Results 101 to 110 of about 619,673 (171)

Heterogeneous foraging swarms can be better. [PDF]

Front Robot AI
Kaminka GA, Douchan Y.
europepmc   +1 more source

A Globally Guided Dual-Arm Reactive Motion Controller for Coordinated Self-Handover in a Confined Domestic Environment. [PDF]

Biomimetics (Basel)
Geng Z, Yang Z, Xu W, Guo W, Sheng X.
europepmc   +1 more source

Explainable dual LSTM-autoencoders with exogenous features for anomaly detection and supply chain forecasting. [PDF]

Sci Rep
Xiaoyang C.
europepmc   +1 more source

Scoring information integration with statistical quality control enhanced cross-run analysis of data-independent acquisition proteomics data. [PDF]

Commun Chem
Gao M, Gupta S, Yang W, Yu R, Röst HL.
europepmc   +1 more source

Thompson Sampling for Non-Stationary Bandit Problems. [PDF]

Entropy (Basel)
Qi H, Guo F, Zhu L.
europepmc   +1 more source

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Enabling Robust Horizon Picking From Small Training Sets

IEEE Transactions on Geoscience and Remote Sensing, 2021
Seismic interpretation is a complex procedure that depends on many and interdependent data analyses. One of the essential steps in this process is picking horizons in seismic images, which is time-consuming and prone to errors when performed manually.
Andrea Britto Mattos, Daniel Civitarese, Daniela Szwarcman, Matheus Oliveira, Semen Zaytsev, Daniil G. Semin, Dario A. B. Oliveira   +6 more
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Simulating the procedure of manual seismic horizon picking

GEOPHYSICS, 2021
Manual seismic horizon picking is the least efficient interpretation technique in terms of time and effort. The loop-tie is a key “element” and the most time-consuming task in manual horizon picking, which ensures the accuracy of horizon picking. Autopicking techniques have been used since the early 1980s. However, there are few studies simulating the
Yihuai Lou, Bo Zhang, Huijing Fang, Danping Cao, Kangning Wang, Zhizhou Huo   +5 more
openaire   +2 more sources

Horizon Picking Using Two-Branch Network With Spatial and Time–Frequency Features

IEEE Geoscience and Remote Sensing Letters, 2022
In seismic interpretation, horizon picking is a very essential but time-consuming and challenging task. Most existing auto-picking algorithms have been proposed to improve the horizon interpretation efficiency. Recently, deep learning approaches have shown promising performance in horizon identification. However, feeding directly seismic time series or
Xiaofang Liao, Junxing Cao, Ya-Juan Xue, Jiachun You, Ming Cheng   +4 more
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Waveform embedding: Automatic horizon picking with unsupervised deep learning

GEOPHYSICS, 2020
Picking horizons from seismic images is a fundamental step that could critically impact seismic interpretation quality. We have developed an unsupervised approach, waveform embedding, based on a deep convolutional autoencoder network to learn to transform seismic waveform samples to a latent space in which any waveform can be represented as an ...
Yunzhi Shi, Xinming Wu, Sergey Fomel
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Comprehensive Sediment Horizon Picking From Subbottom Profile Data

IEEE Journal of Oceanic Engineering, 2019
Manual horizon picking and semiautomatic horizon picking are the traditional methods for data processing of subbottom profiles (SBPs). However, the former is time consuming and laborsome, whereas the latter requires frequent manual intervention and easily suffers from low accuracy and discontinuous picking due to noises.
Jianhu Zhao, Shaobo Li, Hongmei Zhang, Jie Feng   +3 more
openaire   +2 more sources