Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression
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This article is published in Geophysical Journal International.The article was published on 2016-10-01 and is currently open access. It has received 104 citations till now. The article focuses on the topics: Logistic regression.read more
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Machine learning for data-driven discovery in solid Earth geoscience
TL;DR: Solid Earth geoscience is a field that has very large set of observations, which are ideal for analysis with machine-learning methods, and how these methods can be applied to solid Earth datasets is reviewed.
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P Wave Arrival Picking and First-Motion Polarity Determination With Deep Learning
TL;DR: This work trains convolutional neural networks to measure both P-wave arrival times and first-motion polarities, and shows that the classifier picks more polarities overall than the analysts, without sacrificing quality, resulting in almost double the number of focal mechanisms.
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P-wave arrival picking and first-motion polarity determination with deep learning
TL;DR: In this paper, a convolutional neural network (CNN) was trained on 18.2 million manually picked seismograms for the southern California region to determine earthquake hypocenters and focal mechanisms.
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Seismic Waveform Classification and First-Break Picking Using Convolution Neural Networks
TL;DR: This letter investigates the application of CNNs for classifying time-space waveforms from seismic shot gathers and picking FBs of both direct wave and refracted wave and illustrates that CNN is an efficient automatic data-driven classifier and picker.
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STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI
TL;DR: A high-quality, large-scale, and global data set of local earthquake and non-earthquake signals recorded by seismic instruments, which contains two categories: local earthquake waveforms and seismic noise waveforms that are free of earthquake signals.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Applied Logistic Regression
David W. Hosmer,Stanley Lemeshow +1 more
TL;DR: Hosmer and Lemeshow as discussed by the authors provide an accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
The WEKA data mining software: an update
TL;DR: This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
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