Unsupervised Machine Learning Techniques for Subtle Fault Detection

2020 ◽  
Author(s):  
M. Hussein ◽  
R. Stewart ◽  
J. Wu
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Machine Learning Techniques ◽  
Unsupervised Machine Learning ◽  
Learning Techniques

Machine Learning Assisted Interpretation: Integrated Fault Prediction Extraction Case Study from the Groningen Gas Field, Netherlands

2021 ◽  
pp. 1-67
Author(s):  
Stewart Smith ◽  
Olesya Zimina ◽  
Surender Manral ◽  
Michael Nickel
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Seismic Data ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Gas Field ◽  
Seismic Fault ◽  
Learning Techniques ◽  
Groningen Gas Field

Seismic fault detection using machine learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and de-risking activities. Efforts to automate the detection and extraction of geological features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, NE Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D pre-stack depth migrated seismic dataset. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis (PCA) to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geological context to the interpreter and input to dependent workflows more efficiently.

Machine Learning Techniques to Fault Detection in Rotating Machines

2021 ◽  
Author(s):  
Luís Otávio Garavaso ◽  
Gregory Bregion Daniel ◽  
Katia Lucchesi Cavalca Dedini
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Machine Learning Techniques ◽  
Rotating Machines ◽  
Learning Techniques

In-Network Data Aggregation for Information-Centric WSNs using Unsupervised Machine Learning Techniques

2021 ◽  
Author(s):  
Marcelo E. Pellenz ◽  
Rosana Lachowski ◽  
Edgard Jamhour ◽  
Glauber Brante ◽  
Guilherme Luiz Moritz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Aggregation ◽  
Machine Learning Techniques ◽  
Network Data ◽  
Unsupervised Machine Learning ◽  
Learning Techniques

scholarly journals A Novel Semi-Supervised Fuzzy C-Means Clustering Algorithm Using Multiple Fuzzification Coefficients

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 258
Author(s):  
Tran Dinh Khang ◽  
Manh-Kien Tran ◽  
Michael Fowler
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Machine Learning Techniques ◽  
Unsupervised Machine Learning ◽  
Practical Applications ◽  
Fuzzy C Means ◽  
Learning Techniques ◽  
Fuzzy C Means Clustering ◽  
Data Points ◽  
Data Elements

Clustering is an unsupervised machine learning method with many practical applications that has gathered extensive research interest. It is a technique of dividing data elements into clusters such that elements in the same cluster are similar. Clustering belongs to the group of unsupervised machine learning techniques, meaning that there is no information about the labels of the elements. However, when knowledge of data points is known in advance, it will be beneficial to use a semi-supervised algorithm. Within many clustering techniques available, fuzzy C-means clustering (FCM) is a common one. To make the FCM algorithm a semi-supervised method, it was proposed in the literature to use an auxiliary matrix to adjust the membership grade of the elements to force them into certain clusters during the computation. In this study, instead of using the auxiliary matrix, we proposed to use multiple fuzzification coefficients to implement the semi-supervision component. After deriving the proposed semi-supervised fuzzy C-means clustering algorithm with multiple fuzzification coefficients (sSMC-FCM), we demonstrated the convergence of the algorithm and validated the efficiency of the method through a numerical example.

Sign in / Sign up

Export Citation Format

Share Document