Surveys in Geophysics最新文献

This paper is concerned with how the diabatically-forced overturning circulations of the atmosphere, established by the deep convection within the tropical trough zone (TTZ), first introduced by Riehl and (Malkus) Simpson, in Contr Atmos Phys 52:287–305 (1979), fundamentally shape the distributions of tropical and subtropical cloudiness and the changes to cloudiness as Earth warms. The study first draws on an analysis of a range of observations to understand the connections between the energetics of the TTZ, convection and clouds. These observations reveal a tight coupling of the two main components of the diabatic heating, the cloud component of radiative heating, shaped mostly by high clouds formed by deep convection, and the latent heating associated with the precipitation. Interannual variability of the TTZ reveals a marked variation that connects the depth of the tropical troposphere, the depth of convection, the thickness of high clouds and the TOA radiative imbalance. The study examines connections between this convective zone and cloud changes further afield in the context of CMIP6 model experiments of climate warming. The warming realized in the CMIP6 SSP5-8.5 scenario multi-model experiments, for example, produces an enhanced Hadley circulation with increased heating in the zone of tropical deep convection and increased radiative cooling and subsidence in the subtropical regions. This impacts low cloud changes and in turn the model warming response through low cloud feedbacks. The pattern of warming produced by models, also influenced by convection in the tropical region, has a profound influence on the projected global warming.

Rayleigh wave exploration is a powerful method for estimating near-surface shear-wave (S-wave) velocities, providing valuable insights into the stiffness properties of subsurface materials inside the Earth. The dispersion curve inversion of Rayleigh wave corresponds to the optimization process of searching for the optimal solutions of earth model parameters based on the measured dispersion curves. At present, diversified inversion algorithms have been introduced into the process of Rayleigh wave inversion. However, limited studies have been conducted to uncover the variations in inversion performance among commonly used inversion algorithms. To obtain a comprehensive understanding of the optimization performance of these inversion algorithms, we systematically investigate and quantitatively assess the inversion performance of two bionic algorithms, two probabilistic algorithms, a gradient-based algorithm, and two neural network algorithms. The evaluation indices include the computational cost, accuracy, stability, generalization ability, noise effects, and field data processing capability. It is found that the Bound-constrained limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS-B) algorithm and the broad learning (BL) network have the lowest computational cost among candidate algorithms. Furthermore, the transitional Markov Chain Monte Carlo algorithm, deep learning (DL) network, and BL network outperform the other four algorithms regarding accuracy, stability, resistance to noise effects, and capability to process field data. The DL and BL networks demonstrate the highest level of generalization compared to the other algorithms. The comparison results reveal the variations in candidate algorithms for the inversion task, causing a clear understanding of the inversion performance of candidate algorithms. This study can promote the S-wave velocity estimation by Rayleigh wave inversion.

Integration of different kinds of data is an important issue in archaeological prospection. However, the current methodological approaches are underdeveloped and rarely use the data to their maximum potential. Common approaches to integration in the geophysical sciences are mostly just various forms of comparison. We argue that true integration should involve the mathematical manipulation of input data such that the original values of the input data are changed, or that new variables are produced. To address this important research gap, we present an innovative approach to the analysis of geochemical and geophysical datasets in prospection-focused disciplines. Our approach, which we refer to as “multiscalar integration” to differentiate it from simpler methods, involves the application of mathematical methods and tools to process the data in a unified way. To demonstrate our approach, we focus on integrating geophysical data (magnetometry) with geochemical data (elemental content). Our approach comprises three main stages: Quantification of the data deviation from random distributions, linear modelling of geophysical and geochemical data and integration based on weighting of the different elements derived in previous steps. All the steps of the workflow can be also applied separately and independently as needed or preferred. Our approach is implemented in the R environment for statistical computing. All data, functions and scripts used in the work are available from open access repositories (Zenodo.org and Github.com) so that others can test, modify and apply our proposed methods to new cases and problems. Our approach has the following advantages: (1) It allows the rapid exploration of multiple data sources in an unified way; (2) it can increase the utility of geochemical data across diverse prospection disciplines; (3) it facilitates the identification of links between geochemical and geophysical data (or generally, between point-based and raster data); (4) it innovatively integrates various datasets by weighting the information provided by each; (5) it is simple to apply following a step-by-step framework; (6) the code and workflow is fully open to allow for customization, improvements and additions.

Machine learning-based seismic processing models are typically trained separately to perform seismic processing tasks (SPTs) and, as a result, require plenty of high-quality training data. However, preparing training data sets is not trivial, especially for supervised learning (SL). Despite the variability in seismic data across different types and regions, some general characteristics are shared, such as their sinusoidal nature and geometric texture. To learn the shared features and thus, quickly adapt to various SPTs, we develop a unified paradigm for neural network-based seismic processing, called Meta-Processing, that uses limited training data for meta learning a common network initialization, which offers universal adaptability features. The proposed Meta-Processing framework consists of two stages: meta-training and meta-testing. In the former, each SPT is treated as a separate task and the training dataset is divided into support and query sets. Unlike conventional SL methods, here, the neural network (NN) parameters are updated by a bilevel gradient descent from the support set to the query set, iterating through all tasks. In the meta-testing stage, we also utilize limited data to fine-tune the optimized NN parameters in an SL fashion to conduct various SPTs, such as denoising, interpolation, ground-roll attenuation, image enhancement, and velocity estimation, aiming to converge quickly to ideal performance. Extensive numerical experiments are conducted to assess the effectiveness of Meta-Processing on both synthetic and real-world data. The findings reveal that our approach leads to a substantial improvement in the convergence speed and predictive performance of the NN.

Seamount eruptions alter the bathymetry and can occur undetected due to lack of explosive character. We review documented eruptions to define whether they could be detected by a future satellite gravity mission. We adopt the noise level in acquisitions of multi-satellite constellations as in the MOCAST+ study, with a proposed payload of a quantum technology gradiometer and clock. The review of underwater volcanoes includes the Hunga Tonga Hunga Ha’apai (HTHH) islands for which the exposed surface changed during volcanic unrests of 2014/2015 and 2021/2022. The Fani Maoré submarine volcanic eruption of 2018–2021 produced a new seamount 800 m high, emerging from a depth of 3500 m, and therefore not seen above sea surface. We review further documented submarine eruptions and estimate the upper limit of the expected gravity changes. We find that a MOCAST+ type mission should allow us to detect the subsurface mass changes generated by deep ocean submarine volcanic activity for volume changes of 6.5 km³ upwards, with latency of 1 year. This change is met by the HTHH and Fani Maoré volcanoes.

Satellite observations from the Clouds and the Earth’s Radiant Energy System show that Earth’s energy imbalance has doubled from 0.5 ± 0.2 Wm⁻² during the first 10 years of this century to 1.0 ± 0.2 Wm⁻² during the past decade. The increase is the result of a 0.9 ± 0.3 Wm⁻² increase absorbed solar radiation (ASR) that is partially offset by a 0.4 ± 0.25 Wm⁻² increase in outgoing longwave radiation (OLR). Despite marked differences in ASR and OLR trends during the hiatus (2000–2010), transition-to-El Niño (2010–2016) and post-El Niño (2016–2022) periods, trends in net top-of-atmosphere flux (NET) remain within 0.1 Wm⁻² per decade of one another, implying a steady acceleration of climate warming. Northern and southern hemisphere trends in NET are consistent to 0.06 ± 0.31 Wm⁻² per decade due to a compensation between weak ASR and OLR hemispheric trend differences of opposite sign. We find that large decreases in stratocumulus and middle clouds over the sub-tropics and decreases in low and middle clouds at mid-latitudes are the primary reasons for increasing ASR trends in the northern hemisphere (NH). These changes are especially large over the eastern and northern Pacific Ocean, and coincide with large increases in sea-surface temperature (SST). The decrease in cloud fraction and higher SSTs over the NH sub-tropics lead to a significant increase in OLR from cloud-free regions, which partially compensate for the NH ASR increase. Decreases in middle cloud reflection and a weaker reduction in low-cloud reflection account for the increase in ASR in the southern hemisphere, while OLR changes are weak. Changes in cloud cover in response to SST increases imply a feedback to climate change yet a contribution from radiative forcing or internal variability cannot be ruled out.

Integral transformations represent an important mathematical tool for gravitational field modelling. A basic assumption of integral transformations is the global data coverage, but availability of high-resolution and accurate gravitational data may be restricted. Therefore, we decompose the global integration into two parts: (1) the effect of the near zone calculated by the numerical integration of data within a spherical cap and (2) the effect of the far zone due to data beyond the spherical cap synthesised by harmonic expansions. Theoretical and numerical aspects of this decomposition have frequently been studied for isotropic integral transformations on the sphere, such as Hotine’s, Poisson’s, and Stokes’s integral formulas. In this article, we systematically review the mathematical theory of the far-zone effects for the spherical integral formulas, which transform the disturbing gravitational potential or its purely radial derivatives into observable quantities of the gravitational field, i.e. the disturbing gravitational potential and its radial, horizontal, or mixed derivatives of the first, second, or third order. These formulas are implemented in a MATLAB software and validated in a closed-loop simulation. Selected properties of the harmonic expansions are investigated by examining the behaviour of the truncation error coefficients. The mathematical formulations presented here are indispensable for practical solutions of direct or inverse problems in an accurate gravitational field modelling or when studying statistical properties of integral transformations.

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.