A Tool for Predicting Songliao Heat Flow Based on Machine Learning
The manuscript is submitted to Earth and Space Science. It contains relevant code and visualization results.
Currently, it is being further improved.
Heat flow data can be accessed through the website http://heatflow.org and open access of citation office of Goutorbe et al. (2011). Chinese heat flow data can be accessed through https://chfdb.xyz/ obtained from the website or Jiang et al. (2016). The geological and geophysical features of training data can be obtained through the studies of Goutorbe et al. (2011) and Rezvanbehbahani et al. (2017). The DGSA sensitivity analysis code is Open access from Jihoon et al. (2016). The visualization results in the text are obtained through GMT（ https://docs.gmt-china.org ）Implement with Matlab.
1.Goutorbe, B., Poort, J., Lucazeau, F. & Raillard, S. (2011). Global heat flow trends resolved from multiple geological and geophysical proxies. Geophysical Journal International, 187, 1405-1419. 2.Rezvanbehbahani, S., Stearns, L. A., Kadivar, A., Walker, J. D. & van der Veen C. J. (2017). Predicting the geothermal heat flux in Greenland: A machine learning approach. Geophysical Research Letters, 44, 12, 271–279. 3.Jiang, G. Z., Hu, S. B., Shi, Y. Z., Zhang, C., Wang, Z. T. & Hu, Di. (2019). Terrestrial heat flow of continental China: Updated dataset and tectonic implications. Tectonophysics, 753, 36-48. 4.Jihoon, P., Guang, Y., Addy, S., Céline, S. & Jef, C. (2016). DGSA: A Matlab toolbox for distance-based generalized sensitivity analysis of geoscientific computer experiments. Computers & Geosciences, 97, 15-29.