Computers and Geosciences

Thin section photographs with 10X magnification zoom under plane parallel polarized light. Organized in folders with interpreted microfacies. Subcrops of the images are also provided.

We provide the metadata of the experiment. There are 6 groups in total, including 1) Monitored curves of ZG118 station: reservoir water level; 2) Monitored curves of ZG118 station: rainfall; 3) Decomposition results of cumulative displacement; 4) Iterative process of solving hybrid model parameters; 5) Predicted results of periodic displacement using hybrid model; 6) Predicted results of cumulative displacement using SVR, LSTM and hybrid model.

The Code described in the manuscript

Attempting to classify the quarry sources which provided reactive rock aggregate, composed of Carboniferous aged pyritic mudrocks and limestones, to over 12, 500 homes across Ireland has not yet been possible using geochemical data. Using this dataset, a solution to this problem is found by applying machine learning models, such as logistic regression and random forest, to a geochemical dataset of scanning electron microscope energy-dispersive X-ray spectroscopy (SEM-EDS) and Laser ablation-quadrupole-inductively coupled plasma mass spectrometry (LA-Q-ICPMS) of pyrite, and Isotope ratio mass spectrometry (IRMS) of bulk rock aggregate, to predict quarry source location.

pssa.zip: zipped source code for pSSA, including: cmake: CMake dependency folder; include: C++ header files; src: C++ source code files; CMakeLists.txt: CMake build definition file; README.txt: Compilation and run instructions. data_for_pssa: data set used in the paper for testing pSSA, including: *.dat: ascii seismograms files ssa.json: setup file station.dat: sensors coordinates. the first two lines are comment lines. The following lines are organized in columns separated by space: column 1: northing (in km). column 2: easting (in km). column 3: sensor height column 4: not in use. column 5: not in use column 6: sensor code/name.

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

(1) conti2d-win10.zip includes manual, source code and pre-compiled executable file with and without GUI for Win10 system. (2) conti2d-master.zip includes manual, source code for Linux and Mac OS. (3) Tutorial_conti2d.mp4 is a video tutorial for executable program of conti2d with GUI.

This is the application source code of SensorDataGetter.

This is the data obtained by Android smartphone sensors, when phone is placed at different orientations. We have re-examined and modified the formulas for the calculation methods.

Log files produced by the software (FieldOpt) in running the optimizations for this paper. The log files are in a comma-separated values (CSV) format and the JSON format. Also included is simulated production for initial- and optimal cases (in CSV format). Also included is the configuration files used by FieldOpt when running the optimizations. A description of how the data is structured is found in the included README.md file.