Surveys in Geophysics最新文献

Reliable characterization of subsurface fracture information within the oil-bearing reservoirs plays an essential role in reservoir description, particularly in assessing the fracture density and fluid storage capacity. Fracture density indicates the spatial distribution and extent of fracture development, which is traditionally estimated indirectly via tangential fracture weakness predicted from the OVT (offset vector tile) seismic data or azimuthal elliptical fitting techniques. The fracture fluid factor is also a critical parameter for identifying and characterizing fluids within fractured reservoirs. Conventional methods for estimating the fluid factor typically rely on establishing a linear correlation between the fracture fluid factor and anisotropy parameters, leading to challenging fluid identification and low accuracy. To address this limitation, we propose a novel approach that directly and simultaneously inverts the fracture density and a newly defined fluid influence factor (FIF) within a horizontally transverse isotropic (HTI) medium. The specific definition of frequency-dependent FIF is presented, and the reliable mechanism of FIF for identifying fracture-filling fluid is demonstrated based on an anisotropic petrophysical model. Furthermore, we derive a new PP-wave reflection coefficient for HTI media to facilitate azimuthal seismic inversion. A two-step inversion strategy is also introduced to invert weakly anisotropic parameters to enhance inversion accuracy. Combining the superiority of direct simultaneous inversion of fracture density and FIF, these results applied to the carbonate oil-bearing reservoir demonstrate that the proposed method can not only estimate fracture density directly but also predict FIF to identify fractured fluid, providing a valuable reference for the evaluation of fractured oil reservoirs.

The spatiotemporal patterns of groundwater temperature may effectively delineate groundwater flow systems and help to identify aquifer recharge areas and preferential flow pathways. In coastal aquifers, they may also offer valuable insights into the spatial extent of seawater intrusion and saltwater upconing. Applying simple Kriging interpolation and variography techniques on a high-density three-dimensional temperature dataset derived from groundwater temperature–depth profiles has enabled the reconstruction of the three-dimensional thermal field for the southernmost part of the Salento coastal karst aquifer (Southern Italy). This region shows structural complexity, which poses challenges for conceptual modelling assessment. The 3D temperature model produced is a groundbreaking reconstruction derived from field data that highlights crucial insights into a shallow hydrogeological environment. Given the hydrogeological complexity and the regional scale of the aquifer, which pose challenges to straightforward groundwater flow modelling, the information on temperature distribution from maps and cross sections of the three-dimensional thermal field emerges as a pivotal tool in identifying crucial hydrogeological features. This study, bolstered by geological, geomorphological, and structural data, demonstrates that the analysis of the groundwater thermal field, which encapsulates information about aquifer permeability heterogeneity and anisotropy, is instrumental in deducing the hydraulic behaviour of faults and revealing aquifer properties. From a geostatistical perspective, this study underscores the comprehensive nature of the 3D Kriging model: it incorporates all available groundwater temperature data from all explored depths, resulting in temperature maps that show a more accurate spatial distribution than those created by Kriging within ± 2 m of selected depths.

The Arctic is warming several times faster than the rest of the globe. Such Arctic amplification rapidly changes hydrometeorological conditions with consequences for the structuring of cold-adapted terrestrial and aquatic ecosystems. Arctic ecosystems, which have a relatively small buffering capacity, are particularly susceptible to hydrometeorological regime shifts thus frequently undergo system-scale transitions. Abrupt ecosystem changes are often triggered by disturbances and extreme events that shift the ecosystem state beyond its buffering threshold capacity thus irreversibly changing its functioning (ecosystem tipping). The tipping depends on spatial and temporal scales. At the local scale, feedback between soil organic matter and soil physics could lead to multiple steady states and a tipping from high to low soil carbon storages. On the continental scale, local tipping is smoothed and the changes are rather gradual (no clear tipping threshold). However, due to the centennial timescale of soil carbon and vegetation dynamics, Arctic ecosystems are not in equilibrium with the changing climate, so a tipping could occur at a later time. Earth Observation (EO) is useful for monitoring ongoing changes in permafrost and freshwater systems, in particular extreme events and disturbances, as indicators of a possible tipping point. Lake change observations support gradual rather than abrupt transitions in different permafrost regions until a hydrological tipping point where lake areas start to decline leading to regional drying. Due to floodplain abundance, floodplains should be considered separately when using satellite-derived water extent records to analyse potential tipping behaviour associated with lakes. Reduction in surface water extent, increasing autocorrelation of water level of larger lakes and the impact of extreme events on ground ice can all be observed with satellite data across the Arctic. The analysis of Earth System simulations suggests significant impacts of changes in permafrost hydrology on hydroclimate in the tropics and subtropics, but there is no clear threshold in global temperature for these shifts in hydroclimate.

Aquifers in basement terrains, including fractured basement rocks and superimposed alluvial deposits associated with the dissecting ephemeral streams, are very complex and their groundwater accumulation is significantly affected by their structural settings. However, the distribution and intersection of geologic structures, along with their mechanistic controls on aquifer thickness, depth, and groundwater flow, remain unclear. In this study, an integrated approach is developed that combines fieldwork, remote-sensing data, and geophysical techniques (vertical electrical sounding, seismic refraction, ground-penetrating radar) to characterize, and better understand the role of, geologic structures (e.g., faults and shear zones) in controlling groundwater accumulation in the basement aquifer systems of southern Sinai, Egypt. Three major structural elements were identified in southern Sinai; their spatial distribution and intersections predominantly control groundwater accumulation. A total of 334 locations were identified, in a geographic information system (GIS) environment, at the intersections of two or more fault/shear zone systems, representing optimal aquifer conditions. The intersection of N–S, NE–SW, and NW–SE shear zones and/or the N–S shear zone and ENE–WSW fault resulted in a thicker aquifer unit with a shallow depth to water table, at these sites the faults/shear zones act as barriers for groundwater flow. The intersection of N–S with NW–SE shear zones, N–S shear zones with NE–SW faults, and NE–SW shear zones with NW–SE faults produced a thin aquifer with a greater depth to water table; in these, the faults/shear zones act as channels for groundwater flow. These findings provide valuable new insights into the significance of structural elements and their spatial distribution in controlling groundwater availability in basement rock aquifers. The methodologies employed in this research can be used as a framework for similar studies in other regions with highly fractured basement terrains.

Graphical Abstract

With the rapid advancement of artificial intelligence, deep-learning-based inversion frameworks are increasingly being adopted to tackle the challenges associated with surface-wave dispersion curve (DC) inversion. Compared with classical model-driven methods, the deep-learning-based inversion is known for its higher efficiency and independence from the initial model. Existing researches, however, have focused on algorithm design and case applications. The reforms that deep learning techniques can bring to inversion need further exploration. Therefore, we explored the anti-noise ability, stability, performance in joint inversion scenarios, and generalization ability of deep-learning-based inversions. For the first three characteristics, we select a published neural network and the neighborhood algorithm as representatives of deep-learning-based and model-driven inversions, respectively, to compare the corresponding performance of these two methods. The comparative tests and statistical analyses reveal that deep-learning-based inversion exhibits superior anti-noise ability and stability, but shows limited improvement in joint inversion performance. And the statistical results from tests for generalization ability show that the trained neural network can predict the shear-wave velocity (Vs) model whose Vs oversteps the model space of training dataset within 20%. In particular, we discover that the generalization ability is positively correlated with the prediction precision of Vs. This analysis provides valuable insights for choosing appropriate inversion methods and contributes to a deeper understanding of deep-learning-based inversions.

Multigrid (MG) methods solve large linear equations on fine grids by projecting them onto progressively coarser grids, on which the problem can be solved more cheaply. They have become among the most effective and prospective solvers for large linear systems. However, due to the abundant null solution space and the inclusion of the air layer, traditional MG methods struggle to converge in three-dimensional (3D) electromagnetic (EM) numerical forward modeling. Served as one major contribution of this review, we provide a complete review on strategies, introduced in recent decades to develop efficient MG algorithms for EM forward modeling. We focus on how these strategies handle the convergence difficulties encountered in EM numerical forward modeling. Another observation is that most state-of-the-art MG solvers have been developed and examined against traditional Krylov subspace iterative solvers, but there is little knowledge on the numerical performance of different strategies. Therefore, another primary contribution of this work is to provide a complete review of the numerical performance of different strategies used in MG solvers for 3D EM forward modeling in geophysical applications. For this purpose, firstly, we briefly introduce on finite difference and finite element numerical discretization of the electrical field partial differential equations to demonstrate why EM forward modeling is challenging to solve. Subsequently, some background information on MG methods is provided to show how they can be implemented in general. Then, different strategies used in different MG methods are introduced in great detail to address the convergence issues encountered in EM forward modeling in geophysical applications, caused by the abundant null solution space and the inclusion of the air layer. Finally, we present four newly developed MG algorithms and compare their overall numerical performance in terms of their parallel ability, stability, efficiency and memory cost by using two increasingly complex models. Since one major motivation for improving the EM forward modeling efficiency is to speed up the inversion process, their perspective of efficiency improvement in EM inversions has been discussed. On this basis, authors and researchers can choose one particular MG solver for their own EM forward modeling problems.

The Active Plasma and E-field Sounders (APES) mission concept aims to resolve orders-of-magnitude errors in modeling transionospheric radio propagation through the midlatitude trough, and to determine which physical mechanism(s) are responsible for generating plasma irregularities there. APES will observe ionospheric electron density profiles and signals from ground transmitters along its orbital track, allowing for a constrained test of propagation models. The mission will also perform small-scale in situ science, differentiating between the long-held temperature gradient instability and the Kelvin–Helmholtz/gradient drift instabilities as potential causes of irregularities in the trough. The centerpiece of the mission is the first-ever oblique topside ionospheric sounder, providing 2D electron density-altitude profiles along the orbital track through cooperative operation between two satellites. The leading satellite will produce swept-frequency HF transmissions that will reflect off the ionosphere before being received by the follower. The following satellite will also receive signals transmitted by the Super Dual Auroral Radar Network (SuperDARN). Both satellites will observe electron density at 1 m along-track resolution, while single-point electron temperature, vector electric field, neutral density and neutral wind will also be provided. The mission will operate in a nominal 350 × 800 km elliptical orbit, with along-track spacing varied from < 1 to 750 km over 12 months of science operations in an inclination between 50–87° and 103–130° (depending on the rideshare). Each bus carries a 250 m/s propulsion system to control eccentricity and for orbit maintenance. The orbital analysis has been used to select orbits with > 500 passes through the trough in each quarter.

Many components of the Earth system feature self-reinforcing feedback processes that can potentially scale up a small initial change to a fundamental state change of the underlying system in a sometimes abrupt or irreversible manner beyond a critical threshold. Such tipping points can be found across a wide range of spatial and temporal scales and are expressed in very different observable variables. For example, early-warning signals of approaching critical transitions may manifest in localised spatial pattern formation of vegetation within years as observed for the Amazon rainforest. In contrast, the susceptibility of ice sheets to tipping dynamics can unfold at basin to sub-continental scales, over centuries to even millennia. Accordingly, to improve the understanding of the underlying processes, to capture present-day system states and to monitor early-warning signals, tipping point science relies on diverse data products. To that end, Earth observation has proven indispensable as it provides a broad range of data products with varying spatio-temporal scales and resolutions. Here we review the observable characteristics of selected potential climate tipping systems associated with the multiple stages of a tipping process: This includes i) gaining system and process understanding, ii) detecting early-warning signals for resilience loss when approaching potential tipping points and iii) monitoring progressing tipping dynamics across scales in space and time. By assessing how well the observational requirements are met by the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS), we identify gaps in the portfolio and what is needed to better characterise potential candidate tipping elements. Gaps have been identified for the Amazon forest system (vegetation water content), permafrost (ground subsidence), Atlantic Meridional Overturning Circulation, AMOC (section mass, heat and fresh water transports and freshwater input from ice sheet edges) and ice sheets (e.g. surface melt). For many of the ECVs, issues in specifications have been identified. Of main concern are spatial resolution and missing variables, calling for an update of the ECVS or a separate, dedicated catalogue of tipping variables.