Artificial Intelligence Applications in Seismic Data Processing: Leveraging Machine Learning for Enhanced Data Interpretation and Exploration Results.

@article{1709674,
      author = {Nyaknno Umoren, Malvern Iheanyichukwu Odum, Iduate Digitemie Jason, Dazok Donald Jambol},
      title = {Artificial Intelligence Applications in Seismic Data Processing: Leveraging Machine Learning for Enhanced Data Interpretation and Exploration Results.},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {3},
      number = {10},
      pages = {454-464},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709674.pdf},
      abstract = {Artificial intelligence (AI) has emerged as a transformative force in seismic data processing, revolutionizing how subsurface structures are interpreted and how exploration decisions are made. With increasing data complexity and the demand for high-resolution imaging, machine learning techniques offer robust solutions to automate, accelerate, and enhance seismic interpretation workflows. This review examines the application of AI?particularly supervised learning, unsupervised learning, and deep learning?in key areas of seismic processing, including noise attenuation, fault detection, horizon picking, and reservoir characterization. The integration of convolutional neural networks (CNNs), support vector machines (SVMs), and clustering algorithms has led to improved accuracy in facies classification and velocity model building. Additionally, this paper explores recent advances in real-time seismic analytics, predictive modeling, and the fusion of AI with geophysical inversion techniques. Challenges such as data quality, model generalization, and interpretability are addressed, along with opportunities to integrate AI into end-to-end exploration pipelines. Through a synthesis of case studies, technological innovations, and methodological trends, the review highlights how AI-driven seismic processing not only enhances interpretation fidelity but also drives more efficient and informed exploration strategies},
      keywords = {Artificial Intelligence (AI), Seismic Data Processing, Machine Learning, Deep Learning, Fault Detection, Reservoir Characterization.},
      month = {April},
  }