This paper discusses on the use of Spline Interpolation with Mean Square Error (MSE) as tools to process data acquired from scaled down experiments that replicate sea bed logging environment. Sea bed logging (SBL) is a new technique that uses marine controlled source electromagnetic (CSEM) sounding technique and is proven to be very successful in detecting and characterizing hydrocarbon bearing reservoirs in deep water area by using resistivity contrasts. Data collected from this SBL technique shall be used for processing and modeling purposes and to predict location and depth of the resistive bodies. Data collected from this technique is enormous; therefore a good processing tool or technique is required. In this work, a scaled tank with a scale factor of 2000 was built to replicate the SBL environment with varying hydrocarbon positions. Data acquired from series of experiment was processed using spline interpolation technique and mean square error. These data were interpolated using Spline interpolation technique (degree of three) and mean square error (MSE) was calculated between original data and interpolated data. Comparisons were made by studying the trends and relationship between this data. It is found that the MSE is on increasing trends in the experiments that involved the presence of hydrocarbon in the setting than the one without. This shall give indication that combination of spline interpolation and mean square error can be used as new techniques in processing CSEM data. © 2011 IEEE.