Surveys in Geophysics最新文献

The aim of the new generation of Global Geodetic Observing System is a millimeter-level accuracy in positioning, with a crucial role to be played by Global Navigation Satellites Systems (GNSS) in the Precise Point Positioning (PPP) mode. This is of course because GNSS constellations and receivers provide an efficient stand-alone technique with a homogeneous performance over large areas (positions, navigation and meteorology) when used in conjunction with the PPP mode, with also an ever-increasing data flow and different satellite line-of-sights. The requirement of accuracies reaching the millimeter or sub-millimeter implies a knowledge at this level of each line in the GNSS-PPP error budget, including, but not restricted to: clock biases, troposphere and ionosphere delays, multipath and ground deformations. In this review study, we consider this millimeter-/submillimeter level GNSS-PPP error budget, and possible mitigations and improvements in the frame of the existing global constellations: GPS, Galileo, GLONASS and BDS, in view of augmented constellations and/or Low Earth Orbit constellations, which will be available in the near future. We also pay a special attention to systematic biases that can/could exist between constellations.

Thin elongated sources, such as dykes, sills, chimneys, inclined sheets, etc., often encountered in volcano gravimetric studies, pose great challenges to gravity inversion methods based on model exploration and growing sources bodies. The Growth inversion approach tested here is based on partitioning the subsurface into right-rectangular cells and populating the cells with differential densities in an iterative weighted mixed adjustment process, in which the minimization of the data misfit is balanced by forcing the growing subsurface density distribution into compact source bodies. How the Growth inversion can cope with thin elongated sources is the subject of our study. We use synthetic spatiotemporal gravity changes caused by simulated sources placed in three real volcanic settings. Our case studies demonstrate the benefits and limitations of the Growth inversion as applied to sparse and noisy gravity change data generated by thin elongated sources. Such sources cannot be reproduced by Growth accurately. They are imaged with smaller density contrasts, as much thicker, with exaggerated volume. Despite this drawback, the Growth inversion can provide useful information on several source parameters even for thin elongated sources, such as the position (including depth), the orientation, the length, and the mass, which is a key factor in volcano gravimetry. Since the density contrast of a source is not determined by the inversion, but preset by the user to run the inversion process, it cannot be used to specify the nature of the source process. The interpretation must be assisted by external constraints such as structural or tectonic controls, or volcanological context. Synthetic modeling and Growth inversions, such as those presented here, can serve also for optimizing the volcano monitoring gravimetric network design. We conclude that the Growth inversion methodology may, in principle, prove useful even for the detection of thin elongated sources of high density contrast by providing useful information on their position, shape (except for thickness) and mass, despite the strong ambiguity in determining their differential density and volume. However, this yielded information may be severely compromised in reality by the sparsity and noise of the interpreted gravity data.

Stress-induced seismic anisotropy is usually difficult to be separated or decoupled from intrinsic or fracture-induced anisotropy in the subsurface. To distinguish the effects of fracture and stress on azimuthal reflection amplitudes in an anisotropic medium, a feasible approximation for decoupled fracture- and stress-induced PP-wave reflection coefficient is presented used for azimuthal seismic inversion. Following nonlinear acoustoelastic theory, we first present the relationship between horizontal uniaxial stress and decoupled fracture- and stress-induced PP-wave reflection coefficient for an interface between two stressed horizontal transversely isotropic (HTI) media based on weak-contrast, weak-anisotropy and small-stress assumptions. Next, we present an inversion method of amplitude variations with angles of incidence and azimuth to estimate decoupled fracture- and stress-induced anisotropy using the seismic amplitude differences between different azimuths. Finally, both synthetic and real data sets are used to validate our proposed inversion method, and it can provide an alternative way to estimate the decoupled fracture- and stress-induced anisotropic parameters in stressed shale gas reservoir with HTI symmetry from azimuthal reflection amplitude data.

An accurate estimation of ionospheric variables such as the total electron content (TEC) is important for many space weather, communication, and satellite geodetic applications. Empirical and physics-based models are often used to determine TEC in these applications. However, it is known that these models cannot reproduce all ionospheric variability due to various reasons such as their simplified model structure, coarse sampling of their inputs, and dependencies to the calibration period. Bayesian-based data assimilation (DA) techniques are often used for improving these model’s performance, but their computational cost is considerably large. In this study, first, we review the available DA techniques for upper atmosphere data assimilation. Then, we will present an empirical decomposition-based data assimilation (DDA), based on the principal component analysis and the ensemble Kalman filter. DDA considerably reduces the computational complexity of previous DA implementations. Its performance is demonstrated by updating the empirical orthogonal functions of the empirical NeQuick and the physics-based TIEGCM models using the rapid global ionosphere map (GIM) TEC products as observation. The new models, respectively, called ‘DDA-NeQuick’ and ‘DDA-TIEGCM,’ are then used to predict TEC values for the next day. Comparisons of the TEC forecasts with the final GIM TEC products (that are available after 11 days) represent an average (42.46%) and (31.89%) root mean squared error (RMSE) reduction during our test period, September 2017.

We study the reflection and transmission (R/T) characteristics of inhomogeneous plane waves at the interface between two dissimilar fluid-saturated thermoporoelastic media at arbitrary incidence angles. The R/T behaviors are formulated based on the classic Lord–Shulman (LS) and Green–Lindsay (GL) heat-transfer models as well as a generalized LS model, respectively. The latter results from different values of the Maxwell-Vernotte-Cattaneo relaxation times. These thermoporoelastic models can predict three inhomogeneous longitudinal (P1, P2, and T) waves and one shear (S) wave. We first compare the LS and GL models for the phase velocities and attenuation coefficients of plane waves, where the homogeneous wave has a higher velocity but weaker thermal attenuation than the inhomogeneous wave. Considering the oil–water contact, we investigate R/T coefficients associated with phase angles and energy ratios, which are formulated in terms of incidence and inhomogeneity angles, with the latter having a significant effect on the interference energy. The proposed thermoporoelastic R/T model predicts different energy partitions between the P and S modes, especially at the critical angle and near grazing incidence. We observe the anomalous behavior for an incident P wave with the inhomogeneity angle near the grazing incidence. The energy partition at the critical angle is mainly controlled by relaxation times and boundary conditions. Beyond the critical angle, the energy flux predicted by the Biot poroelastic and LS models vanishes vertically, becoming the opposite for the GL and generalized LS models. The resulting energy flux shows a good agreement with the R/T coefficients, and they are well proven by the conservation of energy, where the results are valuable for the exploration of thermal reservoirs.

The accumulation of organic matter is the basis for gas generation and significantly affects the ultimate gas production in shale reservoirs. Estimation of organic enrichment using seismic data is essential for shale gas characterization. The commonly used correlations between elastic properties and organic matter content for a particular area are locally applicable but may not be workable for other zones. Herein, a general physics-based approach is proposed to predict organic enrichment in shales. An organic matter-matrix decoupling amplitude variation versus offset (AVO) formula is constructed to straightforwardly quantify seismic signatures of organic matter via an introduced organic matter-related factor (M_c). Then, the elastic impedance (EI) function is established from the decoupling AVO formula to compute M_c. The proposed EI inversion method is suitable for capturing organic enrichment, particularly in the case of inadequate petrophysics information for reliable evaluation of M_c using log data as a constraint in the inversion. The developed AVO formula and EI function regard the organic matter as solid pore-fillings, presenting a more reasonable model for organic shales. Numerical tests show that M_c exhibits enhanced sensitivity to organic matter content with respect to the regularly used elastic properties. The real data applications indicate that the estimated M_c agrees well with the gas production in horizontal development wells, suggesting that M_c is a good indicator of favorable gas areas. The proposed approach may have broader potential applications and can be extended to detect other fluids and solid-saturated hydrocarbon reservoirs such as shale oil, heavy oil, and gas hydrates.

Linear arrays are popularly used for passive surface wave imaging due to their high efficiency and convenience, especially in urban applications. The unknown characteristics such as azimuth of noise sources, however, make it challenging to extract accurate phase-velocity dispersion information by employing a 1-D linear array. To solve this problem, we proposed an alternative passive surface wave method to capture the dominant azimuth of noise sources and retrieve the phase-velocity dispersion curve by polarization analysis with multicomponent ambient noise records. We verified the proposed method using synthetic data sets under various source distributions. According to the calculated dominant azimuth, it is deduced that noise sources are mainly classified as either inline or offline distribution. For inline noise source distribution, we are able to directly obtain the unbiased phase-velocity measurements; for offline noise source distribution, we should correct the velocity overestimation due to azimuthal effects using the proposed method. Results from two field examples show that the distributions of noise sources are predominantly offline. We eliminated the velocity bias caused by offline source distribution and picked phase velocities following higher amplitude peaks along the trend. After the azimuthal correction, the picked phase-velocity dispersion curves in dispersion images generated from passive source data match well with those from active source data, demonstrating the practicability of the proposed technique.

The understanding of subsurface information on the Earth is crucial in numerous fields such as economics of oil and gas, geophysical exploration, archaeology and hydro-geophysics, particularly in a context of climate change. The methodology consists in reconstructing the seismic velocity model of the near surface, that contains information about the basement structure, by solving the inverse problem and resolving the related complex nonlinear systems with the data collected from seismic experiments and measurements. In the last few years, many deep neural networks have been proposed to simplify the seismic inversion problem based, for instance, on automatic differentiation of the adjoint operator, or on automatic arrival time picking. However, such approaches require a large amount of labeled training data, which are hardly available in real applications. We present here a deep learning approach for arrival time picking, aimed to deal with unlabeled data. The main building blocks are transfer learning, as well as a semi-supervised learning strategy where the pseudo-labels are greedily computed with robust regression, and classification algorithms. The hybrid method showcases very high scores when evaluating on synthetic data, and its application to a real dataset containing a limited amount of labeled data shows the computational efficiency and very accurate results.