2018年9月8日墨江地震及周边地区构造应力场特征分析

2018年9月8日,云南省墨江县发生M_S5.9地震并伴随一系列余震,探究该地震周围的应力场对于理解该地震的发生机制和后续地震的发展趋势具有着重要的参考意义.本研究收集了震源及其邻区中前人研究和Global CMT所给出的震源机制资料,对该地区进行了构造应力场反演,并同时利用反演得到的应力张量模拟墨江地区的震源机制解表现.结果表明：①在应力轴整体分布上,自西向东σ₁轴（压轴）从NNE-SSW向逐渐转向NNW-SSE向,σ₃轴（张轴）从WWN-EES向逐渐转向WWS-EEN向,张轴呈弧形分布,压轴呈放射状分布.②在应力轴倾伏角上,研究区域内的压应力轴和张应力轴倾伏角都比较小,即两轴均接近水平.③R值分布大体是在东南部相较于西北部大,结合当地地质背景分析得到,物质逃逸自西北向东南呈逐渐变缓的趋势.④利用反演得到的应力张量和应力状态计算墨江地震震源区的相对剪应力和相对正应力大小.由此推测,墨江地震恰好发生在相对剪切应力值和相对正应力正值最大的节面上.从而可以确定墨江地震的发震节面的基本参数：走向216.32°,倾角86.91°,滑动角0.27°,相对剪应力值0.9,相对正应力值0.3.本研究为此次墨江地震的发震背景和地震动力学研究提供了基础性资料.

     

沿107.6°E南北向剖面鄂尔多斯地块及周缘地区地壳结构

鄂尔多斯地块紧邻青藏高原东北缘,位于华北克拉通的西部,在我国中生代、新生代以来东部地区的构造活动中起到了重要作用.对鄂尔多斯及其周缘地区的研究可以提供有关华北克拉通的形成、演化和破坏过程的重要信息.本文选取了纵贯鄂尔多斯的107.6°E附近南北剖面上的44个流动地震台站进行分析,采用接收函数方法,进行Kirchhoff偏移成像,并且结合在该区域内前人的地震面波频散进行联合反演,获得剖面下方的地壳内部精细结构.研究结果显示:(1)莫霍面在鄂尔多斯北部较平缓,约45km深;在鄂尔多斯南部有所加深,达到50km;其北边的河套盆地的地壳厚度约为50km;南边的渭河盆地到秦岭地区及四川盆地的地壳厚度从约为40km增厚到47～50km.(2)河套盆地下方存在大规模的低速异常,最深可达25km,反映了其显著的拉张构造和沉积历史.(3)秦岭造山带下方的低速异常对应于其主要为长英质的地壳组分,可能是由于中生代的拆沉作用导致的地壳下部基性岩石层的缺失.(4)以38°N为界的鄂尔多斯地块,南北部地壳速度结构存在差异,可能表明了这两部分经历的构造历史不同.     

空-地-井重力异常正则化协同密度反演方法

围绕综合利用空-地-井多源重力异常联合反演提升精度的目标,提出正则化协同密度反演方法,从而有效利用多维数据的横向和纵向变化特征提高反演精度,且无需先验信息的约束.此外,还提出利用奇异值谱和深度分辨率工具评价航空数据观测高度和层数对反演分辨率的影响.通过理论模型试验本文方法在空-地、地-井等不同情况下的应用效果,结果表明空-地-井重力异常正则化协同密度反演方法能获得更高分辨率、高精度的反演结果,且证明不同的钻孔位置对反演结果有较大的影响,从而可指导实际的空-地-井联合勘探.最后,利用文顿盐丘实际勘探数据进行空地和空地井重力数据协同反演,反演结果垂向分辨率明显优于地面观测数据反演结果,盐盖的顶面及中心埋深与前人研究和地质资料解释相吻合,验证了方法正确性及实用性,为推进我国空-地-井立体重磁勘探提供了重要的技术手段.

     

SESTET: A spatially explicit stream temperature model based on equilibrium temperature

Stream-water temperature is a key variable controlling chemical, biological, and ecological processes in freshwater environments. Most models focus on a single river cross-section; however, temperature gradients along stretches and tributaries of a river network are crucial to assess ecohydrological features such as aquatic species suitability, growth and feeding rates, or disease transmission. We propose SESTET, a deterministic, spatially explicit stream temperature model for a whole river network, based on water and energy budgets at a reach scale and requiring only commonly available spatially distributed datasets, such as morphology and air temperature, as input. Heat exchange processes at the air–water interface are modelled via the widely used equilibrium temperature concept, whereas the effects of network structure are accounted for through advective heat fluxes. A case study was conducted on the prealpine Wigger river (Switzerland), where water temperatures have been measured in the period 2014–2018 at 11 spatially distributed locations. The results show the advantages of accounting for water and energy budgets at the reach scale for the entire river network, compared with simpler, lumped formulations. Because our approach fundamentally relies on spatially distributed air temperature fields, adequate spatial interpolation techniques that account for the effects of both elevation and thermal inversion in air temperature are key to a successful application of the model. SESTET allows the assessment of the magnitude of the various components of the heat budget at the reach scale and the derivation of reliable estimates of spatial gradients of mean daily stream temperatures for the whole catchment based on a limited number of conveniently located (viz., spanning the largest possible elevation range) measuring stations. Moreover, accounting for mixing processes and advective fluxes through the river network allows one to trust regionalized values of the parameters controlling the relationship between equilibrium and air temperature, a key feature to generalize the model to data-scarce catchments.     

Multiscale phase inversion for 3D ocean-bottom cable data

We invert three-dimensional seismic data by a multiscale phase inversion scheme, a modified version of full waveform inversion, which applies higher order integrations to the input signal to produce low-boost signals. These low-boost signals are used as the input data for the early iterations, and lower order integrations are computed at the later iterations. The advantages of multiscale phase inversion are that it (1) is less dependent on the initial model compared to full waveform inversion, (2) is less sensitive to incorrectly modelled magnitudes and (3) employs a simple and natural frequency shaping filtering. For a layered model with a three-dimensional velocity anomaly, results with synthetic data show that multiscale phase inversion can sometimes provide a noticeably more accurate velocity profile than full waveform inversion. Results with the Society of Exploration Geophysicists/European Association of Geoscientists and Engineers overthrust model shows that multiscale phase inversion more clearly resolves meandering channels in the depth slices. However, the data and model misfit functions achieve about the same values after 50 iterations. The results with three-dimensional ocean-bottom cable data show that, compared to the full waveform inversion tomogram, the three-dimensional multiscale phase inversion tomogram provides a better match to the well log, and better flattens angle-domain common image gathers. The problem is that the tomograms at the well log provide an incomplete low-wavenumber estimate of the log's velocity profile. Therefore, a good low-wavenumber estimate of the velocity model is still needed for an accurate multiscale phase inversion tomogram.     

Approximate traveltime inversion in downhole microseismic monitoring

In downhole microseismic monitoring, accurate event location relies on the accuracy of the velocity model. The model can be estimated along with event locations. Anisotropic models are important to get accurate event locations. Taking anisotropy into account makes it possible to use additional data – two S-wave arrivals generated due to shear-wave splitting. However, anisotropic ray tracing requires iterative procedures for computing group velocities, which may become unstable around caustics. As a result, anisotropic kinematic inversion may become time consuming. In this paper, we explore the idea of using simplified ray tracing to locate events and estimate medium parameters. In the simplified ray-tracing algorithm, the group velocity is assumed to be equal to phase velocity in both magnitude and direction. This assumption makes the ray-tracing algorithm five times faster compared to ray tracing based on exact equations. We present a set of tests showing that given perforation-shot data, one can use inversion based on simplified ray-tracing even for moderate-to-strong anisotropic models. When there are no perforation shots, event-location errors may become too large for moderately anisotropic media.     

Incorporating known petrophysical model in the seismic full-waveform inversion using the Gramian constraint

In this paper, we develop a general approach to integrating petrophysical models in three-dimensional seismic full-waveform inversion based on the Gramian constraints. In the framework of this approach, we present an example of the frequency-domain P-wave velocity inversion guided by an electrical conductivity model. In order to introduce a coupling between the two models, we minimize the corresponding Gramian functional, which is included in the Tikhonov parametric functional. We demonstrate that in the case of a single-physics inversion guided by a model of different physical type, the general expressions of the Gramian functional and its gradients become simple and easy to program. We also prove that the Gramian functional has a non-negative quadratic form, so it can be easily incorporated in a standard gradient-based minimization scheme. The developed new approach of seismic inversion guided by the known petrophysical model has been validated by three-dimensional inversion of synthetic seismic data generated for a realistic three-dimensional model of the subsurface.     

Combining discrete cosine transform and convolutional neural networks to speed up the Hamiltonian Monte Carlo inversion of pre-stack seismic data

Markov chain Monte Carlo algorithms are commonly employed for accurate uncertainty appraisals in non-linear inverse problems. The downside of these algorithms is the considerable number of samples needed to achieve reliable posterior estimations, especially in high-dimensional model spaces. To overcome this issue, the Hamiltonian Monte Carlo algorithm has recently been introduced to solve geophysical inversions. Different from classical Markov chain Monte Carlo algorithms, this approach exploits the derivative information of the target posterior probability density to guide the sampling of the model space. However, its main downside is the computational cost for the derivative computation (i.e. the computation of the Jacobian matrix around each sampled model). Possible strategies to mitigate this issue are the reduction of the dimensionality of the model space and/or the use of efficient methods to compute the gradient of the target density. Here we focus the attention to the estimation of elastic properties (P-, S-wave velocities and density) from pre-stack data through a non-linear amplitude versus angle inversion in which the Hamiltonian Monte Carlo algorithm is used to sample the posterior probability. To decrease the computational cost of the inversion procedure, we employ the discrete cosine transform to reparametrize the model space, and we train a convolutional neural network to predict the Jacobian matrix around each sampled model. The training data set for the network is also parametrized in the discrete cosine transform space, thus allowing for a reduction of the number of parameters to be optimized during the learning phase. Once trained the network can be used to compute the Jacobian matrix associated with each sampled model in real time. The outcomes of the proposed approach are compared and validated with the predictions of Hamiltonian Monte Carlo inversions in which a quite computationally expensive, but accurate finite-difference scheme is used to compute the Jacobian matrix and with those obtained by replacing the Jacobian with a matrix operator derived from a linear approximation of the Zoeppritz equations. Synthetic and field inversion experiments demonstrate that the proposed approach dramatically reduces the cost of the Hamiltonian Monte Carlo inversion while preserving an accurate and efficient sampling of the posterior probability.     

新疆皮山地区地震动衰减关系研究

利用2015年7月3日皮山M_S6.5地震序列的波形资料,采用微遗传算法反演得到震源区的5个介质参数,即应力降Δσ为75.95bars,品质因子Q_0为186.33,η值为0.26,几何衰减分段点R_1为72.18km,R_2为139.70km;并由此计算得到的傅氏谱再配合随机相位谱,进而得到地震动时程,建立了震源区地震动衰减关系。由于缺乏强震记录,本文还收集了2008年10月5日乌恰M_S6.9地震、2008年10月6日阿克陶M_S6.2地震、2012年3月9日洛浦M_S6.0地震的强震数据,将计算结果与经验性衰减关系以及3次强震记录进行比较后发现,皮山地震、洛浦地震的强震数据普遍高于经验性衰减关系,乌恰地震的强震数据与俞言祥等文中的衰减关系吻合较好,在远场低于本文的计算结果。     

2013年南斯科舍海岭MW7.8地震的多点震源机制反演

2013年11月17日,在南极南奥克尼群岛北、南极板块与斯科舍板块之间发生了一次M_W7.8级地震(2013年南斯科舍海岭M_W7.8地震),我们利用全球分布的长周期和宽频带地震记录反演确定了这次地震随时间和空间变化的震源机制,验证了提出的一种多点震源机制反演的新方法.首先利用长周期记录的W震相反演了这次地震的矩心矩张量解并利用体波提取了视震源时间函数,同时利用台阵反投影技术从宽频带记录中获得了这次地震的高频源的时空分布,然后基于矩心矩张量解、视震源时间函数以及高频源的时空分布,实现了采用新方法对2013年南斯科舍海岭M_W7.8地震的多点震源机制反演.矩心矩张量解表明,地震矩心在44.50°W/60.18°S,矩心深度19 km,半持续时间49 s,释放标量地震矩4.71×10²⁰ N·m,发震断层走向104°,倾角54°,滑动角8°.视震源时间函数清楚地揭示了地震矩随时间变化的方位依赖性,总体上可以将时间过程分为前60 s和后50 s两个阶段,但前60 s可细分为两次子事件.根据台阵反投影结果,这次地震为沿海沟从西到东的单侧破裂,破裂长度达311 km,可以分为5次子事件,能量释放的峰值点依次为13 s、30 s、51 s、64 s和84 s,平均破裂速度分别为0.6 km·s^-1、2.6 km·s^-1、2.3 km·s^-1、2.8 km·s^-1和3 km·s^-1.多点震源机制反演显示,5次子事件的矩震级分别为M_W7.57,M_W7.48,M_W6.80,M_W7.53和M_W7.08,半持续时间依次为21 s,17 s,6 s,16 s和8 s,走向分别为95°,105°,81°,98°和98°,倾角依次为57°,49°,86°,46°和64°,滑动角-9°,1°,-17°,13°和-4°.这些在震源机制、能量释放以及持续时间方面的变化都是当地构造和应力环境复杂性的反映.