Surveys in Geophysics最新文献

Lacustrine shale oil resources are essential for the maintenance of energy supply. Fluid types and contents play important roles in estimating resource potential and oil recovery from organic-rich shales. Precise identification of fluid types hosted in shale oil reservoir successions that are characterized by marked lithological heterogeneity from only a single well is a significant challenge. Although previous research has proposed a large number of methods for determining both porosity and fluid saturation, many can only be applied in limited situations, and several have limited accuracy. In this study, an advanced logging technique, combinable magnetic resonance logging (CMR-NG), is used to evaluate fluid types. Two-dimensional nuclear magnetic resonance (2D-NMR) experiments on reservoir rocks subject to different conditions (as received, after being dried at 105 ℃, and kerosene imbibed) were carried out to define the fluid types and classification criteria. Then, with the corresponding Rock–Eval pyrolysis parameters and various mineral contents from X-ray diffraction, the contribution of organic matter and mineral compositions was investigated. Subsequently, the content of different fluid types is calculated by CMR-NG (combinable magnetic resonance logging, viz. 2D NMR logging). According to the fluid classification criteria under experimental conditions and the production data, the most favorable model and optimal solution for logging evaluation was selected. Finally, fluid saturations of the Cretaceous Qingshankou Formation in the Gulong Sag were calculated for a single well. Results show that six fluid types (kerogen-bitumen-group OH, irreducible oil, movable oil, clay-bound water, irreducible water, and movable water) can be recognized through the applied 2D NMR test. The kerogen-bitumen-group OH was mostly affected by pyrolysis hydrocarbon (S₂) and irreducible oil by soluble hydrocarbon (S₁). However, kerogen-bitumen-group OH and clay-bound water cannot be detected by CMR-NG due to the effects of underground environmental conditions on the instruments. Strata Q8–Q9 of the Qing 2 member of the cretaceous Qingshankou Formation are the most favorable layers of shale oil. This research provides insights into the factors controlling fluid types and contents; it provides guidance in the exploration and development of unconventional resources, for example, for geothermal and carbon capture, utilization, and storage reservoirs.

Microseismic source localization methods with deep learning can directly predict the source location from recorded microseismic data, showing remarkably high accuracy and efficiency. Two main categories of deep learning-based localization methods are coordinate prediction methods and heatmap prediction methods. Coordinate prediction methods provide only a source coordinate and generally do not provide a measure of confidence in the source location. Heatmap prediction methods require the assumption that the microseismic source is located on a grid point. Thus, they tend to provide lower resolution information and localization results may lose precision. This study reviews and compares previous methods for locating the source based on deep learning. To address the limitations of existing methods, we devise a network fusing a convolutional neural network and a Transformer to locate microseismic sources. We first introduce the multi-modal heatmap combining the Gaussian heatmap and the offset coefficient map to represent the source location. The offset coefficients are utilized to correct the source locations predicted by the Gaussian heatmap so that the source is no longer confined to the grid point. We then propose a fusion network to accurately estimate the source location. A gated multi-scale feature fusion module is developed to efficiently fuse features from different branches. Experiments on synthetic and field data demonstrate that the proposed method yields highly accurate localization results. A comprehensive comparison of coordinate prediction method and heatmap prediction methods with our proposed method demonstrates that the proposed method outperforms the other methods.

Prior model construction is a fundamental component in geophysical inversion, especially Bayesian inversion. The prior model, usually derived from available geological information, can reduce the uncertainty of model characteristics during the inversion. However, the prior geological data for inferring a prior distribution model are often limited in real cases. Our work presents a novel framework to create 3D geophysical prior models using soil geochemistry and borehole rock sample measurements. We focus on the Bayesian inversion, which enables encoding of knowledge and multiple non-geophysical data into the prior. The new framework developed in our research comprises three main parts, namely correlation analysis, prior model reconstruction, and Bayesian inversion. We investigate the correlations between surface and subsurface geochemical features, as well as the correlation between geochemistry and geophysics, using canonical correlation analysis for the surface and borehole geochemistry. Based on the resulting correlations, we construct the prior susceptibility model. The informed prior model is then tested using geophysical forward modeling and outlier detection methods. In this test, we aim to falsify the prior model, which happens when the model cannot predict the field geophysical observation. To obtain the posterior models, the reliable prior models are incorporated into a Bayesian inversion framework. Using a real case of exploration in the Central African Copperbelt, we illustrate the workflow of constructing the high-resolution 3D stratigraphic model conditioned on soil geochemistry, borehole data, and airborne geophysics.

The subsurface in situ stress fields significantly influence the elastic and anisotropic properties of rocks, yet traditional linear elastic theories often overlook the impact of stress on seismic response characteristics. Nonlinear acoustoelastic theory integrates third-order elastic constants (TOECs) to elucidate the influence of stress on changes in elastic and anisotropic properties of stressed rocks. A comprehensive examination of recent scholarly investigations on nonlinear acoustoelastic phenomena precedes the introduction of an innovative stress-dependent equation for the PP-wave reflection coefficient. This equation delineates the dependence of azimuthal seismic response on horizontal uniaxial stress in inherently vertical transversely isotropic (VTI) media, or those VTI formations induced by a single set of horizontal aligned fractures. Emphasis is placed on delineating stress-induced anisotropy and elucidating azimuthal PP-wave reflection characteristics in horizontally uniaxially stressed VTI media. Additionally, this discourse extends to more intricate scenarios involving horizontally biaxially and triaxially stressed VTI media, as delineated by nonlinear acoustoelastic theory. Subsequently, the reflection coefficient of horizontally uniaxially stressed VTI media is expressed in terms of azimuthal Fourier coefficients (FCs), revealing that the unstressed VTI background exhibits heightened sensitivity to zeroth-order FC, while the stress-induced anisotropy manifests greater sensitivity to second-order FC. Through the application of azimuthal FCs-based amplitude versus offset and azimuth (AVOAz) inversion method to both synthetic and field datasets, the proposed model and approach offer promising avenues for reservoir characterization in VTI media subject to horizontal uniaxial stress conditions.

There is a diverging perception of climate tipping points, abrupt changes and surprises in the scientific community and the public. While such dynamics have been observed in the past, e.g., frequent reductions of the Atlantic meridional overturning circulation during the last ice age, or ice sheet collapses, tipping points might also be a possibility in an anthropogenically perturbed climate. In this context, high impact—low likelihood events, both in the physical realm as well as in ecosystems, will be potentially dangerous. Here we argue that a formalized assessment of the state of science is needed in order to establish a consensus on this issue and to reconcile diverging views. This has been the approach taken by the Intergovernmental Panel on Climate Change (IPCC). Since 1990, the IPCC has consistently generated robust consensus on several complex issues, ranging from the detection and attribution of climate change, the global carbon budget and climate sensitivity, to the projection of extreme events and their impact. Here, we suggest that a scientific assessment on tipping points, conducted collaboratively by the IPCC and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, would represent an ambitious yet necessary goal to be accomplished within the next decade.

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.