| 基于MSA-WBiLSTM的测井仪器通过长度预测 |
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| 引用本文：高雅田，朱颖.基于MSA-WBiLSTM的测井仪器通过长度预测[J].计算技术与自动化,2026,(2):113-118 |
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| 中文摘要:为了精准预测测井仪器在复杂井眼中的通过性，提出了一种融合多头自注意力机制与权重修正层的双向长短期记忆神经网络模型MSA-WBiLSTM。旨在通过深化对测井仪器通过长度的理解与分析，提升测井仪器通过性的预测精度。该模型以BiLSTM为基础模型，设计权重修正层调整BiLSTM的输出权重，并引用多头自注意力机制进行关键信息的聚焦与强化，最终实现测井仪器通过长度的精确预测。实验结果表明，MSA-WBiLSTM模型与其他模型相比，平均误差降低67%，模型拟合度提高27%，可以精确反应测井仪器在复杂井眼条件下的实际通过能力，为测井方案设计提供坚实可靠的数据支撑与科学指导。 |
| 中文关键词:多头自注意力机制 权重修正层 双向长短期记忆神经网络 MSA-WBiLSTM 测井仪器通过长度 |
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| Prediction of Logging Instrument Pass-through Length Based on MSA-WBiLSTM |
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| Abstract:In order to accurately predict the passage of logging instruments in complex boreholes, innovatively proposes a bi-directional long and short-term memory neural network model MSA-WBiLSTM, which integrates the multi-head self-attention mechanism and the weight correction layer, aiming to improve the prediction accuracy of the passage of logging instruments by deepening the understanding and analyzing the passage length of logging instruments. The model takes BiLSTM as the base model, designs the weight correction layer to adjust the output weight of BiLSTM, and invokes the multi-head self-attention mechanism for focusing and reinforcing the key information, and finally realizes the accurate prediction of logging instrument passability. The experimental results show that the average error of MSA-WBiLSTM model is reduced by 67%, and the model fit is improved by 27%, which can accurately reflect the actual passing ability of logging instruments under complex borehole conditions, and provide solid and reliable data support and scientific guidance for the design of logging programs. |
| keywords:multi-head self-attention mechanism weight correction layer bidirectional long short-term memory MSA-WBiLSTM passage length of logging instruments |
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