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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引言
井水位观测是地震地下流体前兆监测的重要手段之一(刘耀炜,2006),井水位的异常变化能够客观地、灵敏地反映地壳介质应力-应变变化。在地震孕育、发生的过程中,局部应力的加卸载作用或断层的大尺度活动均会导致岩体发生变形,使含水层孔隙压力产生变化,从而导致地壳介质渗流场的变化,井孔水位也随之动态变化(田竹君,谷圆珠,1985;Montgomery,Manga,2003)。付虹等(1997),Sato等(2004)和车用太等(2008)的观测实践已表明,地震前井水位是存在异常变化的。研究地震孕育、发生过程中井水位的各种异常变化,对揭示地震孕育、发生和发展过程具有重要的物理意义,在地震前兆监测与地震预测实践中发挥着重要作用(车用太等,2003)。
云南省会泽井的井水位自2012年数字化观测以来,该井周边及附近地区发生的中强地震震前井水位均出现了显著的上升,显示出较强的映震能力。本文拟以会泽井水位为研究对象,系统地分析会泽井水位在几次中强地震前出现的显著异常特征,并依据会泽井水位及其周边的地震活动情况,利用Molchan图表法对该井水位观测资料进行预测效能定量化评估与检验,提取其具有前兆指示意义的地震预测预报指标,进而对会泽井水位上升异常的机理进行解释,以期为本地区的震情跟踪及地震形势判定提供可能的判定依据。
1. 观测井概况
会泽井位于云南省曲靖市会泽县城以西18 km处的娜姑湖盆地边缘,地理坐标为(26.52°N,103.15°E),海拔为2 005 m,所属区域属于云贵高原中部盆岭地貌,地势北高南低;位于川滇菱形地块构造的东南部,小江断裂东侧8 km处,白雾街不对称双弧形构造的交会部位。会泽井深103.15 m,套管深度为87.80 m,水位埋深约30.00 m,其中34.06—87.80 m为滤水管,观测段为34.06—103.15 m。会泽井的水文地质条件如图1a所示,地下水为裂隙承压水,水温为16.0℃左右,受区域活动断裂控制和影响,该井的松散堆积层孔隙水的富水性弱,单井涌水量小于100 m3/d,地表以下11.19 m深处为0.15 m厚的棕色黏土隔水层,其隔水效果较好。井孔周边岩性如图1b所示:地表为第四系湖相沉积,观测含水层岩性为第四系风化玄武岩、砾石、碎石、黏土含细砂层;下伏地层为二叠系灰色玄武岩。
2. 会泽井水位异常特征
会泽井水位采用LN-3型数字化水位仪,探头放置于水下4.925 m,自2012年采用数字化观测以来,该井水位的动态特征表现为持续下降趋势,无年变,不受降雨影响,下降速率为0.3 m/a。如图2a所示,在表1所示的4次地震前,该井水位在多年缓降趋势的正常动态背景上,均表现出显著的突升异常现象,异常幅度较大,表明其具有一定的映震能力。
表 1 会泽井水位出现异常变化及对应的地震Table 1. Abnormal changes of water level and corresponding earthquakes发震日期
年-月-日震中位置 地点 MS 井震距
/km水位变化
幅值/m水位变化开始时间
年-月-日水位变化距
发震时间/d北纬/° 东经/° 2012−09−07 27.50 103.95 云南彝良 5.7 136 0.27 2012−08−13 25 2013−04−20 30.30 103.00 四川芦山 7.0 421 0.21 2013−04−06 14 2014−05−07 25.48 101.92 云南元谋 4.7 161 0.38 2014−04−18 19 2014−08−03 27.10 103.40 云南鲁甸 6.5 71 0.33 2014−07−20 14 为进一步分析中强地震前会泽井水位的异常变化特征,分别将图2a中的4次地震前井水位出现的显著异常时段经时间平移后,叠加异常形态曲线,得到各异常动态曲线,如图2b所示,可见4次地震前会泽井水位的异常形态特征较为相似,均为短时间内的上升异常变化,变化幅值存在差异,介于0.21—0.38 m之间,详见表1。其中井震距最短的地震为2014年8月3日鲁甸MS 6.5地震,震前14天井水位呈明显上升变化,幅值为0.33 m,并在地震发生后又出现了一次幅值为0.32 m的明显上升变化(此为震后效应)。几次地震前会泽井水位异常变化距离发震的时间均在1个月以内,说明该井水位具有显著的地震短临异常特征。
从会泽井水位异常的变化特征来看,当井水位出现上升异常变化之后,在一定时间内均会发生中强地震,说明该井水位异常与地震活动存在一定的关系,据此可为该区域的地震形势判定提供可能的参考依据。
3. 地震预测效能检验
会泽井水位出现上升异常与本区或周边地区中强以上地震发生的对应关系,显示出会泽井水位在多次中强震前都有较好的映震能力,但并非该区域内所有地震震前均存在井水位的异常变化。因此,本文将采用Molchan图表法对其预测效能进行定量化检验。Molchan图表法是目前国际上“地震可预测性合作研究”计划中采用的6种统计检验方法之一,该方法不仅能客观地和科学地进行地震预测评估,还能解决固定研究区域内地震时间的预测问题,并能给出相应的概率解释(Jordan,2006),已被广泛应用于常规的确定性和概率性预测的统计检验和效能评估中(Zechar,Jordan,2008)。
研究区范围为(21°—32°N,97°—110°E),地震目录引自中国地震台网中心(2017),震源机制解引自USGS (2017)。依据震级与震中距一般对应关系:MS4.0—4.9地震的震中距小于200 km;MS5.0—5.9地震的震中距小于250 km;MS6.0—6.9地震的震中距小于300 km;MS7.0以上地震的震中距小于500 km,筛选出2012—2015年会泽井周围发生的地震,共31次,其中MS4.0—4.9地震19次,MS5.0—5.9地震8次,MS6.0—6.9地震3次,MS7.0—7.9地震1次。利用实际观测数据进行地震预测分析时,常需要对数据进行预处理。由于会泽井水位无年变特征,水位的异常特征表现为高值异常,对所积累的震例进行日常跟踪分析可知,地震一般发生在井水位异常升高之后,因此,本文选取去趋势处理和差分分析两种方法分别对会泽井水位的原始数据进行预处理,再利用Molchan图表法对水位异常期相应的地震依次进行检验,并对不同预处理方法的检验结果加以分析,最终得到会泽井的预测效能和优势对应异常时间的定量结果。
3.1 Molchan图表法
Molchan图表法主要是针对预测值与目标地震差异度的检验(Molchan,1990),该方法既能直观地反映观测资料的整体预测效能,又能定量地分析异常,提取最佳阈值所对应的异常判定指标。图表法所计算的变量为:漏报率v,即预测无震而实际发震的数量与总的实际发震数之比;异常时空占有率τ,即以不同的阈值提取异常的时空范围与总的时空范围之比。
本文利用此方法对会泽井水位观测数据的预测效果进行检验时,需考虑时间占有率,即所有异常时间及其有效预测期所累积的去除二者重复时段后的总时间长度除以被检验数据的总时间长度。例如:被检验数据的起始时间为2010年1月1日,结束时间为2010年12月31日,总时间长度为365天。在这一年中出现了测值高于某一阈值的异常3次,每次异常的持续时间为15天,预测期为30天,并且3次异常的持续时间和预测期中共计有20天是重复的。那么,其时间占有率为[3×(15+30)−20]/365 ≈ 0.315。通过不断降低预测的“警报”阈值,分别计算异常在时间上的占有率τ和相应的漏报率v,得到Molchan图表中的τ-v曲线。曲线与图表的边界线所包围的面积代表检验的预测效果,面积越小,预测效果越好;此外,还需参考概率增益G。在Molchan图表法中G被定义为(Molchan,1991;Zechar,Jordan,2008)
$G {\text{=}} \frac{{1 - \nu }}{\tau }{\text{,}}$
(1) G越大,预测效果越好,若τ-v曲线接近于直线G=1,表示无统计意义。实际检验的具体过程为给定阈值后,超出阈值的数据为异常值,地震发生在异常值所在时段及其有效预测期之外时,称为漏报。
3.2 会泽井水位检验结果
2012—2015年会泽井周围发生的31次地震的空间分布如图3a所示,利用Molchan检验法对会泽井水位去趋势后数据进行预测效能检验,阈值由大到小滑动,根据阈值确定时间占有率τ和漏投率v,得到τ-v曲线结果如图3b所示。图中点(1.0,0)表示地震全都报准,但其时间占有率也最大,占据整个数据的时间段;点(0,1.0)表示地震全部漏报,其时间占有率最小,相当于未作出预测。因此,评估观测资料的预测效能需要从漏报率和时间占有率这两方面综合判定。
对会泽井水位观测数据经去趋势处理后的Molchan检验结果显示,该井水位对MS≥4.0地震与MS≥5.0地震的预测效果基本相当,且G约为2,只是MS≥5.0地震的时间占有率略小,说明从去趋势曲线来看,会泽井水位对于其周围或附近地区、整个大区域不同的震级档未显示出明显的优势预测效果。
会泽井位于川滇菱形地块的东南部,处于多条断裂的交会部位,其所在地区的地震活动受区域活动断裂控制和影响,因此,针对MS≥5.0地震按会泽井所在川滇菱形地块以东和以西进行分选,地震震中分布如图4a所示;分别对川滇菱形地块以东和以西的地震预测效能进行Molchan检验,结果如图4b所示,可以明显地看出:会泽井水位去趋势曲线对地块以东的地震预测效能较好,检验线与横纵坐标包围的面积较小,大部分数据点G>2;而对地块以西的地震预测效能并不理想,基本都在随机线G=1附近,且G<2,说明当会泽井水位出现异常变化时,中强地震发生的优势预测区域为川滇菱形地块以东,这可以为将来地震发生的地点和强度提供一定的参考依据。
以上分析和检验是基于对原始水位数据进行去趋势预处理后的数据,考虑到会泽井水位震前多表现为上升型高值异常,且不同的预处理方法可能会得到不同的结果,因此为了更加突出会泽井水位的高值异常,分析不同的预处理方法对检验结果的影响,本文还尝试用差分法对其数据进行预处理,结果如图5a和图5c所示,可见绝大部分地震前都出现了井水位高值现象,但并不是所有高值之后均有地震发生。
利用Molchan图表法对差分处理后的会泽井水位进行检验,结果如图5b和图5d所示。由图5b可直观地看出,MS≥5.0地震的红色检验线包围的面积较MS≥4.0地震的绿色检验线包围面积更小,且MS≥5.0地震的概率增益更大,说明会泽井水位5日差分曲线对其附近或周围地区MS≥5.0地震的预测效能更好;同样,如图5d所示,川滇菱形地块以东地震的红色检验线所包围的面积较地块以西地震的绿色检验线所包围面积更小,其概率增益更大,说明会泽井水位5日差分曲线对川滇菱形地块以东地震的预测效能更好。由此可见,利用去趋势法和差分法对会泽井水位进行预处理后,二者的检验效果与已有认识基本一致,即会泽井水位的上升高值异常对周边及川滇菱形地块以东的中强地震具有一定的预测效能。
基于上述分析,分别对会泽井去趋势曲线和5日差分曲线进行预测效能的检验,结果如图6a和图6c所示。可以看出,会泽井水位在去趋势和5日差分处理后的整体预测效能都很好,即概率增益均大于1,而且有效预测时间的预测效能均在0.5以上,尤其是5日差分处理后的会泽井水位绝大多数预测时间的预测效能均在0.6以上,部分在0.8以上,说明会泽井水位用5日差分方法进行预处理后,预测效能更好。
图6b和图6d分别为两种方法预测效能的优势地震对应时段,可见,会泽井水位去趋势处理后,优势地震对应时间段为3个月以内,预测效能介于0.6—0.8之间,3个月之后预测效能降低至同一水平(图6b);会泽井水位5日差分处理后,优势地震对应时间段也为3个月以内,预测效能介于0.7—0.8之间,3个月之后预测效能逐渐降低,直至同一水平(图6d),说明会泽井水位在1—3个月以内的短期预测效果较好。
综上所述,从空间预测角度来看,会泽井水位的上升高值异常对周边及川滇菱形地块以东的中强地震具有一定的预测效能,从时间预测角度来看,其预测期多在1—3个月,且1个月左右的预测效能最佳。定量检验的结果与水位原始曲线(经去趋势处理)的映震效果基本一致,如表1中统计的4次显著震例,其异常出现时间距发震时间均在30天以内。
4. 水位异常机理分析
国内外研究人员已经开展了大量的地下水位异常变化与地震关系的机理研究,提出了多种模式。20世纪70年代初的扩容-扩散模式、裂隙串通或雪崩模式和微裂-顶位移模式,均认为地震孕育到发生的过程中,震源附近应力积累使得地下介质变化,引起地震震中位置或周围地下水位的异常(国家地震局预测预防司,1997)。车用太和杨会年(1985)总结了国外研究关于震前地下水动态异常的3种机理:滑动机理、破裂机理和变形锋传播机理,归纳出震前断裂的预滑或微破裂的产生与发展可形成震中压缩区和拉张区,从而导致井水位上升或下降异常。车用太(1990)进一步研究认为震前地下水位异常与震源区域应力场活动相关。至20世纪80年代,地下水前兆机理研究从“震源效应”转向“大范围应力场效应”,认为井水位上升、下降异常与地壳应力变化—含水层变形—水动力条件改变等作用过程有关,是区域应力场和震源应力场共同作用的结果(车用太,杨会年,1985;王吉易等,2002)。
2012年以来会泽井水位在多次中强地震前均出现了上升高值异常变化,本文利用Molchan图表法的检验结果显示,会泽井水位的高值异常对川滇菱形地块以东地震的预测效能较高,这可能与会泽井所处的构造位置及井孔的水文条件密切相关。从构造区域环境来看,会泽井位于则木河—小江断裂和西鱼河—昭通断裂的交会区附近(图7a),两断裂位于川滇菱形地块与华南地块之间的边界带上,属于活动地块边界带的一部分(张培震等,2003),由一系列大规模逆冲断裂系组成,具有显著逆冲分量的右旋走滑性质,朝东南向推覆(闻学泽等,2013),在青藏高原隆升、川滇地块朝南东运动的区域构造作用下,会泽井所处的区域应力场呈挤压状态,其井水位在本文分析的多次中强地震前表现为上升高值异常变化(图7b)。
从区域水文环境来看,由于处于构造灵敏点,会泽井的观测含水层对区域构造作用具有很好的反映能力。2012年云南彝良MS5.7地震和2014年云南鲁甸MS6.5地震发生在离会泽井较近的西鱼河—昭通断裂附近,从水位观测曲线上也能看出,这两次地震前出现的水位异常变化也是最显著的。但是,并不是所有地震前均会出现类似异常变化,例如2014年云南永善MS5.3地震,一方面此次地震发生在离会泽井较远的ENE向蓬峰断裂附近(近200 km),另一方面可能由于此次地震的孕育过程中会泽井所处位置未受到区域构造作用的影响,即永善MS5.3地震的震源过程与会泽井附近区域的断裂带并无直接相关性。结合先前会泽井异常变化与川滇菱形地块构造活动密切相关的认识,会泽井位于构造灵敏部位,对川滇菱形地块以东区域的整体构造作用较为敏感,但对距其较远的局部应力调整无显著反映。
此外,观测井自身的水文条件也直接影响到水位对构造活动作用的灵敏程度。距会泽井约120 km处的昭通井、昭阳井,虽同处西鱼河—昭通断裂附近(图7a),但会泽井水位在3次地震前均有显著异常出现(图7b);昭通井在2012年云南彝良MS5.7地震和2014年云南鲁甸MS6.5地震前出现了上升高值异常,在2014年云南元谋MS4.7地震前却未出现;而昭阳井仅在2014年云南鲁甸MS6.5地震前出现了上升高值异常变化。可见,同样是水位观测井,会泽井由于处于构造灵敏点,其对地震孕育过程或构造活动的响应能力较好,即使同处一个构造区(如昭通井和昭阳井),不同的井-含水层系统条件也直接影响其对地震的响应能力。从图7b可知,昭通井水位深35 m左右,昭阳井水位埋深4.5 m左右,其观测含水层明显不同,进而对附近区域的地震响应能力也不完全相同。
综上分析,井水位异常的变化机理较为复杂,既与区域构造活动有关,也与井孔自身的水文条件密切相关。会泽井位于则木河—小江断裂和西鱼河—昭通断裂的交会区附近,处于构造敏感部位,其水位对区域构造作用及地震孕育过程较为灵敏,因而具有较好的预测效能,本文定量检验的结果与其一致。
5. 讨论与结论
本文通过对会泽井水位在几次中强地震前出现的显著异常特征的分析,认为会泽井水位对其周围一定范围内的中强地震具有较高的映震能力。利用Molchan图表法对会泽井水位的预测效能进行了定量检验,得到了会泽井的水位优势预测时间,最后从不同的角度对会泽井水位上升异常机理进行了初步分析与解释,结论如下:
1) 会泽井水位在几次显著的上升异常变化后,一个月以内周围一定范围内均有中强地震发生,说明会泽井水位异常与地震活动存在一定的关系,且均为短临异常,这对周边区域的地震预测探索有重要意义。
2) 会泽井水位异常的整体预测效能较好,对地震发生的时间和地点具有一定的指示意义。优势地震对应时间段为3个月以内,优势预测地区为川滇菱形地块以东区域。
3) 处于断裂带交会部位的会泽井,对与震源过程相关的区域构造活动响应较为灵敏,其水位在川滇地区几次中强地震前均出现显著上升异常,与区域构造作用密切相关。
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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
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
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
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
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
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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