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

Deep Learning Assisted Doppler Sensing for Hydrocarbon Downhole Flow Velocity Estimation

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Alberto Marsala ◽  
Virginie Schoepf ◽  
Linda Abbassi
Keyword(s):  
Deep Learning ◽  
Flow Velocity ◽  
Oil And Gas ◽  
Velocity Estimation ◽  
Gas Reservoirs ◽  
Hydrocarbon Production ◽  
Oil And Gas Exploration ◽  
Learning Framework ◽  
Measurement Results ◽  
Interpretative Framework

Abstract Logging hydrocarbon production potential of wells has been at the forefront of enhancing oil and gas exploration and maximize productivity from oil and gas reservoirs. A major challenge is accurate downhole fluid phases flow velocity measurements in production logging due to the criticality of mechanical spinner-based sensor devices. Ultrasonic Doppler based sensors are more robust and deployable either in wireline or logging while drilling (LWD) conditions; however, due to the different sensing physics, the measurement results may not be equivalent. We present in this work an innovative deep learning framework to estimate spinner phase velocities from Doppler based sensor velocities. Tests of the framework on a benchmark dataset displayed strong estimation results. This allows for the real-time automatic interpretative framework implementation and flow velocity estimations either in conventional wireline production logging technologies (PLTs) and potentially also in LWD conditions, when the well is flowing in underbalanced conditions.

scholarly journals MAPPING SURFACE FLOW VELOCITIES OF SIACHEN AND GANGOTRI GLACIERS USING TERRASAR-X AND SENTINEL-1A DATA BY INTENSITY TRACKING

2018 ◽  
Vol IV-5 ◽  
pp. 325-329 ◽  
Author(s):  
P. Lal ◽  
D. S. Vaka ◽  
Y. S. Rao
Keyword(s):  
Surface Flow ◽  
Velocity Estimation ◽  
Accurate Estimation ◽  
Velocity Mapping ◽  
Tracking Method ◽  
X Band ◽  
Gangotri Glacier ◽  
Glacier Velocity ◽  
Offset Tracking ◽  
Band Image

<p><strong>Abstract.</strong> Glaciers are melting at an alarming rate due to global warming. Two major glaciers of India viz. Gangotri and the Siachen are chosen for the velocity mapping. The line-of-sight (LOS) velocity fields are derived using X-band TerraSAR-X and C-band Sentinel-1A datasets. An intensity-based offset tracking method is used to generate LOS velocities of the glaciers. The single look complex (SLC) images of the TerraSAR-X are converted into intensity before applying the offset tracking method, whereas the ground range detected (GRD) products from Sentinel-1A are directly used to estimate the glacier velocities. The Siachen glacier velocity is mapped using three X-band images from 2011 to 2017 and a C-band image between 2017 and 2018. The X-band images in the case of Siachen are available with the long-time interval between the master and slave images. The velocity of the glacier is observed to be around 30&amp;ndash;40<span class="thinspace"></span>cm<span class="thinspace"></span>day<sup>&amp;minus;1</sup> from X-band and around 45&amp;ndash;50<span class="thinspace"></span>cm day<sup>&amp;minus;1</sup> from C-band. Three X-band images in the year 2012 and a C-band image in the year 2018 are used for the Gangotri glacier velocity estimation. These images are very closely separated in time, and the velocity of the glacier is found to be 15&amp;ndash;20<span class="thinspace"></span>cm<span class="thinspace"></span>day<sup>&amp;minus;1</sup>. A dataset with a temporal gap of approximately three years is also used for the Gangotri glacier velocity estimation and observed a large difference in velocity (&amp;sim;10<span class="thinspace"></span>cm<span class="thinspace"></span>day<sup>&amp;minus;1</sup>) from that of shorter interval data. Therefore, for a slow-moving glacier like Gangotri, a dataset with a high temporal gap may not give a reliable result. It is also observed that X-band TerraSAR-X results are more accurate than the C-band Sentinel-1A results. The penetration depth of X-band is less compared to C-band, which might result in accurate estimation of glacier surface flow. According to the results, the velocity of the Siachen glacier is increasing at a very high rate.</p>

scholarly journals Application of deep learning to large scale riverine flow velocity estimation

2021 ◽  
Author(s):  
Mojtaba Forghani ◽  
Yizhou Qian ◽  
Jonghyun Lee ◽  
Matthew W. Farthing ◽  
Tyler Hesser ◽  
...  
Keyword(s):  
Deep Learning ◽  
Flow Velocity ◽  
Large Scale ◽  
Velocity Estimation

scholarly journals First Results on Wake Detection in SAR Images by Deep Learning

2021 ◽  
Vol 13 (22) ◽  
pp. 4573
Author(s):  
Roberto Del Del Prete ◽  
Maria Daniela Graziano ◽  
Alfredo Renga
Keyword(s):  
Deep Learning ◽  
A Priori ◽  
Automatic Target Recognition ◽  
Velocity Estimation ◽  
Sar Images ◽  
Art Object ◽  
Ship Wakes ◽  
First Results ◽  
Wake Detection ◽  
Maritime Domain Awareness

Spaceborne synthetic aperture radar (SAR) represents a powerful source of data for enhancing maritime domain awareness (MDA). Wakes generated by traveling vessels hold a crucial role in MDA since they can be exploited both for ship route and velocity estimation and as a marker of ship presence. Even if deep learning (DL) has led to an impressive performance boost on a variety of computer vision tasks, its usage for automatic target recognition (ATR) in SAR images to support MDA is still limited to the detection of ships rather than ship wakes. A dataset is presented in this paper and several state-of-the-art object detectors based on convolutional neural networks (CNNs) are tested with different backbones. The dataset, including more than 250 wake chips, is realized by visually inspecting Sentinel-1 images over highly trafficked maritime sites. Extensive experiments are shown to characterize CNNs for the wake detection task. For the first time, a deep-learning approach is implemented to specifically detect ship wakes without any a-priori knowledge or cuing about the location of the vessel that generated the wake. No annotated dataset was available to train deep-learning detectors on this task, which is instead presented in this paper. Moreover, the benchmarks achieved for different detectors point out promising features and weak points of the relevant approaches. Thus, the work also aims at stimulating more research in this promising, but still under-investigated, field.

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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