Gravity data requirements for decimetre accuracy regional geoid using Stokes’ Remove Compute Restore technique in Nigeria - ODUMOSU, Joseph Olayemi; NNAM , Victor Chukwuemeka (Contributions to Geophysics and Geodesy, 2020)
The need for dense and accurate gravity data cannot be overemphasised in the development of a precise gravimetric geoid model. Unfortunately, the field observations required are costly, and labour-intensive hence the need to ascertain via numerical simulations the appropriate field specifications before embarking on them. This paper presents an experimental study on the gravimetric data specifications (spatial resolution and data accuracy) required for achieving decimetre-level accuracy geoid using the conventional Stokes’ Remove Compute Restore (RCR) method in Nigeria. A two-step solution approach was used in this study. The steps were determination of the (i) effect of data spacing by a comparative assessment of computation results obtained by using gravity data at four user determined intervals and (ii) effect of observation accuracy by numerical simulation using error propagation analysis. The data intervals (3′×3′, 5′×5′, 10′×10′ and 20′×20′) were selected from a combination of 1815 terrestrial FA anomaly points merged with EGM2008 derived FA anomaly covering the study area. Also, observational errors investigated were 0 mGal, 0.1 mGal, 0.5 mGal, 1 mGal and 5 mGal. The study was conducted in Nigeria having a total land area of approximately 923,768 km2. The study established that gravimetric geoid accuracy improves substantially as the spatial resolution and accuracy of the gravity data improves. Also, the study identified that data spacing contributes more to the overall geoid error than data accuracy. In addition, the study observed that hilly regions should have denser data spacing than plain areas. Within the test region, a data spacing of 3′×3′ with gravity observational errors 5 mGal was found to produce an acceptable gravimetric geoid. The produced gravimetric geoid had a pre-fit Root Mean Square Error (RMSE) of 15.6 cm when compared with GNSS-Levelling data at 27 stations located evenly across the study area.