## 4 Discussion  
The results presented above demonstrate the importance of improving seismic data resolution. The authors of [20] aptly posited that since the seismic trace is a superposition of many overlapping reflections, it is often difficult to identify the exact reflection boundaries. The applied harmonic extrapolation algorithm enhanced the frequency content of the data and made subtle geologic features more easily identifiable. As illustrated by Figures 20 and 21, the enhanced seismic data shows a significant increase in resolution resulting in detailed reservoir stratigraphy, clearer pinch outs, and structures as the event appeared more sharply defined and less affected by low-frequency noise ([PERSON] et al., 2020). In agreement with [PERSON] and [PERSON], 1990, this increment in resolution was possible because wavelet transformation allows the retention of local transient signal characteristics beyond the capabilities of the infinite harmonic basis functions by allowing a multi-resolution representation of a process. The recovery of this transient signal formed the basis for the enhanced resolution of the sub-seismic features presented above, not the  
Figure 21: (a) generalized spectral decomposition of the data before bandwidth extrapolation (time slice—300 ms), (b) generalized spectral decomposition of the data after bandwidth extrapolation (time slice—300 ms). GSD volumes of 10, 30, and 40 Hz frequencies were blended using the RGB tool. The symbols \(\oplus\) indicate the well locations. arbitrary invention of frequency [19], suggesting the recoverability of seismic frequency below the conventional tuning limit. Moreover, the observed reduction of signal amplitude in Figures 12 and 18 for the trace model and the seismic trace, respectively, could give an insight into what happens in the resolution of thick and sub-seismic features. Further, ref. [6] hinted that the differences between thin-bed response and thick-bed response are that thick-bed response has a separate response for the top and bottom of the bed, the two wavelets do not interfere substantially, and the amplitude of the wavelet depends on reflection strength (reflection coefficient). For isolated thin beds, reflections from the top and bottom of the bed interfere. The result is a single wavelet response that approximates the time derivative of the original wavelet. This implies that the amplitude response of a thin bed in narrowband data is not a true response of the bed thickness, but an approximation of the amplitude responses of the interfered wavelets. Thus, it is assumable that when the thin bed is correctly resolved, the actual amplitude responses are reinstated. This explains the reduction in amplitude magnitude after we applied the enhancement algorithm. The thin beds are diminished in amplitude according to the thinness of the bed. An advantage of having this enhanced data is that thin beds are more correctly resolved, and the interference problems are less disruptive. High-frequency enhancement aims to sharpen the data, apparently by better defining structures and pinch-outs [18]. They can aid interpretation by making events appear more markedly defined and less deluged by the low-frequency ringing that characterizes conventional seismic data [59]. Figures 14 and 17 illustrate that the bandwidth of both the trace model and the seismic data spectra were increased by about 137% respectively. This increment translated to increased resolutions as shown in other results above, suggesting that resolution is directly proportional to the bandwidth of the wavelet. The broader the bandwidth, the greater the resolution [6]. Thus, it is safe to say that resolution depends directly on bandwidth and indirectly on frequency.