PDF(2092 KB)
Automatic picking of dispersion curves based on region splitting and merging method
Na KANG, YanQing LI, JingJie CAO
Prog Geophy ›› 2026, Vol. 41 ›› Issue (2) : 732-743.
PDF(2092 KB)
PDF(2092 KB)
Automatic picking of dispersion curves based on region splitting and merging method
Surface wave dispersion curve picking is one of the important steps in surface wave data processing. At present, the dispersion curve picking mainly relies on a large number of manual processing and is time consuming when a large number of dispersion curves need to be picked up, and it is difficult to meet the demand of efficient processing of modern surface wave exploration. How to efficiently and accurately realize picking of surface wave dispersion curves automatically has become an urgent problem. In this paper, the picking of dispersion curves is regarded as an image segmentation problem, and a region splitting and merging algorithm is proposed to realize picking of dispersion curves from the dispersion energy map quickly and accurately. The algorithm uses the standard deviation and average value of the grayscale value of the pixels in the dispersion energy map region to establish the consistency criterion of region splitting and merging, uses the consistency criterion to split and merge them continuously to split the disperse energy region, and finally realizes the automatic picking of the dispersion curve by tracking the peaks of different frequencies in the disperse energy region. To verify the effectiveness of the algorithm, a soft sandwich-containing model and a velocity-increasing model are used for comprehensive testing. The results show that the automatically picking dispersion curves are highly consistent with the theoretical dispersion curves. Meanwhile, the actual surface wave data test further confirms the accuracy of the region splitting and merging algorithm.
Dispersion curves / Automatic picking / Region splitting and merging algorithm / Surface wave exploration
|
Alyousuf T, Colombo D, Rovetta D, et al. 2018. Near-surface velocity analysis for single-sensor data: An integrated workflow using surface waves, AI, and structure-regularized inversion. //SEG Technical Program Expanded Abstracts 2018. Anaheim, California, USA: Society of Exploration Geophysicists, 2342-2346.
|
|
|
|
|
|
Dai R T. 2022. Extraction and inversion of Rayleigh wave dispersion curve based on machine learning[Master's thesis] (in Chinese). Xi'an: Chang'an University, doi: 10.26976/d.cnki.gchau.2022.000361.
|
|
|
|
Dal Moro G, Pipan M, Forte E, et al. 2003. Determination of Rayleigh wave dispersion curves for near surface applications in unconsolidated sediments. //SEG International Exposition and Annual Meeting. SEG.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Long J, Shelhamer E, Darrell T. 2015. Fully convolutional networks for semantic segmentation. //Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 3431-3440.
|
|
|
|
|
|
|
|
Park C B, Miller R D, Xia J H. 1998. Imaging dispersion curves of surface waves on multi-channel record. //SEG Technical Program Expanded Abstracts 1998. Society of Exploration Geophysicists, 1377-1380.
|
|
|
|
Ren L, Gao F C, Wu Y L, et al. 2020. Automatic picking of multi-mode dispersion curves using CNN-based machine learning. //SEG Technical Program Expanded Abstracts 2020. Virtual: Society of Exploration Geophysicists, 1551-1555.
|
|
|
|
Ronneberger O, Fischer P, Brox T. 2015. U-net: Convolutional networks for biomedical image segmentation. //18th International Conference on Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Munich, Germany: Springer, 234-241.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Wu X J. 2020. Research on inversion method of high frequency surface wave based on deep learning[Master's thesis] (in Chinese). Chengdu: University of Electronic Science and Technology of China, doi: 10.27005/d.cnki.gdzku.2020.000908.
|
|
|
|
|
|
Xie P, Boelle J L, Khosro F, et al. 2017. Automatic surface wave dispersion curve picking and symbolic calculation inversion. //79th EAGE Conference and Exhibition 2017. Paris, France: European Association of Geoscientists & Engineers, 1-5.
|
|
|
|
|
|
Yao H, Cao W P, Huang X R, et al. 2021. Automatic extraction of surface wave dispersion curves using unsupervised learning. //First International Meeting for Applied Geoscience & Energy. Denver, Colorado, USA: Society of Exploration Geophysicists, 1826-1830.
|
|
|
|
|
|
Zheng D, Miao X G. 2014. Multimodal Rayleigh wave dispersion curve picking and inversion to build near surface shear wave velocity models. //76th EAGE Conference and Exhibition-Workshops. Amsterdam, Netherlands: European Association of Geoscientists & Engineers, cp-401-00048.
|
|
|
|
|
|
|
|
代瑞涛. 2022. 基于机器学习的瑞雷波频散曲线提取及反演研究[硕士论文]. 西安: 长安大学, doi: 10.26976/d.cnki.gchau.2022.000361.
|
|
|
|
|
|
|
|
|
|
|
|
伍锡军. 2020. 基于深度学习的高频面波反演方法研究[硕士论文]. 成都: 电子科技大学, doi: 10.27005/d.cnki.gdzku.2020.000908.
|
|
|
|
|
感谢审稿专家提出的修改意见和编辑部的大力支持!
/
| 〈 |
|
〉 |