Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool [PDF]
Sensors, 2021 Seismic interpretation is a fundamental process for hydrocarbon exploration. This activity comprises identifying geological information through the processing and analysis of seismic data represented by different attributes.
Alimed Celecia +8 more
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Increasing the quality of seismic interpretation [PDF]
Interpretation, 2016 Acknowledgments E. Macrae was funded by an NERC Open CASE Ph.D. award (NE/F013728/1) with Midland Valley Exploration Ltd. as the industry partner. We thank 763 geoscientists for their participation, and in particular, the REs who gave their time freely ...
Bond, Clare E. +3 more
core +6 more sources
Quantification of uncertainty in 3-D seismic interpretation: implications for deterministic and stochastic geomodeling and machine learning [PDF]
Solid Earth, 2019 In recent years, uncertainty has been widely recognized in geosciences, leading to an increased need for its quantification. Predicting the subsurface is an especially uncertain effort, as our information either comes from spatially highly limited direct
A. Schaaf, C. E. Bond
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Developing a seismic pattern interpretation network (SpiNet) for automated seismic interpretation [PDF]
arXiv, 2018 Seismic interpretation is now serving as a fundamental tool for depicting subsurface geology and assisting activities in various domains, such as environmental engineering and petroleum exploration. However, most of the existing interpretation techniques are designed for interpreting a certain seismic pattern (e.g., faults and salt domes) in a given ...
arxiv +3 more sources
Framing bias : the effect of figure presentation on seismic interpretation [PDF]
, 2017 The authors thank all the participants in the survey, and those who helped to distribute it. We thank Prof. Christopher Jackson and co-authors for allowing the use of their published images in this experiment.
Alcalde, Juan +2 more
core +2 more sources
Learnable Gabor Kernels in Convolutional Neural Networks for Seismic Interpretation Tasks [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2023 The use of convolutional neural networks (CNNs) in seismic interpretation tasks, like facies classification, has garnered a lot of attention for its high accuracy.
Fu Wang, T. Alkhalifah
semanticscholar +1 more source
A Joint Inversion-Segmentation approach to Assisted Seismic Interpretation [PDF]
82nd EAGE Annual Conference & Exhibition, 2021 Structural seismic interpretation and quantitative characterization are historically intertwined processes. The latter provides estimates of the properties of the subsurface, which can be used to aid structural interpretation alongside the original ...
M. Ravasi, C. Birnie
semanticscholar +1 more source
3D seismic interpretation with deep learning: A brief introduction
The Leading Edge, 2021 Understanding the internal structure of our planet is a fundamental goal of the earth sciences. As direct observations are restricted to surface outcrops and borehole cores, we rely on geophysical data to study the earth's interior.
T. Wrona +5 more
semanticscholar +1 more source
Identification and Interpretation of fan deposits in Block H12 of Beixi subsag, Beier depression, Hailar basin [PDF]
E3S Web of Conferences, 2020 Fan deposits in the Nantun formation in Beier depression have been drilled in the past two decades. It’s difficult to identify and describe subtle traps formed by fans by seismic interpretation. By using a combination of seismic interpretation techniques
Yu-lin QI, Yue ZHOU
doaj +1 more source
Generating Paired Seismic Training Data with Cycle-Consistent Adversarial Networks
Remote Sensing, 2023 Deep-learning-based seismic data interpretation has received extensive attention and focus in recent years. Research has shown that training data play a key role in the process of intelligent seismic interpretation.
Zheng Zhang +4 more
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