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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Image Statistic Models Characterize Well Log Image Quality
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Image Statistic Models Characterize Well Log Image Quality

机译：图像统计模型表征测井图像质量

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摘要

Assessing the image quality of well logs is essential to ensure the accuracy of their digitization and subsequent processing. Currently, the suitability of well logs for information retrieval is solely determined on the basis of subjective judgments of their image quality by human experts. The success of natural scene statistics (NSS)-based models that are used to conduct no-reference (NR) quality assessment of photographic images motivates us to try to exploit them to characterize the quality of nonphotographic images, such as well logs. Accordingly, we develop a scheme to characterize the quality of a well log as "acceptable" or "unacceptable" for subsequent processing based on the natural image quality evaluator (NIQE), a successful NR image quality assessment model based on the NSS. Our experimental results show that the objective quality scores thus obtained can be reliably used to eliminate well logs of inferior quality from the processing pipeline, which can serve as a beneficial step to reduce the human hours spent in examining well logs and to improve the rate of information retrieval as well as the accuracy of retrieved information. Source code for the trained well log image quality predictor is available at https://github.com/Somdyuti2/Well_log_IQA.
机译：评估测井的图像质量对于确保其数字化和后续处理的准确性至关重要。当前，仅根据人类专家对其图像质量的主观判断来确定测井记录是否适合信息检索。基于自然景物统计（NSS）的模型用于对摄影图像进行无参考（NR）质量评估的成功促使我们尝试利用它们来表征非摄影图像（例如测井）的质量。因此，我们基于自然图像质量评估器（NIQE）（基于该模型的成功NR图像质量评估模型），开发了一种方案，将测井的质量表征为“可接受”或“不可接受”以用于后续处理。 NSS。我们的实验结果表明，由此获得的客观质量得分可以可靠地用于消除加工管道中劣质的测井记录，这可以作为减少检查测井记录的人工时间并提高测井效率的有益步骤。信息检索以及所检索信息的准确性。训练有素的测井图像质量预测器的源代码可在https://github.com/Somdyuti2/Well_log_IQA获得。

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