Showing papers in "Geophysics in 2019"
TL;DR: Multiple field examples indicate that the neural network (trained by only synthetic data sets) can predict faults from 3D seismic images much more accurately and efficiently than conventional methods.
Abstract: Delineating faults from seismic images is a key step for seismic structural interpretation, reservoir characterization, and well placement. In conventional methods, faults are considered as...
461 citations
TL;DR: A novel method based on the supervised deep fully convolutional neural network (FCN) for velocity-model building (VMB) directly from raw seismograms is investigated, showing promising performances in comparison with conventional FWI even when the input data are in more realistic scenarios.
Abstract: Seismic velocity is one of the most important parameters used in seismic exploration. Accurate velocity models are the key prerequisites for reverse time migration and other high-resolution...
313 citations
TL;DR: Results indicate that the predictions of the trained network are susceptible to facies proportions, the rock-physics model, and source-wavelet parameters used in the training data set.
Abstract: We have addressed the geophysical problem of obtaining an elastic model of the subsurface from recorded normal-incidence seismic data using convolutional neural networks (CNNs). We train th...
233 citations
TL;DR: Deep-learning-based approaches for seismic data antialiasing interpolation are used, which could extract deeper features of the training data in a nonlinear way by self-learning and avoid linear events, sparsity, and low-rank assumptions of the traditional interpolation methods.
Abstract: Seismic data interpolation is a longstanding issue. Most current methods are only suitable for randomly missing cases. To deal with regularly missing cases, an antialiasing strategy should ...
216 citations
TL;DR: In this article, low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to reliable subsurface properties, however, it is challenging to acquire field data with an appropria...
Abstract: Low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to reliable subsurface properties. However, it is challenging to acquire field data with an appropria...
119 citations
TL;DR: In this article, a new approach to record hydraulic fracturing operations in unconventional reservoirs is proposed, which uses geophones located either at the surface or in the adjacent wellbores.
Abstract: Hydraulic fracturing operations in unconventional reservoirs are typically monitored using geophones located either at the surface or in the adjacent wellbores. A new approach to record hyd...
89 citations
TL;DR: In this article, a regularized elastic full-wave waveform was developed to obtain high-resolution models of the earth, especially around the reservoir, which is crucial to properly image and interpret the subsurface.
Abstract: Obtaining high-resolution models of the earth, especially around the reservoir, is crucial to properly image and interpret the subsurface. We have developed a regularized elastic full-wavef...
85 citations
TL;DR: In this article, a new wave of experiments and solutions to solve geophysical problems in the oil and gas industry is generated by advances in machine learning and its applications in various sectors.
Abstract: Recent advances in machine learning and its applications in various sectors are generating a new wave of experiments and solutions to solve geophysical problems in the oil and gas industry...
83 citations
TL;DR: Prestack acoustic full-waveform inversion (FWI) can provide longwavelength components of the P-wave velocity by using low frequencies and long-offset direct/diving/refracted waves as mentioned in this paper.
Abstract: Prestack acoustic full-waveform inversion (FWI) can provide long-wavelength components of the P-wave velocity by using low frequencies and long-offset direct/diving/refracted waves, which c...
68 citations
TL;DR: This work has developed a novel workflow to automatically pick the first arrival of microseismics by using a state-of-the art pixel-wise convolutional image segmentation method and predicted first-arrival result obtained is superior to the result obtained by using the traditional method of short-term average and longterm average.
Abstract: Microseismic imaging plays an important role in hydraulic fracture detection, and the first-arrival picking of microseismic events is the bedrock of microseismic imaging. Manual picking is ...
66 citations
TL;DR: A three-stage workflow to remove the high-frequency motion-induced noise using the wavelet neural network (WNN) is developed, and the results provide a strong data foundation for the subsequent processing procedures.
Abstract: In helicopter-borne transient electromagnetic (HTEM) signal processing, removal of motion-induced noise is one of the most important steps. A special type of short-term noise, which could b...
TL;DR: Permeability is a critical parameter for understanding subsurface fluid flow behavior, managing reservoirs, enhancing hydrocarbon recovery, and sequestering carbon dioxide as mentioned in this paper, and it is the most common parameter for underground fluid flow.
Abstract: Permeability is a critical parameter for understanding subsurface fluid flow behavior, managing reservoirs, enhancing hydrocarbon recovery, and sequestering carbon dioxide. In general, perm...
TL;DR: Reparameterization of the initial velocity model is performed, by the weights in a convolutional neural network (CNN), to automatically capture the salient features in the initial model, as a priori information.
Abstract: Most current full-waveform inversion (FWI) algorithms minimize the data residuals to estimate a velocity model based on the assumption that the updated model is the sum of a background mode...
TL;DR: This work has developed a dip-oriented dictionary learning method, which incorporates an estimation of the dip field into the selection procedure of training patches, and applies a curvelet-transform noise reduction method to remove some fine-scale components that presumably contain mostly random noise.
Abstract: In recent years, sparse representation is seeing increasing application to fundamental signal and image-processing tasks. In sparse representation, a signal can be expressed as a linear com...
TL;DR: In this article, the authors developed an efficient formulation based on a time-based time-viscoelastic full-waveform inversion (TWCIN) algorithm for current acquisition deployment at the crustal scale.
Abstract: Viscoelastic full-waveform inversion is recognized as a challenging task for current acquisition deployment at the crustal scale. We have developed an efficient formulation based on a time-...
TL;DR: In this article, an augmented Lagrangian method equipped with operator splitting (iteratively refined WRI [IR-WRI] was proposed to solve the tuning issue of the penalty parameter.
Abstract: Full-waveform inversion (FWI) is an iterative nonlinear waveform matching procedure subject to wave-equation constraint. FWI is highly nonlinear when the wave-equation constraint is enforced at each iteration. To mitigate nonlinearity, wavefield-reconstruction inversion (WRI) expands the search space by relaxing the wave-equation constraint with a penalty method. The pitfall of this approach resides in the tuning of the penalty parameter because increasing values should be used to foster data fitting during early iterations while progressively enforcing the wave-equation constraint during late iterations. However, large values of the penalty parameter lead to ill-conditioned problems. Here, this tuning issue is solved by replacing the penalty method by an augmented Lagrangian method equipped with operator splitting (iteratively refined WRI [IR-WRI]). It is shown that IR-WRI is similar to a penalty method in which data and sources are updated at each iteration by the running sum of the data and source residuals of previous iterations. Moreover, the alternating direction strategy exploits the bilinearity of the wave-equation constraint to linearize the subsurface model estimation around the reconstructed wavefield. Accordingly, the original nonlinear FWI is decomposed into a sequence of two linear subproblems, the optimization variable of one subproblem being passed as a passive variable for the next subproblem. The convergence of WRI and IR-WRI is first compared with a simple transmission experiment, which lies in the linear regime of FWI. Under the same conditions, IR-WRI converges to a more accurate minimizer with a smaller number of iterations than WRI. More realistic case studies performed with the Marmousi II and the BP salt models indicate the resilience of IR-WRI to cycle skipping and noise, as well as its ability to reconstruct with high-fidelity, large-contrast salt bodies and subsalt structures starting the inversion from crude initial models and a 3 Hz starting frequency.
TL;DR: An automatable and versatile framework for the separation of interfering wavefields through a sequence of coherent data summation and subtraction is proposed, which specifically targets reflected contributions, which are normally favored in automated summation schemes, and adaptively subtracts the resulting reflection stack from the input data.
Abstract: Diffractions encode subwavelength information and superior illumination but are weak in amplitude and strongly interfere with the more dominant reflected wavefield. Accordingly, the success...
TL;DR: In this article, the authors present a forward forward model for solving inverse problems in exploration seismology and show that it is often not possible to afford being physically or numerically accurate.
Abstract: Accurate forward modeling is essential for solving inverse problems in exploration seismology. Unfortunately, it is often not possible to afford being physically or numerically accurate. To...
CGG1
TL;DR: The full waveform inversion (FWI) was proposed by Lailly and Tarantola in the 1980s and is considered to be the most promising data-driven tool for automatically building velocity models as discussed by the authors.
Abstract: Full-waveform inversion (FWI), proposed by Lailly and Tarantola in the 1980s, is considered to be the most promising data-driven tool for automatically building velocity models. Many successful exa...
TL;DR: There is a growing need for detailed investigation of the top 30 to 50 meters of the subsurface, which is critical for infrastructure, water supply, aquifer storage and recovery, farming, waste dep... as mentioned in this paper.
Abstract: There is a growing need for detailed investigation of the top 30–50 m of the subsurface, which is critical for infrastructure, water supply, aquifer storage and recovery, farming, waste dep...
TL;DR: This work uses the encoder-decoder convolutional neural network (CNN) to detect the “ objects” contained in the seismic traces and gives a unique training label to the time window of seismic traces bounded by two manually picked horizons.
Abstract: The seismic horizon is a critical input for the structure and stratigraphy modeling of reservoirs. It is extremely hard to automatically obtain an accurate horizon interpretation for seismi...
TL;DR: In this paper, the authors applied time domain 2D fullwaveform inversion (FWI) to detect a known 10m deep wood-framed tunnel at Yuma Proving Ground, Arizona.
Abstract: We have applied time domain 2D full-waveform inversion (FWI) to detect a known 10 m deep wood-framed tunnel at Yuma Proving Ground, Arizona. The acquired seismic data consist of a series of...
TL;DR: In this article, the authors iteratively update the subsurface model by minimizi cation of the full waveform inversion (FWI) problem by iteratively updating the model.
Abstract: Full-waveform inversion (FWI) promises a high-resolution model of the earth. It is, however, a highly nonlinear inverse problem; thus, we iteratively update the subsurface model by minimizi...
TL;DR: U-net only needs a few annotated samples for learning and is able to efficiently detect first-arrival times with high precision on complicated seismic data from a large survey, indicating that it has the potential to directly identify the first arrivals on new seismic data.
Abstract: In exploration geophysics, the first arrivals on data acquired under complicated near-surface conditions are often characterized by significant static corrections, weak energy, low signal-t...
TL;DR: In this article, the authors evaluated a field test in the city of Singapore to assess the feasibility of the passive seismic survey for bedrock depth determination and further investigate the optimal acquis...
Abstract: We have evaluated a field test in the city of Singapore to assess the feasibility of the passive seismic survey for bedrock depth determination and to further investigate the optimal acquis...
TL;DR: Writing software packages for seismic inversion is a very challenging task, since problems such as full-waveform inversion or least-squares imaging are both algorithmically and computationally demanding due to the large number of unknown parameters and the fact that waves are propagated over many wavelengths.
Abstract: Writing software packages for seismic inversion is a very challenging task because problems such as full-waveform inversion or least-squares imaging are algorithmically and computationally ...
TL;DR: In this article, the authors show that heterogeneous small-scale high-contrast layers and spatial variabilities of soil properties can have a large impact on flow and transport processes in the critical zone.
Abstract: Heterogeneous small-scale high-contrast layers and spatial variabilities of soil properties can have a large impact on flow and transport processes in the critical zone. Because their chara...
TL;DR: In this paper, a Markov chain Monte Carlo (MCMCMC) method was developed for joint inversion of seismic data for the prediction of facies and elastic properties. But the solution of the inverse problem is de...
Abstract: We have developed a Markov chain Monte Carlo (MCMC) method for joint inversion of seismic data for the prediction of facies and elastic properties. The solution of the inverse problem is de...
TL;DR: A DL-based workflow is introduced that uses analog velocity models and realistic raw seismic waveforms as input and produces subsurface velocity models as output andustrates a way to mitigate this problem and opens geology, geophysics, and planetary sciences to more DL applications.
Abstract: Exploration seismic data are heavily manipulated before human interpreters are able to extract meaningful information regarding subsurface structures. This manipulation adds modeling and h...
TL;DR: The custEM toolbox as mentioned in this paper is a toolbox for the simulation of complex 3D controlled-source electromagnetic (CSEM) problems, which is based on the open-source toolbox custEM.
Abstract: We have developed the open-source toolbox custEM (customizable electromagnetic modeling) for the simulation of complex 3D controlled-source electromagnetic (CSEM) problems. It is based on t...