Automated Top Salt Interpretation Using a Deep Convolutional Net

Gramstad, Oddgeir; Nickel, Michael

doi:10.3997/2214-4609.201800731

Cited by 5 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In aggregate, these works laid the ground-work for automatic seismic interpretation using CNNs. While seismic interpretation already is a narrow field of application of machine learning, it can be observed that most applications focus on sub-sections of seismic interpretation such as salt detection [Waldeland et al, 2018, Gramstad and Nickel, 2018, fault interpretation [Araya-Polo et al, 2017, Guitton, 2018, facies classification [Chevitarese et al, 2018, Dramsch and, and horizon picking [Wu and Zhang, 2018]. In comparison, this is however, already a broader application than prior machine learning approaches for seismic interpretation that utilized very specific seismic attributes as input to self-organizing maps (SOM) for e.g.…”

Section: He Et Al [2016]mentioning

confidence: 99%

70 years of machine learning in geoscience in review

Dramsch

2020

Preprint

View full text Add to dashboard Cite

This review gives an overview of the development of machine learning in geoscience. A thorough analysis of the co-developments of machine learning applications throughout the last 70 years relates the recent enthusiasm for machine learning to developments in geoscience. I explore the shift of kriging towards a mainstream machine learning method and the historic application of neural networks in geoscience, following the general trend of machine learning enthusiasm through the decades. Furthermore, this chapter explores the shift from mathematical fundamentals and knowledge in software development towards skills in model validation, applied statistics, and integrated subject matter expertise. The review is interspersed with code examples to complement the theoretical foundations and illustrate model validation and machine learning explainability for science. The scope of this review includes various shallow machine learning methods, e.g. Decision Trees, Random Forests, Support-Vector Machines, and Gaussian Processes, as well as, deep neural networks, including feed-forward neural networks, convolutional neural networks, recurrent neural networks and generative adversarial networks. Regarding geoscience, the review has a bias towards geophysics but aims to strike a balance with geochemistry, geostatistics, and geology, however excludes remote sensing, as this would exceed the scope. In general, I aim to provide context for the recent enthusiasm surrounding deep learning with respect to research, hardware, and software developments that enable successful application of shallow and deep machine learning in all disciplines of Earth science.

show abstract

Section: He Et Al [2016]mentioning

confidence: 99%

70 years of machine learning in geoscience in review

Dramsch

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Initially, methods were mainly based on the computation of external attributes that would help the detection of salt in a supervised approach (Guillen, et al, 2015). More recently, Deep learning methods and in particular convolutional neural networks (CNNs) have been used for automated Salt interpretation (Waldeland, et al, 2018), (Gramstad, et al, 2018). CNNs outperform previous approaches with their capacity of automatically generating features that would best solve a particular classification task (Di et al, 2018).…”

Section: Looking Ahead: Reducing Labor Intensive Tasks Through Machine Learning?mentioning

confidence: 99%

“…The purpose is to replace the human intensive tasks and automate interpretation workflows. (Gramstad et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Status of salt interpretation for velocity model building in Brazilian basins

Marpeau¹,

Belz²

2019

Proceedings of the 16th International Congress of the Brazilian Geophysical Society&Expogef

View full text Add to dashboard Cite

show abstract

70 years of machine learning in geoscience in review

Dramsch

2020

Advances in Geophysics

183

View full text Add to dashboard Cite

This review gives an overview of the development of machine learning in geoscience. A thorough analysis of the codevelopments of machine learning applications throughout the last 70 years relates the recent enthusiasm for machine learning to developments in geoscience. I explore the shift of kriging toward a mainstream machine learning method and the historic application of neural networks in geoscience, following the general trend of machine learning enthusiasm through the decades. Furthermore, this chapter explores the shift from mathematical fundamentals and knowledge in software development toward skills in model validation, applied statistics, and integrated subject matter expertise. The review is interspersed with code examples to complement the theoretical foundations and illustrate model validation and machine learning explainability for science. The scope of this review includes various shallow machine learning methods, e.g., decision trees, random forests, support-vector machines, and Gaussian processes, as well as, deep neural networks, including feed-forward neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks. Regarding geoscience, the review has a bias toward geophysics but aims to strike a balance with geochemistry, geostatistics, and geology, however, excludes remote sensing, as this would exceed the scope. In general, I aim to provide context for the recent enthusiasm surrounding deep learning with respect to research, hardware, and software developments that enable successful application of shallow and deep machine learning in all disciplines of Earth science.

show abstract

Automated Top Salt Interpretation Using a Deep Convolutional Net

Cited by 5 publications

References 0 publications

70 years of machine learning in geoscience in review

70 years of machine learning in geoscience in review

Status of salt interpretation for velocity model building in Brazilian basins

70 years of machine learning in geoscience in review

Contact Info

Product

Resources

About