从现场数据中学习岩石物理模型的有理函数神经网络

IF 1.6 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Journal of Geophysics and Engineering Pub Date : 2023-09-28 DOI:10.1093/jge/gxad079
Weitao Sun, Zhifang Yang
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引用次数: 0

摘要

地震波速度估算是了解地球内部结构的关键。传统的岩石物理模型需要仔细的物理假设和数学推导,在应用于复杂的现场数据时往往面临挑战。经验公式虽然简单,但缺乏坚实的物理基础。为了解决这些限制,我们提出了一种数据驱动的方法,使用有理函数神经网络(RafNN)进行岩石物理建模。通过分析测井数据,RafNN建立了一个合理的方程,可以捕捉岩石模量、基质刚度、孔隙度和流体之间的相互依赖关系。结果表明,当训练数据符合约束条件时,RafNN能够准确地提取出Gassmann方程。此外,RafNN可以从偏离Gassmann方程的测井数据中推导出一般模型。这些数据驱动的模型显示出较低的预测误差,同时保持与Gassmann模型的一致性。RafNN对现场数据可变性的适应性是一个关键优势,有助于更好地理解潜在的数学和物理原理。此外,我们探讨了模量、孔隙率和压缩性之间的关系,揭示了RafNN模型的物理解释。值得注意的是,RafNN直接从现场数据中导出分析模型,减少了对数学推导和物理假设的依赖。虽然还需要进一步的研究来理解RafNN的收敛理论,但这项研究为数据驱动的岩石物理建模提供了一种很有前途的方法。它有助于探索地球的非均质结构,并推动地震波速度估计领域的发展。
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Rational function neural networks for learning rock physics models from field data
Abstract Seismic wave velocity estimation is critical for understanding Earth's internal structure. Traditional rock physics models require careful physical assumptions and mathematical derivations, often facing challenges when applied to complex field data. Empirical formulas, while simple, lack a solid physical foundation. To address these limitations, we propose a data-driven approach using rational function neural networks (RafNN) for rock physics modelling. By analysing logging data, RafNN establishes a rational equation capturing the interdependencies among rock modulus, matrix stiffness, porosity, and fluid. The results show that RafNN accurately extracts the Gassmann's equation when the training data adheres to its constraints. Moreover, RafNN can derive general models from logging data that deviate from the Gassmann's equation. These data-driven models exhibit lower prediction errors while maintaining consistency with Gassmann's model. RafNN's adaptability to field data variability is a key advantage, facilitating better comprehension of the underlying mathematical and physical principles. Additionally, we explore the relationship between modulus, porosity, and compressibility, shedding light on the physical interpretation of RafNN models. Notably, RafNN derives analytical models directly from field data, reducing reliance on mathematical derivations and physical assumptions. Although further research is needed to understand the convergence theory of RafNN, this study presents a promising approach for data-driven rock physics modelling. It contributes to the exploration of Earth's heterogeneous structure and advances the field of seismic wave velocity estimation.
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来源期刊
Journal of Geophysics and Engineering
Journal of Geophysics and Engineering 工程技术-地球化学与地球物理
CiteScore
2.50
自引率
21.40%
发文量
87
审稿时长
4 months
期刊介绍: Journal of Geophysics and Engineering aims to promote research and developments in geophysics and related areas of engineering. It has a predominantly applied science and engineering focus, but solicits and accepts high-quality contributions in all earth-physics disciplines, including geodynamics, natural and controlled-source seismology, oil, gas and mineral exploration, petrophysics and reservoir geophysics. The journal covers those aspects of engineering that are closely related to geophysics, or on the targets and problems that geophysics addresses. Typically, this is engineering focused on the subsurface, particularly petroleum engineering, rock mechanics, geophysical software engineering, drilling technology, remote sensing, instrumentation and sensor design.
期刊最新文献
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