... machine learning exploration geophysicist saturation noise Bayesian RockAVO: direct petrophysical inversion with Hierarchical Conditional GANs Miguel Corrales, Muhammad Izzatullah, Matteo Ravasi, and Hussein Hoteit King Abdullah University of Science and Technology (KAUST), Saudi Arabia SUMMARY...

... Estimating shallow velocity from land seismic data is challenging due to severe noises, which are largely caused by complexities in the near-surface weathering layers. One such complexity arises from velocity reversals, which imposes challenges for conventional methods to estimate near surface...

... Designing seismic acquisition is a blend of science and art, especially in a desert environment with low signal-to-noise ratios (SNR) of -20 to -60 dB. Basic design driven by the square-root model may be overly simplistic. At the same time, conventional numerical feasibility studies are lengthy...

... As field data applications of FWI increase, dealing with both random and coherent noise in seismic data, and the artifacts they create in FWI models, becomes increasingly important; noise suppression or estimation is also increasingly important when we transition to elastic multiparameter...

... & gas international applied geoscience noise energy 10 american association mexico figure dip guided selective stacking society procedure contribution Application of post-stack image enhancement with dip-guided selective stacking in southern Gulf of Mexico Janet Ravelo-Cervantes, Ricardo...

... associated with wide-angle inversions: the advantage is that the inversion becomes more stable in the presence of noise. One of the disadvantages is that extracting the higher-angle traces from offset gathers can be problematic. This paper introduces the concepts of p-gathers and AVP inversion. P-gathers...

... Seismic data usually contain various types of noise. Noise comes from signal collection environment, equipment (current noise), micro seismic, etc. Noise reduces the signal-to-noise ratio and resolution of seismic records, and affects the accuracy of seismic processing and stratigraphic...

... Noise suppression is always a vital procedure in seismic data processing. Under the Gaussian distribution assumption, conventional sparsity-based methods are highly practical and effectual in dealing with random noise. Nevertheless, the field data are always contaminated by erratic noise...

... Self-supervised procedures offer an appealing alternative to supervised denoising techniques that require noisy-clean pairs of training data. However, the capabilities of self-supervised denoising procedures are often limited by the requirement that noise cannot be predicted directly from...

... Modeled and recorded seismic data is contaminated by noise due to a variety of factors including geophones recording ambient noise unrelated to seismic exploration, equipment malfunction and limitations, and restrictive modeling assumptions. The level of noise in seismic data is not known...

... estimation, with satisfactory results. upstream oil & gas artificial intelligence deep learning neural network machine learning storseismic reservoir characterization noise alkhalifah frequency extrapolation estimation applied geoscience arxiv preprint arxiv application first-break...

... Blind-spot networks have been shown to be natural noise suppressors under the assumption that noise is unpredictable based on the information fed into the network during training. Trained in a self-supervised manner, such approaches only utilise the original raw data to determine to remove...

... are developed based on the azimuthal difference in proposed P-wave reflectivity equation. Numerical results illustrate the feasibilities of three methods with no noise and illustrate that three methods can provide reasonable inverted stress results at relatively large incidence angles but show huge bias...

.... Challenges associated with simultaneous-source shooting, acquisition perturbations, and shear-on-vertical noise are addressed using a sparsity-promoting deblending scheme, a novel linearized travel time inversion, and a curvelet-domain shear-noise suppression flow, respectively. When these data are imaged...

... signals and ESP related noise were recorded by fiber optic cables installed inside the wellbore. In this paper, we compare DAS VSP data recorded with and without the ESP running and perform an analysis of the characteristics of flow noise activated by the ESP. We also use a machine learning (ML) algorithm...

... We present the processing workflow of estimating stable, good-quality Rayleigh surface waves from ambient noise recorded by the Distributed Acoustic Sensing (DAS) dark fiber array inside the city. Our example concerns a 660-m long telecom fiber line as a part of the Penn State Fiber-Optic...

... Seismic data is contaminated by random noise, which decreases the accuracy of subsequent seismic data processing. Therefore, random noise attenuation is an important step to improve the signal-to-noise ratio (SNR) of seismic data. We design a Deep Image Prior (DIP) network architecture based...

... Plateau to record ambient noise for a week. The DAS interrogator is specialized by using a 12V-DC battery for power supply due to the power limitation in the field. We show the analysis of four-hour data of a 0.66km-long fiber optical cable using seismic interferometry of ambient noise and multichannel...

... refractions, and the output is the same traces of data with enhanced refractions. Different from noise reduction, this approach intends to focus on refraction signals, similar to the super-virtual interferometry. The training data are selected from field data collected in Daqing oilfield, China. We test...

... reservoir characterization international exploration geophysicist geophysics upstream oil & gas twi odcig application waveform inversion tomographic waveform inversion noise wavefield applied geoscience expanded abstract stanford university Tomographic waveform inversion (TWI) applied...