Non-core drilling has gradually become the primary exploration method in
geological engineering, and well logging curves have increasingly gained
importance as the main carriers of geological information. However, factors
such as geological environment, logging equipment, borehole quality, and
unexpected events can all impact the quality of well logging curves. Previous
methods of re-logging or manual corrections have been associated with high
costs and low efficiency. This paper proposes a machine learning method that
utilizes existing data to predict missing well logging curves, and its
effectiveness and feasibility have been validated through experiments. The
proposed method builds upon the traditional Long Short-Term Memory (LSTM)
neural network by incorporating a self-attention mechanism to analyze the
spatial dependencies of the data. It selectively includes the dominant
computational results in the LSTM, reducing the computational complexity from
O(n^2) to O(nlogn) and improving model efficiency. Experimental results
demonstrate that the proposed method achieves higher accuracy compared to
traditional curve synthesis methods based on Fully Connected Neural Networks
(FCNN) and LSTM. This accurate, efficient, and cost-effective prediction method
holds practical value in engineering applications.

通过引入自注意机制分析数据的空间依赖性，本论文提出了一种机器学习方法用于预测缺失的岩屑曲线，并通过实验证实了其有效性和可行性，该方法将传统的 LSTM 神经网络与自注意机制相结合，降低了计算复杂度，提高了模型的效率。实验结果表明，与基于全连接神经网络和 LSTM 的传统曲线合成方法相比，该方法具有更高的准确性。该准确、高效且经济实惠的预测方法在工程应用中具有实用价值。