Exploiting Correlation Structures for Geoscience
Fan, Bo.
2018
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Abstract: Geoscience is
the scientific study of the planet earth and its many different natural geologic
systems. It has been widely used in geology, archaeology, mineral, oil and energy
exploration, oceanography, and engineering. In geoscience methods, statistical signal
processing, modeling, and machine learning techniques are of great importance. In this
thesis, by exploiting the correlation ... read morestructure from the geophysical data, we propose
novel methods for signal processing, modeling and classification and apply them to three
different geophysical data acquisition systems. For hyperspectral imaging and
reconstruction system in the presence of spectral noise and additive noise, we propose a
novel denoising and reconstruction optimization framework by joining low rank (from
correlated slices), total variation and sparsity based regularization together. Using
parallel proximal algorithm (PPXA) and alternating direction method of multipliers
(ADMM) as solvers, our framework improves the reconstruction SNR by 1db to 8db, compared
to the state of the art. For ultrasonic data online compression and imaging system, we
exploit the high correlation among successively acquired signals through cosine
similarities as measurements, and model the signal as sum of complex exponentials (SOE).
We propose a new method called angle based basis grouping (ABBG), which represents a
group of correlated waveforms sharing the same basis but different amplitudes. ABBG
generates better compression results compared to SOE-MP, SOC-CSD and SOG-SAGE methods in
terms of speed, compression ratio and reconstruction accuracy. It also achieves near
lossless imaging performances in parallel scanning and borehole imaging by retaining
only 43% of the original data. For borehole acoustic array data classification problem
in well integrity diagnosis system, we exploit the cross correlation in each depth
frame, and apply slowness time coherence (STC) processing and band pass filtering to
extract new features. To further exploit the correlation in and across different
channels from the feature maps, we discuss several deep learning models such as
Convolutional Auto Encoder, Alex net, VGG, GoogLeNet, Inception V2, Residual net, and
XCeption, and show the classification accuracy gain by 3-5 % in validation and test
sets. To increase the prediction accuracy on field data set, we propose a new ensemble
learning framework by feeding 6 types of features from 2 modalities into a stacked model
composed of 10 classifiers. The proposed method generates consistent and convincing
results visually, which have been validated by the prior knowledge and
experts.
Thesis (Ph.D.)--Tufts University, 2018.
Submitted to the Dept. of Electrical Engineering.
Advisor: Shuchin Aeron.
Committee: Eric Miller, Sandip Bose, Sameer Sonkusale, and Shuchin Aeron.
Keywords: Electrical engineering, Computer science, and Geotechnology.read less - ID:
- 44558r89z
- Component ID:
- tufts:25017
- To Cite:
- TARC Citation Guide EndNote
