## 1 Introduction  
Accurate interpretation of seismic reflection data is one major contributing factor to successful oil and gas exploration. The success of a seismic geomorphology study is dependent not only on knowledge of sedimentological or stratigraphical principles and the local geological setting, but also on the quality of the seismic geomorphological imaging [1]. Notwithstanding that many advanced technologies, sophisticated computers, and software have been used in data acquisition and processing, there are still many challenges associated with the interpretation of seismic data, one of which is the seismic resolution problem [2]. It is still difficult to uncover the geologic stories recorded in seismic data volumes, especially when they are sub-seismic [3]. Seismic signal-to-noise ratio typically degrades with depth, and interpretation is hampered further at depth if the unit of interest is thin and well below seismic resolution. Furthermore, reflections from multiple thin beds commonly interfere with one another and form merged events, making object- or volume-based imaging more difficult [4]. To improve our understanding of hydrocarbon reservoirs, there is a need to enhance the resolution quality of the seismic data. Resolution is the ability to separate two features that are close together [5, 6]. Because of the limited seismic bandwidth, there is a thickness below which seismic loops cannot be effectively distinguished [7]. Improving the bandwidth of seismic data has always been one of the primary concerns for geophysicists for decades because this would enable seismologists to interpret finer details of the subsurface geology, such as the extraction of stratigraphic, structural, and geomorphological details from seismic data; this has become indispensable because of the emerging complexities in the delineation, characterization, and development of hydrocarbon reservoirs [8]. There is a thickness below which seismic loops essentially have constant separation as a function of frequency bandwidth [9]. This thickness is known as the tuning thickness. The below-tuning effects are a result of the interference of wavelets, which are a function of geology as it changes vertically and laterally [10]. According to [2,8,11], the tuning thickness is a quarter of a wavelength for vertical resolution and \((z+\lambda/4)^{2}\) for horizontal resolution. As shown below, Figure 1 depicts the synthetic seismic response of a theoretical wedge model. A simple layer wedging out from right to left is shown in Figure 1a. It was made from sand's top and bottom reflectivities, with opposite polarities separated by a variable distance. Figure 1b depicts how the extracted amplitude (red dotted line) fluctuates with the actual wedge thickness. The thickness at which the amplitude is maximum is the tuning thickness. Some five decades ago, [PERSON] proposed \(\lambda/8\) as the resolution limit of seismic data, where \(\lambda\) is the predominant wavelength in the data [8]. As a result of the presence of noise and the consequent broadening of the wavelet resulting from the spherical divergence of the seismic wavefront during its subsurface propagation, this resolution is usually assumed to be \(\lambda/4\). According to this theory, the wavelength is the yardstick for resolution, which in turn depends on velocity and frequency. Because velocity generally increases with depth, [PERSON] only variable and major parameter for determining resolution is frequency. Based on this theory and other contributing factors, several techniques have been used by scientists to overcome the interpretation problems posed by the limited seismic data bandwidth. These include detuning of seismic amplitude [9], wedge modeling [8], improvement of signal-to-noise ratio [11], enhancing the shape of wavelets for an improved bandwidth [12,13], spectral inversion [2,14], inverse Q filtering [15], spectral bluing and color inversion [16], sparse layer reflectivity inversion [17], and so on.