محاسبات کوانتومی - Quantum-Computing
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
ieee - ieee Transactions on Geoscience and Remote Sensing;2022;60; ;10:1109/TGRS:2021:3085340
Naihao Liu; Teng Huang; Jinghuai Gao; Zongben Xu; Daxing Wang; Fangyu Li
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.