应用人工神经网络和最小二乘回归技术开发预测岩石参数的新型模型

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Earth Science Informatics Pub Date : 2024-09-04 DOI:10.1007/s12145-024-01464-7
C. C. Agoha, A. I. Opara, D. C. Bartholomew, L. J. Osaki, U. K. Agoha, J. O. Njoku, F. B. Akiang, E. T. Epuerie, O. C. Ibe
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引用次数: 0

摘要

这项研究是在尼日尔三角洲近海盆地进行的,目的是生成用于估算岩石参数的新型预测模型。采用 MATLAB,利用多重普通最小二乘回归(OLSR)方法,获得了四种不同岩石参数关系的模型,包括无压抗压强度(UCS)与体积密度的关系、无压抗压强度与声波穿越时间(STT)的关系、剪切波速度与声波穿越时间的关系以及渗透率与体积密度的关系。此外,还利用自适应神经模糊推理系统(ANFIS)人工智能网络对数据进行建模和优化。在研究模型的预测性能时,应用了统计工具,包括总平方和(SST)、误差平方和(SSE)、回归平方和(SSR)和相关系数(R-squared)。OLSR 分析结果表明,在所有 OLSR 模型中,只有 UCS 对体积密度模型的预测性能较高,线性模型、二次模型、幂模型、对数模型和指数模型的 R 平方值分别为 0.8637、0.8848、0.8216、0.9956 和 0.8108。ANN 模型结果显示,UCS 对体积密度、UCS 对 STT 和剪切波速对 STT 模型的预测性能都很高,R 方值分别为 0.89635、0.99365 和 0.52703,而渗透率对体积密度模型的预测性能较低(0.03378)。这些研究结果表明,所有 OLSR 模型都可以仅根据体积密度信息预测岩石 UCS,而 ANN 生成的模型除了可以根据 STT 预测剪切波速度外,还可以根据体积密度和 STT 预测研究区域及类似地质环境中的 UCS。
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Application of artificial neural network and least squares regression technique in developing novel models for predicting rock parameters

This study was carried out within the offshore Niger Delta Basin to generate novel predictive models for estimating rock parameters. MATLAB was employed in obtaining models for four different rock parameter relationships including unconfined compressive strength (UCS) against bulk density, UCS against sonic transit time (STT), shear wave velocity against STT, and permeability against bulk density using multiple ordinary least-squares regression (OLSR) methods. Also, the Adaptive-Neuro Fuzzy Inference System (ANFIS) artificial intelligence network was utilized for modeling and optimization of the data. Statistical tools including the Sum of Squares Total (SST), the Sum of Squares Error (SSE), the Sum of Squares Regression (SSR), and Correlation Coefficient (R-squared) were applied in investigating the prediction performances of the models. Results of OLSR analysis show that only the UCS against bulk density model gave high prediction performance in all the OLSR models with R-squared values of 0.8637, 0.8848, 0.8216, 0.9956, and 0.8108 for linear, quadratic, power, logarithmic, and exponential models respectively. ANN model results revealed that UCS against bulk density, UCS against STT, and shear wave velocity against STT models all gave high prediction performances with respective R-squared values of 0.89635, 0.99365, and 0.52703, while the permeability against bulk density model gave low performance (0.03378). These findings imply that all the OLSR models can be applied for the prediction of rock UCS from bulk density information only, while ANN-generated models can be used in predicting UCS from bulk density and STT, in addition to shear wave velocity from STT in the study area and similar geologic environments.

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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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