Results 141 to 150 of about 619,673 (171)

Horizon Picking Using AWFF-MT-LSTM Network With the Aid of Geological Simulation Modeling

IEEE Transactions on Geoscience and Remote Sensing
Seismic horizon picking is vital in seismic interpretation, forming the foundation for reservoir exploration and seismic inversion. Traditional horizon picking methods heavily depend on geologists’ experience, making the process time-consuming, labor ...
Hao Wu, Hao Zhang, Naihao Liu, Yang Yang
openaire   +2 more sources

Picking up the pieces: Instrumental Neutron Activation Analysis (INAA) of early intermediate period and middle horizon pottery from Ayacucho, Peru

Nawpa Pacha, 2022
This study compares chemical data of 224 sherds from Early Intermediate Period Huarpa and Middle Horizon Wari pottery to investigate settlement dynamics. All sherds were first identified stylistically and chronologically.
Patricia J. Knobloch, M. Glascock, B. Macdonald   +2 more
semanticscholar   +1 more source

Automated Amplitude and Phase Attribute-Based Horizon Picking Applied to 3-D Sub-bottom Data

IEEE Journal of Oceanic Engineering
The 3-D sub-bottom profiler (SBP) is widely used for observing sub-bottom structures due to its high resolution and spatial coverage. However, traditional picking methods are limited by scattering noise and imbalanced intensity, resulting in poor picking
Shaobo Li, Tie Li, Aiguo Sun, Shiqi Wang, Yunlong Wu   +4 more
openaire   +2 more sources

Automated horizon picking by multiple target tracking

53rd EAEG Meeting, 1991
Geophysical horizons are of interest to the interpreter as an indicator of geological boundaries and structures. As such, they are also important for accurately obtaining the earth's velocity model. Horizon picking is still commonly done by hand, a process which is error-prone and time consuming.
E. Harrigan, T. S. Durrani
openaire   +1 more source

Can Machines Learn to Pick Horizons in Post Stack Data?

81st EAGE Conference and Exhibition 2019 Workshop Programme, 2019
Summary The presented method applies a supervised deep learning (DL) method to detect the horizons throughout a seismic dataset with high detection accuracy.
L. Yalcinoglu, C. Stotter
openaire   +1 more source

Three-Dimensional Mapping of Horizons Picked on Two-Dimensionally Migrated Seismic Sections

Exploration Geophysics, 1987
There are some well-known problems in interpretation and mapping of horizons picked on migrated sections:? horizons do not tie at intersections? traveltimes or depths as posted are erroneous in both position and value, as far as out of dip lines are concerned.
J. W. Sattlegger, H. Egbers
openaire   +1 more source

Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks

International Conference on Learning Representations
Large Language Models (LLMs) have been shown to be capable of performing high-level planning for long-horizon robotics tasks, yet existing methods require access to a pre-defined skill library (e.g.
Murtaza Dalal, Tarun Chiruvolu, Devendra Singh Chaplot, Ruslan Salakhutdinov   +3 more
semanticscholar   +1 more source

Anisotropic Media Tomography based on Automatic Horizons Picking

82nd EAGE Annual Conference & Exhibition, 2021
S. Xu, K. Zhang, Z. Li, P. Jiang, M. Li, W. Peng, G. Wang   +6 more
openaire   +1 more source

Synthesis and alignment of liquid crystalline elastomers

Nature Reviews Materials, 2021
Katie M Herbert, Hayden E Fowler, Joselle M Mccracken   +2 more
exaly  

Self‐organizing neural network for picking seismic horizons

SEG Technical Program Expanded Abstracts 1990, 1990
Kou‐Yuan Huang, William R. I. Chang, H. T. Yen   +2 more
openaire   +1 more source