Measuring rock surface strength based on spectrograms with deep convolutional networks

Shuai Han, Heng Li, Mingchao Li, Xiaochun Luo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


One of the most widely accepted field methods used by geological engineers to measure rock surface strengths is by striking a rock with a geological hammer and using the emitted sound frequencies to determine strength. While the method is a convenient, it is also subjective. To this end, we propose a new method of measurement based on spectrograms using deep convolutional networks. The spectrograms collected through striking rocks with a geological hammer is the input variable to a deep learning model, which is the Inception-v3 model in this study and has a 93% classification accuracy. We then introduced a probability matrix and an error correction model to estimate the surface strength of rocks from the classification results. The experimental results show our method has high potential to underpin the implementation of efficient and objective measurements of rock surface strength in the field.

Original languageEnglish
Article number104312
JournalComputers and Geosciences
Publication statusPublished - Dec 2019


  • Deep convolutional network
  • Inception-v3 model
  • Rebound value
  • Spectrogram analysis
  • Surface strength of rocks

ASJC Scopus subject areas

  • Information Systems
  • Computers in Earth Sciences

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