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Fault Types

Fault Types

When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...

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Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

Published on: August 7, 2017

A gigabyte interpreted seismic dataset for automatic fault recognition.

Yu An¹, Jiulin Guo², Qing Ye³

¹The Insight Centre for Data Analytics, School of Computer Science, University College Dublin, Dublin, Ireland.

|June 30, 2021

Summary

This study introduces a large, open-source seismic dataset with expert fault annotations, crucial for advancing AI in automatic fault recognition. The dataset facilitates AI model training and validation for seismic interpretation.

Keywords:

Computer vision Fault recognition Image processing Seismic interpretation

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Area of Science:

Geophysics
Artificial Intelligence
Data Science

Background:

Limited availability of large-scale, expert-labeled seismic datasets hinders AI applications in automatic fault recognition.
Seismic data interpretation is critical for understanding geological structures and basin evolution.

Purpose of the Study:

To present a novel, large-scale, open-source seismic dataset with expert fault annotations.
To facilitate the development and application of AI techniques for automatic fault recognition in the oil and gas industry.

Main Methods:

Processed seismic images from the Thebe Gas Field survey were curated.
Fault annotations were manually created by expert seismic interpreters.
Data was processed and collected using Petrel software and Python programs.

Main Results:

A comprehensive dataset of processed seismic images and corresponding fault annotations is now available.
The dataset is provided in Python Numpy format for easy integration with AI models.
This resource enables robust training, validation, and evaluation of AI-driven fault recognition workflows.

Conclusions:

The release of this dataset addresses a significant barrier in AI-powered seismic interpretation.
It is expected to accelerate research and development in automatic fault recognition.
Facilitates interdisciplinary collaboration between geoscientists and computer scientists.