## 6 Conclusions  
* The lithological types in the study area predominantly consist of sandstone, siltstone, mudstone, limestone, and dolomite. A single logging curve is inadequate for reservoir identification. Therefore, a lithology indicator curve was developed to effectively identify sandstone, utilizing a cutoff value of less than 83. Subsequently, seismic waveform inversion was employed to predict thin sand bodies, which can be identified vertically at depths of 3 to 8 m and laterally with distinct boundaries. The validation results of the inversion demonstrate a coincidence rate exceeding 90% with the participating wells and over 80% with the verification wells. * Guided by seismic sedimentary theory, a Self-Organizing Map (SOM)-based multi-attribute optimization analysis was conducted utilizing three seismic attributes sensitive to lithological characteristics: Root Mean Square Amplitude (RMS), Total Energy (TE), Average Energy (AE), Amplitude Envelope (ENV), and Peak Amplitude (PA). SOM operations were employed for correlation and clustering analyses to identify a subset of data that reflects lithological features. In conjunction with well-seismic calibration, a threshold for sandstone distribution was established, non-sandstone areas were excluded, and a spatial hollowing-out map of the Lower Member of the Second East Sandstone Formation was produced. The visual representation of this map, in comparison to single attributes, emphasizes the distribution of beach-bar sand bodies and facilitates clearer identification and delineation of the sandbody edges. * Seismic waveform inversion, which embodies phase-controlled principles, operates independently of the quantity and distribution of wells. The Self-Organizing Map (SOM) algorithm is effectively employed for comprehensive attribute clustering analysis. The integration of these two techniques in the study of beach-bar microfacies within the Lower Member of the Second East Sandstone Formation in the HHK Depression reveals a strong correlation between sandstone thickness pinch-out lines and  
Figure 14: Detailed characterization of beach-bar sedimentary microfacies in the Lower E3d2 Submember of the study area. (**a**) SOM-processed attribute hollow map; (**b**) Inversion-predicted sandbody thickness map; (**c**) Characteristics of Beach-Bar Depositional Facies Distribution. the boundaries of attribute amplitude anomalies. By predicting sandbody thickness through waveform inversion and integrating litho-electric characteristics with seismic responses, three microfacies types are delineated: bar body, beach body, and beach edge. The thickness of the bar body sandbody is approximately 8 to 14 m, the beach body ranges from 3 to 8 m, and the beach edge measures between 0 and 3 m.
* Guided by geological principles and seismic sedimentology theory, high-resolution waveform indicator inversion is employed in well-controlled areas to enhance the accuracy of thin-sandstone predictions. In regions lacking well control, Self-Organizing Map (SOM) based seismic attribute clustering mitigates the limitations of inversion precision by clarifying the spatial distribution of sand bodies. This integrated methodology addresses two critical challenges in seismic interpretation: the low vertical resolution of seismic data and the complexities associated with lithological identification. By combining the high-resolution capabilities of waveform inversion with the pattern recognition advantages of SOM clustering, this approach offers a novel solution for predicting thin sandstones and delineating boundaries in sparsely sampled areas. [PERSON]: Contribution roles: Resources, Writing-original draft and writing-review and editing. [PERSON] is a main author responsible for the overall coordination and organization of the paper. He arranged the requirements of the case study, ensuring that all necessary information and data were captured. [PERSON]: Contribution roles: Resources, Supervision.