## 3 Results  
The study area of this field data is located in the Ordos Basin of western China, a major hydrocarbon-producing region characterized by a carbonate rock sedimentary environment. The primary lithology of the target reservoir is dolostone and limestone, which are of significant importance in hydrocarbon exploration due to their fracture porosity and cavernous structures. However, the target layer is overlaid by a coal seam approximately 10 m thick, which has a high acoustic impedance. The strong reflections from the overlying coal seams suppress and shield the effective reflection signals from the reservoir, interfering with the detection of the reservoir's internal structures. The complex fracture-cavity geometries within the carbonate formation, coupled with the low resolution of seismic data, present significant challenges to accurately characterizing the reservoir. Given the pronounced thickness, extensive lateral extension, and internal stability of coal seam, we integrate well-logging lithology data, petrophysical interpretation, and seismic horizon interpretation to precisely characterize the spatial distribution of coal seams within the stratigraphic framework. Building upon this foundation, by incorporating the coal-seam distribution as a geological steering matrix into the reflectivity inversion process, this method is expected to enhance resolution and effectively restore reservoir reflections affected by coal-seam reflection shielding effects. Both our proposed reflectivity inversion method based on geologically guided sparse criteria and the conventional sparse reflectivity inversion method were tested on field data. To better align with field data processing scenarios, the inversion results were convolved with the seismic wavelet to synthesize high-resolution seismic data for comparison. Figure 11a shows the profile of inline 7710 from the observed seismic data, revealing the relatively low resolution of the original data (indicated by black arrows). Additionally, the reservoir (highlighted by the green dashed box) exhibits weak reflection signals due to shielding by the overlying coal seams. Figure 11b presents the high-resolution seismic data obtained after conventional sparse inversion processing. Compared to the original data, the resolution has improved, but the continuity of the strata remains poor, with some stratigraphic layers still overlapping along the same reflection axis, making it difficult to distinguish between them, especially at the reservoir location beneath the coal seam. In Figure 11c, we present the results after applying the geologically guided sparsity criteria inversion method. Compared to the conventional method, the resolution is significantly improved, with much clearer details of the strata. The continuity of the strata is notably enhanced, especially the reflection of the reservoir, which shows a marked improvement in clarity and allows for distinct differentiation of the reflection axes of the coal and reservoir layers. To further assess the effectiveness of the inversion methods, we zoomed in on the area around the coal seam and reservoir for detailed analysis. Figure 12 shows the seismic data, with the black line indicating the well position and the blue curve representing the gamma curve of the blind well. The yellow band marks the coal seam, while the red dashed box highlights the fracture-cavity reservoir primarily composed of limestone. Figure 12a presents the observed seismic data, where the strong reflection amplitude from the coal seam is evident, along with high gamma values. Below the coal seam, in the limestone reservoir section, the gamma values remain very low, and there is almost no visible stratigraphic information, with the seismic reflection signals being very weak. Figure 12b displays the high-resolution seismic data obtained after sparse inversion processing. Some new geological features below the coal seam emerge (as indicated by the green arrows), but their continuity is poor.