# High-Resolution Mapping of Subsurface Sedimentary Facies and Reservoirs Using Seismic Sedimentology
## 3 Methodology and Workflow
### Step 2: Select Proper Seismic Attributes for Seismic Lithology  
For the processed seismic data available to interpreters, the main aspects influencing actual resolution are choosing attributes, selecting frequency bandwidth, and choosing the wavelet shape. Specific strategies are more effective for geologic and geophysical studies, including stratigraphy, sedimentology, structure, rock physics, or fluid content. A comprehensive understanding of these issues requires an extensive literature review. This section will concentrate specifically on the effective strategies for stratigraphic and sedimentological interpretations. _1. Choice of attributes_: A seismic attribute can be any measurement of seismic data. Many seismic attributes can connect seismic signals to a reservoir formation's acoustic impedance, lithology, and porosity profile. Notable examples include the 90\({}^{\circ}\)-phase trace, integrated trace, and model-based inversion, which are all valuable attributes. Currently, seismic trace (waveform) serves as the primary platform for investigation. Based on the author's experience, the 90\({}^{\circ}\)-phase trace is straightforward, geologist-friendly, and preferred (as discussed in the next section). Additionally, some specific attributes known as thin-bed indicators can indicate thin beds at or below seismic resolution. These thin-bed attributes include instantaneous attributes [34], singularity attributes [35; 36; 37], and phase residues [38]. We hope to see more model and case studies in the near future to promote the widespread application of these attributes in the industry, hoping to reduce the bias of using limited attributes. _2. Wavelet shape and compactness_: Zero-phase and 90\({}^{\circ}\)-phase waveforms represent two of the most important symmetric waveforms used by seismic interpreters in the petroleum industry. Seismic interpreters often prefer using zero-phase wavelets due to their symmetric waveform, which offers the most compressed representation and the best temporal resolution [39]. However, this advantage mainly applies when analyzing a single reflection surface, such as structural mapping. Suppose the subject is a thin bed (see Figure 2). In that case, the composite waveform centered at the thin bed turns antisymmetric (meaning a main waveform lobe followed an equal amplitude but opposite polarity lobe), removing the benefits of the symmetric wavelet. Researchers can employ a 90\({}^{\circ}\) wavelet to recreate a symmetric waveform to address this issue while preserving compactness and optimal resolution [40; 41] (see Figure 4). When comparing a series of striatal slices created with 90\({}^{\circ}\) data to those derived from a zero-phase cube within the same strata, interpreters will notice that the 90\({}^{\circ}\) slice series displays fewer channel fingerprints [41]. This difference is due to there being less mixing of the vertically stacked reflections, leading to reduced interference. This method is particularly effective for interpreting interbedded thin-bed sequences. 3 Approaches to using frequency information  
Frequency components in subsurface seismic data are intrinsically related to bed-thickness distribution in a geologic formation. Seismic frequency analysis and utilization have significantly progressed during the last three decades. [PERSON] et al. [42] invented the spectral decomposition method, applying the short-window discrete Fourier transform (SWDFT) to calculate the spectral energy for time-frequency cubes. In contrast, [PERSON] and [PERSON] [43] developed the continuous wavelet transform (CWT), which correlated a set of wavelets across a travel time trace to create localized frequency images of the seismic trace in time. This method was refined by matching pursuit decomposition (MPD) to achieve higher vertical resolution in the frequency domain [44; 45; 46]. Moreover, avoiding mixing low-frequency components with high-frequency subsets in a display can significantly enhance thin-bed resolution.