3D survey geometry corrections and surface velocity mapping via deep learning

2021 ◽  
Author(s):  
Xiangjun Ding ◽  
Jie Zhang ◽  
Ji Zhang
Keyword(s):  
Deep Learning ◽  
Surface Velocity ◽  
Velocity Mapping

Near-surface full-waveform inversion in a transmission surface-consistent scheme

Geophysics ◽  
2020 ◽  
pp. 1-57
Author(s):  
Daniele Colombo ◽  
Ernesto Sandoval ◽  
Diego Rovetta ◽  
Apostolos Kontakis
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Surface Velocity ◽  
Full Waveform Inversion ◽  
Velocity Mapping ◽  
Signal To Noise ◽  
Near Surface ◽  
Velocity Modeling ◽  
Full Waveform

Land seismic velocity modeling is a difficult task largely related to the description of the near surface complexities. Full waveform inversion is the method of choice for achieving high-resolution velocity mapping but its application to land seismic data faces difficulties related to complex physics, unknown and spatially varying source signatures, and low signal-to-noise ratio in the data. Large parameter variations occur in the near surface at various scales causing severe kinematic and dynamic distortions of the recorded wavefield. Some of the parameters can be incorporated in the inversion model while others, due to sub-resolution dimensions or unmodeled physics, need to be corrected through data preconditioning; a topic not well described for land data full waveform inversion applications. We have developed novel algorithms and workflows for surface-consistent data preconditioning utilizing the transmitted portion of the wavefield, signal-to-noise enhancement by generation of CMP-based virtual super shot gathers, and robust 1.5D Laplace-Fourier full waveform inversion. Our surface-consistent scheme solves residual kinematic corrections and amplitude anomalies via scalar compensation or deconvolution of the near surface response. Signal-to-noise enhancement is obtained through the statistical evaluation of volumetric prestack responses at the CMP position, or virtual super (shot) gathers. These are inverted via a novel 1.5D acoustic Laplace-Fourier full waveform inversion scheme using the Helmholtz wave equation and Hankel domain forward modeling. Inversion is performed with nonlinear conjugate gradients. The method is applied to a complex structure-controlled wadi area exhibiting faults, dissolution, collapse, and subsidence where the high resolution FWI velocity modeling helps clarifying the geological interpretation. The developed algorithms and automated workflows provide an effective solution for massive full waveform inversion of land seismic data that can be embedded in typical near surface velocity analysis procedures.

A robust first-arrival picking workflow using convolutional and recurrent neural networks

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. U109-U119
Author(s):  
Pengyu Yuan ◽  
Shirui Wang ◽  
Wenyi Hu ◽  
Xuqing Wu ◽  
Jiefu Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Model Building ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Velocity Model ◽  
Surface Velocity ◽  
Data Sets ◽  
Near Surface ◽  
Segmentation Boundary ◽  
First Arrival

A deep-learning-based workflow is proposed in this paper to solve the first-arrival picking problem for near-surface velocity model building. Traditional methods, such as the short-term average/long-term average method, perform poorly when the signal-to-noise ratio is low or near-surface geologic structures are complex. This challenging task is formulated as a segmentation problem accompanied by a novel postprocessing approach to identify pickings along the segmentation boundary. The workflow includes three parts: a deep U-net for segmentation, a recurrent neural network (RNN) for picking, and a weight adaptation approach to be generalized for new data sets. In particular, we have evaluated the importance of selecting a proper loss function for training the network. Instead of taking an end-to-end approach to solve the picking problem, we emphasize the performance gain obtained by using an RNN to optimize the picking. Finally, we adopt a simple transfer learning scheme and test its robustness via a weight adaptation approach to maintain the picking performance on new data sets. Our tests on synthetic data sets reveal the advantage of our workflow compared with existing deep-learning methods that focus only on segmentation performance. Our tests on field data sets illustrate that a good postprocessing picking step is essential for correcting the segmentation errors and that the overall workflow is efficient in minimizing human interventions for the first-arrival picking task.

Automated Velocity Estimation by Deep Learning Based Seismic-to-Velocity Mapping

2019 ◽  
Author(s):  
L. Duque ◽  
G. Gutiérrez ◽  
C. Arias ◽  
A. Rüger ◽  
H. Jaramillo
Keyword(s):  
Deep Learning ◽  
Velocity Estimation ◽  
Velocity Mapping

Epicardial surface velocity mapping

1992 ◽  
Vol 25 ◽  
pp. 111 ◽  
Author(s):  
Michael D. Herr ◽  
Joseph J. McInerney ◽  
Jerry C. Luck ◽  
Jeffrey D. Orledge ◽  
Gary L. Copenhaver
Keyword(s):  
Surface Velocity ◽  
Velocity Mapping ◽  
Epicardial Surface

scholarly journals COMPILING TECHNIQUES FOR EAST ANTARCTIC ICE VELOCITY MAPPING BASED ON HISTORICAL OPTICAL IMAGERY

2018 ◽  
Vol XLII-3 ◽  
pp. 2625-2628
Author(s):  
X. Li ◽  
R. Li ◽  
G. Qiao ◽  
Y. Cheng ◽  
W. Ye ◽  
...  
Keyword(s):  
Flow Velocity ◽  
Image Matching ◽  
Landsat Imagery ◽  
East Antarctica ◽  
Surface Velocity ◽  
Velocity Mapping ◽  
Large Space ◽  
Ice Flow ◽  
Ice Velocity ◽  
Optical Imagery

Ice flow velocity over long time series in East Antarctica plays a vital role in estimating and predicting the mass balance of Antarctic Ice Sheet and its contribution to global sea level rise. However, there is no Antarctic ice velocity product with large space scale available showing the East Antarctic ice flow velocity pattern before the 1990s. We proposed three methods including parallax decomposition, grid-based NCC image matching, feature and gird-based image matching with constraints for estimation of surface velocity in East Antarctica based on ARGON KH-5 and LANDSAT imagery, showing the feasibility of using historical optical imagery to obtain Antarctic ice motion. Based on these previous studies, we presented a set of systematic method for developing ice surface velocity product for the entire East Antarctica from the 1960s to the 1980s in this paper.

scholarly journals ICE FLOW VELOCITY MAPPING OF EAST ANTARCTICA FROM 1963 TO 1989

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1735-1739
Author(s):  
Y. Cheng ◽  
X. Li ◽  
G. Qiao ◽  
W. Ye ◽  
Y. Huang ◽  
...  
Keyword(s):  
Flow Velocity ◽  
East Antarctica ◽  
Surface Velocity ◽  
Velocity Mapping ◽  
Landsat Images ◽  
Ice Flow ◽  
Continental Scale ◽  
Optical Images ◽  
Long Time ◽  
Surface Ice

<p><strong>Abstract.</strong> Long-time serial observation of surface ice flow velocity in Antarctic is a crucial component in estimating the mass balance of Antarctic ice sheet. However, there is a lack of historical continental scale velocity maps of Antarctica before the 1990s. Historical optical images such as ARGON and Landsat images before 1990s are difficult to be used for ice flow velocity mapping, due to the fact that they are mostly not strictly geo-processed (e.g., ortho-rectified) and the image quality is lower than those of recent sensors. This paper presents a systematic framework for developing a surface velocity map of East Antarctica from 1963 to 1989 based on historical ARGON and Landsat images, followed by analysis of spatial-temporal changes of the ice flow velocity in some major glaciers, as well as the dynamic changes. The preliminary comparison with existing products suggests that the glaciers in Wilkes Land experienced an increasing trend with obvious fluctuations during the past &amp;sim;50 years, while the glaciers near Transantarctic Mountains tended to be stable or slightly fluctuating to a certain degree.</p>

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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