Surveys in Geophysics最新文献

As the Earth system is exposed to large anthropogenic interferences, it becomes ever more important to assess the resilience of natural systems, i.e., their ability to recover from natural and human-induced perturbations. Several, often related, measures of resilience have been proposed and applied to modeled and observed data, often by different scientific communities. Focusing on terrestrial ecosystems as a key component of the Earth system, we review methods that can detect large perturbations (temporary excursions from a reference state as well as abrupt shifts to a new reference state) in spatio-temporal datasets, estimate the recovery rate after such perturbations, or assess resilience changes indirectly from stationary time series via indicators of critical slowing down. We present here a sequence of ideal methodological steps in the field of resilience science, and argue how to obtain a consistent and multi-faceted view on ecosystem or climate resilience from Earth observation (EO) data. While EO data offers unique potential to study ecosystem resilience globally at high spatial and temporal scale, we emphasize some important limitations, which are associated with the theoretical assumptions behind diagnostic methods and with the measurement process and pre-processing steps of EO data. The latter class of limitations include gaps in time series, the disparity of scales, and issues arising from aggregating time series from multiple sensors. Based on this assessment, we formulate specific recommendations to the EO community in order to improve the observational basis for ecosystem resilience research.

This paper delivers an in-depth bibliometric analysis of distributed acoustic sensing (DAS) research within the realm of geophysics, covering the period from 2012 to 2023 and drawing on data from the Web of Science. By employing bibliographic and structured network analysis methods, including the use of Bibliometrix and VOSviewer^®, the study highlights the most influential scholars, leading institutions, and pivotal research contributions that have significantly shaped the field of DAS in geophysics. The research delves into key collaborative dynamics, unraveling them through co-authorship network analysis, and delves into thematic developments and trajectories via comprehensive co-citation and keyword co-occurrence network analyses. These analyses elucidate the most robust and prominent areas within DAS research. A critical insight gained from this study is the rise of ‘photonic seismology’ as an emerging interdisciplinary domain, exemplifying the fusion of photonic sensing techniques with seismic science. This paper also discusses certain limitations inherent in the study and concludes with implications for future research.

The wave reflection and transmission (R/T) coefficients in fluid-saturated porous media with the effect of effective pressure are rarely studied, despite the ubiquitous presence of in situ pressure in the subsurface Earth. To fill this knowledge gap, we derive exact R/T coefficient equations for a plane wave incident obliquely at the interface between the dissimilar pressured fluid-saturated porous half-spaces described by the theory of poro-acoustoelasticity (PAE). The central result of the classic PAE theory is first reviewed, and then a dual-porosity model is employed to generalize this theory by incorporating the impact of nonlinear crack deformation. The new velocity equations of generalized PAE theory can describe the nonlinear pressure dependence of fast P-, S- and slow P-wave velocities and have a reasonable agreement with the laboratory measurements. The general boundary conditions associated with membrane stiffness are used to yield the exact pressure-dependent wave R/T coefficient equations. We then model the impacts of effective pressure on the angle and frequency dependence of wave R/T coefficients and synthetic seismic responses in detail and compare our equations to the previously reported equations in zero-pressure case. It is inferred that the existing R/T coefficient equations for porous media may be misleading, since they lack consideration for inevitable in situ pressure effects. Modeling results also indicate that effective pressure and membrane stiffness significantly affect the amplitude variation with offset characteristics of reflected seismic signatures, which emphasizes the significance of considering the effects of both in practical applications related to the observed seismic data. By comparing the modeled R/T coefficients to the results computed with laboratory measured velocities, we preliminarily confirm the validity of our equations. Our equations and results are relevant to hydrocarbon exploration, in situ pressure detection and geofluid discrimination in high-pressure fields.

Different observation data are utilized to obtain a unified geophysical model based on the correlations of underground geological bodies in joint inversions. By specifying a type of Gramian constraints, Gramian as a coupling term can link geophysical models through relationships of physical properties or structural similarities. Considering the complex relationships of physical properties of underground geological bodies, we proposed an adaptive zoning method to automatically divide the whole inversion area into subregions with different relationships of physical properties and to determine the number and range of subregions that utilized correlation between geophysical data before joint inversions. On this basis, we considered the use of a combination of Gramian coupling terms rather than one term to link petrophysical and structural domains during joint inversions. Synthetic tests showed that the algorithm is capable of having a robust estimate of the spatial distribution and relationships between density and magnetization intensity of geological bodies. The idea was also applied to the ore concentration area in the middle and lower reaches of the Yangtze River to obtain the three-dimensional (3-D) distribution model of magnetite-bearing rocks within 5 km underground, which corresponds well with the existing shallow ore sites and demonstrates the existence of available deep resources in the study area.

As an emerging seismic acquisition technology, distributed acoustic sensing (DAS) has drawn significant attention in earth science for long-term and cost-effective monitoring of underground activities. Field seismic experiments with optical fibers in a vertical seismic profile (VSP) configuration were conducted at the Newell County Facility of Carbon Management Canada in Alberta, Canada, for ({text{CO}}_2) injection and storage monitoring. Seismic full-waveform inversion (FWI) represents one promising approach for high-resolution imaging of subsurface model properties. In this study, anisotropic FWI with variable density is applied to the DAS-recorded walk-away VSP data for characterizing the subsurface velocity, anisotropy, and density structures, serving as baseline models for future time-lapse studies at the pilot site. Synthetic inversion experiments suggest that, without accounting for anisotropy, the inverted density structures by isotropic FWI are damaged by strong trade-off artifacts. Anisotropic FWI can provide more accurate P-wave velocity, density, and valuable anisotropy models. Field data applications are then performed to validate the effectiveness and superiority of the proposed methods. Compared to the inversion outputs of isotropic FWI, the inverted P-wave velocity by anisotropic FWI matches trend variation of the well log more closely. In the inverted density model, the ({text{CO}}_2) injection formation can be clearly resolved. The inverted anisotropy parameters provide informative references to interpret the structures and lithology around the target ({text{CO}}_2) injection zone.

Accurate diagnosis of regional atmospheric and surface energy budgets is critical for understanding the spatial distribution of heat uptake associated with the Earth’s energy imbalance (EEI). This contribution discusses frameworks and methods for consistent evaluation of key quantities of those budgets using observationally constrained data sets. It thereby touches upon assumptions made in data products which have implications for these evaluations. We evaluate 2001–2020 average regional total (TE) and dry static energy (DSE) budgets using satellite-based and reanalysis data. For the first time, a consistent framework is applied to the ensemble of the 5th generation European Reanalysis (ERA5), version 2 of modern-era retrospective analysis for research and applications (MERRA-2), and the Japanese 55-year Reanalysis (JRA55). Uncertainties of the computed budgets are assessed through inter-product spread and evaluation of physical constraints. Furthermore, we use the TE budget to infer fields of net surface energy flux. Results indicate biases < 1 W/m² on the global, < 5 W/m² on the continental, and ~ 15 W/m² on the regional scale. Inferred net surface energy fluxes exhibit reduced large-scale biases compared to surface flux data based on remote sensing and models. We use the DSE budget to infer atmospheric diabatic heating from condensational processes. Comparison to observation-based precipitation data indicates larger uncertainties (10–15 Wm⁻² globally) in the DSE budget compared to the TE budget, which is reflected by increased spread in reanalysis-based fields. Continued validation efforts of atmospheric energy budgets are needed to document progress in new and upcoming observational products, and to understand their limitations when performing EEI research.

Sparse representation and inversion have been widely used in the acquisition and processing of geophysical data. In particular, the low-rank representation of seismic signals shows that they can be determined by a few elementary modes with predominantly large singular values. We review global and local low-rank representation for seismic reflectivity models and then apply it to least-squares migration (LSM) in acoustic and viscoacoustic media. In the global singular value decomposition (SVD), the elementary modes determined by singular vectors represent horizontal and vertical stratigraphic segments sorted from low to high wavenumbers, and the corresponding singular values reflect the contribution of these basic modes to form a broadband reflectivity model. In contrast, local SVD for grouped patch matrices can capture nonlocal similarity and thus accurately represent the reflectivity model with fewer ranks than the global SVD method. Taking advantage of this favorable sparsity, we introduce a local low-rank regularization into LSM to estimate subsurface reflectivity models. A two-step algorithm is developed to solve this low-rank constrained inverse problem: the first step is for least-squares data fitting and the second is for weighted nuclear-norm minimization. Numerical experiments for synthetic and field data demonstrate that the low-rank constraint outperforms conventional shaping and total-variation regularizations, and can produce high-quality reflectivity images for complicated structures and low signal-to-noise data.

The rheological models of Lomnitz and Jeffreys have been widely used in earthquake seismology (to simulate a nearly constant Q medium) and to describe the creep and relaxation behavior of rocks as a function of time. Other similar models, such as those of Becker, Scott Blair and Kolsky, show similar properties, particularly the Scott Blair model describes a perfectly constant Q as a function of frequency. We first give a historical overview of the main scientists and the development and versions of the various models and priorities of discovery. Then, we clarify the relationship between the different versions of these models in terms of mathematical expressions of the complex modulus and calculate the phase velocity and quality factor Q as a function of frequency, illustrating the various special cases. In addition, we give useful hints for the numerical calculation of these moduli, which include special cases of the hypergeometric function.

Since the first Global Energy and Water Exchanges cloud assessment a decade ago, existing cloud property retrievals have been revised and new retrievals have been developed. The new global long-term cloud datasets show, in general, similar results to those of the previous assessment. A notable exception is the reduced cloud amount provided by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Science Team, resulting from an improved aerosol–cloud distinction. Height, opacity and thermodynamic phase determine the radiative effect of clouds. Their distributions as well as relative occurrences of cloud types distinguished by height and optical depth are discussed. The similar results of the two assessments indicate that further improvement, in particular on vertical cloud layering, can only be achieved by combining complementary information. We suggest such combination methods to estimate the amount of all clouds within the atmospheric column, including those hidden by clouds aloft. The results compare well with those from CloudSat-CALIPSO radar–lidar geometrical profiles as well as with results from the International Satellite Cloud Climatology Project (ISCCP) corrected by the cloud vertical layer model, which is used for the computation of the ISCCP-derived radiative fluxes. Furthermore, we highlight studies on cloud monitoring using the information from the histograms of the database and give guidelines for: (1) the use of satellite-retrieved cloud properties in climate studies and climate model evaluation and (2) improved retrieval strategies.

The accurate extraction of useful signals from the measurement data is one of the important parts and challenges of the understanding of subsurface information. The desired signal is usually hidden in the background noise, and the amplitude is weak due to the particular geological environment of the subsurface or the consistency of the measuring instrument. By extending the theory of seismic interferometry to include the effects of adjacent virtual channels and by combining super- and reverse-virtual interferometry, we obtain a hybrid virtual interferometry technique based on stacking neighboring virtual traces for wave reconstruction. We have verified the effectiveness of the processing method in suppressing noise interference and extracting useful signals using synthetic data tests. The method is applied to the processing and interpretation of acoustic measurements acquired in a cased borehole of a coal formation and an open hole of an igneous formation, where the processed waveforms are finely reconstructed, and the estimated slowness results are in good agreement with other measurements, thus providing an effective tool for data analysis.