Results 111 to 120 of about 619,673 (171)

INTERPRETASI DATA SEISMIK REFLEKSI : PICKING HORIZON DAN PICKING FAULT

Journal of Renewable Engineering
Seismic method practicum has been carried out with the title "Interpretation of reflection seismic data: picking horizon and picking fault". The seismic method is a geophysical method that is often used in imaging the subsurface conditions of the earth. The purpose of this practicum is to find out how to do picking horizon and picking fault.
Barrend Wellyadi Wibowo, Soly Deo Gloria Hutagalung   +1 more
openaire   +2 more sources

Horizon picking in 3D seismic data volumes

Machine Vision and Applications, 2004
In this paper, we present an automatic horizonpicking algorithm, based on a surface detection technique, to detect horizons in 3D seismic data. The surface detection technique, and the use of 6-connectivity, allows us to detect fragments of horizons that are afterwards combined to form full horizons.
Maria Faraklioti, Maria Petrou
openaire   +2 more sources

Horizon Picking For Multidimensional Data: An Integrated Aproach

6th International Congress of the Brazilian Geophysical Society, 1999
3-D seismic surveys generate 5-D data volume. In order to estimate the horizons for interpretation and further processing, the traveltime picking needs to be performed on n-D subsets of this 5-D data volume (n≤5). Horizon picking (HP) is complicated by the irregular sampling, faults, discontinuities, and low signal-to-noise ratio areas.
BIENATI, NICOLA, NICOLI, MONICA BARBARA, SPAGNOLINI, UMBERTO   +2 more
openaire   +3 more sources

Seismic horizon picking via marching semblance dynamic time warping

GEOPHYSICS
Seismic horizon interpretation is critical for reservoir modeling, but it remains labor intensive and subjective when performed manually. Although automated approaches can be efficient, they struggle with complex geologic features (e.g., faults and unconformities) and noisy data.
Hao Wu, Weimin Wang, Sandong Zhou
openaire   +2 more sources

Seismic horizon picking by integrating reflector dip and instantaneous phase attributes

GEOPHYSICS, 2020
Seismic horizons are the compulsory inputs for seismic stratigraphy analysis and 3D reservoir modeling. Manually interpreting horizons on thousands of vertical seismic slices of 3D seismic survey is a time-consuming task. Automatic horizon interpreting algorithms are usually based on the seismic reflector dip.
Yihuai Lou, Bo Zhang, Tengfei Lin, Danping Cao   +3 more
openaire   +2 more sources

Seismic horizon picking using an artificial neural network

[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992
In seismic data interpretation, horizon picking is important for structural analysis, feature recognition, and site appraisal. However, horizon picking is still commonly done by hand, a process which is error prone and time consuming. Attempts to automate horizon picking are hindered by the absence of a clear, robust, and universal picking algorithm. A
E. Harrigan, J.R. Kroh, W.A. Sandham, T.S. Durrani   +3 more
openaire   +2 more sources

A two-step mechanism for automated 3D horizon picking

SEG Technical Program Expanded Abstracts 2014, 2014
Summary The spatial distribution characteristics of seismic extrema in 3D seismic data are rendered that extrema are high density in the same horizon, while density of different horizons is low in time direction. Based on this understanding, we propose a novel automatic tracking algorithm that mainly contains two steps: (1) form horizon fragments using
Feng Qian*, Chang Luo, Zhaojie Su, Xingmiao Yao, Guangmin Hu, Xiaoer Cheng   +5 more
openaire   +2 more sources

Cellular Neural Network for Seismic Horizon Picking

2005 9th International Workshop on Cellular Neural Networks and Their Applications, 2005
Cellular neural network has the property of local connection. We use this property for seismic horizon linking. The constraint conditions for detecting seismic horizons are used to construct the Lyapunov energy function. The connection weights between neurons are extracted from Lyapunov energy function.
null Kou-Yuan Huang, null Chin-Hua Chang, null Wen-Shiang Hsieh, null Shan-Chih Hsieh, L.K. Wang, null Fan-Jen Tsai   +5 more
openaire   +2 more sources

Horizon Picking on Subbottom Profiles Using Multiresolution Analysis

Digital Signal Processing, 2001
Abstract Maroni, C.-S., Quinquis, A., and Vinson, S., Horizon Picking on Subbottom Profiles Using Multiresolution Analysis, Digital Signal Processing 11 (2001) 269–287 A fully automatic algorithm is proposed for the mapping of sediment layers on subbottom profiles. This mapping should significantly speed up data analysis and sedimentary data base
Claire-Sophie Maroni, André Quinquis, Samuel Vinson   +2 more
openaire   +2 more sources

Neural network for seismic horizon picking

1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227), 2002
A Hopfield neural network can solve optimization problems. We use a Hopfield net for seismic horizon picking. The peak position of each seismic wavelet corresponds to one neuron. We transform the constraints for detecting local horizon patterns and the constraints for extracting one horizon each time into the system energy function.
Kou-Yuan Huang
openaire   +2 more sources