摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑新闻-关于人工智能的新研究结果已经发表。根据消息来源来自中国青岛的NewsRx编辑，这项研究指出：“方解石是一种特殊的矿物。”有利生产岩性指标，对生产能力也有积极影响。新闻编辑们从华东中国宠物大学的研究中获得了一句话：因此,方解石的识别是页岩储层勘探的一个重要内容,但由于对方解石的测井响应较弱,样品尺寸有限,难以准确识别。挑战使用传统方法。为了应对这一挑战，我们提出了数据增强和基于注意力的机器学习算法，指定测井曲线a s实验数据。最初，利用合成小数过采样技术(SMOTE)和小波变换对数据集进行优化。然后，训练一个基于atten的卷积神经网络(CNN)来提取非线性fe输入测井曲线的特征。这些非线性特征随后被馈入双向长波短时记忆(BiLSTM)提取深度域序列和深度域信息实现对方解石的准确识别。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – New study results on artificial intelligence have been published. According to news originatingfrom Qingdao, People’s Republic o f China, by NewsRx editors, the research stated, “Sparry calcite is anindicator of favorable production lithology and it also exerts a positive influence on pr oduction capacity.”The news editors obtained a quote from the research from China University of Pet roleum East China:“Therefore, sparry calcite identification is an important par t of shale reservoir exploration. However, the logging response to sparry calcit e is weak and sample sizes are limited, making accurate identificationchallengi ng using conventional methods. To address this challenge, we propose data augmen tation andattention-based machine learning algorithms, specify logging curves a s experimental data. Initially, thedataset is optimized using Synthetic Minorit y Over-sampling Technique (SMOTE) and wavelet transforms.Subsequently, an atten tion-based Convolutional Neural Network (CNN) is trained to extract nonlinear features from the input logging curves. These nonlinear features are then fed into the Bi-directional LongShort-Term Memory (BiLSTM) to extract potential informa tion regarding the depth domain sequence andachieve accurate identification of sparry calcite.”
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