Error-based correlation coefficient: An alternative to combine error and coefficient of correlation and its application in geophysical data

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science Pub Date : 2025-05-02 DOI:10.1016/j.jocs.2025.102611

Waskito Pranowo , Adhitya Ryan Ramadhani

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引用次数: 0

Abstract

The coefficient of correlation and error values are two standard metrics for determining the similarity between two data sets. Correlated errors in experimental data are a common problem that is often overlooked. In addition, traditional error estimation approaches do not consider pattern similarity. An error-based method for estimating correlation coefficients is proposed, combining the fundamental principles of ' 'Pearson's correlation coefficient and error. This method represents a generalised form of the concordance correlation coefficient (CCC). The experiment with synthetic geophysical data pairs demonstrates that the suggested method effectively evaluates pattern and amplitude similarity. The proposed error-based correlation coefficient is comparable to the concordance coefficient of correlation but with some modifications. These modifications increase the new method's sensitivity to scale shifting, a vital element in geophysical data processing and analysis.

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基于误差的相关系数：误差与相关系数相结合的一种替代方法及其在地球物理资料中的应用

相关系数和误差值是确定两个数据集之间相似性的两个标准度量。实验数据中的相关误差是一个经常被忽视的常见问题。此外，传统的误差估计方法没有考虑模式相似度。结合Pearson相关系数和误差的基本原理，提出了一种基于误差的相关系数估计方法。该方法代表了一致性相关系数（CCC）的一种广义形式。用合成地球物理资料对进行的实验表明，该方法能有效地评价图幅相似度。提出的基于误差的相关系数与相关的一致性系数相当，但做了一些修改。这些改进提高了新方法对尺度变化的敏感性，这是地球物理数据处理和分析中的一个重要因素。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).