基于声波方程的井间地震数据快速WTW反演方法

WTW(Wave equation traveltime+Waveform inversion)反演是基于波动方程的走时反演（WT反演）和波形反演的联合反演方法.WT反演利用波动方程计算走时和走时关于速度的导数,和传统以射线为基础的走时反演相比,具有不必射线追踪、不必拾取初至、不必高频假设以及初始模型和实际模型差别较大时也能较好收敛等优点,但WT反演与波形反演相比其结果分辨率低.与之互补的是,波形反演的反演结果分辨率高,但是当所给初始模型和实际模型相差太大时,波形反演迭代算法容易陷入局部极小点.可见结合两种方法的WTW反演是一种比较好的联合反演方法.常规WTW迭代算法是首先以WT反演为主反演得到地质模型的整体特征,然后再以波形反演为主反演模型细节,该算法耗时和占用计算机存储空间接近WT反演或波形反演的两倍.为了节省运算耗时和计算机存储空间,往往采取首先单独利用WT反演然后再单独利用波形反演的算法.这样做的缺点是不能紧密结合两种反演方法,使得它们的优缺点在每一次迭代中无法得到互补,从而影响了最终的反演结果.针对以上事实,本文提出一种新的方法实现WTW,使得WTW运算速度和存储空间在任何情况下等同于WT反演或波形反演.模型计算表明新的算法具有更好的收敛性.     

利用sPn震相测定芦山MS7.0级地震余震的震源深度

利用南北地震带南段密集流动地震台阵的观测数据,采用波形互相关方法拾取Pn波走时,应用滑动时窗相关法识别sPn震相,通过sPn与Pn震相之间的走时差测定了芦山地震序列中28个M_L4.0级以上余震的震源深度.结果表明,震源深度集中在10~20 km范围内,垂直余震带的北西-南东向震源深度剖面揭示,余震分布表现出西深东浅的特点,倾角大约为39°.这些余震在空间上具有较好的线性分布特征,推测可能发生在与主震有关的破裂面上或邻近位置,由此推测主震的破裂面倾角大约为39°.根据余震的空间分布特征,认为芦山地震的发震断层并非双石-大川断裂,可能是其东侧的一条隐伏断层.     

Validation of 3-D basin structure models for long-period ground motion simulation in the Osaka basin, western Japan

We studied the applicability of two types of existing three-dimensional (3-D) basin velocity structure models of the Osaka basin, western Japan for long-period ground motion simulations. We synthesized long-period (3–20 s) ground motions in the Osaka basin during a M6.5 earthquake that occurred near the hypothetical Tonankai earthquake source area, approximately 200 km from Osaka. The simulations were performed using a 3-D finite-difference method with nonuniform staggered grids using the two basin velocity structure models. To study the ground motion characteristics inside the basin, we evaluated the wave field inside the basin using the transfer functions derived from the synthetics at the basin and a reference rock site outside the basin. The synthetic waveforms at the basin site were obtained by a convolution of the calculated transfer function and the observed waveform at the reference rock site. First, we estimated the appropriate Q values for the sediment layers. Assuming that the Q value depends on the S wave velocity V _S and period T, it was set to Q = (1/3V _S)(T ₀/T) where V _S is in m/s and the reference period T ₀ is 3.0 s. Second, we compared the synthetics and the observations using waveforms and pseudovelocity response spectra, together with a comparison of the velocity structures of the two basin models. We also introduced a goodness-of-fit factor to the pseudovelocity response spectra as an objective index. The synthetics of both the models reproduced the observations reasonably well at most of the stations in the central part the basin. At some stations, however, especially where the bedrock depth varies sharply, there were noticeable discrepancies in the simulation results of the models, and the synthetics did not accurately reproduce the observation. Our results indicate that the superiority of one model over the other cannot be determined and that an improvement in the basin velocity structure models based on simulation studies is required, especially along the basin edges. We also conclude that our transfer function method can be used to examine the applicability of the basin velocity structure models for long-period ground motion simulations.     

地震反演成像中的Hessian算子研究

总结了牛顿类地震反演方法中Hessian算子的作用,对其在地震反演成像中的数学物理含义进行了分析.Hessian算子是误差泛函对模型参数的二阶导数,反映了误差泛函对模型变化的二次型特征.分析声波方程下的Hessian算子的格林函数表达形式,发现其表达了整个观测系统和子波频带等因素对地震数据空间到模型空间投影过程的影响.提出了两种分别适用于最小二乘偏移和全波形反演的Hessian算子简化格式.平面波Hessian算子应用于最小二乘偏移能够得到相对保真的成像结果,改善了地震偏移成像的精度.地下偏移距Hessian算子应用于全波形反演能够加快反演迭代的计算效率.最后,对Hessian算子在地震反演成像中的价值进行了讨论和评价.     

An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data

Large footprint waveform LiDAR sensors have been widely used for numerous airborne studies. Ground peak identification in a large footprint waveform is a significant bottleneck in exploring full usage of the waveform datasets. In the current study, an accurate and computationally efficient algorithm was developed for ground peak identification, called Filtering and Clustering Algorithm (FICA). The method was evaluated on Land, Vegetation, and Ice Sensor (LVIS) waveform datasets acquired over Central NY. FICA incorporates a set of multi-scale second derivative filters and a k-means clustering algorithm in order to avoid detecting false ground peaks. FICA was tested in five different land cover types (deciduous trees, coniferous trees, shrub, grass and developed area) and showed more accurate results when compared to existing algorithms. More specifically, compared with Gaussian decomposition, the RMSE ground peak identification by FICA was 2.82 m (5.29 m for GD) in deciduous plots, 3.25 m (4.57 m for GD) in coniferous plots, 2.63 m (2.83 m for GD) in shrub plots, 0.82 m (0.93 m for GD) in grass plots, and 0.70 m (0.51 m for GD) in plots of developed areas. FICA performance was also relatively consistent under various slope and canopy coverage (CC) conditions. In addition, FICA showed better computational efficiency compared to existing methods. FICA’s major computational and accuracy advantage is a result of the adopted multi-scale signal processing procedures that concentrate on local portions of the signal as opposed to the Gaussian decomposition that uses a curve-fitting strategy applied in the entire signal. The FICA algorithm is a good candidate for large-scale implementation on future space-borne waveform LiDAR sensors.     

Frequency‐ and angle‐dependent poroelastic seismic analysis for highly attenuating reservoirs

We extend the frequency‐ and angle‐dependent poroelastic reflectivity to systematically analyse the characteristic of seismic waveforms for highly attenuating reservoir rocks. It is found that the mesoscopic fluid pressure diffusion can significantly affect the root‐mean‐square amplitude, frequency content, and phase signatures of seismic waveforms. We loosely group the seismic amplitude‐versus‐angle and ‐frequency characteristics into three classes under different geological circumstances: (i) for Class‐I amplitude‐versus‐angle and ‐frequency, which corresponds to well‐compacted reservoirs having Class‐I amplitude‐versus‐offset characteristic, the root‐mean‐square amplitude at near offset is boosted at high frequency, whereas seismic energy at far offset is concentrated at low frequency; (ii) for Class‐II amplitude‐versus‐angle and ‐frequency, which corresponds to moderately compacted reservoirs having Class‐II amplitude‐versus‐offset characteristic, the weak seismic amplitude might exhibit a phase‐reversal trend, hence distorting both the seismic waveform and energy distribution; (iii) for Class‐III amplitude‐versus‐angle and ‐frequency, which corresponds to unconsolidated reservoir having Class‐III amplitude‐versus‐offset characteristic, the mesoscopic fluid flow does not exercise an appreciable effect on the seismic waveforms, but there exists a non‐negligible amplitude decay compared with the elastic seismic responses based on the Zoeppritz equation.     

Inversion of ocean-bottom seismometer (OBS) waveforms for oceanic crust structure: a synthetic study

The waveform inversion method is applied—using synthetic ocean-bottom seismometer(OBS) data—to study oceanic crust structure. A niching genetic algorithm(NGA) is used to implement the inversion for the thickness and P-wave velocity of each layer, and to update the model by minimizing the objective function, which consists of the misfit and cross-correlation of observed and synthetic waveforms. The influence of specific NGA method parameters is discussed, and suitable values are presented.The NGA method works well for various observation systems, such as those with irregular and sparse distribution of receivers as well as single receiver systems. A strategy is proposed to accelerate the convergence rate by a factor of five with no increase in computational complexity; this is achieved using a first inversion with several generations to impose a restriction on the preset range of each parameter and then conducting a second inversion with the new range. Despite the successes of this method,its usage is limited. A shallow water layer is not favored because the direct wave in water will suppress the useful reflection signals from the crust. A more precise calculation of the air-gun source signal should be considered in order to better simulate waveforms generated in realistic situations; further studies are required to investigate this issue.     

四川汶川8.0级地震震源过程

2008年5月12日在青藏高原与四川盆地交界的龙门山山脉发生了M_s8.0级强烈地震,引发山体滑坡等地质灾害,造成了巨大的人员伤亡和经济损失.本文利用远场体波波形记录结合近场同震位移数据,根据地质资料和地震形成的地表破裂轨迹,构造了一个双“铲状”有限地震断层模型,利用反演技术重建地震的破裂过程.结果显示汶川大地震主要是沿龙门山构造带的映秀-北川断裂和灌县-江油断裂发生的逆冲兼右旋走滑破裂事件.断层面上的滑动分布显示两个高滑动区先后发生在地震破坏最为严重的映秀和北川地区,最大滑动量高达1200～1250 cm,且破裂过程也显示一定的复杂性.地震破裂的平均走滑量略大于平均倾滑量,与多种观测资料获得的震前龙门山断裂带构造变形相一致,推断是由于长期区域应力场作用和龙门山地区特殊的物质组成和结构孕育了这次千年尺度的强烈地震.     

2019年吉林宁江MS5.1地震全矩张量反演及其发震机理初探

采用吉林、黑龙江、辽宁和内蒙古地震台网记录的地震波形数据,利用ISOLA近震全波形反演方法对2019年5月18日吉林宁江M_S5.1地震进行全矩张量反演。结果表明,该地震的最佳断层面解节面Ⅰ走向304°/倾角81°/滑动角26°,节面Ⅱ走向210°/倾角65°/滑动角170°;最佳矩心深度6km,矩震级M_W5.0。根据宁江M_S5.1地震序列展布形态,推断节面Ⅱ可能为优势发震断层面,即本次地震的主控断裂为扶余-肇东断裂,和与其正交的第二松花江断裂共同控制着余震展布方向。全矩张量解在Husdon震源类型图上的投影显示本次地震具有明显的非双力偶成分,是1次体积增加的张性破裂。根据区域地质构造特征和震源区接收函数、电磁测深和地下热结构等地球物理研究结果,综合分析认为在西太平洋板块作用形成俯冲带的同时,也相应地产生了热物质上涌,这些地球物理过程可能会改变莫霍面形态,使其向上突起并作用于活动断层,从而形成此次吉林宁江M_S5.1地震。     

2014年3月10日加州西北岸M_w6.9级地震震源破裂过程

2014年3月10日13时18分(北京时间)美国加利福尼亚州西北岸发生Mw6.9级地震,震中位于戈尔达板块内部.本文利用国际地震学研究联合会(IRIS)地震数据中心提供的远场体波数据,通过波形反演的方法来研究此次地震的震源破裂过程,并分析未造成重大人员伤亡及诱发海啸的原因,为该地区地球动力学的研究提供依据.选取19个方位角覆盖均匀的远场P波垂向波形记录和13个近场P波初动符号进行约束,基于剪切位错点源模型确定此次地震的震源机制解.结合地质构造背景资料,确定断层破裂面的走向.在考虑海水层多次反射效应的影响下,采用18个远场P波垂向波形数据和21个远场SH波切向波形数据,利用有限断层模型,将断层面剖分为17×9块子断层单元来模拟破裂面上滑动的时空分布,通过波形反演的方法获得此次地震的震源破裂过程.利用海水层地壳模型,剪切位错点源模型的反演结果为:走向323°,倾角86.1°,滑动角-180°,震源深度为10.6km.有限断层模型的反演结果表明,此次地震的破裂过程相对简单,主要滑动量集中于震源上方35km×9km的区域内,破裂时间持续19s左右,平均破裂传播速度约为2.7km·s-1,较大滑动量均沿着走向分布,最大滑动量为249cm.此次地震为发生在戈尔达板块内部的一次Mw6.9级的陡倾角走滑型地震.此次地震为单纯的走滑型地震,断层面接近竖直方向,且发生在洋壳底部,因此破坏力不大,不会对沿岸城市造成重大损失.陡倾角断层在走滑错动的过程中不会使海底地形发生大幅度变化,不会引起大面积水体的突然升降,因此不会诱发大规模海啸.