Journal of Seismic Exploration

Aims & Scope

The Journal of Seismic Exploration is an international medium for the publication of research in seismic modeling, processing, inversion, interpretation, field techniques, borehole techniques, tomography, instrumentation and software.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	0.4
2024	0.30

SJR (SCImago Journal Rank)

Year	Value
2024	0.191

Quartile

Year	Value
2024	Q4

h-index

Year	Value
2024	29

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Earth and Planetary Sciences, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Earthquake and blast recognition based on CEEMDAN multiscale fuzzy entropy and NSGAIII optimized 1D-CNN neural networks

  Citation: 1

  Authors: Cong, Tianwen, Guoqing, Chawei, Zhongya, Piyapatr, Pornntiwa

• Time-lapse earthquake difference prediction based on physics-informed long short-term memory coupled with interpretability boosting

  Citation: 0

  Authors: Tianwen, Guoqing, Cong, Palakorn, Nipada, Piyapatr

• An innovative method for computing static corrections using seismic reflection horizons analysis

  Citation: 0

  Authors: Youcef, Mohamed Cherif, Said

• Optimization of the SOM neural network model using CEEMDAN distribution entropy and ALO for seismic and blasting identification

  Citation: 0

  Authors: Ailing, Cong, Guoqing, Chawei, Tianwen

• Multi-stage progressive network for seismic random noise suppression

  Citation: 0

  Authors: Guanghui, Huiwei, Shoufeng, Li

• Seismic signal denoising using variational mode decomposition and a denoising convolutional neural network

  Citation: 0

  Authors: Shengrong, Liang, Xuesha

• Modeling the seismic wave equation using a staggered grid finite-difference method optimized with a genetic algorithm

  Citation: 0

  Authors: Mounika, Maheswar

• Predicting channel sandstone thickness through a VIF-NRBO-XGBoost model

  Citation: 0

  Authors: Weichao, Junhua, Zheng, Jingqiang, Deyong, Shugang

• Efficient wave-equation-based Kirchhoff-style migration using interpolated excitation information

  Citation: 0

  Authors: Sumin, Jiho, Wookeen

• Data-driven high-resolution gas-bearing prediction in tight sandstones: A case study from block L, Eastern Ordos Basin

  Citation: 0

  Authors: Lixin, Shuai, Qixin, Jingxue

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