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Figure 4. Topography on the study area of Bilogora mountainous For the accuracy assessment, fitting was made on a set of six GNSS/levelling points located in the study area, The red line shows the behaviour of the standard devi- ation for the input gravity data. Even if the RMS would be neglected, the last three models could be eliminated regarding the too large interval of extreme values showing incapability to compute a solid consistent quasi-geoid model from such a small density of terrestrial gravity data. Observing other models, it is visible how the gravity

Figure 4 Topography on the study area of Bilogora mountainous For the accuracy assessment, fitting was made on a set of six GNSS/levelling points located in the study area, The red line shows the behaviour of the standard devi- ation for the input gravity data. Even if the RMS would be neglected, the last three models could be eliminated regarding the too large interval of extreme values showing incapability to compute a solid consistent quasi-geoid model from such a small density of terrestrial gravity data. Observing other models, it is visible how the gravity

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Figure 1. The location of Bilogora valley in the context of Croatian territory
Figure 1. The location of Bilogora valley in the context of Croatian territory
Table 1. Gravity data transformed from Potsdam to IGSN71, in [mGal].
Table 1. Gravity data transformed from Potsdam to IGSN71, in [mGal].
le 3. Preparation of the gravity points datasets for geoic computations by reducing data spacing. N is the index of gravity dataset and geoid solution.
le 3. Preparation of the gravity points datasets for geoic computations by reducing data spacing. N is the index of gravity dataset and geoid solution.
Table 2. Computed free-air and Bouguer disturbances and anomalies, in [mGal].
Table 2. Computed free-air and Bouguer disturbances and anomalies, in [mGal].
re2. Spatial distribution of gravity datasets with different spacing of gravity anomalies. (a) The dataset No.1 — original num- ber of gravity points: 24 263 pts. (b) The dataset No.4 — 10x sparser than the original: 2427 pts. (c) The dataset No.5 — 20x sparser than the original: 1214 pts. (d) The dataset No.6 — 50x sparser than the original: 486 pts. (e) The dataset No.7 — 100x sparser than the original: 243 pts. (f) The dataset No.8 — 200x sparser than the original: 122 pts. (g) The dataset No.9 — 500x sparser than the original: 49 pts. (h) The dataset No.10 — 1000 sparser than the original: 25 pts
re2. Spatial distribution of gravity datasets with different spacing of gravity anomalies. (a) The dataset No.1 — original num- ber of gravity points: 24 263 pts. (b) The dataset No.4 — 10x sparser than the original: 2427 pts. (c) The dataset No.5 — 20x sparser than the original: 1214 pts. (d) The dataset No.6 — 50x sparser than the original: 486 pts. (e) The dataset No.7 — 100x sparser than the original: 243 pts. (f) The dataset No.8 — 200x sparser than the original: 122 pts. (g) The dataset No.9 — 500x sparser than the original: 49 pts. (h) The dataset No.10 — 1000 sparser than the original: 25 pts
fable 4. Statistics for the free-air gravity anomalies (only available terrestrial gravity data, without EGM2008), in [mGal].
fable 4. Statistics for the free-air gravity anomalies (only available terrestrial gravity data, without EGM2008), in [mGal].
jure 3. Models of gravity anomalies created from gravity datasets with different spacing of gravity anomalies, in [10-° ms~7]. (a) The dataset No.1 — original number of data: 24 263 pts. (b) The dataset No.4: 2427 pts. (c) The dataset No.5: 1214 pts. (d) The dataset No.6: 486 pts. (e) The dataset No.7: 243 pts. (f) The dataset No.8: 122 pts. (g) The dataset No.9: 49 pts. (h) The dataset No.10: 25 pts
jure 3. Models of gravity anomalies created from gravity datasets with different spacing of gravity anomalies, in [10-° ms~7]. (a) The dataset No.1 — original number of data: 24 263 pts. (b) The dataset No.4: 2427 pts. (c) The dataset No.5: 1214 pts. (d) The dataset No.6: 486 pts. (e) The dataset No.7: 243 pts. (f) The dataset No.8: 122 pts. (g) The dataset No.9: 49 pts. (h) The dataset No.10: 25 pts
The basic statistical parameters which were used and indicate internal and external accuracy of developed geoid models are mean, standard deviation and root mean square (RMS): where AN; are differences between GNSS/levelling points and geoid model obtained from Equation (4), 1 is the number of control GNSS/levelling points.
The basic statistical parameters which were used and indicate internal and external accuracy of developed geoid models are mean, standard deviation and root mean square (RMS): where AN; are differences between GNSS/levelling points and geoid model obtained from Equation (4), 1 is the number of control GNSS/levelling points.
fable 5. The RMS of geoid models depending on the 3-, 4- and 5-parametric fitting model, in [cm].
fable 5. The RMS of geoid models depending on the 3-, 4- and 5-parametric fitting model, in [cm].
Table 6. The RMS trend depending on the spatial resolutior of terrestrial data, in [cm]. data spacing impacts the RMS of a computed quasi-geoid model carrying 3mm RMS difference between quasi- geoid model computed by 10 pts/km? to the quasi-geoid solution computed by 1 pt/km7’, seen in Table 6.
Table 6. The RMS trend depending on the spatial resolutior of terrestrial data, in [cm]. data spacing impacts the RMS of a computed quasi-geoid model carrying 3mm RMS difference between quasi- geoid model computed by 10 pts/km? to the quasi-geoid solution computed by 1 pt/km7’, seen in Table 6.
Figure 5. The range of the computed geoids versus the anomaly’s standard deviation
Figure 5. The range of the computed geoids versus the anomaly’s standard deviation
Figure 6. Standard deviations of anomalies and 5-parameter fit regards to the density of terrestrial gravity data
Figure 6. Standard deviations of anomalies and 5-parameter fit regards to the density of terrestrial gravity data
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