... The multiples in seismic data cause distortions of primary reflections from the target layer, which will affect the accuracy of the final imaging and hence reduce the reliability of seismic interpretation. In this paper, we describe an efficient method that attenuates multiples from post-stack...

... Five-dimensional (5D) seismic data reconstruction is a crucial step to improve seismic imaging. We introduce a deep complex-valued neural network for constructing an unsupervised frequency–space domain deep learning framework to reconstruct each frequency component of 5D seismic data. We apply...

... The construction of subsurface velocity models and reservoir characterization depend heavily on the resolution of seismic interpretation. In the field of seismic exploration, combining multiple stacks, e.g. multi-angle, multi-azimuth, multi-frequency, of seismic data is becoming more and more...

... study introduces a self-supervised deep-learning architecture to complete the interpolation method. The dataset comprises small-scale marine seismic survey data acquired from Youngil Bay, Pohang, near the East Sea of the Korean Peninsula. We randomly extract 2D planes from 3D seismic data to generate...

... pitfalls in salt tectonic interpretation from legacy seismic data has led to the misidentification of these features in the past. We also demonstrate the importance of outcrop analogs in building a holistic understanding of megaflap geometries and supporting seismic structural interpretations. This has...

... transformer initial model neural network seismic data geology information reservoir characterization interpolation geophysics applied geoscience & energy 10 acquisition american association architecture deep learning yangkang chen 1 Pushing the limit of 5D interpolation using deep learning...

... techniques. When applied to actual seismic data analysis, this method effectively indicates the distribution relationship between faults and karst caves, reduces manual interpretation costs, and provides guiding suggestions for oil and gas exploration. sedimentary rock deep learning machine learning...

... Estimating the seismic impedance from the band-limited seismic data has proven to be a difficult task, especially due to the absence of low frequencies. This situation can be mitigated using the information obtained from well logs which can be used as a constraint. This prior information can...

... The near surface layer causes a strong absorption and attenuation effect on seismic waves, which seriously affects the resolution of seismic data, distorts seismic waveforms, and affects seismic imaging and reservoir identification. In order to eliminate the near-surface absorption...

.... The proposed method has potent compatibility for embracing diverse datasets and a strong ability to model complex dynamics and interactions between multiple datasets. Through an example application on field passive seismic data at the Utah FORGE site, this method demonstrates its potential to capitalize...

... The amplitude versus azimuth (AVAZ) method based on the variation of amplitude with azimuth has shown good predictive performance for small-scale fractures. However, there are few fracture prediction methods based on post-stack seismic data (Bakulin et al., 2000; Bachrach et al., 2009; Barone...

... when elastic effects are small, obtaining reliable amplitudes from crosswell seismic is difficult. We apply different imaging algorithms to real and synthetic crosswell seismic data, and show that generalized radon transform (GRT) migration provides the best imaging, with the highest resolution...

... interpretation alkhalifah petroleum geology machine learning exploration geophysicist seismic data inversion pitfall ravasi Fourth International Meeting for Applied Geoscience & Energy 10.1190/image2024-4101010.1 © 2024 Society of Exploration Geophysicists and the American Association of Petroleum...

... geophysicist seismic data offshore trinidad fracture characterization normal fault columbus basin applied geoscience & energy 10 relay ramp interpretation connectivity fault system offshore trinidad summary An integrated approach to subsurface uncertainty and risk analysis from offshore...

... We demonstrate the application of t-x-p domain decomposition of seismic data to remove residual noise from post-stack images. The t-p-x domain is an extension of the t-x domain by local slope decomposition. We apply the strategy to a dataset with multiple residual events from a steeply dipping...

... With the deepening of exploration and development of oil and gas reservoirs, optical fibers sensing systems have been applied to the acquisition of land 3C seismic and borehole-surface joint seismic data, which has promoted the application of optical fiber sensing techniques in geophysis...

... Traditionally, seismic post-stack impedance inversion is implemented using linear optimization algorithms. Recently, supervised neural networks have been successfully used to estimate the impedance from seismic data. In this paper, we propose to compute seismic impedance using physicsinformed...

... guided semisupervised (GPKGS) deep learning framework for amplitude-versus-angle (AVA) inversion. Based on physical priori knowledge, the prestack seismic data are decoupled into prestack seismic attribute data of the reservoir parameters. Meanwhile, according to the prestack seismic attribute data...

... This paper presents an innovative approach to generating structured labels from 4D time-lapse seismic data, a crucial step in enhancing CO 2 sequestration monitoring and predictive modeling. Utilizing local orthogonal weights, shape regularization techniques, and advanced computer vision...

... information of the dictionary and representation coefficients, enhancing the flexibility of the convolutional dictionary and coefficients. The advantages of this approach include: leveraging convolutional sparse coding to decompose images fully, considering the local correlation in seismic data, which...