Computers and Geosciences

______V1____________2019____________________ This data set includes a 3D modeled ground-penetrating radar (GPR) reflection data set as well as the underlying realistic sedimentary model. We provide a 3D porosity model showing heterogeneities down to the sub-facies scale. We have inferred this model from the publicly available 3D Herten hydrofacies model ('Realization 1' in Supplementary Material of Comunian et al., 2011; see related links down below) and the associated porosity values and ranges (Bayer et al., 2011; see related links down below). Details on the generation of our porosity model are found in the associated article. We deliver our unprocessed 3D GPR reflection data set modeled using gprMax (Warren et al., 2016; see related links down below) across the entire model surface assuming fresh-water saturated sediments. Details on the transformation of the porosity model into electrical parameter fields used as input for GPR modeling as well as information on the GPR modeling procedure are found in the associated article. Additionally, we provide basic code to read and visualize the provided data in MATLAB and python. The Readme-file comprises detailed descriptions of the data files and formats and step-by-step instructions on code usage and ParaView visualization. ______V2____________2025____________________ We deliver a unprocessed 3D Multi-Offset (MO) dataset modeled using gprMax (Warren et al., 2016; see related links down below) along selected 2D lines (y = 2, 4, 6, 8 m) assuming fresh-water saturated sediments. Details on the GPR modeling procedure are found in the associated article.

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.