Ggm

ABSTRACT Considering the rigors of terrain based gravity observation and subsequent reductions, EGM 2008 derived gravity anomaly is herein suggested as an alternative measure in reconnaissance exploration. Aerial gravity anomaly maps of Niger State (north-central Nigeria) have been used to make inference(s) on the geologic information and the nature of crust. Attempt was made to interpret the Bouguer map based on distribution, nature, and characteristics of anomalies and integrate the data with known geologic and tectonic features. The Bouguer anomaly map was superimposed on geographical map of Niger State to determine the geographical locations with gravity/lows and highs and corresponding closures. It reveals that the gravity anomaly map helps delineate major tectonic boundaries and infer the structural trends of the area. It is observed that the gravity high zones are associated with geologic structure-like lineaments in their proximity.

This paper evaluates a number of latest releases of GOCE/GRACE global geopotential models (GGMs) using the GPS-levelling geometric geoid heights, terrestrial gravity data and existing local gravimetric models. We investigate each global model at every 5 degree of spherical harmonics. Our analysis shows that the satellite-only models derived by space-wise and time-wise approaches (SPW_R1, SPW_R2 TIM_R1 and TIM_R2), GOCO01S together with EGM08 (combined model) are very distinct and consistent to the local data, which guarantees one of them to be selected as the best of candidate models and then to be utilized in our further geoid studies. One of Satellite-only models will be employed for acquiring the long wavelength geoid component which is one of major steps in the geoid determination. EGM08 will be used to compensate and restore the missing gravity data points in the un-surveyed parts within the target area. We expect further improvements in geoid studies in Sudan due to the improved medium wavelength part of the gravity field from GOCE mission.

The legacy of Sudan gravity data was extensively collected over 15 years for oil exploration after small-scale gravity campaigns designated for groundwater investigations. However, the acquired gravity data sets were very sparse due to the large area of the country (Sudan and South Sudan). This study conducts an intensive procedure to refine and reconstruct the local gravity grid by selecting an appropriate geopotential model (GGM) to fill the gaps of un-surveyed areas. The digital elevation model is used to smooth the terrestrial gravity and reduce the gross error using the cross-validation technique. The best-fit agreement utilizing the standard deviation (SD) between the tested GGMs and ground data varies from a model to another. Three models, namely EGM2008, EIGEN-6C4, and XGM2019e, have close agreements with the ground data (8.35 to 8.77 mGals), while GOCO05C yields 10.75 mGal. XGM2019e has the lowest SD detected at d/o 760, and the satellite altimetry data are used over the Red Sea marine area. Investigations, comparisons, and spectral analysis are demonstrated to select the most suitable geopotential model to improve the Sudan gravity field.

The jointly adjusted levelling networks at the South and North Islands of New Zealand (in combination with GPS data) are used to assess the accuracy of the currently available regional gravimetric geoid/quasigeoid models (NZGeoid2009, BEM-quasigeoid and KTH-geoid). The least squares analysis is applied to combine the gravity and GPS-levelling data using a three-parametre model. The GPS and levelling data are further used to evaluate the accuracy of the recently released satellite-only global geopotential models (GGMs) compiled using GRACE and GOCE data. The regional accuracy of these GGMs in New Zealand is compared with the combined models EGM2008 and EIGEN-GL04C. The analysis of regional gravimetric solutions reveals that the fit of the KTH geoid model with GPS-levelling data is 12 cm in terms of the standard deviation (STD) of differences. The STD fit of the NZGeoid2009 and BEM quasigeoid models with GPS-levelling data is 14 and 15 cm, respectively. This accuracy is better (except for NZGeoid2009) than the STD fit obtained when applying the average offsets of individual local vertical data. The analysis of GGMs reveals that the GOCE satellite-only model GO-CONS-GCF-2 version TIM-R2 has the best agreement with GPS-levelling data; the STD of differences is 57 cm and the mean of differences is 2 cm.