Geophysical Prospecting: Most Recent Articles
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https://www.earthdoc.org/content/journals/10.1111/1365-2478.13495?TRACK=RSS
<div></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13495?TRACK=RSS2024-02-21T00:00:00ZPerformance of old and new mass‐lumped triangular finite elements for wavefield modelling
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13383?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Finite elements with mass lumping allow for explicit time stepping when modelling wave propagation and can be more efficient than finite differences in complex geological settings. In two dimensions on quadrilaterals, spectral elements are the obvious choice. Triangles offer more flexibility for meshing, but the construction of polynomial elements is less straightforward. The elements have to be augmented with higher‐degree polynomials in the interior to preserve accuracy after lumping of the mass matrix. With the classic accuracy criterion, triangular elements suitable for mass lumping up to a polynomial degree 9 were found. With a newer, less restrictive criterion, new elements were constructed of degree 5–7. Some of these are more efficient than the older ones. To assess which of all these elements performs best, the acoustic wave equation is solved for a homogeneous model on a square and on a domain with corners, as well as on a heterogeneous example with topography. The accuracy and runtimes are measured using either higher‐order time stepping or second‐order time stepping with dispersion correction. For elements of polynomial degree 2 and higher, the latter is more efficient. Among the various finite elements, the degree‐4 element appears to be a good choice.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13383?TRACK=RSSW. A. Mulder2024-02-21T00:00:00ZMultichannel seismic data attenuation compensation via curvelet‐based sparsity promotion
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13442?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Due to subsurface viscosity and heterogeneity, the vertical resolution of observed seismic data is decreased after wave propagation, generating nonstationary seismic data with amplitude attenuation and phase distortion. Inverse Q filtering techniques are always used to enhance the vertical resolution of seismic data. However, the majority of inverse Q filtering methods treat attenuation compensation trace by trace, which may produce non‐robust compensation results with poor transverse continuity and amplify noise energy in noisy cases. Thus, we develop a novel sparsity‐promoting inversion‐based multichannel seismic data attenuation compensation approach by introducing a sparse constraint for curvelet coefficients of multichannel compensated data, which takes the transverse continuity of compensated data into account. Besides, the proposed method with a sparse constraint for curvelet coefficients has a better noise‐resistance property, which can attenuate the noise energy in noisy cases during attenuation compensation, improving compensation accuracy and robustness. To improve its computational efficiency, a fast iterative shrinkage–thresholding algorithm is adopted to solve the established lasso problem. Synthetic data examples with different noise levels and two post‐stack field data examples validate the effectiveness of the proposed multichannel method. Its compensation results have superior vertical resolution, transverse continuity and noise robustness in comparison to the conventional single‐channel compensation method using a damped least squares algorithm.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13442?TRACK=RSSTongtong Mo, Ying Yin, Ren Luo and Benfeng Wang2024-02-21T00:00:00ZAttenuating free‐surface multiples and ghost reflection from seismic data using a trace‐by‐trace convolutional neural network approach
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13443?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>The presence of the air–water interface (or free‐surface) creates two major problems in marine seismic data for conventional seismic processing and imaging: free‐surface multiples and ghost reflections. The attenuation of free‐surface multiples remains one of the most challenging noise attenuation problems in seismic data processing. Current solutions suffer from the removal of the primary events along with the multiple events especially when the primary and multiple events overlap (e.g., adaptive subtraction). The effective attenuation of ghost reflections (or <span class="jp-italic">deghosting</span>) requires acquisition‐ and/or processing‐related solutions which generally address the source‐side and receiver‐side ghosts separately. Additionally, an essential requirement for a successful implementation of free‐surface multiple attenuation and seismic dehosting is the requirement of dense seismic data acquisition parameters which is not realistic for two‐dimensional and/or three‐dimensional marine cases. We present a convolutional neural network approach for free‐surface multiple attenuation and seismic deghosting. Unlike the existing solutions, our approach operates on a single trace at a time, and neither relies on the dense acquisition parameters nor requires a subtraction process to eliminate free‐surface multiples, and it removes both the source ghost and receiver ghost simultaneously. We train a network using subsets of the Marmousi and Pluto velocity models and make predictions using subsets of the Sigsbee velocity model. We show that the convolutional neural network predictions give a correlation coefficient of 0.97 on average with the numerically modeled data for the synthetic examples. We illustrate the efficacy of our convolutional neural network–based technique using the Mobil AVO Viking Graben field data set. The application of our algorithm demonstrates that our convolutional neural network–based approach removes different orders of free‐surface multiples (e.g., first and second orders) and recovers the low‐frequency content of the seismic data (which is essential for, for instance, full‐waveform inversion applications and broadband processing) by successfully removing the ghost reflections while preserving and increasing the continuity of the primary reflection.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13443?TRACK=RSSMert S. R. Kiraz, Roel Snieder and Jon Sheiman2024-02-21T00:00:00ZBlended acquisition with temporally signatured/modulated and spatially dispersed source array: The first pilot survey onshore Abu Dhabi
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13445?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Recently, we established a blended‐acquisition method: temporally signatured and/or modulated and spatially dispersed source array that jointly uses various signaturing and/or modulation in the time dimension and dispersed source array in the space dimension. We acquired and processed the first pilot programme with our method onshore Abu Dhabi. In this paper, we review the resulting acquisition‐productivity enhancement in the time dimension and discuss it in the space dimension as well. We then review the deblended‐data‐reconstruction processing, followed by imaging processing, and discuss their performance and resulting data quality. We last establish a relationship between the acquisition productivity and the processing performance. We found that this method significantly enhances the acquisition productivity compared to conventional blending methods. For the processing performance, the deblended data can successfully be reconstructed from the blended data; afterwards, the subsurface sections can naturally be imaged from the deblended data. Furthermore, this method owns a relationship: The deblended‐data‐reconstruction performance increases with the acquisition time (i.e. the acquisition effort that is inversely proportional to the acquisition productivity) up to a plateau; the imaging processing improves the data quality as the flow makes progress and eventually reaches a high data quality after poststack processing, regardless of the acquisition effort.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13445?TRACK=RSSTomohide Ishiyama2024-02-21T00:00:00ZDispersion computation of guided waves in a layered transversely isotropic elastic medium sandwiched between two half‐spaces
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13447?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>A transversely isotropic elastic medium with a vertical axis of symmetry is considered. We obtain dispersion equations in real terms for guided Love and Rayleigh waves in a such a medium consisting of horizontal layers sandwiched between two half‐spaces by brief modifications of the available literatures on dispersion equations in elastic layered media through transfer matrix. To illustrate the applicability, dispersion curves of guided waves are computed for three‐ and five‐layered symmetric models with transversely isotropic coal seam in the middle. The Airy phase is marked by a minimum or maximum of group velocity in a dispersion curve, and this phase is important to get seam structure for mining safety. For the first three modes, the effect of Thomsen anisotropy parameters <span class="jp-italic">γ</span>, <span class="jp-italic">ε</span> and <span class="jp-italic">δ</span> of a coal seam on frequency (<span class="jp-italic">f</span><span class="jp-sub">A</span>) and group velocity (<span class="jp-italic">U</span><span class="jp-sub">A</span>) of the Airy phase is similar in three‐ and five‐layered models. For guided Love waves, <span class="jp-italic">f</span><span class="jp-sub">A</span> and <span class="jp-italic">U</span><span class="jp-sub">A</span> have nearly a uniform increase with the increase of <span class="jp-italic">γ</span>. For guided Rayleigh waves, the increase of <span class="jp-italic">ε</span> causes <span class="jp-italic">f</span><span class="jp-sub">A</span> and <span class="jp-italic">U</span><span class="jp-sub">A</span> to increase; however, the increase of <span class="jp-italic">δ</span> causes <span class="jp-italic">f</span><span class="jp-sub">A</span> and <span class="jp-italic">U</span><span class="jp-sub">A</span> to decrease.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13447?TRACK=RSSSankar N. Bhattacharya2024-02-21T00:00:00ZSemi‐blind‐trace algorithm for self‐supervised attenuation of trace‐wise coherent noise
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13448?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Trace‐wise noise is a type of noise often seen in seismic data, which is characterized by vertical coherency and horizontal incoherency. Using self‐supervised deep learning to attenuate this type of noise, the conventional blind‐trace deep learning trains a network to blindly reconstruct each trace in the data from its surrounding traces; it attenuates isolated trace‐wise noise but causes signal leakage in clean and noisy traces and reconstruction errors next to each noisy trace. To reduce signal leakage and improve denoising, we propose a new loss function and masking procedure in a semi‐blind‐trace deep learning framework. Our hybrid loss function has weighted active zones that cover masked and non‐masked traces. Therefore, the network is not blinded to clean traces during their reconstruction. During training, we dynamically change the masks' characteristics. The goal is to train the network to learn the characteristics of the signal instead of noise. The proposed algorithm enables the designed U‐net to detect and attenuate trace‐wise noise without having prior information about the noise. A new hyperparameter of our method is the relative weight between the masked and non‐masked traces' contribution to the loss function. Numerical experiments show that selecting a small value for this parameter is enough to significantly decrease signal leakage. The proposed algorithm is tested on synthetic and real off‐shore and land data sets with different noises. The results show the superb ability of the method to attenuate trace‐wise noise while preserving other events. An implementation of the proposed algorithm as a Python code is also made available.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13448?TRACK=RSSMohammad Mahdi Abedi, David Pardo and Tariq Alkhalifah2024-02-21T00:00:00ZLoss functions in machine learning for seismic random noise attenuation
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13449?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Seismic random noise is one of the main factors that degrade the quality of seismic data. Therefore, seismic random noise attenuation should be performed appropriately through several stages during seismic data processing, and this requires sufficient experience and knowledge because the proper hyperparameters need to be determined based on the features of the noise in the target seismic data. Recently, machine learning–based seismic noise attenuation has been widely studied because it provides suitable results by learning noise features from noisy data, unlike conventional physics‐based approaches. There are many important factors in machine learning, and, here, we focus on the loss functions of machine learning in terms of seismic random noise attenuation. The most widely used loss function is <span class="jp-italic">l</span><span class="jp-sub">2</span>, but we train a model with various kinds of single and multiple loss functions and attenuate seismic random noise. We analyse the efficiency of loss functions by comparing the noise‐attenuated results of synthetic and field seismic data qualitatively and quantitatively. Our analysis indicates that the multiple loss function with the <span class="jp-italic">l</span><span class="jp-sub">1</span> norm can be a proper choice for random noise suppression of seismic data.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13449?TRACK=RSSHyunggu Jun and Han‐Joon Kim2024-02-21T00:00:00ZA novel automatic source point deviating method based on dynamic programming
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13453?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Seismic geometry design is the first step for the seismic prospecting, which would substantially influence the quality of acquired seismic data. Earlier researchers have focused more on tuning systemic parameters and offsetting source points. However, due to the complex geographical factors, the real layout is often different from the pre‐plan design, which is designed by experts in advance. In this paper, we aim to address the source point deviating problem when the pre‐plan design is unsuitable for geographical conditions. Although there exist many research works in this aspect, few of them have been applied in practice and the offset of source points in the field still mainly relies on manual work. In this paper, we propose a method based on dynamic programming to deviate source points automatically while maintaining the smoothness of source lines and taking the uniformity of fold distribution into account. To verify the effectiveness of our method, we also compare the uniformity of offset and azimuth after automatically deviating source points with the pre‐plan design. Experimental results show that our proposed method achieves better uniformity and keeps more smooth source lines which are conducive to the construction. Moreover, on the data set collected in a mountainous area, we observe that our method slightly suppresses the acquisition footprints.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13453?TRACK=RSSLi Long, Chunxia Zhang, Hongtao Wang, Jiangshe Zhang, Yan Wang, Xujiang Zhu, Huibing Zhao, Yinpo Xu and Long Wu2024-02-21T00:00:00ZSensitivity study of the critical angle in elastic orthorhombic media
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13457?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>The critical angle plays a crucial role in data processing for refraction seismology. In the context of three‐dimensional data, the critical angle exhibits azimuthal dependence, particularly in the presence of an anisotropic model. In this paper, we propose a method to determine the critical angle (phase angle) and analyse the sensitivity of the critical angle to the model parameters and the available azimuthal range for both transversely isotropic medium with a vertical symmetry axis and orthorhombic models. For more complex orthorhombic models, the critical angle can be computed while considering changes in azimuth. In a numerical example, we apply sensitivity analysis to examine the existence of the critical angle and determine its corresponding values to variations in model parameters and the azimuthal range. Additionally, we conduct computations of the critical angle for the simplified acoustic and elliptical orthorhombic models. This analysis can be extended to encompass all model parameters for different wave modes (pure and converted waves), and they provide generalized predictions for the available range of data in seismic data processing applications.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13457?TRACK=RSSShibo Xu and Alexey Stovas2024-02-21T00:00:00ZTime‐lapse applications of the Marchenko method on the Troll field
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13463?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>The data‐driven Marchenko method is able to redatum wavefields to arbitrary locations in the subsurface, and can, therefore, be used to isolate zones of specific interest. This creates a new reflection response of the target zone without interference from over‐ or underburden reflectors. Consequently, the method is well suited to obtain a clear response of a subsurface reservoir, which can be advantageous in time‐lapse studies. The isolated responses of a baseline and monitor survey can be more effectively compared; hence, the retrieval of time‐lapse characteristics is improved. This research aims to apply Marchenko‐based isolation to a time‐lapse marine data set of the Troll field in Norway in order to acquire an unobstructed image of the primary reflections and retrieve small time‐lapse traveltime difference in the reservoir. It is found that the method not only isolates the primary reflections but can also estimate internal multiples outside the recording time. Both the primaries and the multiples can then be utilized to find time‐lapse traveltime differences. More accurate ways of time‐lapse monitoring will allow for a better understanding of dynamic processes in the subsurface, such as observing saturation and pressure changes in a reservoir or monitoring underground storage of hydrogen and CO<span class="jp-sub">2</span>.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13463?TRACK=RSSJohno van IJsseldijk, Joeri Brackenhoff, Jan Thorbecke and Kees Wapenaar2024-02-21T00:00:00ZAcoustic signatures of porous rocks permeated by partially saturated, aligned planar fractures
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13444?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>The presence of sets of vertical, parallel fractures is very common in the Earth's upper crust. Notably, open fractures exert critical control on the mechanical and hydraulic properties of the host formation. There is great interest in understanding how fractures interact with seismic waves, as this knowledge could be used to detect and characterize fractures from seismic data. When a seismic wave travels through a fractured formation, it induces oscillatory fluid pressure diffusion between the fractures and the embedding porous background, a physical process that produces attenuation and dispersion of the seismic wave. Although there are numerous studies on this topic, the case of parallel fractures saturated with different immiscible fluids, such as brine and CO<span class="jp-sub">2</span>, remains rather unexplored. With these motivations, in this work, we propose an analytical approach to compute the phase velocity and attenuation of P‐waves travelling perpendicularly to a set of planar, parallel fractures. While we consider that the background is saturated with brine, the fractures can be saturated with brine or gas. Our numerical analysis shows for the first time that two manifestations of fluid pressure diffusion arise in these cases. One of them, associated with relatively high levels of attenuation, is due to fluid pressure diffusion occurring between consecutive fractures saturated with different fluids. In this case, the fluid pressure diffusion process is initiated at a fracture saturated with brine and reaches a consecutive fracture saturated with gas. The other fluid pressure diffusion manifestation, on the other hand, arises at higher frequencies, is characterized by lower levels of attenuation and results from the interaction in the background of fluid pressure diffusion processes initiated at consecutive fractures, irrespective of the fluid content. Finally, by considering a stochastic distribution of the two fluids and a classical frequency of interest in seismic experiments, we obtain P‐wave attenuation and phase velocity as functions of the fracture gas saturation. This approach could be of interest for the remote detection and quantification of pore fluids using seismic waves.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13444?TRACK=RSSNatalia N. Salva, Gabriel H. Paissan, Santiago G. Solazzi and J. Germán Rubino2024-02-21T00:00:00ZThird‐order elasticity of transversely isotropic field shales
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13446?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>The formations above a producing reservoir can exhibit large mechanical changes, creating a risk of significant subsidence and loss of rock integrity. These changes can be monitored by time‐lapse seismic acquisition, which measures the corresponding velocity changes via time‐shifts. Third‐order elastic theory can be used to connect subsurface strains and stress changes to these seismic attribute changes. Existing models assume isotropic strain dependence of the dynamic stiffness in shales. It is important to re‐evaluate this isotropic assumption considering the inherent anisotropy of shales and their abundance in the overburden. Thus, we instead propose a third‐order elastic model with a transversely isotropic strain dependence of the dynamic stiffness. When calibrated, this new model satisfactorily predicted P‐wave velocity changes determined in undrained laboratory experiments conducted on overburden field shales, covering a wide range of propagation directions and stress variations. The shales exhibit anisotropic dynamic strain sensitivity, resulting in a significantly higher strain sensitivity predicted for Thomsen's anisotropy parameters epsilon and delta subjected to a uniaxial strain parallel to the horizontal bedding plane compared to the vertical direction. Geomechanical modelling, considering a depleting disk‐shaped reservoir surrounded by shales, was employed to predict the dynamic stiffness changes of the overburden using the laboratory‐calibrated third‐order elastic model. The overburden time‐shifts increased with offset angle, peaking at about 45°, suggesting a strong influence of shear strains on the time‐shifts. In contrast, a corresponding model with an isotropic third‐order elastic tensor, calibrated to the same data, exhibited a significantly lower sensitivity to the shear strains. These results underscore the importance of considering the anisotropic strain dependence of the dynamic stiffness when studying shales. Interpreting offset‐dependent trends in pre‐stack time‐lapse seismic data, along with geomechanical modelling and an appropriate strain‐dependent rock physics model, can assist in quantifying subsurface strains and stress changes.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13446?TRACK=RSSAudun Bakk, Marcin Duda, Xiyang Xie, Jørn F. Stenebråten, Hong Yan, Colin MacBeth and Rune M. Holt2024-02-21T00:00:00ZJoint inversion for facies and petrophysical properties based on a bi‐level optimization model
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13450?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>In many subsurface studies, facies and petrophysical properties are two important reservoir parameters that are closely correlated. They are routinely used in well interpretation, hydrocarbon reserve calculation and production profile prediction. These two parameters have commonly been determined in two separate tasks because of their mathematical differences (facies are discrete, and petrophysical properties are continuous). However, this is incorrect because facies and petrophysical properties are often strongly correlated. Therefore, we propose a new joint inversion method of facies and petrophysical properties based on a bi‐level optimization model. In the bi‐level optimization model, the upper‐level problem is the petrophysical property inversion while the lower‐level problem can identify the facies and add a facies‐related constraint for the upper‐level optimization. We also develop a new genetic algorithm for the discrete‐continuous inversion problem based on the bi‐level optimization model because the inversion problem usually has multiple local solutions. In addition, rock physics and statistics are combined in the inversion process. A rock physics model is used to establish the basic relationships between the petrophysical and elastic parameters, and the statistical approach is used to describe the intrinsic connection among the multiple reservoir parameters based on well log data. The numerical experiments indicate that the traditional separate prediction method and the new joint inversion method can quickly obtain more accurate results. In the application examples of real data, the inversion results can be matched to the well log data within the limits of the input data resolution, which further verifies the reliability and application potential of this new method.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13450?TRACK=RSSJin Wen, Dinghui Yang, Yuanfeng Cheng, Zhipeng Qu, Hongwei Han, Xingmou Wang, Jianbing Zhu, Xijun He and Fan Bu2024-02-21T00:00:00ZDeep carbonate fault–karst reservoir characterization by multi‐task learning
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13460?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>The carbonate fault–karst reservoir is a special and significant reservoir in the Shunbei area, and the development of the cave has been controlled by strike‐slip faults. Due to the complex subsurface structures, fault–karst reservoir characterization is generally divided into fault and cave detection tasks. The potential spatial relationships between faults and caves might be neglected by using the separate detection scheme. The multi‐task learning network can perform multiple tasks simultaneously and exploit the potential features of training data by using a deep neural network. In this study, we built fault–karst models based on the geological background of the Shunbei area and synthesized fault–karst training data using three‐dimensional point spread function convolution. Then, we developed a multi‐task learning network to learn fault–karst features and detect faults and caves simultaneously. The test result demonstrates that the multi‐task learning network trained by synthetic fault–karst data can effectively identify the faults and caves in field seismic data. The comparisons of the multi‐task learning network, single‐task learning networks and conventional methods demonstrate the importance of spatial relationships between faults and caves and show the superiority of the multi‐task learning network. This technique could significantly assist in the exploration, development and well deployment for an ultra‐deep carbonate reservoir.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13460?TRACK=RSSZheng Zhang, Haiying Li, Zhe Yan, Jiankun Jing and Hanming Gu2024-02-21T00:00:00ZUltra‐resolution surface‐consistent full waveform inversion
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13461?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Full waveform inversion for land seismic data requires the development of specific strategies for modelling the complex response associated to the near surface. Seismic wave propagation is distorted by several effects, such as topographic relief, wavefield scattering, attenuation (often frequency‐dependent) and anisotropy. The modelling of such shallow complexities is often unmanageable by parametric inversions such that data preconditioning and surface‐related corrections are required before tackling the full waveform inversion velocity modelling problem. We developed a unified framework addressing both surface‐consistent corrections and full waveform inversion by using the transmitted portion of the wavefield. The problem of surface‐consistent decomposition is recast in terms of the transmitted wavefield leading to kinematic and dynamic corrections to account for sub‐resolution wave propagation distortions occurring in the near surface weathering layer. Seismic data are deconvolved from the effects of the near surface to better represent the deeper subsurface wavefield propagation. Signal‐to‐noise enhancement is obtained after the surface‐consistent transmission preconditioning by the generation of virtual super gathers reconstructed in the midpoint‐offset sorting domain. An original scheme of 1.5D Laplace–Fourier full waveform inversion, involving 3D radiation and 1D velocity inversion, is then applied for the velocity reconstruction where the amplitude information of the seismic data is fully preserved and utilized. The unified approach of surface‐consistent transmission preconditioning and velocity modelling is demonstrated on the Society of Exploration Geophysicists Advanced Modelling arid model dataset as well as on two complex field datasets containing near surface complexities typical of arid regions. The approach provides the solution of the near surface distortions by performing data preconditioning and velocity inversions in a unified scheme. The surface‐consistent full waveform inversion approach has been utilized for large land seismic projects and represents a robust tool for supporting land seismic imaging.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13461?TRACK=RSSDaniele Colombo, Ernesto Sandoval‐Curiel, Ersan Turkoglu, Diego Rovetta and Apostolos Kontakis2024-02-21T00:00:00ZNumerical simulation of fracking and gas production in shale gas reservoirs
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13462?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>In this work, different stages of gas production in shale reservoirs are modelled. First, hydraulic fracturing is considered by injecting water at high pressures to crack the formation and increase the flow capacity of the reservoir. During the fluid injection, rock properties are modified and water appears in the stimulated area. Then, these changes can be detected through seismic monitoring. Finally, once the fracking stage is completed, the simulation of gas production begins. The simultaneous gas–water flow in the injection and production stages is modelled using the Black‐Oil formulation. Furthermore, a fracture criterion is applied under the hypothesis of constant temperature and constant stress field in this first analysis of the problem. The numerical simulations allow us to analyse the propagation of the fracture and the behaviour of the pore pressure and water saturation in the stimulated area. The advance of the fracturing fluid is delayed in relation to the breakdown of the rock. Besides, the presence of new fractures is detected by applying a poroviscoelastic wave propagation simulator that considers mesoscopic losses induced by heterogeneities in rock and fluids. After the fracture network is created, the injection well becomes a producer, allowing the extraction of gas and the flowback of the injected fluid. The simulated gas flow rates are compared with those obtained by a simplified single‐phase analytical solution used for practical applications, achieving optimum matching results.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13462?TRACK=RSSNaddia D. Arenas Zapata, Gabriela B. Savioli, Juan E. Santos and Patricia M. Gauzellino2024-02-21T00:00:00ZLimits of three‐dimensional target detectability of logging while drilling deep‐sensing electromagnetic measurements from numerical modelling
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13451?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Subsurface energy resources are often found in three‐dimensional and non‐spatially continuous rock formations that exhibit electrical anisotropy. Deep‐sensing tri‐axial borehole electromagnetic measurements are currently being used to detect three‐dimensional fluid‐bearing subsurface formations, but borehole environmental, geometrical and instrument‐design factors, together with measurement noise, constrain their practical range of detection and spatial resolution. By understanding the interplay of the above factors on the detectability and sensitivity of borehole deep electromagnetic measurements, one can potentially quantify the uncertainty of both target spatial location (relative to the well trajectory) and electrical resistivity contrast, thereby improving the certainty of three‐dimensional well navigation in real time. We implement a finite‐volume method to numerically solve Maxwell's equations for three‐dimensional electrically anisotropic heterogeneous rock formations in the calculation of magnetic fields measured with deep‐sensing tri‐axial borehole electromagnetic instruments. The calculated magnetic fields at measurement locations are described as the per cent difference between measurements acquired in rock formations with and without conductive or resistive three‐dimensional targets. We quantify the maximum radial distance of detection from the well trajectory and the spatial sensitivity of a commercially available deep‐sensing electromagnetic instrument with respect to environmental factors and measurement acquisition parameters by assuming that the borehole electromagnetic instrument can reliably detect offset three‐dimensional targets if the corresponding per cent measurement difference exceeds the threshold for measurement noise. Results indicate that commercially available tri‐axial deep‐sensing borehole electromagnetic instruments can achieve maximum detection distances between a quarter of and a full transmitter–receiver spacing. In addition, we show that radial detection distances vary from 0.3 to 2 skin depths depending on the geological environment, that is, target conductivity contrast with respect to the embedding background, electrical anisotropy of the background formation and targets and measurement acquisition parameters, that is, frequency and transmitter–receiver spacings. The above findings are not only important for instrument design and measurement‐acquisition planning but also for the effective implementation of real‐time inversion‐based measurement interpretation procedures.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13451?TRACK=RSSNazanin Jahani, Carlos Torres‐Verdín and Junsheng Hou2024-02-21T00:00:00ZApplication of sensitivity patterns to inversion of magnetotelluric field data in Utah for selecting optimal input
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13455?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Many different magnetotelluric response functions such as the impedance and tipper which are converted from measured electromagnetic fields to remove the effects of natural sources have been proposed and studied. To obtain good inversion results, it is important to know that the inversion results depend on the sensitivity patterns of the magnetotelluric response functions used as the input, indicating parts to which the magnetotelluric responses are sensitive. We introduce how to use the sensitivity patterns to determine optimal input magnetotelluric response functions for interpreting field data. For magnetotelluric field data, we use the data acquired at the Utah Frontier Observatory for Research in Geothermal Energy survey area. The target structure in the Utah Frontier Observatory for Research in Geothermal Energy survey area is associated with geothermal energy, and most of the sites are located to the left of the target structure. Examining the sensitivity patterns of the major magnetotelluric response functions, that is the components of the impedance tensor and tipper vector, for the target anomaly, the pattern of <span class="jp-italic">y‐</span>component of the tipper vector (<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:msub><mml:mi>T</mml:mi><mml:mi>y</mml:mi></mml:msub><mml:annotation encoding="application/x-tex">${T_y}$</mml:annotation></mml:semantics></mml:math></script>) covers the areas where most of the sites are distributed. This means information about the target structure is mainly contained in <script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-2" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>T</mml:mi><mml:mi>y</mml:mi></mml:msub><mml:annotation encoding="application/x-tex">${T_y}$</mml:annotation></mml:semantics></mml:math></script>. To investigate the impact of <script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-3" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>T</mml:mi><mml:mi>y</mml:mi></mml:msub><mml:annotation encoding="application/x-tex">${T_y}$</mml:annotation></mml:semantics></mml:math></script> on inversion of the target anomalous body, we compare the inversion results for four cases that (1) the <span class="jp-italic">xy</span>‐ and <span class="jp-italic">yx</span>‐components of the impedance tensor (<script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-4" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>Z</mml:mi><mml:mrow><mml:mi>x</mml:mi><mml:mi>y</mml:mi></mml:mrow></mml:msub><mml:annotation encoding="application/x-tex">${Z_{xy}}$</mml:annotation></mml:semantics></mml:math></script> and <script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-5" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>Z</mml:mi><mml:mrow><mml:mi>y</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:annotation encoding="application/x-tex">${Z_{yx}}$</mml:annotation></mml:semantics></mml:math></script>) are used; (2) <script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-6" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>Z</mml:mi><mml:mrow><mml:mi>x</mml:mi><mml:mi>y</mml:mi></mml:mrow></mml:msub><mml:annotation encoding="application/x-tex">${Z_{xy}}$</mml:annotation></mml:semantics></mml:math></script>, <script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-7" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>Z</mml:mi><mml:mrow><mml:mi>y</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:annotation encoding="application/x-tex">${Z_{yx}}$</mml:annotation></mml:semantics></mml:math></script> and <script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-8" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>T</mml:mi><mml:mi>y</mml:mi></mml:msub><mml:annotation encoding="application/x-tex">${T_y}$</mml:annotation></mml:semantics></mml:math></script> are used; (3) <script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-9" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>Z</mml:mi><mml:mrow><mml:mi>x</mml:mi><mml:mi>y</mml:mi></mml:mrow></mml:msub><mml:annotation encoding="application/x-tex">${Z_{xy}}$</mml:annotation></mml:semantics></mml:math></script>, <script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-10" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>Z</mml:mi><mml:mrow><mml:mi>y</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:annotation encoding="application/x-tex">${Z_{yx}}$</mml:annotation></mml:semantics></mml:math></script> and <span class="jp-italic">x‐</span>component of the tipper vector (<script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-11" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>T</mml:mi><mml:mi>x</mml:mi></mml:msub><mml:annotation encoding="application/x-tex">${T_x}$</mml:annotation></mml:semantics></mml:math></script>) are applied; and (4) the all components of the impedance tensor (<script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-12" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>Z</mml:mi><mml:mrow><mml:mi>x</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:annotation encoding="application/x-tex">${Z_{xx}}$</mml:annotation></mml:semantics></mml:math></script>, <script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-13" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>Z</mml:mi><mml:mrow><mml:mi>x</mml:mi><mml:mi>y</mml:mi></mml:mrow></mml:msub><mml:annotation encoding="application/x-tex">${Z_{xy}}$</mml:annotation></mml:semantics></mml:math></script>, <script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-14" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>Z</mml:mi><mml:mrow><mml:mi>y</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:annotation encoding="application/x-tex">${Z_{yx}}$</mml:annotation></mml:semantics></mml:math></script> and <script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-15" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>Z</mml:mi><mml:mrow><mml:mi>y</mml:mi><mml:mi>y</mml:mi></mml:mrow></mml:msub><mml:annotation encoding="application/x-tex">${Z_{yy}}$</mml:annotation></mml:semantics></mml:math></script>) are used as the input. This comparison shows that <script type="math/mml" xmlns="http://pub2web.metastore.ingenta.com/ns/"><mml:math display="inline" id="jats-math-16" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:msub><mml:mi>T</mml:mi><mml:mi>y</mml:mi></mml:msub><mml:annotation encoding="application/x-tex">${T_y}$</mml:annotation></mml:semantics></mml:math></script> selected as the optimal input from the sensitivity patterns for this case study contributes to retrieving the target anomaly. Through the case study for the Utah Frontier Observatory for Research in Geothermal Energy site, we show how to design a strategy of selecting optimal magnetotelluric response functions based on the sensitivity patterns to effectively image target structures. Our experiment supports that the sensitivity patterns can be used to increase the reliability of magnetotelluric inversion.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13455?TRACK=RSSJanghwan Uhm, Dong‐Joo Min and Junyeong Heo2024-02-21T00:00:00ZGravity and magnetic exploration applied to iron ore deposits in the Sierra Grande area, Río Negro Province, Argentina
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13458?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>The Ordovician–Devonian Sierra Grande Formation, Río Negro Province, Argentina contains three quarzitic members with two iron horizons. Its South Deposit includes both iron horizons. However, the East Deposit is relatively unknown, lacking information about geometry, depth and reserves. To answer these questions, we apply geophysical methods for the rapid evaluation of the East Deposit, using gravity and magnetic measures. The processing of these data allows the suggestion of two 2D models for calculating thicknesses, angles and depth of the iron horizons. The adjustments between the calculated and the observed curve are less than 6%. One model proposes the existence of the Alfaro iron horizon, and the other one proposes the presence of the Rosales iron horizon at depth. The Bouguer anomaly gravimetric maps allow us to calculate the mineral mass, resulting in 125 million iron tons. Thus, this study allowed us to calculate the thicknesses, angles and depth of both iron horizons, and to adjust and evaluate the mineral reserves with the maximum reliability that potential methods allow when applied to mineral prospecting. These results provide new and valuable information for future mining prospects.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13458?TRACK=RSSMarcos E. Bahía, Leonardo Strazzere, Leonardo Benedini, Daniel A. Gregori and José Kostadinoff2024-02-21T00:00:00ZResearch note: Application of refraction full‐waveform inversion of ocean bottom node data using a squared‐slowness model parameterization
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13454?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Full‐waveform inversion is a wave equation–based imaging technique for obtaining subsurface model parameters by matching modelled with field data. Full‐waveform inversion is often formulated as a local optimization problem in which the model parameterization influences the gradient preconditioner and the convergence rate associated with the full‐waveform inversion objective function. Model parameterization governs the radiation pattern of the so‐called secondary Born source. In this work, we assess model parameterization effects on the estimation of P‐wave velocities using a three‐dimensional acoustic time‐domain full‐waveform inversion procedure. These include the three commonly used parameterization: velocity, slowness and squared slowness. In this context, we consider a field data set from a deepwater Brazilian pre‐salt field using a recently introduced circular shot ocean bottom node acquisition which favours refracted waves. The results reveal that the squared slowness model parameterization provides a satisfactory trade‐off between the reconstruction of the deep pre‐salt target area and convergence rate, saving 50% of runtime compared to the velocity and slowness cases.</p></div>Wed, 21 Feb 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13454?TRACK=RSSSérgio Luiz da Silva, Felipe Costa, Ammir Karsou, Felipe Capuzzo, Roger Moreira, Jorge Lopez and Marco Cetale2024-02-21T00:00:00ZIssue Information
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13482?TRACK=RSS
<div></div>Tue, 30 Jan 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13482?TRACK=RSS2024-01-30T00:00:00ZTemporal dispersion correction for wave‐propagation modelling with a series approach
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13411?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Temporal dispersion correction of second‐order finite‐difference time stepping for numerical wave propagation modelling exploits the fact that the discrete operator is exact but for the wrong frequencies. Mapping recorded traces to the correct frequencies removes the numerical error. Most of the implementations employ forward and inverse Fourier transforms. Here, it is noted that these can be replaced by a series expansion involving higher time derivatives of the data. Its implementation by higher‐order finite differencing can be sensitive to numerical noise, but this can be suppressed by enlarging the stencil. Tests with the finite‐element method on a homogeneous acoustic problem with an exact solution show that the method can achieve the same accuracy as higher‐order time stepping, similar to that obtained with Fourier transforms. The same holds for an inhomogeneous problem with topography where the solution on a very fine mesh is used as reference. The series approach costs less than dispersion correction with the Fourier method and can be used on the fly during the time stepping. It does, however, require a wavelet that is sufficiently many times differentiable in time.</p></div>Tue, 30 Jan 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13411?TRACK=RSSW. A. Mulder2024-01-30T00:00:00ZAutomatic microseismic signal classification for mining safety monitoring using the WaveNet classifier
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13398?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>Microseismic monitoring is a promising method for the safety monitoring of underground mines. However, it is crucial to isolate microseismic signals related to the collapse of a mine from others for successful monitoring because the monitoring system records various signals. Recently, deep learning‐based classification techniques have achieved high performance in such data classification problems. In this context, we develop an automatic signal classification technique using the modified WaveNet classifier. The main characteristic of the WaveNet structure is its ability to extract features at various frequencies from very long time‐series data, and such an advantage makes the WaveNet suitable for seismic data processing. The data imbalance problem coming from the safe condition of the monitoring target is solved by augmenting the training data with those acquired from another mine and employing class weighting. After training, an optimal classifier is chosen considering the loss function, accuracy and <span class="jp-italic">F</span><span class="jp-sub">β</span> score. The optimal classifier shows very high accuracy and excellent performance for the test data prediction. Compared to the random forest model and another one‐dimensional convolutional neural network–based network, the suggested classifier has higher reliability in predicting microseismic signals. Even though the proposed WaveNet model has a much more complex structure than the random forest model, the actual application examples demonstrate that the proposed model achieves high efficiency without any preprocessing. The automatic signal classifier developed in this study can be directly applied to various safety monitoring problems, not only mines, to improve the efficiency and reliability of monitoring systems.</p></div>Tue, 30 Jan 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13398?TRACK=RSSWoochang Choi, Sukjoon Pyun and Dae‐Sung Cheon2024-01-30T00:00:00ZFocal deblending: Marine data processing experiences
https://www.earthdoc.org/content/journals/10.1111/1365-2478.13404?TRACK=RSS
<div><span class="tl-main-part">Abstract</span>
<p>In contrast to conventional acquisition practices, simultaneous source acquisition allows for overlapping wavefields to be recorded. Relaxing the shot schedule in this manner has certain advantages, such as allowing for faster acquisition and/or denser shot sampling. This flexibility usually comes at the cost of an extra step in the processing workflow, where the wavefields are deblended, that is, separated. An inversion‐type algorithm for deblending, based on the focal transform, is investigated. The focal transform uses an approximate velocity model to focus seismic data. The combination of focusing with sparsity constraints is used to suppress blending noise in the deblended wavefield.</p>
<p>The focal transform can be defined in different ways to better match the spatial sampling of different types of marine surveys. To avoid solving a large inverse problem, involving a large part of the survey simultaneously, the input data can be split into sub‐sets that are processed independently. We discuss the formation of such sub‐sets for ocean bottom node and streamer‐type acquisitions. Two deblending experiments are then carried out. The first is on numerically blended ocean bottom node field data. The second is on field‐blended towed streamer data with a challenging signal overlap. The latter experiment is repeated using curvelet‐based deblending for comparison purposes, showing the virtues of the focal deblending process. Several challenges of basing deblending around the focal transform are discussed as well as some suggestions for improved implementations.</p></div>Tue, 30 Jan 2024 00:00:00 GMThttps://www.earthdoc.org/content/journals/10.1111/1365-2478.13404?TRACK=RSSApostolos Kontakis and Dirk Jacob Verschuur2024-01-30T00:00:00Z