本文为国家自然科学基金地质联合基金重点项目（编号U2244221）、国家自然科学基金重大研究计划“西太平洋地球系统多圈层相互作用”重点项目（编号92158205）和南方海洋科学与工程广东省实验室（广州）人才团队引进重大专项（编号2019BT02H594）联合资助的成果
WANG Zhenteng
1) Key Laboratory of Ocean and Marginal Sea Geology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, Guangdong 510301, China;2) Guangdong Provincial Laboratory of Southern Ocean Science and Engineering (Guangzhou), Guangzhou, Guangdong 511458, China;3) University of Chinese Academy of Sciences, Beijing 100049, ChinaSUN Zhen
1) Key Laboratory of Ocean and Marginal Sea Geology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, Guangdong 510301, China;2) Guangdong Provincial Laboratory of Southern Ocean Science and Engineering (Guangzhou), Guangzhou, Guangdong 511458, ChinaSUN Liheng
1) Key Laboratory of Ocean and Marginal Sea Geology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, Guangdong 510301, China;2) Guangdong Provincial Laboratory of Southern Ocean Science and Engineering (Guangzhou), Guangzhou, Guangdong 511458, China地壳厚度是地质体的重要参数，对认识区域地壳形变以及量化物质循环至关重要。由于变形破坏以及地壳厚度在较窄空间上的急剧变化，地震探测方法对俯冲增生带的莫霍面成像质量较差，很难获得准确的地壳厚度。学者们常利用重力数据反演俯冲带的地壳厚度，由于缺乏其他地球物理数据的约束，加上传统的 Parker- Oldenburg 算法在迭代过程中常需引入低通滤波以确保计算稳定。然而，如果低通滤波的范围选择不当，可能会使反演结果过于光滑。这些问题导致重力反演得到的地壳厚度结果与地震数据的一致性较差。本文基于卫星重力数据和大量海底地震仪（ocean bottom seismometer, OBS）剖面，运用优化的反向传播(back propagation, BP)神经网络的方法，建立了一个多影响指标控制的定量估计地壳厚度的模型，并利用该模型反演了马尼拉俯冲带与花东海盆区域地壳厚度。与传统重力反演（Parker- Oldenburg）法相比，在地壳厚度变化显著的区域，BP神经网络重力反演方法具有明显的优势，与OBS剖面较吻合，更符合实际地质情况。结果表明，南海海盆地壳厚度在4~9 km，东部海盆残余扩张中心呈SW- NE向一直延伸至马尼拉海沟，地壳厚度局部增厚至10 km以上。花东海盆地壳厚度为4~7 km，推测为洋壳属性。通过分析不同构造单元地壳厚度的对称性，本研究推测加瓜脊可能是西菲律宾海盆向花东海盆俯冲的产物。
Determining crustal thickness is crucial for understanding regional crustal deformation and material cycling. However, in subduction accretionary zones, complex deformation and rapid changes in crustal thickness over narrow spaces often lead to poor imaging quality of Moho surfaces, thereby hindering the accurate determination of crustal thickness through seismic methods. Gravity data are frequently used to infer crustal thickness in these zones due to the dearth of alternative geophysical data and the fact that the conventional Parker- Oldenburg algorithm often necessitates low- pass filtering to ensure stability during the iterative calculation process. However, inappropriate filtering can lead to overly smooth inversion results, creating discrepancies between gravity inversion and seismic data regarding crustal thickness. In this paper, we utilize satellite gravity data and a substantial number of ocean bottom seismometer (OBS) profiles to develop a model for quantitatively estimating crustal thickness, taking into account a range of influencing factors. We apply this model to invert the crustal thicknesses of the Manila subduction zone and the Huatung basin. In comparison to the conventional Parker- Oldenburg method, the BP neural network gravity inversion method demonstrates notable advantages in regions characterized by significant crustal thickness variations. This approach aligns more closely with OBS profiles and is more consistent with the actual geological situation. Our results demonstrate that the crustal thickness of the South China Sea basin ranges from 4 to 9 km. A localized thickening to over 10 km occurs near the residual spreading center of the eastern basin, extending SW- NE towards the Manila trench. The crustal thickness of the eastern basin is 4 to 7 km, which is assumed to be oceanic crust. Furthermore, analysis of crustal thickness symmetry in different tectonic units suggests that the Gagua Ridge may be a product of subduction from the West Philippine basin to the Huatung basin.
王贞腾,孙珍,孙李恒.2024.基于优化的BP神经网络的重力数据反演马尼拉俯冲带及花东海盆区域地壳厚度[J].地质学报,98(11):3316-3331.
WANG Zhenteng, SUN Zhen, SUN Liheng.2024. Optimised BP neural network- based gravity data inversion of crustal thickness in the Huatung basin and Manila subduction zone region[J]. Acta Geologica Sinica,98(11):3316-3331.
