Depth of Moho beneath the Tanghai-Shangdu seismic array profile from ambient noise autocorrelation
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摘要:
基于唐海—商都宽频带地震台阵2006—2009年连续三年的波形记录，利用环境噪声相位自相关函数对台阵下方的莫霍面反射P波进行分析。通过对同一个台站多个时间段的自相关结果进行分组、采用两步叠加处理增加信号强度：① 在组内进行线性叠加，对组间的叠加结果进行相位加权叠加；② 基于华北地区的背景速度结构信息，在地壳平均速度5%不确定性的时窗内，根据自相关函数包络线的二阶导数最大值确定P波的莫霍面反射时间，经时间−深度转化，获得台阵下方的莫霍面深度。结果显示，莫霍面从东南向西北总体由浅变深，中间有小幅度的起伏。噪声自相关方法确定的莫霍面平均深度相较于参考的接收函数结果的偏差为0.8 km，相应的双程走时偏差约为0.3 s。以月份叠加的自相关函数结果显示，PmP信号的噪声源具有显著的季节性变化。自相关函数的波形特征显示华北地区的地壳−地幔转换带的速度梯度模式不同。
Abstract:Ambient noise encompasses both surface and body waves. Although surface-wave extraction is more common, the extraction of body waves is not as widespread, with its application in the Earth’s deep subsurface exploration typically confined to regions with simpler geological structures. A linear broadband seismic array, deployed in the North China Craton, spans from the southeast （Tanghai） to the northwest （Shangdu） and traverses plain, mountainous, and plateau regions. This study endeavors to extract Moho-reflected P-waves （PmP） from three years of array recordings （2006–2009）, with the extracted body waves utilized to determine the depth of the Moho interface beneath the array. The process of extracting PmP from ambient noise involves six steps. In the first step, continuous recordings of the vertical component of the PmP are divided into segments of 1 h durations. In the second step, the recorded segments undergo bandpass filtering in a frequency range of 2−4 Hz for most stations and 1−2 Hz for a select few. The third step involves phase autocorrelation of the filtered segments. In the fourth step, a two-step stacking process is applied to the phase autocorrelation functions from multiple time intervals at the same seismic station. A linear stacking is initially performed within the group of autocorrelation functions, followed by a phase-weighted stacking of the group results to generate a seismic trace. In the fifth step, the second-order derivative of the envelope of the stacked traces is computed. Finally, in the sixth step, within a time window containing vertically reflected P-waves from the Moho interface which is determined based on prior information （i.e., Moho depth and assumed error of 5% in average crustal P-wave velocity）, the time corresponding to the maximum value of the second-order derivative is selected. This time is converted to the Moho depth using the average crustal velocity. Following the six steps, the data from the Tanghai–Shangdu array are processed. In terms of the time window for P-wave selection, the prior Moho depth was obtained from the results of receiver function inversion. The assumed average crustal P-wave velocity was 6.3 km/s in the northwest section （Inner Mongolia Plateau and Yanshan Orogenic Belt） and 5.7 km/s in the southeast section （Bohai Bay） of the seismic array. Data were unavailable for two of the 51 seismic stations within the array, while the autocorrelation functions of three stations displayed periodic oscillations, thus posing challenges in identifying the PmP signals. Ultimately, data for Moho depths were obtained for 46 stations. Along the Tanghai–Shangdu array, the Moho depth demonstrated a general trend of deepening from southeast to northwest, ranging from approximately 33 to 42 km. Minor fluctuations of a few kilometers were observed on the array profile. Compared to the receiver function results used as prior information, the Moho depths determined utilizing noise autocorrelation functions exhibited an average deviation of 0.8 km, with a corresponding two-way travel time deviation of approximately 0.3 s. The PmP in the ambient noise cross-correlation functions of three groups of station pairs were investigated to validate the extracted P-waves and determined Moho depths. These station- pair groups had three common midpoint stations respectively located in two end sections and in the middle section of the seismic array. The optimal crustal average velocity （v*） and PmP arrival time with zero offset （$t_{0}^{*} $） were chosen within a considerably wide velocity range and time window. This selection aimed to maximize the energy of the stacked PmP waves for the common midpoint. The Moho depths determined from v* and $t_{0}^{*} $ were highly consistent with the results obtained from autocorrelation functions. The discrepancies in Moho depths at the three common midpoint stations, progressing from northwest to southeast, were 0.5 km, 0.68 km, and 2.02 km, respectively. The PmP obtained through monthly stacking of autocorrelation functions showed distinct seasonal variations. By contrast, the results from annual stacking remained relatively stable over the entire three-year observation period. This indicated that the noise sources that contributed to the PmP in North China showed substantial variations at the seasonal scale but exhibited great stability on an annual scale. For various stations, the resulting stacked traces of autocorrelation functions exhibited notable variations in the shape, amplitude, and duration of the PmP. To elucidate the features of the reflected waves, we conducted simulations using two representative velocity models, each characterized by distinct crust-mantle transition zones. The results indicated that PmP waves from the thin crust-mantle transition zone with a large velocity gradient exhibited shorter durations and stronger amplitudes, resembling the reflection waves observed at station K005. By contrast, reflection waves from the thick crust-mantle transition zone with a small velocity gradient showed longer durations and weaker amplitudes, similar to those observed at station K040. Based on these findings, we concluded that the crust-mantle transition zones beneath the Tanghai–Shangdu array feature distinct velocity gradient patterns. The prevalence of seismic ambient noise facilitates the acquisition of valuable body wave data, particularly in regions that experience fewer seismic events. Body waves extracted from ambient noise carry rich information about the Earth’s interior, akin to seismic waves generated by active or passive sources. Thus, extracting reflected P-waves from autocorrelation functions of ambient noise and using them to delineate internal Earth interfaces such as the Moho holds significant promise.
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Keywords:
- North China /
- Moho depth /
- seismic ambient noise /
- phase autocorrelation /
- reflected P waves
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图 1 唐海—商都测线的台站分布图
Figure 1. Distribution of the seismic stations on Tanghai-Shangdu survey line
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图 2 K035台不同方式叠加的自相关结果
（a） 线性叠加；（b） 相位加权叠加；（c） 两步叠加.红色线为根据武岩等（2018）的接收函数结果换算的莫霍面垂直反射P波时间
Figure 2. Autocorrelograms stacked with different methods for station K035
（a） Linear stack；（b） Phase-weighted stack；（c） Two-step stack. The red line indicates the traveltime converted from the receiver function result （Wu et al，2018） for the P wave vertically reflected from the Moho interface
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图 3 K006台自相关函数的分组分步叠加
（a） 叠加道为10组函数相位加权叠加的结果；（b） 每组函数约86天的自相关函数线性叠加的结果。图中红色部分为PmP波信号
Figure 3. Grouped stacks and the final two-step stack of the autocorrelations for station K006
（a） The final trace results from the phase-weighted stack of 10 group-stacked autocorrelations；（b） Individual traces result from the linear stack of autocorrelations for about 86-day periods. The red waveforms are Moho P reflections
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图 4 唐海—商都测线K005台站的叠加自相关函数及其包络线（a）和包络线的二阶导数（b）
图中，灰色区域为基于先验信息计算的包含PmP信号的时窗，蓝色线为根据武岩等（2018）接收函数结果计算的PmP走时，红色线为本文二阶导数的最大值，下同
Figure 4. The stacked autocorrelogram （a） and the second derivative of its envelope （b） for station K005 on the Tanghai−Shangdu survey line
The grey area is the time window containing PmP wave，based on prior information，and the blue line indicates the PmP travel time according to the receiver function result （Wu et al，2018），and the red line corresponds to themaximum of the second derivative，the same below
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图 5 唐海—商都测线K040台站的叠加自相关函数及其包络线（a）和包络线的二阶导数（b）
Figure 5. Autocorrelogram （a） and the second derivative of its envelope （b） for station K040 on the Tanghai−Shangdu survey line
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图 6 唐海—商都测线K031台站不同滤波频带的叠加自相关函数及其包络线（a，c）和包络线的二阶导数（b，d）
Figure 6. Stacked autocorrelograms （a，c） and the second derivatives of their envelopes （b，d） using different filtering frequency bands for station K031 on the Tanghai−Shangdu survey line
（a，b） 1—2 Hz；（c，d） 2—4 Hz
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图 8 唐海—商都地震台阵的环境噪声自相关函数剖面
蓝线为根据武岩等（2018）接收函数结果计算的PmP信号走时，红色线为基于自相关函数包络线二阶导数拾取的PmP信号走时
Figure 8. Autocorrelograms of ambient noise recorded by stations along the Tanghai-Shangdu survey line
Blue bars indicate the PmP travel times from the receiver function results （Wu et al，2018），and the red bars correspond to those based on the second derivatives of envelopes of the ambient noise autocorrelograms
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图 9 噪声互相关函数的共中心点水平叠加分析
（a） 共中心点台对的噪声互相关函数；（b） 叠加信号的归一化能量分布，最大值点（十字符号所示）在$ {v} $=6.30 km/s，$ {t}_{0}^{\mathrm{*}} $=13.62 s，相应的时距曲线如图a中红色虚线所示；（c） 对应$ {v}^{\mathrm{*}} $和$ {t}_{0}^{\mathrm{*}} $的互相关叠加结果，红线和绿色虚线分别对应自相关函数和互相关函数中PmP信号的走时
Figure 9. Stack analysis of ambient noise cross correlations for station pairs with the common middle point
（a） Cross correlations with common middle point；（b） Normalized energy of the stacked cross correlations function. The maximum point （indicated by the cross） lies at v*＝6.30 km/s and ${t}_{0}^{\mathrm{*}} $＝13.62 s，and the corresponding distance-time curve is shown as a red dashed line in Fig.（a）； （c） The stacked cross-correlation trace for v* and ${t}_{0}^{\mathrm{*}} $，the red solid line and the green dotted line indicate the travel times of PmP wave in the autocorrelogram and the cross correlation function，respectively
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图 10 K005台不同频带滤波的自相关函数
Figure 10. Autocorrelations function filtered with different filtering frequency bands for the station K005
（a） 0.1—0.3 Hz；（b） 0.5—1 Hz；（c） 1—2 Hz；（d） 2—4 Hz；（e） 4—6 Hz
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图 11 K007台PmP波信噪比随分步叠加中分组个数的变化
Figure 11. Signal-to-noise ratios of the PmP wave with the number of groups in two-step stacks for station K007
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图 12 壳幔转换带（红色折线所示）速度梯度较大（a）和较小（b）时的垂直反射P波响应
Figure 12. P-waves vertical reflected from the crust-mantle transition zones （denoted by red lines） with a large （a） and small （b） velocity gradients，respectively
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图 13 台站K006不同月份自相关函数中PmP信号能量
Figure 13. Monthly energy of PmP wave in the autocorrelation function for station K006
表 1 唐海—商都台阵剖面下方的莫霍面深度
Table 1 Moho depth below the profile from Tanghai to Shangdu
台站 PmP信号
双程走时/s本研究莫霍
深度/km接收函数
莫霍深度/km偏差 台站 PmP信号
双程走时/s本研究莫霍
深度/km接收函数
莫霍深度/km偏差 K001 13.28 41.832 41.43 0.402 K027 11.60 36.54 35.65 0.89 K002 13.62 42.903 41.47 1.433 K028 11.16 35.154 35.09 0.064 K003 12.58 39.627 41.50 1.873 K029 10.86 34.209 34.77 0.561 K004 13.90 43.785 41.60 2.185 K030 11.34 35.721 34.10 1.621 K005 13.14 41.391 41.77 0.379 K031 11.86 33.801 33.88 0.079 K006 13.46 42.399 41.88 0.519 K032 11.58 33.003 33.76 0.757 K007 13.28 41.832 42.54 0.708 K033 11.76 33.516 33.28 0.236 K008 13.32 41.958 42.14 0.182 K034 11.88 33.858 33.76 0.098 K009 14.10 44.415 42.40 2.015 K035 12.80 36.48 34.57 1.91 K010 13.58 42.777 42.20 0.577 K036 12.08 34.428 35.36 0.932 K011 13.58 42.777 42.56 0.217 K037 12.62 35.967 35.36 0.607 K012 14.10 44.415 42.73 1.685 K038 12.54 35.739 35.32 0.419 K013 13.64 42.966 42.74 0.226 K039 11.86 33.801 35.35 1.549 K014 13.72 43.218 42.80 0.418 K040 12.02 34.257 35.10 0.843 K016 13.26 41.769 42.10 0.331 K041 12.34 35.169 34.93 0.239 K017 13.28 41.832 41.51 0.322 K042 12.62 35.967 34.75 1.217 K018 12.64 39.816 41.13 1.314 K044 11.22 31.977 33.42 1.443 K020 12.14 38.241 39.99 1.749 K045 12.58 35.853 34.32 1.533 K021 13.06 41.139 39.35 1.789 K046 11.60 33.06 33.90 0.84 K022 11.74 36.981 38.28 1.299 K047 11.82 33.687 33.54 0.147 K023 11.74 36.981 37.45 0.469 K048 11.78 33.573 33.27 0.303 K025 11.26 35.469 36.68 1.211 K049 11.72 33.402 32.94 0.462 K026 11.94 37.611 37.09 0.521 K051 11.72 33.402 32.33 1.072 
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陈俊磊,郑定昌,龙飞. 2019. 基于接收函数与背景噪声联合反演的研究与应用[J]. 地球物理学进展,34(3):862–869. Chen J L,Zheng D C,Long F. 2019. Joint inversion of receiver function and ambient noise:Research and application[J]. Progress in Geophysics,34(3):862–869 (in Chinese).
房立华,吴建平,吕作勇. 2009. 华北地区基于噪声的瑞利面波群速度层析成像[J]. 地球物理学报,52(3):663–671. Fang L H,Wu J P,Lü Z Y. 2009. Rayleigh wave group velocity tomography from ambient seismic noise in North China[J]. Chinese Journal of Geophysics,52(3):663–671 (in Chinese). doi: 10.1002/cjg2.1388
付媛媛,高原. 2016. 东北地区背景噪声的Rayleigh和Love波相速度层析成像[J]. 地球物理学报,59(2):494–503. Fu Y Y,Gao Y. 2016. Phase velocity tomography of Rayleigh and Love waves using ambient noise in Northeast China[J]. Chinese Journal of Geophysics,59(2):494–503 (in Chinese).
嘉世旭,刘昌铨. 1995. 华北地区人工地震测深震相与地壳结构研究[J]. 地震地质,17(2):97–105. Jia S X,Liu C Q. 1995. Study on the seismic phase of DSS in North China[J]. Seismology and Geology,17(2):97–105 (in Chinese).
嘉世旭,张先康. 2005. 华北不同构造块体地壳结构及其对比研究[J]. 地球物理学报,48(3):611–620. Jia S X,Zhang X K. 2005. Crustal structure and comparison of different tectonic blocks in North China[J]. Chinese Journal of Geophysics,48(3):611–620 (in Chinese). doi: 10.1002/cjg2.694
李奇,张智,侯爵,俞贵平,王敏玲,徐涛. 2021. 背景噪声提取体波方法研究进展[J]. 地震科学进展,51(10):433–451. Li Q,Zhang Z,Hou J,Yu G P,Wang M L,Xu T. 2021. Research progress of the extraction body waves from ambient noise[J]. Progress in Earthquake Sciences,51(10):433–451 (in Chinese).
刘瑞丰,高景春,陈运泰,吴忠良,黄志斌,徐志国,孙丽. 2008. 中国数字地震台网的建设与发展[J]. 地震学报,30(5):533–539. Liu R F,Gao J C,Chen Y T,Wu Z L,Huang Z B,Xu Z G,Sun L. 2008. Construction and development of digital seismograph networks in China[J]. Acta Seismologica Sinica,30(5):533–539 (in Chinese).
鲁来玉,何正勤,丁志峰,姚志祥. 2009. 华北科学探测台阵背景噪声特征分析[J]. 地球物理学报,52(10):2566–2572. Lu L Y,He Z Q,Ding Z F,Yao Z X. 2009. Investigation of ambient noise source in North China array[J]. Chinese Journal of Geophysics,52(10):2566–2572 (in Chinese).
马梦丹,赵爱华. 2021. 华北地区地壳P波和S波速度结构的双差层析成像[J]. 地震学报,43(1):13–33. Ma M D,Zhao A H. 2021. Double-difference tomography of crustal P- and S-wave velocity structures beneath North China[J]. Acta Seismologica Sinica,43(1):13–33 (in Chinese).
潘桂棠,肖庆辉,陆松年,邓晋福,冯益民,张克信,张智勇,王方国,邢光福,郝国杰,冯艳芳. 2009. 中国大地构造单元划分[J]. 中国地质,36(1):1–28. Pan G T,Xiao Q H,Lu S N,Deng J F,Feng Y M,Zhang K X,Zhang Z Y,Wang F G,Xing G F,Hao G J,Feng Y F. 2009. Subdivision of tectonic units in China[J]. Geology in China,36(1):1–28 (in Chinese).
潘素珍,王夫运,郑彦鹏,段玉玲,刘兰,邓晓果,宋向辉,孙一男,马策军,李怡靑. 2015. 胶东半岛地壳速度结构及其构造意义[J]. 地球物理学报,58(9):3251–3263. Pan S Z,Wang F Y,Zheng Y P,Duan Y L,Liu L,Deng X G,Song X H,Sun Y N,Ma C J,Li Y Q. 2015. Crustal velocity structure beneath Jiaodong Peninsula and its tectonic implications[J]. Chinese Journal of Geophysics,58(9):3251–3263 (in Chinese).
孙伟家,符力耘,魏伟,林羿,唐清雅. 2018. 中国东部地区的壳−幔过渡带结构[J]. 地球物理学报,61(3):845–855. Sun W J,Fu L Y,Wei W,Lin Y,Tang Q Y. 2018. The crust-mantle transition structures beneath eastern China[J]. Chinese Journal of Geophysics,61(3):845–855 (in Chinese).
王椿镛,吴庆举,段永红,王志铄,楼海. 2017. 华北地壳上地幔结构及其大地震深部构造成因[J]. 中国科学:地球科学,47(6):684–719. Wang C Y,Wu Q J,Duan Y H,Wang Z S,Lou H. 2017. Crustal and upper mantle structure and deep tectonic genesis of large earthquakes in North China[J]. Science China Earth Sciences,60(5):821–857. doi: 10.1007/s11430-016-9009-1
王芳,王伟涛,袁松湧. 2020. 大孔径地震台阵噪声互相关函数中体波信号的研究:以ChinArray二期数据为例[J]. 地球物理学报,63(9):3370–3386. Wang F,Wang W T,Yuan S Y. 2020. P wave signals in the noise cross-correlation functions of a large-aperture array illustrated by the data of ChinArray phase Ⅱ[J]. Chinese Journal of Geophysics,63(9):3370–3386 (in Chinese).
王琼,高原. 2012. 噪声层析成像在壳幔结构研究中的现状与展望[J]. 地震,32(1):70–81. Wang Q,Gao Y. 2012. Present state and prospect of ambient noise tomography in the study of crust-mantle structure[J]. Earthquake,32(1):70–81 (in Chinese).
王爽,孙新蕾,秦加岭,何立朋,邓阳凡. 2018. 利用密集地震台网高频环境噪声研究广东新丰江库区浅层地下结构[J]. 地球物理学报,61(2):593–603. Wang S,Sun X L,Qin J L,He L P,Deng Y F. 2018. Fine fault structure of Xinfengjiang water reservoir area from high-frequency ambient noise tomography[J]. Chinese Journal of Geophysics,61(2):593–603 (in Chinese).
王伟涛,倪四道,王宝善. 2011. 地球背景噪声干涉应用研究的新进展[J]. 中国地震,27(1):1–13. Wang W T,Ni S D,Wang B S. 2011. New advances in application of ambient noise interferometry[J]. Earthquake Research in China,27(1):1–13 (in Chinese).
吴庆举,曾融生. 1998. 用宽频带远震接收函数研究青藏高原的地壳结构[J]. 地球物理学报,41(5):669–679. Wu Q J,Zeng R S. 1998. The crustal structure of Qinghai-Xizang Plateau inferred from broadband teleseismic waveform[J]. Acta Geophysica Sinica,41(5):669–679 (in Chinese).
武岩,丁志峰,王兴臣,朱露培. 2018. 华北克拉通地壳结构及动力学机制分析[J]. 地球物理学报,61(7):2705–2718. Wu Y,Ding Z F,Wang X C,Zhu L P. 2018. Crustal structure and geodynamics of the North China Craton derived from a receiver function analysis of seismic wave data[J]. Chinese Journal of Geophysics,61(7):2705–2718 (in Chinese).
徐义贤,罗银河. 2015. 噪声地震学方法及其应用[J]. 地球物理学报,58(8):2618–2636. Xu Y X,Luo Y H. 2015. Methods of ambient noise-based seismology and their applications[J]. Chinese Journal of Geophysics,58(8):2618–2636 (in Chinese).
张智奇,姚华建,杨妍. 2020. 青藏高原东南缘地壳上地幔三维S波速度结构及动力学意义[J]. 中国科学:地球科学,50(9):1242–1258. Zhang Z Q,Yao H J,Yang Y. 2020. Shear wave velocity structure of the crust and upper mantle in Southeastern Tibet and its geodynamic implications[J]. Science China Earth Sciences,63(9):1278–1293. doi: 10.1007/s11430-020-9625-3
赵玲云,王伟涛,王芳,李娜. 2021. 噪声源的时空分布及其对噪声互相关函数的影响:以ChinArray二期数据为例[J]. 地球物理学报,64(12):4327–4340. Zhao L Y,Wang W T,Wang F,Li N. 2021. The distribution of noise source both in space and time and its influence on noise cross-correlation functions[J]. Chinese Journal of Geophysics,64(12):4327–4340 (in Chinese).
朱光,郑天愉,段永红,汤艳杰,朱日祥. 2020. 华北克拉通破坏图集[M]. 北京:科学出版社:68. Zhu G,Zheng T Y,Duan Y H,Tang Y J,Zhu R X. 2020. Atlas of the North China Craton Destruction[M]. Beijing:Science Press:68 (in Chinese).
朱日祥,徐义刚,朱光,张宏福,夏群科,郑天愉. 2012. 华北克拉通破坏[J]. 中国科学:地球科学,42(8):1135–1159. Zhu R X,Xu Y G,Zhu G,Zhang H F,Xia Q K,Zheng T Y. 2012. Destruction of the North China Craton[J]. Science China Earth Sciences,55(10):1565–1587. doi: 10.1007/s11430-012-4516-y
Becker G,Knapmeyer-Endrun B. 2018. Crustal thickness across the Trans-European suture zone from ambient noise autocorrelations[J]. Geophys J Int,212(2):1237–1254. doi: 10.1093/gji/ggx485
Bensen G D,Ritzwoller M H,Barmin M P,Levshin A L,Lin F,Moschetti M P,Shapiro N M,Yang Y. 2007. Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements[J]. Geophys J Int,169(3):1239–1260. doi: 10.1111/j.1365-246X.2007.03374.x
Buffoni C,Schimmel M,Sabbione N C,Rosa M L,Connon G. 2019. Crustal structure beneath Tierra del Fuego,Argentina,inferred from seismic P-wave receiver functions and ambient noise autocorrelations[J]. Tectonophysics,751:41–53. doi: 10.1016/j.tecto.2018.12.013
Carbonell R,Gallart J,Pérez-Estaún A. 2002. Modelling and imaging the Moho transition:The case of the southern Urals[J]. Geophys J Int,149(1):134–148. doi: 10.1046/j.1365-246X.2002.01623.x
Chen L,Ai Y S. 2009. Discontinuity structure of the mantle transition zone beneath the North China Craton from receiver function migration[J]. J Geophys Res: Solid Earth,114(B6):B06307.
Cheng S H,Xiao X,Wu J P,Wang W L,Sun L,Wang X X,Wen L X. 2021. Crustal thickness and vP/ vS variation beneath continental China revealed by receiver function analysis[J]. Geophys J Int,228(3):1731–1749. doi: 10.1093/gji/ggab433
Dantas O A B,do Nascimento A F,Schimmel M. 2018. Retrieval of body-wave reflections using ambient noise interferometry using a small-scale experiment[J]. Pure Appl Geophys,175(6):2009–2022. doi: 10.1007/s00024-018-1794-0
Deng L,Wang W T,Wang F,Yuan S Y. 2022. Teleseismic body waves extracted from ambient noise cross correlation between F-net and ChinArray phase Ⅱ[J]. Earthquake Research Advances,2(1):100068. doi: 10.1016/j.eqrea.2021.100068
Ding S B,Ni S D,Kim Y H,He X H. 2019. Constraints on crust-mantle transition zone with Pn waveforms:A case study of eastern China and southern Korean Peninsula[J]. Phys Earth Planet Inter,289:11–19. doi: 10.1016/j.pepi.2019.01.008
Draganov D,Wapenaar K,Mulder W,Singer J,Verdel A. 2007. Retrieval of reflections from seismic background-noise measurements[J]. Geophys Res Lett,34(4):L04305.
Draganov D,Campman X,Thorbecke J,Verdel A,Wapenaar K. 2013. Seismic exploration-scale velocities and structure from ambient seismic noise (>1 Hz)[J]. J Geophys Res: Solid Earth,118(8):4345–4360. doi: 10.1002/jgrb.50339
Feng J K,Yao H J,Wang W T. 2018. Imaging mantle transition zone discontinuities in southwest China from dense array ambient noise interferometry[J]. Earthquake Science,31(5/6):301–310.
Feng J K,Yao H J,Wang Y,Poli P,Mao Z. 2021. Segregated oceanic crust trapped at the bottom mantle transition zone revealed from ambient noise interferometry[J]. Nat Commun,12(1):2531. doi: 10.1038/s41467-021-22853-2
Feng J K,Yao H J,Poli P,Fang L H,Wu Y,Zhang P. 2017. Depth variations of 410 km and 660 km discontinuities in eastern North China Craton revealed by ambient noise interferometry[J]. Geophys Res Lett,44(16):8328–8335. doi: 10.1002/2017GL074263
Forghani F,Snieder R. 2010. Underestimation of body waves and feasibility of surface-wave reconstruction by seismic interferometry[J]. Lead Edge,29(7):790–794. doi: 10.1190/1.3462779
Heath B A,Hooft E E E,Toomey D R. 2018. Autocorrelation of the seismic wavefield at Newberry volcano:Reflections from the magmatic and geothermal systems[J]. Geophys Res Lett,45(5):2311–2318. doi: 10.1002/2017GL076706
Helmberger D V. 1968. The crust-mantle transition in the Bering Sea[J]. Bull Seismol Soc Am,58(1):179–214.
Ito Y,Shiomi K,Nakajima J,Hino R. 2012. Autocorrelation analysis of ambient noise in northeastern Japan subduction zone[J]. Tectonophysics,572-573:38–46. doi: 10.1016/j.tecto.2011.09.019
Kennett B L N. 2015. Lithosphere-asthenosphere P-wave reflectivity across Australia[J]. Earth Planet Sci Lett,431:225–235. doi: 10.1016/j.jpgl.2015.09.039
Kennett B L N,Saygin E,Salmon M. 2015. Stacking autocorrelograms to map Moho depth with high spatial resolution in southeastern Australia[J]. Geophys Res Lett,42(18):7490–7497. doi: 10.1002/2015GL065345
Le Breton M,Bontemps N,Guillemot A,Baillet L,Larose É. 2021. Landslide monitoring using seismic ambient noise correlation:Challenges and applications[J]. Earth-Science Reviews,216:103518. doi: 10.1016/j.earscirev.2021.103518
Lin F C,Tsai V C,Schmandt B,Duputel Z,Zhan Z W. 2013. Extracting seismic core phases with array interferometry[J]. Geophys Res Lett,40(6):1049–1053. doi: 10.1002/grl.50237
Liu X,Beroza G C,Yang L,Ellsworth W L. 2021. Ambient noise Love wave attenuation tomography for the LASSIE array across the Los Angeles basin[J]. Sci Adv,7(22):eabe1030. doi: 10.1126/sciadv.abe1030
Liu Z K,Huang J L,Yao H J. 2016. Anisotropic Rayleigh wave tomography of Northeast China using ambient seismic noise[J]. Phys Earth Planet Inter,256:37–48. doi: 10.1016/j.pepi.2016.05.001
Mordret A,Courbis R,Brenguier F,Chmiel M,Garambois S,Mao S J,Boué P,Campman X,Lecocq T,van der Veen W,Hollis D. 2020. Noise-based ballistic wave passive seismic monitoring,part 2:Surface waves[J]. Geophys J Int,221(1):692–705. doi: 10.1093/gji/ggaa016
Mroczek S,Tilmann F. 2021. Joint ambient noise autocorrelation and receiver function analysis of the Moho[J]. Geophys J Int,225(3):1920–1934. doi: 10.1093/gji/ggab065
Nakata N,Chang J P,Lawrence J F,Boué P. 2015. Body wave extraction and tomography at Long Beach,California,with ambient-noise interferometry[J]. J Geophys Res: Solid Earth,120(2):1159–1173. doi: 10.1002/2015JB011870
Olivier G,Brenguier F,Campillo M,Lynch R,Roux P. 2015. Body-wave reconstruction from ambient seismic noise correlations in an underground mine[J]. Geophysics,80(3):KS11–KS25. doi: 10.1190/geo2014-0299.1
Oren C,Nowack R L. 2017. Seismic body-wave interferometry using noise auto-correlations for crustal structure[J]. Geophys J Int,208(1):321–332. doi: 10.1093/gji/ggw394
Panea I,Draganov D,Vidal C A,Mocanu V. 2014. Retrieval of reflections from ambient noise recorded in the Mizil area,Romania[J]. Geophysics,79(3):Q31–Q42. doi: 10.1190/geo2013-0292.1
Poli P,Pedersen H A,Campillo M. 2012. Emergence of body waves from cross-correlation of short period seismic noise[J]. Geophys J Int,188(2):549–558. doi: 10.1111/j.1365-246X.2011.05271.x
Rabbel W,Kaban M,Tesauro M. 2013. Contrasts of seismic velocity,density and strength across the Moho[J]. Tectonophy sics,609:437–455. doi: 10.1016/j.tecto.2013.06.020
Romero P,Schimmel M. 2018. Mapping the basement of the Ebro basin in Spain with seismic ambient noise autocorrelations[J]. J Geophys Res: Solid Earth,123(6):5052–5067. doi: 10.1029/2018JB015498
Roux P,Sabra K G,Gerstoft P,Kuperman W A,Fehler M C. 2005. P-waves from cross-correlation of seismic noise[J]. Geophys Res Lett,32(19):L19303.
Schimmel M,Paulssen H. 1997. Noise reduction and detection of weak,coherent signals through phase-weighted stacks[J]. Geophys J Int,130(2):497–505. doi: 10.1111/j.1365-246X.1997.tb05664.x
Schimmel M. 1999. Phase cross-correlations:Design,comparisons,and applications[J]. Bull Seismol Soc Am,89(5):1366–1378. doi: 10.1785/BSSA0890051366
Schimmel M,Gallart J. 2007. Frequency-dependent phase coherence for noise suppression in seismic array data[J]. J Geophys Res: Solid Earth,112(B4):B04303.
Schimmel M,Stutzmann E,Gallart J. 2011. Using instantaneous phase coherence for signal extraction from ambient noise data at a local to a global scale[J]. Geophy J Int,184(1):494–506. doi: 10.1111/j.1365-246X.2010.04861.x
Schimmel M,Stutzmann E,Ventosa S. 2018. Low-frequency ambient noise autocorrelations:Waveforms and normal modes[J]. Seismol Res Lett,89(4):1488–1496. doi: 10.1785/0220180027
Schimmel M,Stutzmann E,Lognonné P,Compaire N,Davis P,Drilleau M,Garcia R,Kim D,Knapmeyer-Endrun B,Lekic V,Margerin L,Panning M,Schmerr N,Scholz J R,Spiga A,Tauzin B,Banerdt B. 2021. Seismic noise autocorrelations on Mars[J]. Earth Space Sci,8(6):e2021EA001755. doi: 10.1029/2021EA001755
Shapiro N M,Campillo M. 2004. Emergence of broadband Rayleigh waves from correlations of the ambient seismic noise[J]. Geophys Res Lett,31(7):L07614.
Shapiro N M,Campillo M,Stehly L,Ritzwoller M H. 2005. High-resolution surface-wave tomography from ambient seismic noise[J]. Science,307(5715):1615–1618. doi: 10.1126/science.1108339
Sun W J,Zhao L,Yuan H Y,Fu L Y. 2020. Sharpness of the midlithospheric discontinuities and craton evolution in North China[J]. J Geophys Res: Solid Earth,125(9):e2019JB018594. doi: 10.1029/2019JB018594
Taylor G,Rost S,Houseman G. 2016. Crustal imaging across the North Anatolian fault zone from the autocorrelation of ambient seismic noise[J]. Geophys Res Lett,43(6):2502–2509. doi: 10.1002/2016GL067715
Tibuleac I M,von Seggern D. 2012. Crust-mantle boundary reflectors in Nevada from ambient seismic noise autocorrelations[J]. Geophys J Int,189(1):493–500. doi: 10.1111/j.1365-246X.2011.05336.x
Ventosa S,Schimmel M,Stutzmann E. 2017. Extracting surface waves,hum and normal modes:Time-scale phase-weighted stack and beyond[J]. Geophys J Int,211(1):30–44. doi: 10.1093/gji/ggx284
Ventosa S,Schimmel M,Stutzmann E. 2019. Towards the processing of large data volumes with phase cross-correlation[J]. Seismol Res Lett,90(4):1663–1669.
Wang J N,Wu G X,Chen X F. 2019. Frequency-Bessel transform method for effective imaging of higher-mode Rayleigh dispersion curves from ambient seismic noise data[J]. J Geophys Res: Solid Earth,124(4):3708–3723. doi: 10.1029/2018JB016595
Wang Q Y,Yao H J. 2020. Monitoring of velocity changes based on seismic ambient noise:A brief review and perspective[J]. Earth Planet Phys,4(5):532–542.
Wapenaar K,Draganov D,Snieder R,Campman X,Verdel A. 2010a. Tutorial on seismic interferometry,part 1:Basic principles and applications[J]. Geophysics,75(5):75A195–75A209. doi: 10.1190/1.3457445
Wapenaar K,Slob E,Snieder R,Curtis A. 2010b. Tutorial on seismic interferometry,part 2:Underlying theory and new advances[J]. Geophysics,75(5):75A211–75A227. doi: 10.1190/1.3463440
Weemstra C,Boschi L,Goertz A,Artman B. 2013. Seismic attenuation from recordings of ambient noise[J]. Geophysics,78(1):Q1–Q14. doi: 10.1190/geo2012-0132.1
Zhan Z W,Ni S D,Helmberger D V,Clayton R W. 2010. Retrieval of Moho-reflected shear wave arrivals from ambient seismic noise[J]. Geophys J Int,182(1):408–420.
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