A neural network approach for classification of fault-slip data in geoscience

YAMAN S., Karakaya B., Köküm M.

Ain Shams Engineering Journal, cilt.15, sa.1, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.asej.2023.102325
  • Dergi Adı: Ain Shams Engineering Journal
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Anahtar Kelimeler: Artificial neural network, Geoscience, Machine learning, Paleostress analysis
  • Hakkari Üniversitesi Adresli: Evet

Özet

In geoscience, paleostress studies are a vital tool for understanding the tectonic evolution of the region. The collected hundreds or even thousands of heterogeneous fault-slip data need to be divided into homogeneous (i.e., belonging to similar tectonic environments) subgroups by geologists. Computer-based paleostress inversion programs are able to run homogenous data sets. Here, we are aiming for the classification of heterogeneous fault-slip data into homogeneous sub-data which is performed by using several techniques in Machine Learning (ML) algorithms, which are Artificial Neural Network (ANN), Naïve Bayes (NB) and Logistic Regression (LR) models. When these models are executed on the Anaconda Navigator interface with python language, the accuracies are obtained as 87.17 % for ANN, 79.71 % LR, and 62.1 % NB.