地球与行星物理论评（中英文）2026，Vol.57Issue(6)：647-662,16.DOI:10.19975/j.dqyxx.2026-002
利用深度学习识别接收函数的地球X间断面信号
Detecting the X-discontinuity in receiver functions by using deep learning technique
摘要
Abstract
The X-discontinuity in the upper mantle of the Earth's interior,typically located at depths of approxi-mately 250-350 km,is critical for unraveling mantle composition and dynamics.However,its global distribution and underlying formation mechanisms remain poorly constrained.Conventional methods for identifying this dis-continuity using teleseismic P-wave receiver functions rely heavily on post-stacking empirical interpretation,which is inefficient and unsuitable for processing large volumes of seismic data.To address the challenges posed by massive datasets and the scarcity of manually labeled samples,this study proposes a transfer learning strategy termed"pretraining on synthetic data followed by fine-tuning with real data".We construct a convolutional neural network-based binary classification model to determine the presence or absence of the X-discontinuity.The proce-dure is as follows:First,synthetic receiver function datasets—with and without the X-discontinuity—are generated through forward modeling based on three classical velocity models(AK135,IASP91,and PREM).Noise augmen-tation and temporal trimming are applied to improve model generalization.Second,high-quality observed data are selected from global seismic networks and manually annotated to create a real dataset.Finally,a two-phase training approach is adopted,where the model is sequentially trained on the synthetic and real datasets to achieve auto-mated detection of the X-discontinuity.Experimental results show that the trained model achieves an accuracy of approximately 90%in classifying receiver function images.In determining the presence of the X-discontinuity be-neath seismic stations,the model exhibits about 80%agreement with results derived from conventional methods,indicating its effectiveness in automated detection.Based on this approach,subsequent research will systematically investigate the global distribution of the X-discontinuity.关键词
X间断面/接收函数/深度学习/EfficientNet-B7/迁移学习Key words
X-discontinuity/receiver function/deep learning/EfficientNet-B7/transfer learning分类
天文与地球科学引用本文复制引用
屈梦伟,邓阳凡,胡仲发,马骥骁,叶秀薇..利用深度学习识别接收函数的地球X间断面信号[J].地球与行星物理论评（中英文）,2026,57(6):647-662,16.基金项目
国家自然科学基金资助项目(92479204) （92479204）
中国科学院战略先导项目(XDB0840200) （XDB0840200）
国家重点研发计划项目(2023YFC3008602) Supported by the National Natural Science Foundation of China(Grant No.92479204),the Strategic Priority Research Program(B)of the Chinese Academy of Sciences(Grant No.XDB0840200),and National Key R&D Program of China(Grant No.2023YFC3008602) （2023YFC3008602）
