Geophysical Prospecting最新文献

{"title":"Accelerating target-oriented multi-parameter elastic full-waveform uncertainty estimation by reciprocity","authors":"W. A. Mulder,&nbsp;B. N. Kuvshinov","doi":"10.1111/1365-2478.13650","DOIUrl":"https://doi.org/10.1111/1365-2478.13650","url":null,"abstract":"<p>The accuracy of a model obtained by multi-parameter full-waveform inversion can be estimated by analysing the sensitivity of the data to perturbations of the model parameters in selected subsurface points. Each perturbation requires the computation of the seismic response in the form of Born scattering data for a typically very large number of shots, making the method time consuming. The computational cost can be significantly reduced by placing sources of different types at the Born scatterer, the point where the subsurface parameters are perturbed. Instead of modelling each shot separately, reciprocity relations provide the wavefields from the shot positions to the scatter point in terms of wavefields from the scatterer to the shot positions. In this way, the Born scattering data from a single point in the isotropic elastic case for a marine acquisition with pressure sources and receivers can be expressed in terms of the wavefields for force and moment tensor sources located at the scatterer and only a small number of forward runs are required. A two-dimensional example illustrates how the result can be used to determine the Hessian and local relative covariance matrix for the model parameters at the scatterer at the cost of five forward simulations. In three dimensions, that would be nine.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"38-48"},"PeriodicalIF":1.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.13650","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing porosity prediction: Integrating seismic inversion utilizing sparse layer reflectivity, and particle swarm optimization with radial basis function neural networks","authors":"Ravi Kant,&nbsp;Brijesh Kumar,&nbsp;Ajay P. Singh,&nbsp;G. Hema,&nbsp;S. P. Maurya,&nbsp;Raghav Singh,&nbsp;K. H. Singh,&nbsp;Piyush Sarkar","doi":"10.1111/1365-2478.13651","DOIUrl":"https://doi.org/10.1111/1365-2478.13651","url":null,"abstract":"<p>Seismic inversion, a crucial process in reservoir characterization, gains prominence in overcoming challenges associated with traditional methods, particularly in exploring deeper reservoirs. In this present study, we propose an inversion approach based on modern techniques like sparse layer reflectivity and particle swarm optimization to obtain inverted impedance. The proposed sparse layer reflectivity and particle swarm optimization techniques effectively minimize the error between recorded seismic reflection data and synthetic seismic data. This reduction in error facilitates accurate prediction of subsurface parameters, enabling comprehensive reservoir characterization. The inverted impedance obtained from both methods serves as a foundation for predicting porosity, utilizing a radial basis function neural network across the entire seismic volume. The study identifies a significant porosity zone (&gt;20%) with a lower acoustic impedance of 6000–8500 m/s g cm³, interpreted as a sand channel or reservoir zone. This anomaly, between 1045 and 1065 ms two-way travel time, provides high-resolution insights into the subsurface. The particle swarm optimization algorithm shows higher correlation results, with 0.98 for impedance and 0.73 for porosity, compared to sparse layer reflectivity's 0.81 for impedance and 0.65 for porosity at well locations. Additionally, particle swarm optimization provides high-resolution subsurface insights near well location and across a broader spatial range. This suggests particle swarm optimization's superior potential for delivering higher resolution outcomes compared to sparse layer reflectivity.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"49-66"},"PeriodicalIF":1.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Poynting and polarization vectors mixed imaging condition of source time-reversal imaging","authors":"Nan Hu,&nbsp;Hao Li,&nbsp;Yunsheng Zhao,&nbsp;Yongming Lu,&nbsp;Tao Lei,&nbsp;Mei He,&nbsp;Xingda Jiang,&nbsp;Wei Zhang","doi":"10.1111/1365-2478.13649","DOIUrl":"https://doi.org/10.1111/1365-2478.13649","url":null,"abstract":"<p>Source time-reversal imaging based on wave equation theory can achieve high-precision source location in complex geological models. For the time-reversal imaging method, the imaging condition is critical to the location accuracy and imaging resolution. The most commonly used imaging condition in time-reversal imaging is the scalar cross correlation imaging condition. However, scalar cross-correlation imaging condition removes the directional information of the wavefield through modulus operations to avoid the direct dot product of mutually orthogonal P- and S-waves, preventing the imaging condition from leveraging the wavefield propagation direction to suppress imaging artefacts. We previously tackled this issue by substituting the imaging wavefield with the energy current density vectors of the decoupled wavefield, albeit at the cost of increased computational and storage demands. To balance artifact suppression with reduced computational and memory overhead, this work introduces the Poynting and polarization vectors mixed imaging condition. Poynting and polarization vectors mixed imaging condition utilizes the polarization and propagation direction information of the wavefield by directly dot multiplying the undecoupled velocity polarization vector with the Poynting vector, eliminating the need for P- and S-wave decoupling or additional memory. Compared with scalar cross-correlation imaging condition, this imaging condition can accurately image data with lower signal-to-noise ratios. Its performance is generally consistent with previous work but offers higher computational efficiency and lower memory usage. Synthetic data tests on the half-space model and the three-dimensional Marmousi model demonstrate the effectiveness of this method in suppressing imaging artefacts, as well as its efficiency and ease of implementation.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 4","pages":"1037-1059"},"PeriodicalIF":1.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Velocity model building via combining seismic slope tomography and supervised deep learning","authors":"Yao Huang,&nbsp;Huachen Yang,&nbsp;Jianzhong Zhang","doi":"10.1111/1365-2478.13652","DOIUrl":"https://doi.org/10.1111/1365-2478.13652","url":null,"abstract":"<p>Seismic slope tomography is an effective method to build macro velocity model. In order to improve the accuracy and resolution of the slope tomography, we proposed a novel approach that combines slope tomography with supervised deep learning. First, the slope tomography is used to obtain the macro velocity model and the positions of reflection points. Subsequently, the slope tomographic model, positions of reflection points and the corresponding observed traveltimes are used as inputs simultaneously for a neural network, whereas the actual velocity models are used as the labels. Through training the neural network with sufficient samples, the mapping from the inputs to the real velocity model is established. The neural network learns the background velocity of the real model from the smooth tomographic model, the velocity details from the traveltimes and the formation interface information from the positions of reflection points. Consequently, a high-accuracy and high-resolution velocity model is obtained on the basis of the slope tomographic model. Both tests on synthetic seismic data and applications to field seismic data demonstrate the effectiveness of the proposed method.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"67-79"},"PeriodicalIF":1.8,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel strategy for simultaneous super-resolution reconstruction and denoising of post-stack seismic profile","authors":"Wenshuo Yu,&nbsp;Shiqi Dong,&nbsp;Shaoping Lu,&nbsp;Xintong Dong","doi":"10.1111/1365-2478.13646","DOIUrl":"https://doi.org/10.1111/1365-2478.13646","url":null,"abstract":"<p>Post-stack seismic profiles are images reflecting geological structures which provide a critical foundation for understanding the distribution of oil and gas resources. However, due to the limitations of seismic acquisition equipment and data collecting geometry, the post-stack profiles suffer from low resolution and strong noise issues, which severely affects subsequent seismic interpretation. To better enhance the spatial resolution and signal-to-noise ratio of post-seismic profiles, a multi-scale attention encoder–decoder network based on generative adversarial network is proposed. This method improves the resolution of post-stack profiles and effectively suppresses noises and recovers weak signals as well. A multi-scale residual module is proposed to extract geological features under different receptive fields. At the same time, an attention module is designed to further guide the network to focus on important feature information. Additionally, to better recover the global and local information of post-stack profiles, an adversarial network based on a Markov discriminator is proposed. Finally, by introducing an edge information preservation loss function, the conventional loss function of the Generative Adversarial Network is improved, which enables better recovery of the edge information of the original post-stack profiles. Experimental results on simulated and field post-stack profiles demonstrate that the proposed multi-scale attention encoder–decoder network based on generative adversarial network method outperforms two advanced convolutional neural network-based methods in noise suppression and weak signal recovery. Furthermore, the profiles reconstructed by the multi-scale attention encoder–decoder network based on generative adversarial network method preserve more geological structures.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"96-112"},"PeriodicalIF":1.8,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Broadband seismic data acquisition and processing of iron oxide deposits in Blötberget, Sweden","authors":"Lea Gyger,&nbsp;Alireza Malehmir,&nbsp;Musa Manzi,&nbsp;Lilas Vivin,&nbsp;Jean Lépine,&nbsp;Ayse Kaslilar,&nbsp;Oleg Valishin,&nbsp;Paul Marsden,&nbsp;Ronne Hamerslag","doi":"10.1111/1365-2478.13648","DOIUrl":"https://doi.org/10.1111/1365-2478.13648","url":null,"abstract":"<p>In June 2022, an innovative seismic survey was conducted in Blötberget, central Sweden, to evaluate the effectiveness of employing both a broadband seismic source and broadband receivers for mineral exploration in a challenging hardrock setting. The Blötberget mine hosts high-quality iron oxides, predominantly magnetite and hematite, sometimes enriched with apatite. These deposits comprise 10–50 m thick sheet-like horizons with a moderate eastward dip (<span></span><math>\u0000 <semantics>\u0000 <mo>∼</mo>\u0000 <annotation>$sim$</annotation>\u0000 </semantics></math>45°) along an NNE-trending zone. The survey employed a combination of co-located micro-electromechanical sensors, three-component recorders, surface and borehole distributed acoustic sensing, along with a 77-kN broadband seismic vibrator operating with 2–200 Hz linear sweeps. A tailored processing workflow was applied to preserve the broadband nature of the recorded data, and a one-dimensional velocity model was derived from the borehole distributed acoustic sensing data for migration and time-to-depth conversion purposes. Compared to the previous seismic surveys, the resulting seismic cross section reveals several well-defined reflections with improved resolution. Notably, a reflection intersecting the main deposits at a depth of approximately 1200 m exhibits a distinct polarity reversal relative to the reflection from the mineralization, providing further evidence for its interpretation as originating from a fault zone. Shallow reflections align with geological boundaries and partially coincide with weak magnetic anomalies. Additional reflections were revealed underneath the known mineralization on both sides of the fault zone and may suggest the presence of potential additional resources. The delineation of these reflections and the fault zone is critical for future mine planning and development in the region. This case study underscores the potential of broadband data in achieving high-resolution subsurface imaging in hardrock environment and its pivotal role in mineral resource assessment processes.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"80-95"},"PeriodicalIF":1.8,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.13648","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-resolution velocity model building with fault control: Methods and applications","authors":"Hua Xue,&nbsp;Min Du,&nbsp;Wenbin Jiang,&nbsp;Bin Liu,&nbsp;Xi Chen,&nbsp;Li Yang,&nbsp;Yan Li,&nbsp;Baojin Zhang,&nbsp;Ruwei Zhang,&nbsp;Yuan Gu,&nbsp;Yong Yang,&nbsp;Gaowen He,&nbsp;Xiaoming Sun","doi":"10.1111/1365-2478.13645","DOIUrl":"https://doi.org/10.1111/1365-2478.13645","url":null,"abstract":"<p>In seismic exploration, particularly within the domain of oil and gas reservoirs, the accurate imaging of complex fault blocks and the identification of structural traps are important. Geological risk factors, including the implementation of structural traps, reservoir delineation, and precise target drilling, require immediate attention in practical exploration. Addressing these factors involves two primary challenges: ensuring imaging accuracy and minimizing structural distortions. This study introduces a high-resolution velocity modelling technique with fault control, specifically developed to mitigate misties between seismic image and well-log data and improve the accuracy of seismic depth imaging and well depth correlation. The method offers a targeted solution to the challenges of implementing structural traps, delineating reservoirs and executing precise drilling operations. By incorporating fault control, it accounts for the structural complexity of subsurface media, enabling an accurate inversion of velocity variations across fault blocks. This approach ensures that velocity models, constrained by geological and structural models, exhibit a high degree of consistency. Utilizing fault-controlled travel time inversion, the method resolves mistier between seismic imaging and well-log data, guaranteeing the precision of velocity models and imaging. The methodology provides reliable seismic data for target evaluation, effectively reducing exploration risks and improving the accuracy of velocity modelling.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 4","pages":"1027-1036"},"PeriodicalIF":1.8,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based inversion with discrete cosine transform discretization for two-dimensional basement relief imaging of sedimentary basins from observed gravity anomalies","authors":"Arka Roy,&nbsp;Yunus Levent Ekinci,&nbsp;Çağlayan Balkaya,&nbsp;Hanbing Ai","doi":"10.1111/1365-2478.13647","DOIUrl":"https://doi.org/10.1111/1365-2478.13647","url":null,"abstract":"<p>Sedimentary basins, integral to Earth's geological history and energy resource exploration, undergo complex changes driven by sedimentation, subsidence and geological processes. Gravity anomaly inversion is a crucial technique offering insights into subsurface structures and density variations. Our study addresses the challenge of complex subsurface structure assessment by leveraging deep neural networks to invert observed gravity anomalies. Optimization approaches traditionally incorporate known density distributions obtained from borehole data or geological logging for inverting basement depth in sedimentary basins using observed gravity anomalies. Our study explores the application of deep neural networks in accurate architectural assessment of sedimentary basins and demonstrates their significance in mineral and hydrocarbon exploration. Recent years have witnessed a surge in the use of machine learning in geophysics, with deep learning models playing a pivotal role. Integrating deep neural networks, such as the feedforward neural networks, has revolutionized subsurface density distribution and basement depth estimation. This study introduces a deep neural network specifically tailored for inverting observed gravity anomalies to estimate two-dimensional basement relief topographies in sedimentary basins. To enhance computational efficiency, a one-dimensional discrete cosine transform based discretization approach is employed. Synthetic data, generated using non-Gaussian fractals, compensates for the scarcity of true datasets for training the deep neural network model. The algorithm's robustness is validated through noise introduction with comparisons against an efficient and traditional global optimization-based approach. Gravity anomalies of real sedimentary basins further validate the algorithm's efficacy, establishing it as a promising methodology for accurate and efficient subsurface imaging in geological exploration.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"113-129"},"PeriodicalIF":1.8,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Amplitude versus offset attribute inversion method for characterizing gas hydrate: Insights from high resolution seismic imaging and drilling results in the Shenhu area, South China Sea","authors":"Xudong Zhang,&nbsp;Fanxiang Zeng,&nbsp;Wenbin Jiang,&nbsp;Dajiang Meng,&nbsp;Sheng Yan","doi":"10.1111/1365-2478.13641","DOIUrl":"https://doi.org/10.1111/1365-2478.13641","url":null,"abstract":"<p>Amplitude versus offset attribute inversion primarily utilizes the change in amplitude with offset to extract lithologic information of the reservoir. We performed high resolution imaging using three-dimensional seismic data and simulated four different models of gas hydrate and free gas to optimize the selection of sensitive attributes for gas hydrate characterization. We optimized the selection of sensitive attributes for gas hydrate characterization. Our research identified the gradient parameter <i>G</i> as a highly sensitive attribute for characterizing gas hydrate reservoirs. By comparing theoretical models with drilling site data, we predicted the saturation variations of gas hydrate based on the amplitude of <i>G</i> and summarized the amplitude versus offset characteristics and sensitive attributes of gas hydrate at different saturation levels. The results offer valuable insights for identification of gas hydrate in seismic data and provide a reference for their characterization.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 4","pages":"1008-1026"},"PeriodicalIF":1.8,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytical formulas for geometrical factor and sensitivity for long electrodes","authors":"S. L. Butler","doi":"10.1111/1365-2478.13644","DOIUrl":"https://doi.org/10.1111/1365-2478.13644","url":null,"abstract":"<p>In the electrical resistivity method, electrodes are usually modelled as point current sources and point voltage measurements. If the burial depth of the electrode is significant compared with the spacing between electrodes, this point approximation may not be accurate. Common situations employing long electrodes include the use of metal-cased boreholes as electrodes and small-scale, high-resolution environmental, engineering and archaeological surveys where electrode spacings may be very small. In this contribution, I present analytical expressions for the mutual resistance between long electrodes modelled as line current sources. Mutual resistances are then used to calculate geometrical factors. Additionally, I present an expression for the current density and use it to derive an analytical expression for the sensitivity of electrode arrays with long electrodes. The sensitivity is, in turn, used to calculate the mean depth and position which can be used as estimates of depth and position of investigation and as pseudosection plot points. Example calculations using the geometrical factor, sensitivity and mean depth are shown, and comparisons are made with simulations and lab-scale experiments.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"130-141"},"PeriodicalIF":1.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}