@conference {bougher2016SEGava,
	title = {AVA classification as an unsupervised machine-learning problem},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {553-556},
	abstract = {Much of AVA analysis relies on characterizing background trends and anomalies in pre-stack seismic data. Analysts reduce a seismic section into a small number of these trends and anomalies, suggesting that a low-dimensional structure can be inferred from the data. We describe AVA-attribute characterization as an unsupervised-learning problem, where AVA classes are learned directly from the data without any prior assumptions on physics and geological settings. The method is demonstrated on the Marmousi II elastic model, where a gas reservoir was successfully delineated from a background trend in a depth migrated image.},
	keywords = {AVA, machine learning, SEG},
	doi = {10.1190/segam2016-13874419.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/bougher2016SEGava/bougher2016SEGava.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/bougher2016SEGava/bougher2016SEGava_pres.pdf},
	author = {Ben B. Bougher and Felix J. Herrmann}
}
@mastersthesis {bougher2016THmla,
	title = {Machine learning applications to geophysical data analysis},
	year = {2016},
	note = {(MSc)},
	month = {08},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {The sedimentary layers of the Earth are a complex amorphous material formed from chaotic, turbulent, and random natural processes. Exploration geophysicists use a combination of assumptions, approximate physical models, and trained pattern recognition to extract useful information from complex remote sensing data such as seismic and well logs. In this thesis I investigate supervised and unsupervised machine learning models in geophysical data analysis and present two novel applications to exploration geophysics. Firstly, interpreted well logs from the Trenton-Black River study are used to train a classifier that results in a success rate of 67\% at predicting stratigraphic units from gamma ray logs. I use the scattering transform, a multiscale analysis transform, to extract discriminating features to feed a K-nearest neighbour classifier. A second experiment frames a conventional pre-stack seismic data characterization workflow as an unsupervised machine learning problem that is free from physical assumptions. Conventionally, the Shuey model is used to fit the angle dependent reflectivity response of seismic data. I instead use principle component based approaches to learn projections from the data that improve classification. Results on the Marmousi II elastic model and an industry field dataset show that unsupervised learning models can be effective at segmenting hydrocarbon reservoirs from seismic data.},
	keywords = {AVA, machine learning, MSc, PCA, scattering transform, thesis, well logs},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2016/bougher2016THmla/bougher2016THmla.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2016/bougher2016THmla/bougher2016THmla_pres.pdf},
	author = {Ben B. Bougher}
}
@article {bougher2015CSEGust,
	title = {Using the scattering transform to predict stratigraphic units from well logs},
	journal = {CSEG Recorder},
	volume = {41},
	number = {1},
	year = {2016},
	note = {(CSEG Recorder)},
	month = {01},
	pages = {22-25},
	abstract = {Much of geophysical interpretation relies on trained pattern recognition of signals and images, a workflow that can be modeled by supervised machine learning. A challenge of supervised learning is determining a physically meaningful feature set that can successfully classify the data. Defined by a network of cascading wavelets, the scattering transform provides a non-linear multiscale analysis that has deep connections to the fractal statistics of the signal. Interestingly, the scattering transform takes the form of a pre-trained convolutional neural network. This paper uses the scattering transform to extract features from well logs in order to train a classifier that can predict stratigraphic units. The methodology is tested on interpreted well logs from Trenton-Black River project and initial results are presented.},
	keywords = {machine learning, scattering transform, well logs},
	url = {https://slim.gatech.edu/Publications/Public/Journals/CSEGRecorder/2016/bougher2015CSEGust/bougher2015CSEGust.html},
	url2 = {http://csegrecorder.com/articles/view/using-the-scattering-transform-to-predict-strat-units-from-well-logs},
	author = {Ben B. Bougher and Felix J. Herrmann}
}
@conference {bougher2015CSEGpsu,
	title = {Prediction of stratigraphic units from spectral co-occurance coefficients of well logs},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2015},
	note = {(CSEG, Calgary)},
	month = {05},
	abstract = {Well logging is the process of making physical measurements down bore holes in order to characterize geological and structural properties. Logs are visually interpreted and correlated to classify regions that are similar in structure, a process that can be modelled with machine learning. This project applies supervised learning methods to labelled well logs from the Trenton Black River data set in order to classify major stratigraphic units. Spectral co-occurance coefficients were used for feature extraction, and a k-nearest-neighbours approach was used for classification. This novel approach was applied to real field data in a high-impact domain, yielding promising results for future research.},
	keywords = {CSEG, machine learning, scattering transform, well logs},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2015/bougher2015CSEGpsu/bougher2015CSEGpsu.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2015/bougher2015CSEGpsu/bougher2015CSEGpsu_pres.pdf},
	author = {Ben B. Bougher and Felix J. Herrmann}
}
