# Uncertainty of the 2D Resistivity Survey on the Subsurface Cavities
## 3 Results and Discussion
### Model Accuracy, Resolution, and Sensitivity
#### 3.3.3 Sensitivity to Resistivity Contrasts  
The detection of subsurface structure using the resistivity method is mainly dependent on resistivity contrast, which may constrain the inverted model resolution [16]. We used different resistivity perturbations (\(\pm\)20%, 40%, 60%, and 80%) to examine array (DD, PD, WS, and PP) sensitivities. As shown in Figure 26, the DD array can resolve the first two rows of the checkerboards for the tested perturbations. However, the model resolution of the DD array was considerably enhanced for increased resistivity perturbation, reflecting the high sensitivity of the DD array. The high sensitivity of the DD array helped to study various subsurface structures with low [25; 74] to high [32; 75] resistivity contrasts. In contrasts, the PD array recovered the first row of the checkerboard patterns (Figure 27); however, the anomaly sizes were significantly overestimated for the higher values of negative perturbations (\(-\)60% and \(-\)80%). It can slightly detect the positive 20% perturbation of the second-row checkerboards. The PD array resolution was slightly improved as the resistivity contrast increased, indicating the less sensitivity of the PD array to the resistivity perturbations. The WS array can recover the first row of the checkerboard anomalies in all the perturbations as well as high perturbations (60% and 80%) of the second row (Figure 28). However, the resolution of the WS array is relatively lower than the DD array, and its resolution considerably improved for the increased resistivity contrasts, indicating  
Figure 25: Scenario 3 spatial resolution test for the 4 m (vertical) \(\times\) 8 m (horizontal) checkerboard cell sizes. (**a**) Checkerboard model, (**b**) DD array, (**c**) PD array, (**d**) WS array, (**e**) PP array. high sensitivity to resistivity perturbations. The PP array adequately reproduced the first-row checkerboards for all the perturbations. Its resolution slightly increased, but the improvements are negligible. This is likely related to the low sensitivity of the PP array for both low- and high-resistivity contrasts (Figure 29). As shown in Figures 26-29, all the arrays recovered the first row of the checkerboard patterns for the tested resistivity perturbations. This may be associated with the high sensitivity of the resistivity methods near the vicinity of the electrode positions. Furthermore, the checkerboard tests were used to assess the inversion artefacts. The extensive anomaly features observed below 2 m depth in Figure 23, below 4 m depth in Figures 24 and 26-29, and below 8 m depth in Figure 25 were not in the input model and interpreted as inversion artefacts. The pronounced artefacts at the edge of the profiles (e.g., Figure 26) are likely related to limited data points or no data coverages [76]. As shown in Figures 26-29, the artefact contaminations were slightly increased as the degree of resistivity perturbation increases, as the cross-borehole resistivity experiment for analyzing subsurface heterogeneity between two boreholes has also shown [70]. Even though the DD array has high resolving power, the recovered models were highly contaminated by the inversion artefacts (Figure 26). The PD and PP arrays produced moderate artefacts (Figures 27 and 29), whereas the WS array showed smaller artefacts (Figure 28). The inversion artefacts show a linear association with signal-to-noise ratio; for instance, the low signal-to-noise ratio of the DD array correlates with greater artefacts and the high signal-to-noise ratio of the WS array correlates with lesser inversion artefacts [25; 77]. We present the summary of the results from the experimental model and all the synthetic modeling performed in this study in Table 1. It can help readers to decide appropriate array types for the planned subsurface cavity studies. Figure 26: Dipole-dipole (DD) array sensitivity to resistivity perturbations. The inverted model for: (**a**) \(\pm\)20% perturbation, (**b**) \(\pm\)40% perturbation, (**c**) \(\pm\)60% perturbation, (**d**) \(\pm\)80% perturbation.