Predicting heat flow in the Iranian plateau and surrounding areas based on machine learning approach

IF 2.7 3区 地球科学 Q2 GEOCHEMISTRY & GEOPHYSICS
Naeim Mousavi, Mohammad Tatar
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Abstract

While Surface Heat Flow (HF) is an important constraint unveiling the Earth interior's thermal structure, estimates over the Iranian plateau are sparse. In the presence of sparse estimates, machine learning provides a statistical-based prediction of HF based on a supervised predictor trained in the far-field regions. Here, we imply the machine learning technique of Gradient Boosting Regression Tree (GBRT) which has been proved to be efficient for predicting HF projecting complexities and nonlinearities of input features into predicted HF. Our results provide a robust map of HF with resolution of one degree and uncertainty of up to ±6 mW/m2 over Iran and surrounding regions. The predicted HF has an average value and minimum standard deviation of 59 and 10 mW/m2, respectively. The quality of the algorithm performance is 16%, indicated by normalized Root-Mean-Square Error (RMSE), and linear correlation of predicted HF with validation set is 97%. Total number of trees considerably prevents overfitting which is believed to be solely controllable by shrinkage factor, maximum tree depth and cross-validation scheme. The three most important features, having the highest influence on the output HF, are thermal Lithosphere-Asthenosphere Boundary (LAB), distance to volcanoes and distance to trenches. The extreme importance of LAB in HF prediction of Iran indicates that the lithospheric thermal structure is significantly controlled by lithospheric thickness in the Iranian plateau. Selection of petrologically and seismologically consistent LAB guarantees the precision of the predicted HF. Our results imply that high HF in central Iran is in agreement with extensive magmatism since the Paleozoic. Additionally, the high HF in Zagros keel (originally Proterozoic as the Zagros keel appears to be the Arabian plate front) indicates the tectonically active system of the Arabia-Eurasia collision zone, high likely, in the form of lithospheric mantle deformation.

基于机器学习方法预测伊朗高原及周边地区的热流
虽然地表热流(HF)是揭示地球内部热结构的一个重要约束条件,但伊朗高原上的估计值却很稀少。在估计值稀少的情况下,机器学习可以根据在远场区域训练的监督预测器对地表热流进行基于统计的预测。在此,我们采用梯度提升回归树(GBRT)机器学习技术,该技术已被证明可有效预测高频,将输入特征的复杂性和非线性投射到预测的高频中。我们的研究结果提供了伊朗及周边地区高频的稳健地图,分辨率为一度,不确定性高达 ±6 mW/m2。预测高频的平均值和最小标准偏差分别为 59 mW/m2 和 10 mW/m2。根据归一化均方根误差(RMSE),该算法的性能质量为 16%,预测高频与验证集的线性相关率为 97%。树的总数在很大程度上防止了过度拟合,而过度拟合被认为完全可以通过收缩因子、最大树深度和交叉验证方案来控制。对输出高频影响最大的三个最重要特征是热岩石圈-热成层边界(LAB)、到火山的距离和到海沟的距离。岩石圈-对流层边界在伊朗高频预测中的极端重要性表明,岩石圈热结构在很大程度上受伊朗高原岩石圈厚度的控制。选择岩石学和地震学上一致的岩石圈可保证高频预测的精确性。我们的研究结果表明,伊朗中部的高高频与古生代以来广泛的岩浆活动相一致。此外,扎格罗斯龙骨(原为新生代,因为扎格罗斯龙骨似乎是阿拉伯板块前沿)的高HF表明阿拉伯-欧亚碰撞带构造活跃,很可能是岩石圈地幔变形的形式。
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
Tectonophysics
Tectonophysics 地学-地球化学与地球物理
CiteScore
4.90
自引率
6.90%
发文量
300
审稿时长
6 months
期刊介绍: The prime focus of Tectonophysics will be high-impact original research and reviews in the fields of kinematics, structure, composition, and dynamics of the solid arth at all scales. Tectonophysics particularly encourages submission of papers based on the integration of a multitude of geophysical, geological, geochemical, geodynamic, and geotectonic methods
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