Geophysical Prospecting: Most Recent Articles
https://www.earthdoc.org/content/journals/gpr?TRACK=RSS
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https://www.earthdoc.org/content/journals/10.1111/1365-2478.13113?TRACK=RSS
<div></div>Thu, 16 Jun 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13113?TRACK=RSS2022-06-16T00:00:00ZHow does anisotropy of focal region change structure of moment tensors?
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13182?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>The invariants of the moment tensor such as its norm, eigenvalues and trace are closely related to the physical properties of the seismic source and the focal region, for example, seismic moment, radiation pattern, non‐double‐couple components. In this study, we investigate the relationship between these invariants and the eigenvalues and eigenvectors of the transversely isotropic elasticity tensor of the focal region. More specifically, we study how these invariants change as the source orientations vary with respect to the symmetry axes of the transversely isotropic elasticity tensor, by plotting these invariants on the stereographic net. Fortunately, one can plot them since they are independent of the strike of the fault when the focal region is a vertical transversely isotropic medium . Eigenvalues of the elasticity tensor control the invariants of the moment tensor; for instance, the ratio of the maximum and minimum norms achieved for some orientations of source is equal to the ratio of the two specific eigenvalues of the elasticity tensor. Moreover, the separation of the eigenvectors of the moment tensor from the eigenvectors of the source tensor is related to the deviation of the eigenvalues of the transversely isotropic elasticity tensor from the eigenvalues of the closest isotropic elasticity tensor. It is also found that this deviation is responsible for the percentages of non‐double‐couple components of the resulting moment tensor. This linear algebra point of view makes it easier to understand why and how the structure of the moment tensor changes for different orientations of sources.</p></div>Thu, 16 Jun 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13182?TRACK=RSSÇağrı Diner2022-06-16T00:00:00ZHigh‐resolution and robust microseismic grouped imaging and grouping strategy analysis
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13216?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>As an advanced real‐time monitoring technique, microseismic source‐location imaging provides valuable information during hydraulic fracturing, for example, the development of fracture networks and the effective reservoir reconstruction volume. However, microseismic data always suffer from weak induced energy and susceptibility to noise interference. In the case of a low signal‐to‐noise ratio, it is extremely challenging to perform robust microseismic imaging. Here, we first introduce several state‐of‐the‐art imaging conditions and two hybrid imaging conditions, which are followed by a detailed analysis of the impact of different grouping strategies. Then, we briefly analyse the sensitivity of different imaging conditions to noise using a one‐dimensional signal. Next, several benchmark models, including two‐dimensional Marmousi‐II and three‐dimensional SEG Advanced Modeling, are used as numerical examples for testing the passive‐source imaging algorithms. Finally, three‐dimensional real microseismic data are used to further investigate the impact of the grouping strategy on the imaging. The numerical examples and field data demonstrate the effectiveness of the proposed grouping strategy for the grouped imaging conditions.</p></div>Thu, 16 Jun 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13216?TRACK=RSSGuangtan Huang, Xiaohong Chen, Omar M. Saad, Yunfeng Chen, Alexandros Savvaidis, Sergey Fomel and Yangkang Chen2022-06-16T00:00:00ZSparse seismic reflectivity inversion using an adaptive fast iterative shrinkage‐thresholding algorithm
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13211?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>Seismic reflectivity inversion using <span class="jp-italic">l</span><span class="jp-sub">1</span>‐norm regularization produces sparse solutions by applying an <span class="jp-italic">l</span><span class="jp-sub">1</span>‐norm constraint. The fast iterative shrinkage‐thresholding algorithm is one of the most effective methods to solve <span class="jp-italic">l</span><span class="jp-sub">1</span>‐norm regularized inversion problems. A large number of iterations are commonly required in the fast iterative shrinkage‐thresholding algorithm because its solution converges slowly towards the sparse solution. To improve its convergence rate, we introduce a modifying strategy for the traditional fast iterative shrinkage‐thresholding algorithm. When implementing the soft‐thresholding operator, the thresholding value is adaptively adjusted by assigning the reciprocal of the solution in the previous iteration as a weight to the thresholding value of the current iteration. In this way, small variables in the solution produce large coefficients applied to the thresholding value, which causes small variables to quickly converge to zero. The adaptive fast iterative shrinkage‐thresholding algorithm shows significantly improved computational efficiency and accuracy compared to the traditional fast iterative shrinkage‐thresholding algorithm. It produces good results for both numerical and field examples of <span class="jp-italic">l</span><span class="jp-sub">1</span>‐norm regularized seismic reflectivity inversion.</p></div>Thu, 16 Jun 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13211?TRACK=RSSChuanhui Li and Xuewei Liu2022-06-16T00:00:00ZRatio‐Euler deconvolution and its applications
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13201?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>Euler deconvolution of potential field data is widely applied to obtain the location of concealed sources automatically. A lot of improvements have been proposed to eliminate the dependence of Euler deconvolution on the structural index that are based on the use of high‐order derivatives of the potential field and, therefore, sensitive to data noise. We describe the elimination of the dependence on the Ratio‐Euler method, which is based on the original Euler deconvolution function. The proposed method does not involve high‐order derivatives. Testing on simulated and field data indicates that the proposed method has better noise resistance than the existing Tilt‐Euler method, which is based on high‐order derivatives. The proposed method is first applied to the ground magnetic data from Weigang iron deposit, Eastern China. It reveals that the ore body could be approximated by a horizontal prism with a considerable vertical extent with a top depth of 55 m, which are very close to the information obtained from drill holes. In addition, the proposed method works well in estimating the depth to a cavity centre from the ground gravity anomaly.</p></div>Thu, 16 Jun 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13201?TRACK=RSSLiang Huang, Henglei Zhang, Chun‐Feng Li and Jie Feng2022-06-16T00:00:00ZJoint inversion of muon tomography and gravity gradiometry for improved monitoring of steam‐assisted gravity drainage reservoirs
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13205?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>Steam‐assisted gravity drainage reservoirs require an immense amount of energy and water resources, and proper monitoring of steam evolution and depletion patterns is integral to the economic and environmental efficiency of the operation. Muon tomography is a passive sensing technique, which has proven to successfully model density anomalies in a variety of applications but has not yet been applied to the oil and gas field. A previous study simulated muon intensity data to model density changes in a realistic steam‐assisted gravity drainage reservoir at 1.25 and 5 years after initial production. The results showed that muon tomography is a promising technique for monitoring steam‐assisted gravity drainage reservoirs with high spatial resolution and over short time intervals of weeks to months. Here we demonstrate the advantage of using vertical gravity gradient data and muon tomography data in a joint inversion to improve the muon‐only inverse models. Forward models for simulated muon and gravity gradient data are jointly inverted for a realistic steam‐assisted gravity drainage reservoir at 230 and 130 m total vertical depth at 1.25 years after initial production. Results show that the addition of gravity gradient data helps to constrain the density change models mainly in depth and to a smaller extent laterally. For a sparse muon sensor array of 48 sensors over a <script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:mrow><mml:mn>1000</mml:mn><mml:mspace width="0.16em"/><mml:mi mathvariant="normal">m</mml:mi></mml:mrow><mml:annotation encoding="application/x-tex">$1000\, \text{m}$</mml:annotation></mml:semantics></mml:math></script><script type="math/mml"><mml:math display="inline" id="jats-math-2" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:mo>×</mml:mo><mml:annotation encoding="application/x-tex">$\ensuremath{\times}$</mml:annotation></mml:semantics></mml:math></script><script type="math/mml"><mml:math display="inline" id="jats-math-3" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:mrow><mml:mn>600</mml:mn><mml:mspace width="0.16em"/><mml:mi mathvariant="normal">m</mml:mi></mml:mrow><mml:annotation encoding="application/x-tex">$600\, \text{m}$</mml:annotation></mml:semantics></mml:math></script> reservoir at <script type="math/mml"><mml:math display="inline" id="jats-math-4" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:mrow><mml:mn>100</mml:mn><mml:mspace width="0.16em"/><mml:mi mathvariant="normal">m</mml:mi></mml:mrow><mml:annotation encoding="application/x-tex">$100\, \text{m}$</mml:annotation></mml:semantics></mml:math></script> depth, the joint inversion using gravity gradient data reduces the difference between the inverse and true model by 12% compared to a muon‐only inversion. The improvement is smaller at <script type="math/mml"><mml:math display="inline" id="jats-math-5" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:mrow><mml:mn>200</mml:mn><mml:mspace width="0.16em"/><mml:mi mathvariant="normal">m</mml:mi></mml:mrow><mml:annotation encoding="application/x-tex">$200\, \text{m}$</mml:annotation></mml:semantics></mml:math></script> depth with 6%. The improvement in resolvability metrics is summarized, and limitations are discussed. The addition of multiple data types in a joint inversion improves the resulting models leading to an overall decrease in model uncertainty which can be used for improved operational efficiency in steam‐assisted gravity drainage operations.</p></div>Thu, 16 Jun 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13205?TRACK=RSSSara Pieczonka, Doug Schouten and Alexander Braun2022-06-16T00:00:00ZFine grid model for the dielectric characteristics of ground‐penetrating radar in mixed media
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13214?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>The Fisher–Yates random shuffling algorithm combined with the finite‐difference time‐domain method is proposed to construct a fine grid model for the forward simulation of ground‐penetrating radar in mixed media. First, the finite‐difference time‐domain method was used to divide the coarse grid model into several fine grid models by conforming to the boundary conditions of different media, and the corresponding dielectric parameters were assigned to Yee cells in each fine grid model. Then, the Fisher–Yates random shuffling algorithm was used to randomly scramble all Yee cells with equal probability, and the array of scrambled Yee cells was recombined into a coarse grid model. Finally, the geoelectric model of mixed media was generated with the finite‐difference time‐domain method, and a ground‐penetrating radar image excited by electromagnetic wave pulses was obtained. To explore the characteristic signals and dielectric properties of the ground‐penetrating radar electromagnetic response in mixed media, image entropy theory was used to describe the ground‐penetrating radar image, and waveform analysis and wavelet transform mode maximum methods were used to analyse the single‐channel ground‐penetrating radar signal of the mixed media. The results showed that the Fisher–Yates random shuffling–finite‐difference time‐domain method can be used to construct a valid and stable fine grid model for simulating ground‐penetrating radar in mixed media. The model effectively inhibits electromagnetic attenuation and energy dissipation, and the wavelet transform mode maximum method explains the relative dielectric permittivity distribution of the mixed media. The findings of this study can be used as a theoretical basis for correcting radar parameters and interpreting images when ground‐penetrating radar is applied to mixed media.</p></div>Thu, 16 Jun 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13214?TRACK=RSSTonghua Ling, Wenchao He, Xianjun Liu, Sheng Zhang, Fu Huang and Fei Hua2022-06-16T00:00:00ZRegularized 2D Savitzky–Golay derivative filter in estimating the second vertical derivative of potential field data
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13210?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>A robust method of estimating the second vertical derivative of two‐dimensional (2D) potential field data is proposed. The proposed method uses a 2D variant of Savitzky–Golay derivative filtering. The design of the 2D Savitzky–Golay derivative filter, unlike its one‐dimensional (1D) counterpart, is a non‐trivial exercise. This is due to the inherent complexity associated with 2D polynomial regression, which increases with the increase in the degree of the polynomial and the dimension of the filter window. The measure of complexity increases manifold, as the polynomial order increases from cubic to quintic. A larger polynomial order demands a larger dimension of the filter window patch as a minimum requirement. The large window patch which, in turn, poses computational challenges, becomes an <span class="jp-italic">ill‐posed</span> problem and is computationally inefficient. To alleviate such a problem, an appropriate set of filter parameters is proposed, which ensures computational efficiency while maintaining sufficient robustness. The computational issue arising from the <span class="jp-italic">ill‐posed</span> condition of the system matrix is addressed via <span class="jp-italic">shrinkage</span>‐based regularization. A numerical experiment was conducted on a synthetically generated 2D dataset without and with a moderate amount of Gaussian random noise in order to check the applicability of the proposed method. The performance in terms of robustness was also compared with the other, usually considered as a benchmark, method. The proposed method is then successfully applied to determine the second vertical derivative of the high‐resolution Bouguer gravity anomaly data over an impact crater in Lake Wanapitaei, Canada. A qualitative interpretation of the second vertical derivative map over Lake Wanapitei is given.</p></div>Thu, 16 Jun 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13210?TRACK=RSSIndrajit G. Roy2022-06-16T00:00:00ZIssue Information
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13112?TRACK=RSS
<div></div>Wed, 18 May 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13112?TRACK=RSS2022-05-18T00:00:00ZRobust local slope estimation by deep learning
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13208?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>In the seismic community, the local slope is essential for various applications, including structure prediction, seislet transform, trace interpolation and denoising. The most popular method to calculate slope is the plane‐wave destruction, which assumes that the seismic data can be represented by local plane waves. However, the plane‐wave destruction method fails when the seismic data become very noisy. Taking random noise into consideration, we adopt the deep learning method to calculate the local slope. In the deep learning architecture, the input is noisy data and the target is an accurate slope estimated from the clean data using plane‐wave destruction. The deep learning architecture includes a convolutional layer, deconvolutional layer, normalization layer and activation layer which are suitable to suppress noise and learn the nonlinear relationship between the input and the target. After training, the network is applied to other test examples to calculate local slopes. In addition, we implement the seislet transform and structure prediction applications based on the estimated slope. Both the estimated slope and the related applications indicate that the proposed method can robustly obtain the local slope from noisy seismic data, compared with the plane‐wave destruction method.</p></div>Wed, 18 May 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13208?TRACK=RSSShaohuan Zu, Junxing Cao, Sergey Fomel, Liuqing Yang, Omar M. Saad and Yangkang Chen2022-05-18T00:00:00ZMultiparameter vector‐acoustic least‐squares reverse time migration
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13207?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>The use of dual‐sensor acquisitions enabled different studies using the so‐called vector‐acoustic equations, which admit particle velocity (displacement or acceleration) information instead of solely the pressure wavefield. With cost far from elastic formulations but comparable with the usually used second‐order acoustic equation, previous works involving the use of vector‐acoustic equations along with multicomponent data have been applied to conventional reverse time migration and full‐waveform inversion, always emphasizing the benefits of using wavefields containing directivity information, which make the receiver ghosts interact constructively with the backpropagated reflected wavefield. Thus, generated results are superior to those of conventional single‐component data imaging techniques, particularly with spatial subsampling of marine seismic data. To assess whether the effects of applying the vector‐acoustic equations persist in a linearized inversion, we developed a multiparameter vector‐acoustic least‐squares reverse time migration, inverting reflectivities associated with velocity and density. To demonstrate the method's performance, we apply it to two‐dimensional numerical examples and compare the results with those obtained by the conventional acoustic least‐squares reverse time migration. The results obtained by the vector‐acoustic least‐squares reverse time migration method are accurate for all inverted parameters and also deliver better convergence when compared with the conventional least‐squares reverse time migration.</p></div>Wed, 18 May 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13207?TRACK=RSSFernanda Farias, Alan Souza and Reynam C. Pestana2022-05-18T00:00:00ZWavefield simulation of fractured porous media and propagation characteristics analysis
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13198?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>Most fractured reservoirs are two‐phase media, that is, mixture of solid matrix and void. It contains rock skeleton and fractures or pores filled with oil, gas and water. These fractures are the channels of oil and gas storage and migration. In two‐phase media, the interaction between the fluid and solid phases will further complicate the seismic wave propagation. Natural fractures are typically irregular in shape, thereby causing difficulties in the exploration of fractured reservoirs. Therefore, the key to the prediction of fractures is to study the equation of motion of seismic waves and energy distribution of seismic waves at the fracture interface. To derive the propagation law for complex irregular shape fractures in two‐phase media, we combined the stiffness matrix of the media with linear slip theory and derived a numerical simulation scheme. The simulation scheme considers the fractures in the two‐phase media to be in any direction. In addition, seismic wave energy distribution at the fracture interface was obtained. The linear slip boundary condition was introduced into the conventional Zoeppritz equation, and a modified Zoeppritz equation was proposed for two‐phase fractured media. The reflection and transmission due to the fracture interface were considered in the new equation, thereby making the equation more flexible. Using the new numerical simulation scheme, we analysed the elastic waves produced by the linear slip fracture interface in two‐phase media and provided the long‐term stability results of the new scheme. Moreover, we provided the relationship between the reflection and transmission coefficients of the linear slip fracture interface and the incident angle and compliance in two‐phase media using the new Zoeppritz equation. The results show that the reflection wave of two‐phase fractured media can be divided into wave impedance and fracture parts to accurately describe the properties of underground rocks.</p></div>Wed, 18 May 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13198?TRACK=RSSKang Wang, Suping Peng, Yongxu Lu and Xiaoqin Cui2022-05-18T00:00:00ZConvolutional neural network‐based classification of microseismic events originating in a stimulated reservoir from distributed acoustic sensing data
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13199?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>This study presents a workflow of using a convolutional neural network to automatically classify microseismic events originating from a more productive oil and gas‐bearing formation (the Eagle Ford Shale), as compared to events originating in less productive formations (the Austin Chalk and Buda Limestone). These microseismic events occur due to hydraulic stimulation and are recorded by fibre optic–distributed acoustic sensing measurements from a horizontal monitoring well. The convolutional neural network is trained to recognize guided wave energy in distributed acoustic sensing seismograms, since microseismic events originating within or close to a low‐velocity reservoir (such as the Eagle Ford) generate significant guided wave energy. The training of convolutional neural network is conducted using synthetic seismograms overlain with real noise profiles from field data. Field events with guided waves are then classified by the convolutional neural network as occurring within or close to the Eagle Ford, while events without guided waves are classified as occurring far outside the Eagle Ford. Noise attenuation steps (including a bandpass filter, median filter and non‐local means filter) are implemented to increase the signal‐to‐noise ratio of the field data and improve the classification accuracy. The accuracy of the convolutional neural network is measured by comparison with labels of the events determined by human inspection of guided wave presence. We also evaluate the impact of different network architectures and noise attenuation methods on classification accuracy. The accuracy and F1‐score of the final classification are both 0.85 when tested on a high signal‐to‐noise ratio subset of the field data. On the complete dataset including low signal‐to‐noise ratio events, an accuracy and F1‐score of 0.80 are achieved. These results demonstrate the high effectiveness of the trained convolutional neural network on guided wave detection and classification of microseismic events inside and outside the Eagle Ford formation.</p></div>Wed, 18 May 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13199?TRACK=RSSYoufang Liu, Owen Huff, Bin Luo, Ge Jin and James Simmons2022-05-18T00:00:00ZIntelligent pore type characterization: Improved theory for rock physics modelling
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13204?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>Thanks to the recent developments in both hardware and software capabilities of computers, intelligent rock physics modelling has emerged as an alternative to the conventional approach to the rock physics. Respecting the crucial contribution of the pore geometry into the rock physics modelling, I propose an accurate yet cost‐ and time‐efficient intelligent framework to measure pore space based on digital rock physics. In this method, total pore space was calculated after estimating the pore geometry through pattern recognition on thin section images captured through polarized‐light microscopy. Next, applying three different multi‐class classifiers (radial basis function, support vector machine and k‐nearest neighbours) for estimating pore type and aspect ratio, the best results were obtained using the fuzzy Sugeno integral, and the pore types were classified according to the most widely used pore type classification scheme. Next, an artificial neural network was applied to interpolate discrete data points (thin sections) into continuous profiles of pore type and aspect ratio. Subsequently, as a case study, the proposed approaches were applied to a real‐world carbonate reservoir for modelling the P‐ and S‐wave velocities through a rock physics model. Verifying the modelling results against ultrasonic and measured well‐logging data, the methodology showed promising performance at acceptable levels of uncertainty. The most significant advantage of the intelligent pore type quantification over the conventional methods was found to be its ability to estimate elastic properties with good accuracy. The key findings of this research include automatic detection of the pore types and aspect ratio, provision of a database of pore geometry, attenuation of uncertainty in pore type characterization and improvement of rock physics modelling in the absence of reliable S‐wave velocity data.</p></div>Wed, 18 May 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13204?TRACK=RSSJavad Sharifi2022-05-18T00:00:00ZProbabilistic inversions of electrical resistivity tomography data with a machine learning‐based forward operator
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13189?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>Casting a geophysical inverse problem into a Bayesian setting is often discouraged by the computational workload needed to run many forward modelling evaluations. Here we present probabilistic inversions of electrical resistivity tomography data in which the forward operator is replaced by a trained residual neural network that learns the non‐linear mapping between the resistivity model and the apparent resistivity values. The use of this specific architecture can provide some advantages over standard convolutional networks as it mitigates the vanishing gradient problem that might affect deep networks. The modelling error introduced by the network approximation is properly taken into account and propagated onto the estimated model uncertainties. One crucial aspect of any machine learning application is the definition of an appropriate training set. We draw the models forming the training and validation sets from previously defined prior distributions, while a finite element code provides the associated datasets. We apply the approach to two probabilistic inversion frameworks: A Markov chain Monte Carlo algorithm is applied to synthetic data, while an ensemble‐based algorithm is employed for the field measurements. For both the synthetic and field tests, the outcomes of the proposed method are benchmarked against the predictions obtained when the finite element code constitutes the forward operator. Our experiments illustrate that the network can effectively approximate the forward mapping even when a relatively small training set is created. The proposed strategy provides a forward operator that is three orders of magnitude faster than the accurate but computationally expensive finite element code. Our approach also yields the most likely solutions and uncertainty quantifications comparable to those estimated when the finite element modelling is employed. The presented method allows solving the Bayesian electrical resistivity tomography with a reasonable computational cost and limited hardware resources.</p></div>Wed, 18 May 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13189?TRACK=RSSMattia Aleardi, Alessandro Vinciguerra, Eusebio Stucchi and Azadeh Hojat2022-05-18T00:00:00ZCORRIGENDUM
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13203?TRACK=RSS
<div></div>Wed, 18 May 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13203?TRACK=RSS2022-05-18T00:00:00ZIssue Information
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13111?TRACK=RSS
<div></div>Thu, 14 Apr 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13111?TRACK=RSS2022-04-14T00:00:00ZIn search of the vibroseis first arrival
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13196?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>The first step in correcting for time delays of land seismic data due to low‐velocity weathered layers is to pick the first‐arrival times of the refracting energy. But doing so for vibroseis data can be difficult, as the seismic wavelet is often ringy and uncompact, resulting in cycle‐skipped picks. Even when we manage to pick a waveform feature consistently, it is not clear where the first‐arrival time is in relation to it. I present a novel method that shapes the seismic wavelet to a Ricker wavelet whose peak is located at the true arrival time, so the time of the first arrival is unambiguous. Further, the arrivals are less ringy and their energy more focused, so that they are less likely to cycle skip or be overwhelmed by random noise. The result is more accurate and consistent first‐arrival picks.</p></div>Thu, 14 Apr 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13196?TRACK=RSSStewart Trickett2022-04-14T00:00:00Z3D high‐order sparse radon transform with L1–2 minimization for multiple attenuation
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13185?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>Among many multiple attenuation methods, parabolic Radon transform has been widely used due to its efficiency and effectiveness. However, there are two factors that restrict the application of Radon transform: (1) the smearing caused by finite seismic acquisition aperture and discrete sampling of seismic data and (2) the destruction of the amplitude versus offset signature in seismic data. Therefore, a high‐order sparse Radon transform in the mixed frequency–time domain with <span class="jp-italic">L</span><span class="jp-sub">1–2</span> minimization is proposed. The <span class="jp-italic">L</span><span class="jp-sub">1–2</span> metric has been proved an unbiased approximation to <span class="jp-italic">L</span><span class="jp-sub">0</span> norm, which helps improve the sparsity and resolution of the Radon model. By combining the orthogonal polynomial transform, which can fit the amplitude variations of seismic data, the amplitude versus offset signature is also considered. Furthermore, the 2D Radon transform is extended to 3D by modifying the augmented Lagrangian in the alternating direction method of multipliers algorithm. Compared with the 2D algorithm, 3D Radon transform considers seismic wave propagation in three dimensions, which can describe the wavefield more accurately. The proposed method is applied to multiple attenuation examples based on both synthetic and real 3D data examples to demonstrate its effectiveness, compared with some existing high‐resolution techniques and the corresponding 2D algorithm.</p></div>Thu, 14 Apr 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13185?TRACK=RSSWeiheng Geng, Jingye Li, Xiaohong Chen, Jitao Ma, Jiamin Xu, Guang Zhu and Wei Tang2022-04-14T00:00:00ZWarped mapping–based blind deconvolution for resolution improvement
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13186?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>Owing to the influence of absorption and scattering, the components of seismic waves, especially those with higher frequencies, always suffer amplitude attenuation and phase distortion during their propagation through the earth. Nonstationary blind deconvolution takes such attenuations into account for prestack seismic data. This not only compensates for attenuation‐induced energy loss but also simultaneously addresses the energy loss associated with the reflectivity series and wavelet. Additionally, nonstationary blind deconvolution can greatly improve the resolution of seismic data. However, prestack nonstationary blind deconvolution is difficult to implement because it is not easy to estimate the attenuation function of prestack seismic data unless the quality factor of the Earth is assumed to be constant. In this regard, we introduced a nonstationary blind deconvolution method for prestack gathering. First, we used warped mapping to calculate the attenuation function of the prestack data. Then, we incorporated the attenuation function into the nonstationary sparse spike deconvolution based on Toeplitz‐sparse matrix factorization for reflectivity series and wavelet estimation. The proposed method is velocity‐independent and can be adapted to the time‐varying Q model. To validate its stability and effectiveness, numerical and real data examples were adopted. The results show that the proposed method provides a straightforward routine for the estimation of prestack reflectivity coefficients for further processing and inversion.</p></div>Thu, 14 Apr 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13186?TRACK=RSSChao Li and Guochang Liu2022-04-14T00:00:00ZAn improved 25‐point finite‐difference scheme for frequency‐domain elastic wave modelling
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13188?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>Frequency‐domain finite‐difference modelling is widely used in exploration geophysics. However, it involves solving a large system of linear algebraic equations, which may cause huge computational costs, especially for large‐scale models. To address this issue, we have proposed an improved 25‐point finite‐difference scheme for wavefield modelling of two‐dimensional frequency‐domain elastic wave equations. The biggest difference between the improved 25‐point scheme with other 25‐point schemes is the finite‐difference formulas for spatial derivatives. The proposed 25‐point scheme applies to equal and unequal grid intervals, and its optimization coefficients depend on the grid‐spacing ratio and Poisson's ratio. The dispersion analysis indicates that within the phase velocity errors of 1% and 2%, the improved 25‐point scheme only requires approximately 2.3 and 2.2 grid points per shear wavelength, which has greater accuracy than the existing schemes. To eliminate artificial boundary reflections, we apply the perfectly matched layer boundary conditions to the model edges. Several numerical examples are presented to prove the validity of the improved 25‐point scheme.</p></div>Thu, 14 Apr 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13188?TRACK=RSSShizhong Li and Chengyu Sun2022-04-14T00:00:00ZOriented extrapolation of common‐midpoint gathers in the absence of near‐offset data using predictive painting
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13195?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>Seismic reconstruction of missing traces is an extremely important subject in seismic data processing. It includes both interpolation and extrapolation of sparsely recorded data. Extrapolation is often performed in the absence of near‐offset seismic data recorded through marine acquisition. Several reconstruction methods have been designed to circumvent this sparsity in time–offset, frequency–offset and time–frequency domains. In this research, I propose an oriented extrapolation workflow to reconstruct near‐offset missing traces. The term oriented or velocity‐independent refers to those techniques that are based on the use of local slopes. In the proposed workflow, I use an oriented time‐warping algorithm called predictive painting. This algorithm is suitable to predict two‐way traveltimes between two distinctive points of an event. Seismic events recorded by an off‐end array very rarely contain dips of both signs with respect to their zero‐offset location in common‐midpoint domain. This makes the domain an ideal choice to run the algorithm. The proposed algorithm is demonstrated on synthetic and field data examples. I decimate near‐offset seismic traces and reconstruct them through the algorithm. The reconstruction results are compared with the original data before decimation. Furthermore, insensitivity of the proposed workflow to the presence of class II amplitude‐versus‐offset anomalies is demonstrated on a synthetic example. I also perform a velocity‐dependent (a normal‐moveout‐based) technique on the field data and compare the corresponding outcomes with the results achieved by the application of the proposed velocity‐independent approach. All the results suggest that the proposed technique has the potential to be used in the exploration industry.</p></div>Thu, 14 Apr 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13195?TRACK=RSSM. Javad Khoshnavaz2022-04-14T00:00:00ZA physics‐guided neural network‐based approach to velocity model calibration for microseismic data
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13191?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>The physics‐guided neural network framework combines the effectiveness of data‐driven and physics‐based models, and it is, therefore, becoming increasingly popular in geophysical applications. We present a physics‐guided neural network–based approach to calibrate velocity models for microseismic data. In our implementation, the physics‐guided neural network comprises of a user‐selected number of fully connected layers, a scaling and shifting layer and a forward modelling operator layer. We input the observed P‐ and S‐wave arrival times to the neural network. In the forward pass, the network's output layer produces normalized P‐ and S‐wave velocities for the subsurface model. The scaling and shifting layer converts the normalized output to realistic velocity values. The forward modelling operator (i.e. a ray‐shooting algorithm) layer computes traveltimes using the velocities from the preceding scaling and shifting layer and the known source–receiver locations. We then evaluate a loss function that compares the predicted traveltimes with the input observed arrival times, and update network's weights and bias parameters. We also use a weight‐based saliency measure to evaluate whether the selected network architecture (i.e. number of hidden layers and neurons) is optimal for the model calibration problem. Finally, using synthetic data examples, we demonstrate that our unsupervised physics‐guided neural network–based approach can provide robust velocity model and uncertainty estimates.</p></div>Thu, 14 Apr 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13191?TRACK=RSSHongliang Zhang, Jubran Akram and Kristopher A. Innanen2022-04-14T00:00:00ZCombined fluid factor and brittleness index inversion for coal‐measure gas reservoirs
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13172?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>Gas content and brittleness characteristics, which are mostly quantified using the fluid factor and brittleness index, are the key factors in the evaluation of coal‐measure gas reservoirs, which are similar to traditional unconventional gas reservoirs. However, most previous seismic inversion research and evaluations only focused on one of the parameters. Therefore, in this study, we performed a combined inversion of the fluid factor and brittleness index based on pre‐stack seismic records collected from a typical coal‐measure gas block in the Sichuan Basin. First, a new P–P wave reflection coefficient approximation based on both parameters was derived, and the precision and sensitivity of the inversion parameters were analysed. We then constructed a new inversion equation based on Bayes’ theorem. The logging curves obtained for a typical coal‐measure gas well and through pre‐stack seismic profile were used to evaluate the inversion method. The results of the model test and profile inversion at the well location were in good agreement with the original logging values. Finally, we analysed and discussed the results of the inversion of both parameters for the evaluation of the gas content and brittleness characteristics of the target reservoirs.</p></div>Thu, 14 Apr 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13172?TRACK=RSSHaibo Wu, Rongxin Wu, Pingsong Zhang, Yanhui Huang, Yaping Huang and Shouhua Dong2022-04-14T00:00:00ZPetrophysics applied in mineralizations, hydrothermal alterations and lithology mapping: A case study from the Zn–Pb (Cu–Ag) epithermal deposit of Santa Maria, Brazil
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13180?TRACK=RSS
<div><span class="tl-main-part">ABSTRACT</span>
<p>The epithermal Zn–Pb (Cu–Ag) deposit of Santa Maria represents a distal magmatic‐hydrothermal system, whose mineralizations are controlled by fault systems located in the sedimentary units of the upper Camaquã Basin, above the tectonic units of the Sul‐Riograndense Shield. The hydrothermal alteration zones contain illite, chlorite and pyrite, besides galena, sphalerite, chalcopyrite and bornite. To improve the knowledge of this mineral system, this work investigated the petrophysical footprints of samples representing the predominant lithology, altered rocks and hydrothermal mineralization. The core samples of the predominant lithology, altered rocks and hydrothermal deposit mineralizations were used to determine the following petrophysical properties, density, magnetic susceptibility, primary wave velocity, resistivity, conductivity and chargeability. Moreover, the quantitative evaluation of minerals by scanning electron microscopy coupled with an automated image analysis system allowed us to map lithological and alteration processes. The results indicate density as the most effective physical property to map lithology, hydrothermal alteration and the Zn–Pb (Cu–Au) mineralization. Furthermore, all studied physical properties have moderate effectiveness in the alteration zones of known geological and geophysical anomalies in the Santa Maria deposit. Chargeability could be used, especially when sulphides are disseminated, but additional geological factors complicate its interpretation. The mineralogical and petrophysical diversity of the Santa Maria deposit provided vital data for geological–geophysical interpretations while allowing the creation of a key exploration plan to investigate the Zn–Pb (Cu–Au) mineralization. Finally, petrophysics should be used in prospection to help understand complex geological processes, their overlapping subpopulations and to accelerate mineral research while reducing the use of technical and financial resources and expenditure on ineffective geophysical methodologies.</p></div>Thu, 14 Apr 2022 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13180?TRACK=RSSMoriá Caroline de Araújo, Adalene Moreira Silva, Paola Ferreira Barbosa, João Henrique Boniatti, Allan Früchting, Samuel Bouças do Lago and Ram Horizonte Seixas Betancourt2022-04-14T00:00:00Z