## 5 Conclusions  
In this study, we explored the feasibility and effectiveness of using an adaptive spectrogram convolution model which considers both depth sequence correlation graphs and feature correlation graphs for logging porosity prediction. Through ablation and comparison experiments, we found that the ADF-GCN performed well in this task, with significant performance advantages. The experimental results showed that the ADF-GCN exhibited better distribution characteristics in the relative error interval and relative frequency histogram. The chart's peaks and positive curves demonstrate the model's reliability and precision across various error ranges, visually affirming the ADF-GCN's enhanced performance in predicting log porosity. We explored the rationale behind the model's superior performance; the ADF-GCN model, by adaptively creating depth sequence correlation graphs and feature correlation graphs via an attention mechanism, transcends the constraints of static graph construction, substantially enhancing the prediction model's precision and robustness. Additionally, by using a multi-head attention-based selection mechanism, we automatically detected and focused on key high-dimensional features resulting from the attention-aware graph convolution, efficiently eliminating redundant and overlapping information. Our study offers a robust approach to predicting logging porosity and introduces novel insights for the use of deep learning within geological and geoscience applications. The successful application of ADF-GCN in processing deep sequence data demonstrates its unique advantages and opens up new possibilities for further geoscientific tasks.  
Despite the remarkable achievements of our research, there is still room for improvement. Future research could focus on optimizing the architecture and parameters of ADF-GCN to better adapt it to different geological structures and logging data types. Moreover, incorporating geological and geoscientific expertise could further refine the model's capacity to comprehend and analyze geological characteristics. Collectively, our research presents a potent predictive tool for log porosity, a crucial contribution to the geosciences domain. We anticipate that our findings will offer valuable lessons and inspiration for subsequent studies, potentially advancing logging techniques and geoscientific research.  
L.H.: Writing--original draft (equal). X.W.: Writing--review and editing. Y.D.: Visualization. P.Z.: Writing--review and editing. P.H.: Writing--review and editing. X.L.: Writing--review and editing. F.J.: Methodology. C.Z.: Data curation. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key Research and Development Project of China (2021 YFA1000103, 2021 YFA1000102), National Natural Science Foundation of China (Grant Nos.62272479, 62372469, 62202498), Taishan Scholarship (tstp20240506), Shandong Provincial Natural Science Foundation (ZR2021 QF023).  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.