Surveys in Geophysics最新文献

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.

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.

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.