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GeoPDNN 1.0

Authors: Jiateng Guo; Xuechuang Xu;

GeoPDNN 1.0

Abstract

A program to build 3D geological model from borehole data. PDNN depends on several standard Python packages that should be shipped with any standard distribution (and are easy to install): numpy torch scipy. Before running this program, you need to change the file path in the code. The borehole data is stored in the mdb file. In the borehole data, the following parameters used for processing are saved in the txt file: ZKX, ZKY, TCHD, TCCDSD, ZKBG, TCZCBH, ZKID, GCSY, TCYCBH, TCCYCBH. These represent borehole coordinates x, borehole coordinates y, soil layer thickness, soil layer bottom depth, borehole elevation, main layer label, borehole id, engineering index, sublayer label, sublayer label of sublayer. Finally, after training with GeoPDNN, you get the pt file, the training accuracy, and the test accuracy. You can predict the results based on the modeling range of your data, and the gird data will be saved in txt file by using Pridict.py. HUll3D.py is from https://github.com/rgmyr/pyConvexHull3D, it is used by Predict.py. If you have any questions, please contact us.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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