دانلود مقاله انگلیسی رایگان:تفسیر لیتولوژی مبتنی بر یادگیری عمیق کوانتومی از Well Logs - 2022
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  • Quantum-Enhanced Deep Learning-Based Lithology Interpretation From Well Logs Quantum-Enhanced Deep Learning-Based Lithology Interpretation From Well Logs
    Quantum-Enhanced Deep Learning-Based Lithology Interpretation From Well Logs

    دسته بندی:

    محاسبات کوانتومی - Quantum-Computing


    سال انتشار:

    2022


    عنوان انگلیسی مقاله:

    Quantum-Enhanced Deep Learning-Based Lithology Interpretation From Well Logs


    ترجمه فارسی عنوان مقاله:

    تفسیر لیتولوژی مبتنی بر یادگیری عمیق کوانتومی از Well Logs


    منبع:

    ieee - ieee Transactions on Geoscience and Remote Sensing;2022;60; ;10:1109/TGRS:2021:3085340


    نویسنده:

    Naihao Liu; Teng Huang; Jinghuai Gao; Zongben Xu; Daxing Wang; Fangyu Li


    چکیده انگلیسی:

    Lithology interpretation is important for understanding subsurface properties. Yet, the common manual well log interpretation is usually with low efficiency and bad consistency. Therefore, the automatic well log interpretation tools based on machine learning and deep learning have been developed. Although the state-of-the-art sophisticated models can show fine interpretation performance with acceptable accuracies, “blind” tests do not always exhibit satisfactory results because of the complexity of lithology interpretation with respect to subsurface rock properties and the data-labeling quality. To solve this generalization challenge, we propose to leverage the parameterized quantum circuits in the deep-learning model. The quantum computing takes advantages of the superposition and entanglement quantum systems, which could potentially endow the generalization power or capability to the deep-learning model. Using the proposed quantum-enhanced deep-learning (QEDL) model, we have tested the model performance on field well log data from different wells. Compared with the classic fine convolutional neural network (CNN) model and the long short-term memory (LSTM) model, the proposed QEDL model achieves comparable model performance with a clearly improved generalization power for interpreting both thin and thick lithology layers. In addition, because of the quantum circuit structure, the QEDL model needs much fewer model parameters than LSTM and CNN models, i.e., the QEDL parameter number in our study can be approximately 75% less than that of LSTM and 89% less than that of CNN.
    keywords: Deep learning | lithology interpretation | quantum computing.


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 13
    حجم فایل: 5696 کیلوبایت

    قیمت: رایگان


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