Surveys in Geophysics最新文献

Electromagnetic (EM) imaging aims to produce large-scale, high-resolution soil conductivity maps that provide essential information for Earth subsurface exploration. To rigorously generate EM subsurface models, one must address both the forward problem and the inverse problem. From these subsurface resistivity maps, also referred to as volumes of resistivity distribution, it is possible to extract useful information (lithology, temperature, porosity, permeability, among others) to improve our knowledge about geo-resources on which modern society depends (e.g., energy, groundwater, and raw materials, among others). However, this ability to detect electrical resistivity contrasts also makes EM imaging techniques sensitive to metallic structures whose EM footprint often exceeds their diminutive stature compared to surrounding materials. Depending on target applications, this behavior can be advantageous or disadvantageous. In this work, we review EM modeling and inverse solutions in the presence of metallic structures, emphasizing how these structures affect EM data acquisition and interpretation. By addressing the challenges posed by metallic structures, our aim is to enhance the accuracy and reliability of subsurface EM characterization, ultimately leading to improved management of geo-resources and environmental monitoring. Here, we consider the latter through the lens of a triple helix approach: physics behind metallic structures in EM modeling and imaging, development of computational tools (conventional strategies and artificial intelligence schemes), and configurations and applications. The literature review shows that, despite recent scientific advancements, EM imaging techniques are still being developed, as are software-based data processing and interpretation tools. Such progress must address geological complexities and metallic casing measurements integrity in increasing detail setups. We hope this review will provide inspiration for researchers to study the fascinating EM problem, as well as establishing a robust technological ecosystem to those interested in studying EM fields affected by metallic artifacts.

The adjoint method is a popular method used for seismic (full-waveform) inversion today. The method is considered to give more realistic and detailed images of the interior of the Earth by the use of more realistic physics. It relies on the definition of an adjoint wavefield (hence its name) that is the time-reversed synthetics that satisfy the original equations of motion. The physical justification of the nature of the adjoint wavefield is, however, commonly done by brute force with ad hoc assumptions and/or relying on the existence of Green’s functions, the representation theorem and/or the Born approximation. Using variational principles only, and without these mentioned assumptions and/or additional mathematical tools, we show that the time-reversed adjoint wavefield should be defined as a premise that leads to the correct adjoint equations. This allows us to clarify mathematical inconsistencies found in previous seminal works when dealing with viscoelastic attenuation and/or odd-order derivative terms in the equation of motion. We then discuss some methodologies for the numerical implementation of the method in the time domain and to present a variational formulation for the construction of different misfit functions. We here define a new misfit travel-time function that allows us to find consensus for the longstanding debate on the zero sensitivity along the ray path that cross-correlation travel-time measurements show. In fact, we prove that the zero sensitivity along the ray path appears as a consequence of the assumption on the similarity between data and synthetics required to perform cross-correlation travel-time measurements. When no assumption between data and synthetics is preconceived, travel-time Fréchet kernels show an extremum along the ray path as one intuitively would expect.

Geophysical logging series are valuable geological data that record the physical and chemical information of borehole walls and in-situ formations, and are widely used by geologists for interpreting geological problems due to their continuity, high resolution, and ease of access. Recently, machine learning methods are gradually bringing data science and geoscience closer together, and Intelligent Recognition using Logging Data (IRLD) is increasingly becoming an important interpretation task. However, due to the specificity of geological information, relatively low data quality makes the direct application of machine learning models to IRLD often not optimal. And to the best of our knowledge, IRLDs are not highly generalizable and technical surveys are still lacking. Therefore, this paper presents a comprehensive review of IRLD. Specifically, after systematically reviewing geophysical well logging and machine learning techniques, the main applications and general processes for the cross-discipline task of IRLD are summarized. More importantly, the key challenges of IRLD in the four stages of data acquisition, feature engineering, model building, and practical application are discussed in this review. The potential risks of these challenges are visualized by using real logging data from a study area in the South China Sea and the example of a lithology identification task. For these challenges, we give the current state-of-the-art methods and feasible strategies in conjunction with published research. This comprehensive review is expected to provide insights for practitioners to construct more robust models and achieve more effective application results in IRLD.

The Earth exhibits an equatorial flattening specified by the ellipticity and the east longitude (or orientation) of the equatorial major axis, which is uniquely determined by the degree 2 and order 2 gravitational coefficients, C₂₂ and S₂₂. The 31-year SLR (satellite laser ranging) and 22-year GRACE/GRACE-FO (gravity recovery and climate experiment) data are analyzed to study the climate-related secular and 5.7 years to decadal variations in C₂₂ and S₂₂, in turn, the drift and decadal variation in the Earth’s equatorial ellipticity and orientation of the principal axis of the least moment of inertia. The effects of the surface floating mass changes (including atmosphere, ocean and surface water redistribution and the melting of the mountain and polar glaciers) and the interior fluid convective (Earth’s core flows) were evaluated. Results reveal that the equatorial ellipticity of the Earth is linearly increasing along with a remarkable decadal variation and the Earth’s equator is flattening by ~ 0.16 mm/yr.

Earth’s energy imbalance (EEI) is a fundamental metric of global Earth system change, quantifying the cumulative impact of natural and anthropogenic radiative forcings and feedback. To date, the most precise measurements of EEI change are obtained through radiometric observations at the top of the atmosphere (TOA), while the quantification of EEI absolute magnitude is facilitated through heat inventory analysis, where ~ 90% of heat uptake manifests as an increase in ocean heat content (OHC). Various international groups provide OHC datasets derived from in situ and satellite observations, as well as from reanalyses ingesting many available observations. The WCRP formed the GEWEX-EEI Assessment Working Group to better understand discrepancies, uncertainties and reconcile current knowledge of EEI magnitude, variability and trends. Here, 21 OHC datasets and ocean heat uptake (OHU) rates are intercompared, providing OHU estimates ranging between 0.40 ± 0.12 and 0.96 ± 0.08 W m⁻² (2005–2019), a spread that is slightly reduced when unequal ocean sampling is accounted for, and that is largely attributable to differing source data, mapping methods and quality control procedures. The rate of increase in OHU varies substantially between − 0.03 ± 0.13 (reanalysis product) and 1.1 ± 0.6 W m⁻² dec⁻¹ (satellite product). Products that either more regularly observe (satellites) or fill in situ data-sparse regions based on additional physical knowledge (some reanalysis and hybrid products) tend to track radiometric EEI variability better than purely in situ-based OHC products. This paper also examines zonal trends in TOA radiative fluxes and the impact of data gaps on trend estimates. The GEWEX-EEI community aims to refine their assessment studies, to forge a path toward best practices, e.g., in uncertainty quantification, and to formulate recommendations for future activities.

Low-rank approximation has emerged as a promising technique for recovering five-dimensional (5D) seismic data, yet the quest for higher accuracy and stronger rank robustness remains a critical pursuit. We introduce a low-rank approximation method by leveraging the complete graph tensor network (CGTN) decomposition and the learnable transform (LT), referred to as the LRA-LTCGTN method, to simultaneously denoise and reconstruct 5D seismic data. In the LRA-LTCGTN framework, the LT is employed to project the frequency tensor of the original 5D data onto a small-scale latent space. Subsequently, the CGTN decomposition is executed on this latent space. We adopt the proximal alternating minimization algorithm to optimize each variable. Both 5D synthetic data and field data examples indicate that the LRA-LTCGTN method exhibits notable advantages and superior efficiency compared to the damped rank-reduction (DRR), parallel matrix factorization (PMF), and LRA-CGTN methods. Moreover, a sensitivity analysis underscores the remarkably stronger robustness of the LRA-LTCGTN method in terms of rank without any optimization procedure with respect to rank, compared to the LRA-CGTN method.

The quality factor Q is a dimensionless measure of the energy loss per cycle of a wave field, and a proper understanding of this factor is important in a variety of fields, from seismology, geophysical prospecting to electrical science. Here, the focus is on viscoelasticity. When interpreting experimental values, several factors must be taken into account, in particular the shape of the medium (rods, bars or unbounded media) and the fact that the measurements are made on stationary or propagating modes. From a theoretical point of view, the expressions of Q may differ due to different definitions, the spatial dimension and the inhomogeneity of the wave, i.e. the fact that the vectors of propagation (or wavenumber) and attenuation do not point in the same direction. We show the difference between temporal and spatial Q, the relationships between compressional and shear Q, the dependence on frequency, the case of poro-viscoelasticity and anisotropy, the effect of inhomogeneous waves and various loss mechanisms, and consider the analogy between elastic and electromagnetic waves. We discuss physical theories describing relaxation peaks, bounds on Q and experiments showing the behaviour of Q as a function of frequency, saturation and pore pressure. Finally, we propose an application example where Q can be used to estimate porosity and saturation.

Blended acquisition offers significant cost and period reduction in seismic data acquisition. However, fired blended sources are usually deployed at off-the-grid (OffG) samples due to obstacle limitation and economic cost considerations. The irregular distribution of coordinates, along with the blending noise, has a detrimental effect on the performance of subsequent seismic processing and imaging. The interpolated multichannel singular spectrum analysis (I-MSSA) algorithm effectively provides on-the-grid deblended results by employing an interpolator, in conjunction with a projected gradient descent strategy. However, the deblending accuracy and computational efficiency of the I-MSSA are still a concern due to the limitations of the traditional singular value decomposition (SVD). To address these limitations, we propose an interpolated fast damped multichannel singular spectrum analysis (I-FDMSSA) rank-reduction algorithm. The proposed algorithm incorporates the damping operator, the randomized SVD (RSVD) and the fast Fourier transform (FFT) strategy. The damping operator can further attenuate the remaining noise in the estimated signal obtained from the truncated SVD, resulting in an improved deblending performance. The RSVD accelerates the rank-reduction process by shrinking the size of the Hankel matrix. To expedite the rank-reduction and anti-diagonal averaging stages without explicitly constructing large-scale block Hankel matrices, the FFT strategy is employed. By incorporating a 2D separable sinc interpolator, the I-FDMSSA enables an efficient and accurate deblending of 3D OffG blended data. The deblending performance and operational efficiency improvements of the proposed I-FDMSSA algorithm over the traditional I-MSSA algorithm are demonstrated through OffG synthetic and field blended data examples.

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.