Geophysical Prospecting: Most Recent Articles
https://www.earthdoc.org/content/journals/gpr?TRACK=RSS
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https://www.earthdoc.org/content/journals/10.1111/1365-2478.13116?TRACK=RSS
<div></div>Wed, 19 Oct 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13116?TRACK=RSS2022-10-19T00:00:00ZExplosive source signature determination: Two unequal shots in the same hole
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13251?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Buried explosive charges generate seismic waves with unrivalled bandwidth. The source time function is not always repeatable and is difficult to measure, but is required for subsequent data processing including reverse time migration and full waveform inversion. I present a new method for estimating the explosive seismic source time function for every shot, using two unequal shots in the same shot hole. The measured data for each charge are deconvolved for the modelled monopole seismic source time function to recover estimated earth impulse responses or Green's functions. The source model parameters are adjusted to maximize the likeness of the estimated Green's functions from the two shots, applying physical constraints to obtain the prior probability density functions for each parameter. Ten parameters specify the two source models, but only five of these are independent and a grid search is used to find them. The method is tested on an experimental data set, obtained for a different purpose, consisting of a line of single geophones and three in‐line shot holes, each with two unequal charges, the larger one at twice the depth of the smaller. The method works well where the charge size ratio is at least 2: the estimated source time functions are recovered and the estimated Green's functions are more similar than the seismograms before deconvolution. To apply the method in practice, the smaller charge should be deeper than the larger. If the small charge is small enough, the vertical separation between the charges can be reduced to less than 1 m. The Green's functions for the two charges would then be almost identical for frequencies up to 250 Hz, and the differences in the measured data from the two charges would be caused principally by the differences in the two source time functions.</p></div>Wed, 19 Oct 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13251?TRACK=RSSAnton Ziolkowski2022-10-19T00:00:00ZGeneralization of Bayesian inversion with application to PP reflection in a vertical transverse isotropy medium
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13235?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>Seismic inversion of amplitude variation with offset is an ill‐conditioned problem, in which small errors in the observed data result in large errors in the estimates, and therefore regularization functions are necessary. The Bayes' theorem regularizes ill‐posed problems using the statistical properties of the model parameters of interest; however, our knowledge of these statistical properties is poor. Meanwhile, the Bayesian framework is limited to particular forms of a priori probability distributions, due to poor knowledge of the statistical properties of individual probability distributions. Moreover, each particular a priori probability distribution requires a reformulation of the inversion under a Bayesian framework, which is not practically preferred. Here, we construct a Bayesian framework that enables the use of various types of a priori probability distributions without the need to reformulate the problem and to obtain well‐established statistical information on the model parameters of interest, specifically a lower bound on the variance of the a priori model, for example. Fisher information. Consequently, different probability distributions that best address the parameters of interest are first fitted using the maximum likelihood estimator . A lower bound on the variance of each a priori model is then estimated, which provides adequate statistical information between different model parameters; hence, it is ideally suited for the Bayesian framework. Thereafter, an iterative approach is proposed that utilizes the Hessians of the data and model spaces, and an adaptive learning rate to compute the optimal directions for model updates. The approach is applied to synthetic and real seismic data to estimate the elastic and seismic anisotropy parameters of shale formations. The regularization from the a priori probability density successfully stabilizes the updates of the variant‐sensitivity parameters by imposing the correlation information in the optimization process.</p></div>Wed, 19 Oct 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13235?TRACK=RSSAhmed Zidan, Yunyue Li and Arthur Cheng2022-10-19T00:00:00ZRegularized seismic amplitude inversion via variational inference
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13248?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Over the years, seismic amplitude variation with offset has been successfully applied for predicting the elastic properties of the subsurface. Nevertheless, the solution of the amplitude inversion is not stable due to insufficient information in the recorded seismic data. To enhance the stability, various amplitude inversions under a Bayesian framework have been introduced and most of which are based on single‐component a priori probability densities, which can be solved deterministically. For multi‐component a priori densities, Monte Carlo sampling methods are often used to obtain the posterior distribution of the inverted parameters. The variational methods, on the other hand, provide the gradients of the parameters with respect to the data. Here, we present an inversion framework that uses the gradients of the variational inference as regularization. Due to the ill‐posedness of the amplitude inversion, solving the likelihood of the data leads to solutions that may exist within the low‐probability regions of the a priori density. The rule of the regularization (variational inference gradient) is, therefore, to force solutions within the high‐probability regions that enhance the stability of the inversion process. The performance of the regularization is tested using synthetic and field data examples to jointly estimate the elastic and Thomsen's anisotropy parameters. The proposed constrained optimization successfully predicts the P‐wave velocity, S‐wave velocity and P‐wave anisotropy and preferably constrains the density and near‐vertical anisotropy parameters.</p></div>Wed, 19 Oct 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13248?TRACK=RSSAhmed Zidan, Yunyue Li and Arthur Cheng2022-10-19T00:00:00ZElastic reverse time migration method in vertical transversely isotropic media including surface topography
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13261?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Compared with acoustic seismic data, multi‐wave and multi‐component seismic data can offer some special media properties. Elastic reverse time migration is a high‐precision migration method to image multi‐wave and multi‐component seismic data. Anisotropy widely exists in the earth, and it is important to take anisotropy into account and correct the anisotropy effect for obtaining high‐quality image results during migration. However, accurate elastic reverse time migration in anisotropic media is still a challenging task, particularly for areas with irregular surface topography. To resolve these issues, a new elastic reverse time migration method is developed to correct the anisotropy effect in vertical transversely isotropic media with surface topography. We first use the modified pseudo‐acoustic wave equations of vertical transversely isotropic media to assist the elastic wavefield separation in vertical transversely isotropic media for suppressing the crosstalk artefacts between different wave modes and further generating high‐quality images. We then use a topography‐related filter to remove the influence of surface topography on the migration results of elastic reverse time migration. The advantage of the proposed topography‐related filter is that we do not need to make any changes to the conventional finite‐difference method based on regular grids but can calculate the wavefield in the areas with irregular surface topography efficiently. The synthetic examples with different surface topographies models demonstrate that our proposed elastic reverse time migration method can correct the anisotropy effect effectively, remove the influence of surface topography on elastic reverse time migration results, and generate high‐quality images.</p></div>Wed, 19 Oct 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13261?TRACK=RSSYu Zhong, Hanming Gu, Yangting Liu, Xia Luo and Qinghui Mao2022-10-19T00:00:00ZDo cracks improve the conductive ability of porous rocks?
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13256?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Cracks widely exist in rocks and are important in various geophysical applications. However, although electrical methods are conventionally employed for the detection and characterization of cracks, the fundamental question whether cracks improve the conductive ability of porous rocks (a quantification of their electrical conductivity if their porosity and saturant are the same) remains largely unaddressed. We address this knowledge gap through theoretical models with confirmed validity. We show that the conductive ability of a rock containing non‐interacting penny‐shaped cracks with random orientation will be improved only in the case when the aspect ratio of the cracks is below a certain value, which is referred to as the critical crack aspect ratio. We also show that the critical crack aspect ratio is uniquely determined by the porosity and electrical conductivity (or cementation exponent) of the porous rock where the cracks reside. We further demonstrate that the critical crack aspect ratio is some representation of the pore structure, and using two times the critical crack aspect ratio as the pore geometry can give rise to a reasonable agreement between modelled and measured <span class="jp-italic">P</span>‐ and <span class="jp-bold">S</span>‐wave velocities. The critical crack aspect ratio offers a consistent microstructure for the joint elastic–electrical modelling for the improved characterization of cracks through integrated seismic and electrical surveys.</p></div>Wed, 19 Oct 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13256?TRACK=RSSTongcheng Han and Li‐Yun Fu2022-10-19T00:00:00ZHeterogeneity evaluation of pore types based on dipole shear sonic imager logs by means of statistical parameters, the central Persian Gulf
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13262?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Heterogeneity analysis in carbonate reservoirs is of great importance for the interpretation of their characteristics. This study focuses on the evaluation of pore‐type heterogeneity by means of the sonic wave velocities in the carbonate–evaporite series of Permian–Triassic sequences in the Persian Gulf. Thin section petrography and laboratory measured the porosity and permeability of 1576 samples, as well as routine wire‐line logs, were employed. To manage heterogeneity, six electrofacies (EF) were determined based on an integrated analysis of compressional, shear and Stoneley waves as well as three routine logs (gamma, neutron and density). The frequency of pore types in each electrofacies was calculated. Then, the heterogeneity of pore‐type distribution in each electrofacies was measured by standard error and coefficient of variation. Sonic velocity has been compared with the geological properties of electrofacies. Results indicated that velocity is affected by factors other than the amount of porosity, facies or lithology. The standard error values in all electrofacies were close to zero. The coefficient of variation values ranges from 1.08 to 4.60. The lowest amount of coefficient of variation was observed in EF6 in the Khuff (K) 4 zone for moldic porosity. This value for intercrystalline porosity distribution in EF1 in the K1 zone was low, too. These results indicate the lowest standard deviation and the highest homogeneity in the distribution of intercrystalline and moldic porosities in K1 and K4. Also, comparing the mean values of sonic logs in each electrofacies with the total mean value in the whole sequence indicated that there is a relationship between sonic velocity logs and the type of porosity. High values of sonic velocity correspond to intercrystalline porosity in K1 and low values of velocity correspond to moldic porosity in K4. Results show that integrated pore‐type studies should be carried out to understand the sonic velocities in carbonate reservoir rocks.</p></div>Wed, 19 Oct 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13262?TRACK=RSSAdeleh Jamalian and Vahid Tavakoli2022-10-19T00:00:00ZThe use of a semi‐structured finite‐element mesh in 3‐D resistivity inversion
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13260?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Calculating the electric potential for 3‐D resistivity inversion algorithms can be time consuming depending on the structure of the mesh. There have been generally two approaches to generating finite‐element meshes. One approach uses a structured rectangular mesh with hexahedral elements on a rectangular model grid. The distribution of model cells can be designed to follow known boundaries, and directional roughness constraints can be easily imposed. A 1‐D wavelet transform that takes advantage of the regular arrangement of the model cells can also be used to reduce the computer time and memory required to solve the smoothness‐constrained least‐squares equation. However, the structured rectangular mesh uses an unnecessarily fine mesh in parts of the model that are far away from the electrodes where the potential changes gradually. A second approach uses an unstructured mesh with tetrahedral elements created automatically by a mesh generation program with finer elements nearer the electrodes and coarser elements in the more remote regions. This generates a mesh with a much smaller number of nodes. The disadvantage is that an irregular model grid is normally used. We examine an alternative approach that combines structured and unstructured meshes. We employ a regular model grid with a finer mesh near the surface and a coarser mesh in deeper regions using a combination of hexahedral and tetrahedral elements. The semi‐structured mesh reduces the calculation time by more than three times compared with a structured mesh. An adaptive semi‐structured mesh that also uses a coarser mesh for model cells near the surface if they are more than one unit electrode spacing from the nearest electrode was also developed for surveys with non‐uniform data coverage. For the Bonsall Leys field survey, which used a capacitively coupled mobile system and collected a data set with nearly a million electrode positions, the adaptive mesh reduces the calculation time by about 80%. The calculation time can be further reduced by about 93% when it is combined with a mesh segmentation method.</p></div>Wed, 19 Oct 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13260?TRACK=RSSM. H. Loke, P. B. Wilkinson, O. Kuras, P. I. Meldrum and D. F. Rucker2022-10-19T00:00:00ZA comparison of the joint and independent inversions for magnetic and gravity data over kimberlites in Botswana
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13265?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Complementary independent and joint focusing inversion investigations are applied to the magnetic and gravity datasets for two kimberlite pipes, BK54 and BK55, in Botswana. The magnetic data are high resolution and, clearly, indicate two anomalies in the survey area. Independent inversion of this magnetic data provides a focused image of the subsurface with sharp boundaries for both pipes. The extensions of the pipes are close to those estimated by the available boreholes in the area. The spatial resolution of the gravity data is, on the other hand, relatively poor. The BK54 pipe does not appear on the residual gravity anomaly, while the BK55 is characterized by a positive anomaly. Independent inversion of the gravity data provides the geometry of the BK55 but does not identify the BK54 pipe. While there are some lower density materials in the place of the BK54 pipe, no specific target is revealed. Furthermore and relevant is that the reconstructed model for BK55 is not focused. A joint focusing inversion based on the structural cross‐gradient linkage is applied for the combined gravity and magnetic datasets. This algorithm produces structurally similar magnetic susceptibility and density models. A lower density distribution of material, similar to that of the magnetic susceptibility distribution, is revealed for the BK54 pipe. Moreover, in contrast to the result obtained by an independent inversion, the structure for BK54 is connected at depth, and the reconstructed models of BK54 and BK55 share the sparsity. The models are not as sparse as the magnetic susceptibility model that is obtained independently, but they are more focused than the independently obtained density model. A comprehensive comparison of the reconstructed models, and also computational CPU time, for independent and joint inversions is presented.</p></div>Wed, 19 Oct 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13265?TRACK=RSSSaeed Vatankhah, Rosemary Anne Renaut, Kevin Mickus, Shuang Liu and Kitso Matende2022-10-19T00:00:00ZIssue Information
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13115?TRACK=RSS
<div></div>Wed, 14 Sep 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13115?TRACK=RSS2022-09-14T00:00:00ZViscoelastic and viscoacoustic modelling using the Lie product formula
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13241?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>We present efficient and accurate modelling of seismic wavefields in anelastic media. We use a first‐order viscoelastic wave equation based on the generalized Robertsson's formulation. In our work, viscoacoustic and viscoelastic wave equations use the standard linear solid mechanism. To numerically solve the first‐order wave equation, we employed a scheme derived from the Lie product formula, where the time evolution operator of the analytic solution is written as a product of exponential matrices, and each exponential matrix term is approximated by the Taylor series expansion. The accuracy of the proposed scheme is evaluated by comparison with the analytical solution for a homogeneous medium. We also present simulations of some geological models with different structural complexities, whose results confirm the accuracy of the proposed scheme and illustrate the attenuation effect on the seismic energy during its propagation in the medium. Our results demonstrate that the numerical scheme employed can be used to extrapolate wavefields stably for even larger time steps than those usually used by traditional finite‐difference schemes.</p></div>Wed, 14 Sep 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13241?TRACK=RSSEdvaldo S. Araujo and Reynam C. Pestana2022-09-14T00:00:00ZThe high‐resolution seismic deconvolution method based on joint sparse representation using logging–seismic data
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13232?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>Seismic high‐resolution processing is an essential part of seismic processing. Sparse‐spike deconvolution is a widely used method for improving the resolution of seismic data. However, the stratigraphic reflection coefficients do not fully satisfy the hypothesis of sparse‐spike deconvolution, and this method does not make full use of prior information, such as well‐logging data. In this paper, we have developed a high‐resolution processing method based on joint sparse representation using logging and seismic data. This method can extract stratigraphic information from well‐logging reflection coefficients and observational seismic data at the same location by joint dictionary learning. Through joint sparse representation, the relationship between observed seismic data and the reflection coefficient is established. Under the framework of joint sparse representation, the deconvolution of seismic data is realized. The synthetic data and field data tests show that our method can reveal thin layers and can invert reflection coefficients from strongly noisy seismic data accurately. Moreover, the deconvolution results of our method match well with the well‐logging data. The tests demonstrate that the improvement of accuracy of deconvolution results with our method, compared to sparse‐spike deconvolution.</p></div>Wed, 14 Sep 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13232?TRACK=RSSYaojun Wang, Guiqian Zhang, Haishan Li, Wuyang Yang and Wanli Wang2022-09-14T00:00:00ZCoherency analysis of polarity reversed diffracted wavefields using local semblance
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13240?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>Diffractions carry out important information about subsurface features. These features include small‐scale objects, fracture zones and faults. There have been several robust pre‐ and post‐stack diffraction imaging workflows in the literature to attribute diffraction locations and properties. Most of the traditional workflows are not fully capable of dealing with polarity reversals in the case of polarity reversed diffracted wavefields. This challenge causes null measures at the location of such diffractions. To overcome this issue, which is an ongoing subject of research, we propose to implement local semblance analysis along moveout curves. To do so, the global scanning window is subdivided into smaller windows followed by semblance analysis over each window. The final coherency measure in each image point is computed by averaging the semblance measures from all the subdivided windows. We demonstrated the proposed workflow on synthetic as well as field recorded datasets in the post‐stack domain. The results prove the capability of the proposed method in circumventing polarity reversals without any need to conduct polarity correction prior to imaging. At the end, we studied the seismic imaging resolution in the presence of white noise through the proposed approach.</p></div>Wed, 14 Sep 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13240?TRACK=RSSMohammad Hosseini, M. Javad Khoshnavaz and Hamid Reza Siahkoohi2022-09-14T00:00:00ZErrors in positioning of borehole measurements and how they influence seismic inversion
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13243?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Inversion of seismic data using information from horizontal wells is often hampered by cumulative well‐location errors. These errors can have a significant influence on the final subsurface model derived from the data. To achieve a proper data integration and arrive at correct uncertainty estimates, we formulate the problem in a fully probabilistic framework and present a numerical approach for improving subsurface imaging using uncertain well‐log data and their uncertain locations as well as uncertain seismic data. The result is improved model error quantification in the seismic inversion process.</p></div>Wed, 14 Sep 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13243?TRACK=RSSIris Fernandes and Klaus Mosegaard2022-09-14T00:00:00ZHigh temporal accuracy elastic wave simulation with new time–space domain implicit staggered‐grid finite‐difference schemes
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13244?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Implicit staggered‐grid finite‐difference methods are attractive for elastic wave modelling due to significantly enhanced spatial accuracy compared to explicit ones. However, the central‐grid finite‐difference operators used to approximate the temporal derivatives result in a limited accuracy in time. Temporal high‐order finite‐difference methods have the ability to weaken the temporal dispersion and improve the modelling stability. It is noted that the previous temporal high‐order and spatial implicit finite‐difference methods are all designed in the space domain for performing acoustic wave propagation. To implement 2‐D elastic wave simulation with high‐order accuracy both in space and time, we propose two time–space domain implicit staggered‐grid finite‐difference schemes, in which the spatial derivatives are approximated by the weighted average of a few extra off‐axial nodes and axial nodes of the conventional cross‐stencil. We derive the P‐ and S‐wave dispersion relations of the whole elastic wave equation and estimate the finite‐difference coefficients via a variable substitution‐based Taylor‐series expansion. Our Taylor‐series expansion‐based new scheme yields high‐order temporal and spatial accuracy. Besides, the spatial accuracy can be further enhanced by our newly proposed linear optimization strategy, which benefits from easy implementation since we only optimize the axial spatial coefficients via a least‐squares strategy and set the off‐axial temporal coefficients the same as the solution of the Taylor‐series expansion method. Besides, the P‐ and S‐wave separation approach is adopted to propagate the P‐ and S‐wavefields with the P‐ and S‐wave dispersion relation‐based finite‐difference operators, respectively. Our two new schemes are more capable of suppressing the numerical dispersion and exhibit better stability performance compared to conventional one, as we will illustrate via a detailed analysis of dispersion, stability and numerical experiments. In addition, a comparison of computation times demonstrates the efficiency advantage of two new schemes since small operator lengths and large time steps are allowed.</p></div>Wed, 14 Sep 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13244?TRACK=RSSJing Wang, Yang Liu and Hongyu Zhou2022-09-14T00:00:00ZMicroseismic wavefield propagation in a fracture‐induced anisotropic medium based on a general dislocation source model
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13247?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>The propagation of microseismicity in subsurface media is complex. We developed an anisotropic petrophysical model based on an empirical formula, linear slip theory and the bond transform method considering real underground conditions. Using Biot's equations, the seismic response to a general dislocation source in fracture‐induced anisotropic two‐phased media was solved using a staggered grid high‐order finite‐difference algorithm. To understand the relationship between the microseismic wave and fracture parameters, we then designed several different models to calculate the wavefield snapshots and analyse the effect of the fracture volume ratio and orientation. The modelling results indicate that the shear faulting source generates three types of waves in two‐phase media. Fractures can render homogeneous media anisotropic, and anisotropy becomes more noticeable with an increase in the fracture volume ratio. This decreases the velocity of the fast longitudinal and transverse waves in the vertical direction and increases the velocity of the slow longitudinal wave in a certain range. The fracture orientation mainly influences the moment tensor and then affects the wavefronts. These simulations lay the foundation for further studies on microseismic wave propagation in fracture‐induced anisotropic media.</p></div>Wed, 14 Sep 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13247?TRACK=RSSYi Yao, Yibo Wang and Liyun Kong2022-09-14T00:00:00ZOn the association between fast induced polarization in frozen rocks and dielectric polarization of ice
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13246?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Most often, fast induced polarization in frozen rocks appears in the transient electromagnetic data at early (less than a few hundreds of microseconds) times as a nonmonotone voltage response. Usually, such transients are interpreted in terms of Pelton's resistivity/conductivity model. The relaxation time <span class="jp-italic">τ</span><span class="jp-sub">IS</span> found in this way determines the decay rate of transient voltage response to a current step. Conversion of τ<span class="jp-sub">IS</span> into the relaxation time <span class="jp-italic">τ</span><span class="jp-sub">VS</span>, determining the decay rate of the current response to a voltage step, gives <span class="jp-italic">τ</span><span class="jp-sub">VS</span> values typical for dielectric relaxation of ice at temperatures of about several degrees below zero. This result, along with the fact that both fast induced polarization and dielectric polarization of ice are described by the Debye relaxation model, suggests that fast induced polarization in frozen rocks is controlled by the dielectric polarization of ice. As a possible mechanism for such a control, the effect of ice polarization on the surface conductivity of frozen rocks is considered.</p></div>Wed, 14 Sep 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13246?TRACK=RSSNikolai O. Kozhevnikov2022-09-14T00:00:00ZA machine‐learning framework to estimate saturation changes from 4D seismic data using reservoir models
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13249?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Time‐lapse seismic (four‐dimensional seismic) data play a preeminent role in closed‐loop reservoir management by providing a full‐field image of dynamic reservoir behaviour during production. Nonetheless, the multidisciplinary nature of four‐dimensional closed‐loop approaches demands more quantitative and fast methods to integrate rock physics models, reservoir flow simulation models and four‐dimensional seismic analysis. In this work, we tackle this time‐consuming and expensive process and develop a data‐driven quantitative approach to leverage the inherent physics between four‐dimensional seismic and reservoir property changes. We propose an inversion flow method using machine learning strategies to estimate changes in reservoir properties directly from a fast‐derived four‐dimensional seismic attribute. This study was carried out in a Brazilian deep‐water field where production started in 2013 with 3 years of production and injection history. For this reservoir, the estimation of fluid saturation maps is a critical objective to assist engineers with data assimilation procedures. We also generated millions of training data samples using 200 simulation models from the field mentioned above (before history matching) to highlight the benefits of restricting training samples to proper fluid flow consistent combinations. Results demonstrate high prediction accuracy for the targeted reservoir property changes. Additionally, it provides insights for the detection of sweet spots and positioning of infill wells. We significantly simplify the four‐dimensional seismic integration process, allowing initial engagements of reservoir engineers with decision‐making processes and data assimilation applications.</p></div>Wed, 14 Sep 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13249?TRACK=RSSMasoud Maleki, Marcos Cirne, Denis José Schiozer, Alessandra Davolio and Anderson Rocha2022-09-14T00:00:00ZEstimation of shear sonic logs in the heterogeneous and fractured Lower Cretaceous of the Danish North Sea using supervised learning
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13252?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Shear wave velocity information is valuable in many aspects of seismic exploration and characterization of reservoirs. However, shear wave logs are not always available in the interval of interest due to cost and time‐saving purposes. In this study, we present a tailored supervised learning approach to estimate shear wave velocity from well‐log measurements in the Lower Cretaceous succession of the Valdemar and Boje fields in the Danish North Sea. Our objective is to investigate the performance of four supervised learning regression models (linear, random forest, support vector and multi‐layer perceptron). A limited well‐log data set from six wells is used for training and testing the supervised learning models. A set of well data containing normalized gamma ray, compressional wave velocity, neutron porosity and medium resistivity logs gave reasonable shear wave velocity estimates in the test wells with root‐mean‐square error scores within the range of other published studies. Based on limited input data and complex geology, the multi‐layer perceptron was the most successful model in predicting the reservoir sections of the test wells. However, all models lacked stability in the overburden zones. Lastly, re‐training the multi‐layer perceptron on the six wells to predict missing shear wave velocity in a nearby well showed promising results for further reservoir characterization. The obtained results can yield useful input into, for example, seismic pre‐stack inversion, amplitude versus offset analysis and rock physics analysis.</p></div>Wed, 14 Sep 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13252?TRACK=RSSMads Lorentzen, Kenneth Bredesen, Klaus Mosegaard and Lars Nielsen2022-09-14T00:00:00ZSubsurface characterization using passive seismic in the urban area of Dublin City, Ireland
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13255?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>We investigate the shallow geological features and previously unknown depths of the main geological interfaces in the highly urbanized Dublin City area by using a set of complementary ambient noise seismic methods. A sparse seismic array, composed of 19 broadband sensors, was installed in a 5 × 5 km area across the city centre. The dataset was acquired over a 5‐month‐long deployment and used to perform horizontal/vertical spectral ratios and frequency–wave number analysis cross‐correlation interferometry. From the horizontal/vertical high‐frequency resonance peaks, we estimate the depth to bedrock. Then we use a subset of six seismic stations to obtain the frequency–wavenumber Rayleigh wave fundamental mode in the 0.8–3 Hz frequency band. Next, the ambient noise dataset is cross‐correlated in order to extract the empirical Green's functions before measuring surface‐wave phase velocities by undertaking a dispersion analysis in the 0.5–9 Hz frequency band. Then, a Monte Carlo global optimization algorithm is used to invert the phase velocity dispersion measurements. We generate a reference one‐dimensional depth S velocity profile along with a set of localized one‐dimensional S velocity profiles. Finally, a smooth three‐dimensional shear wave velocity model is derived for the top 1000 m in the sedimentary Dublin Basin. From the one‐dimensional velocity profiles and three‐dimensional shear velocity model, with some sensitivity down to approximately 1.2 km, we observe velocity changes with depth that highlight the presence of three consistent interfaces. We discuss interpretative scenarios that correlate the velocity features to potential stratigraphic boundaries occurring within the sedimentary Dublin Basin, suggesting that basement rocks could be located at a depth significantly greater than the top kilometre. The results of this study may indicate that future geothermal studies should be directed at structures deeper than 1 km, towards the bottom of the sedimentary basin beneath the city.</p></div>Wed, 14 Sep 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13255?TRACK=RSSGiuseppe Maggio, Senad Subašić and Christopher J. Bean2022-09-14T00:00:00ZJoint one‐dimensional inversion of magnetotelluric data and surface‐wave dispersion curves using correspondence maps
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13239?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>We use a correspondence map to jointly invert surface‐wave dispersion curves and magnetotelluric data for subsurface shear velocity and resistivity but also for a possible relationship between them. Our first experiments consist of inversions of synthetic data computed from models linked by first‐ and second‐order polynomial relationships. Our methodology produces joint inversion model pairs from which 100% fit the ‘observed’ parameter relationship within a 5% error vs only 15% of the separate inversion pairs for the degree 1 relationship experiment. For the degree 2 relationship synthetic test, 80% of the joint inversion model pairs fit the ‘observed’ relationship within a 5% error while 45% of the separate inversion pairs. This reduces the number of acceptable models without compromising the data fit (‘reduction of non‐uniqueness'). The next experiment involves synthetic data from models of known physical properties, taken from well logs, but without a known relationship. We show how to select an appropriate polynomial degree for joint inversion when the relationship is unknown. Having validated the approach with synthetic cases, we apply our methodology to field data. We compare separate and joint inversions, and we find that the one‐dimensional subsurface models retrieved from joint inversions are more similar to previous models documented in the area than the separate inversion models.</p></div>Wed, 14 Sep 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13239?TRACK=RSSM. Aquino, G. Marquis and J. Vergne2022-09-14T00:00:00ZCorrigendum
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13259?TRACK=RSS
<div></div>Wed, 14 Sep 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13259?TRACK=RSS2022-09-14T00:00:00ZIssue Information
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13114?TRACK=RSS
<div></div>Tue, 23 Aug 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13114?TRACK=RSS2022-08-23T00:00:00ZThe S waves geometrical spreading in elliptical orthorhombic media
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13212?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>The presence of S waves singularity points in low‐symmetry anisotropic models significantly affects the topology of the slowness surfaces of S waves in the vicinity of these points and, consequently, results in complications in the geometrical spreading. Thus, we analyse the effect of a singularity point in a simple elliptical orthorhombic model with decoupled P wave.</p></div>Tue, 23 Aug 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13212?TRACK=RSSAlexey Stovas, Yuriy Roganov and Vyacheslav Roganov2022-08-23T00:00:00ZConvolution neural network application for first‐break picking for land seismic data
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13192?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>An automatic and robust algorithm for the first‐break picking is necessary to build the near‐surface velocity model. We propose the algorithm based on a convolution neural network. The introduced first‐break picking strategy and neural network architecture are suited for processing large volumes of seismic exploration data with reasonable computational resources. To develop an optimal neural network topology and architecture, extensive testing was performed. We compared several architectures of neural networks, including one‐ and two‐dimensional approaches. Our tests justify that the one‐dimensional approach (trace‐by‐trace processing) provides the most reliable results in the case of first‐break travel‐time variations typical of complicated near‐surface structures. This study demonstrates that the four‐layered neural network trained on 5,000 traces is enough for robust first‐break picking. The algorithm is evaluated on two land‐acquisition field datasets from West Siberia with a total used size of about 7 million traces. The first dataset is used for training, and the second one is used only for testing. For both datasets, the error between the original and the predicted first breaks is not more than three samples for 95% of traces. The final evaluation is done by a comparison of seismic stacks to prove the benefits of the approach and its robustness for offsets over 600 m. Finally, the influence of choosing the locations for the training dataset is discussed, and a strategy for using the proposed approach in production work is introduced.</p></div>Tue, 23 Aug 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13192?TRACK=RSSGeorgy N. Loginov, Anton A. Duchkov, Dmitry A. Litvichenko and Sergey A. Alyamkin2022-08-23T00:00:00Z