Page 52 - Machine Learning for Subsurface Characterization
P. 52
Unsupervised outlier detection techniques Chapter 1 37
[7] Akkurt R, Conroy T, Psaila D, Paxton A, Low J, Spaans P. Accelerating and enhancing pet-
rophysical analysis with machine learning: a case study of an automated system for well log
outlier detection and reconstruction. In: Presented at the SPWLA 59th Annual Logging Sym-
posium, London, UK; 2018.
[8] Raschka S, Mirjalili V. Python Machine Learning: Machine Learning and Deep Learning With
Python, Scikit-Learn, and TensorFlow. 2nd ed. Packt; 2017.
[9] Ester M, Kriegel H-P, Sander J, Xu X. A density based algorithm for discovering clusters in
large spatial database with noise. In: Presented at the International Conference on Knowledge
Discovery and Data Mining, Portland, Oregon; 1996.
[10] Wenig P. Local outlier factor for anomaly detection. In: Towards Data Science, 2018.
[11] Pedregosa F, et al. Scikit-learn: machine learning in python. J Mach Learn Res
2011;12:2825–30.
[12] Osborne J, Overbay A. The power of outliers (and why researchers should ALWAYS check for
them). Pract Assess Res Eval 2004;9(6):1–8.
[13] Ferdowsi H, Jagannathan S, Zawodniok M. An online outlier detection and removal scheme
for improving fault detection performance. IEEE Trans Neural Netw Learn Syst 2014;25
(5):908–19.
[14] Orr J, Sackett P, Dubois C. Outlier detection and treatment in I/O psychology: a survey of
researcher beliefs and an empirical illustration. Pers Psychol 1991;44:473–86.
[15] He J, Misra S. Generation of synthetic dielectric dispersion logs in organic-rich shale forma-
tions using neural-network models. Geophysics 2019;84(3):D117–29.
[16] He J, Misra S, Li H. Comparative study of shallow learning models for generating compres-
sional and shear traveltime logs. Petrophysics 2018;59(06):826–40.
[17] Wu Y, Misra S, Sondergeld C, Curtis M, Jernigen J. Machine learning for locating organic
matter and pores in scanning electron microscopy images of organic-rich shales. Fuel
2019;253:662–76.