 23.2
Vp-p, frequencies were varied was 1000% and step sizes were varied, was 94%. These
high percentage increased in NMSE obtained are able to distinguish data with and with
hydrocarbon.