对于使用全球粒子群优化 (GPSO) 的多层1D地球模型的电阻非线性反向探测数据
Kehinde D Oyeyemi1,2, Ahzegbobor P Aizebeokhai1, Chukwuemeka S Ukabam1
1Applied Geophysics Programme, Department of Physics, Covenant University, Nigeria.
Heliyon
|May 30, 2023
概括
粒子群集优化 (PSO) 为地物理数据逆转提供了传统最小平方方法的优越替代方案. 这项研究表明,PSO有效地解决了复杂的非线性问题,产生了更准确的地球模型,具有更低的不合适误差.
科学领域:
- 地质物理学 地质物理学
- 计算科学 计算科学
- 地球科学 地球科学 地球科学
背景情况:
- 地理物理数据的解释通常涉及解决复杂的反向非线性优化问题.
- 传统的分析方法,如最小平方反转,面临着诸如缓慢收和维度问题等局限性.
- 基于启发式的群集智能算法,特别是粒子群集优化 (PSO),为应对这些挑战提供了一个有希望的替代方案.
研究的目的:
- 评估全球粒子群集优化 (GPSO) 的有效性,以逆转地电电阻数据.
- 为了比较GPSO反转与传统最小平方反转方法的性能.
- 评估GPSO的准确性和收性质,以从垂直电探测 (VES) 数据中构建1D多层地球模型.
主要方法:
- 开发和应用粒子集群优化 (PSO) 算法用于地质电数据逆转.
- 扭转电场垂直电探测 (VES) 数据,以确定一个多层1D地球模型.
- 将GPSO反转结果与使用Winresist 1.0软件的最小平方反转方法获得的结果进行比较分析.
主要成果:
- GPSO在不到200个粒子和不到100次代的融合中取得了令人满意的反转结果.
- 与最小平方反转 (4.0) 相比,GPSO显示出明显较低的不适合错误 ().
- 结合参数极限的GPSO反转模型更好地适应了真地球模型,比最小平方方法更准确的反转模型.
结论:
- 全球粒子集群优化 (GPSO) 是一种有效的技术来逆转地电电阻数据,比最小平方方法提供更高的准确性和更快的融合.
- 虽然PSO倒置表现出较慢的执行时间,并且需要先前了解层数,但其能够估计更接近真实解决方案的模型的能力是一个显著的优势.
- 这项研究验证了GPSO作为地质物理逆转的强大工具,特别是对于地球建模中的复杂,大规模的非线性优化问题.
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