Machine learning for data-driven discovery in solid Earth geoscience
BACKGROUND The solid Earth, oceans, and atmosphere together form a complex
interacting geosystem. Processes relevant to understanding Earth's geosystem behavior …
interacting geosystem. Processes relevant to understanding Earth's geosystem behavior …
Machine learning for volcano-seismic signals: Challenges and perspectives
Environmental monitoring is a topic of increasing interest, especially concerning the matter
of natural hazards prediction. Regarding volcanic unrest, effective methodologies along with …
of natural hazards prediction. Regarding volcanic unrest, effective methodologies along with …
Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
The continuously growing amount of seismic data collected worldwide is outpacing our
abilities for analysis, since to date, such datasets have been analyzed in a human-expert …
abilities for analysis, since to date, such datasets have been analyzed in a human-expert …
Source localization in an ocean waveguide using supervised machine learning
H Niu, E Reeves, P Gerstoft - The Journal of the Acoustical Society of …, 2017 - pubs.aip.org
Source localization in ocean acoustics is posed as a machine learning problem in which
data-driven methods learn source ranges directly from observed acoustic data. The pressure …
data-driven methods learn source ranges directly from observed acoustic data. The pressure …
A deep convolutional neural network for localization of clustered earthquakes based on multistation full waveforms
M Kriegerowski, GM Petersen… - Seismological …, 2019 - pubs.geoscienceworld.org
Earthquake localization is both a necessity within the field of seismology, and a prerequisite
for further analysis such as source studies and hazard assessment. Traditional localization …
for further analysis such as source studies and hazard assessment. Traditional localization …
A gradient boosting decision tree algorithm combining synthetic minority oversampling technique for lithology identification
K Zhou, J Zhang, Y Ren, Z Huang, L Zhao - Geophysics, 2020 - library.seg.org
Lithology identification based on conventional well-logging data is of great importance for
geologic features characterization and reservoir quality evaluation in the exploration and …
geologic features characterization and reservoir quality evaluation in the exploration and …
Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression
SM Mousavi, SP Horton, CA Langston… - Geophysical Journal …, 2016 - academic.oup.com
We develop an automated strategy for discriminating deep microseismic events from
shallow ones on the basis of the waveforms recorded on a limited number of surface …
shallow ones on the basis of the waveforms recorded on a limited number of surface …
[HTML][HTML] Classification of clustered microseismic events in a coal mine using machine learning
Discrimination of seismicity distributed in different areas is essential for reliable seismic risk
assessment in mines. Although machine learning has been widely applied in seismic data …
assessment in mines. Although machine learning has been widely applied in seismic data …
Hierarchical exploration of continuous seismograms with unsupervised learning
Continuous seismograms contain a wealth of information with a large variety of signals with
different origin. Identifying these signals is a crucial step in understanding physical …
different origin. Identifying these signals is a crucial step in understanding physical …
A seismic shift in scalable acquisition demands new processing: Fiber-optic seismic signal retrieval in urban areas with unsupervised learning for coherent noise …
With the development of fiber-optic seismic acquisition systems, dense seismic monitoring of
the near surface in urban areas is quickly becoming much easier than ever before. We …
the near surface in urban areas is quickly becoming much easier than ever before. We …