Data for: Identifying microseismic events in a mining scenario using a convolutional neural network

Published: 31 January 2020| Version 1 | DOI: 10.17632/bj5ypvnw8z.1
Contributors:
Andrew Wilkins,
Xun Luo,
Yi Duan,
Andrew Strange

Description

Name: learn.py Author: Andy Wilkins, andrew.wilkins@csiro.au, +61 7 3327 4497, Queensland Centre for Advanced Technologies, PO Box 883, Kenmore, Qld, 4069, Australia Year: 2019 Software required: python2 software stack, including numpy, optparse, pandas, keras, sklearn and matplotlib Language: python Program size: 17kB Name: out.txt Author: Andy Wilkins, andrew.wilkins@csiro.au, +61 7 3327 4497, Queensland Centre for Advanced Technologies, PO Box 883, Kenmore, Qld, 4069, Australia Year: 2019 Description: Output from learn.py when operating on cnn_data.txt Name: cnn_data.csv Author: Andy Wilkins, andrew.wilkins@csiro.au, +61 7 3327 4497, Queensland Centre for Advanced Technologies, PO Box 883, Kenmore, Qld, 4069, Australia Year: 2019 ASCII plaintext, comma-separated values, with comment-lines indicated by a ``#''. Header precisely define the file format Size: 266MB

Files

Categories

Machine Learning, Microseismic Monitoring

License