Seismic Coherence for Discontinuity Interpretation

Li, Fangyu; Lyu, Bin; Qi, Jie; Verma, Sumit; Zhang, Bo

doi:10.1007/s10712-021-09670-4

Cited by 15 publications

(2 citation statements)

References 145 publications

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“…Dorn et al 12 proposed automatic fault extraction, which uses a priori information to realize the task of autonomous fault detection. Li et al 13 proposed an ant colony algorithm with direction constraints, and Wu and Hale 4 proposed a method of constructing fault planes based on linked data structure. This method constructs faults by connecting similar fault attributes in a local area and estimates its corresponding slip vector at the same time.…”

Section: Related Workmentioning

confidence: 99%

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Rapid fault extraction from seismic images via deep learning

Zhu¹,

Zheng

Wang³

et al. 2022

J. Electron. Imag.

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. Using deep learning to automatically and quickly extract faults from seismic images is of practical significance. An improved U-Net algorithm is proposed by reducing convolutional layers, designing skip connections, enforcing deep supervision, and improving the loss function and learning rate to build a new model. In the operation, the feature map parameters in the network are further revised, the number of training iterations is increased, a callback function is added, and the parameter adjustment training consumes less time and space and has higher accuracy. Experiments on real public datasets show that the improved network can limit the time required to extract a 128 × 128 × 128 three-dimensional image within 200 ms, which not only requires less time and computing power than existing methods but also has an extraction accuracy as high as 97.6%.

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Section: Related Workmentioning

confidence: 99%

“…proposed automatic fault extraction, which uses a priori information to realize the task of autonomous fault detection. Li et al 13 . proposed an ant colony algorithm with direction constraints, and Wu and Hale 4 proposed a method of constructing fault planes based on linked data structure.…”

Section: Related Workmentioning

confidence: 99%

Rapid fault extraction from seismic images via deep learning

Zhu¹,

Zheng

Wang³

et al. 2022

J. Electron. Imag.

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Unsupervised machine learning and multi-seismic attributes for fault and fracture network interpretation in the Kerry Field, Taranaki Basin, New Zealand

Ismail,

Radwan,

Leila

et al. 2023

Geomech. Geophys. Geo-energ. Geo-resour.

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Unsupervised machine learning using an unsupervised vector quantization neural network (UVQ-NN) integrated with meta-geometrical attributes as a novel computation process as opposed to traditional methodologies is currently used effectively in the 3D seismic structural interpretation for high-resolution detection of fault patterns, fracture network zones, and small-scale faults (SSFs). This technology has a crucial role in locating prospective well sites and building a 3D structural model while saving time and cost. The innovation of the current workflow involves combining geostatistical and structural filtering, optimal geometrical seismic attributes, UVQ-NN for automatic major faults, fracture network zones, and SSFs volumes extraction due to the unavailability of well logs and cores. To sharpen the fault edges and discontinuities, a steered volume was first extracted. Structural filters were then applied to the 3D volume, first with a dip-steered median filter (DSMF), followed by a dip-steered diffusion filter (DSDF), and finally, both DSMF and DSDF were combined to generate the fault enhancement filter (FEF). After that, optimal geometrical attributes were computed and extracted, such as similarity, FEF on similarity, maximum curvature, polar dip, fracture density, and thinned fault likelihood (TFL) attributes. Finally, selected attributes were inserted as the input layer to the UVQ-NN to generate segmentation and matching volumes. On the other hand, the TFL was used with the voxel connectivity filter (VCF) for 3D automatic fault patches extraction. The results from the UVQ-NN and VCF identified the locations, orientations, and extensions of the main faults, SSFs, and fracture networks. The implemented approach is innovative and can be employed in the future for the identification, extraction, and classification of geological faults and fracture networks in any region of the world.

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3D seismic attribute visualization and analysis for fault characterization

Verma,

Bhattacharya

2023

Developments in Structural Geology and Tectonics

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Seismic Coherence for Discontinuity Interpretation

Cited by 15 publications

References 145 publications

Rapid fault extraction from seismic images via deep learning

Rapid fault extraction from seismic images via deep learning

Unsupervised machine learning and multi-seismic attributes for fault and fracture network interpretation in the Kerry Field, Taranaki Basin, New Zealand

3D seismic attribute visualization and analysis for fault characterization

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