App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 8

Enhanced deep operator network for seismic source localization

Pang Shanchen1a,1b,1c,1d,1e
Wang Chenyu1a,1b,1c,1d,1e
1. a. State Key Laboratory of Deep Oil and Gas, b. College of Computer Science, c. Qingdao College of Software, d. Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, e. State Key Laboratory of Chemical Safety, China University of Petroleum(East China), Qingdao 266580, China
DOI: 10.19734/j.issn.1001-3695.2025.12.0517

Abstract

In complex geological settings, deep learning methods based on deep neural operators struggle to comprehensively capture the latent features of distorted velocity fields in a timely manner, leading to a significant degradation in source localization performance. To address these limitations, this study proposes a Multi Feature Deep Operator Network (MF-DeepONet) tailored for complex geological conditions. This architecture captures travel-time distortion features induced by complex structures such as fault boundaries and salt bodies through multi-layer intermediate feature interactions. It further integrates a lightweight channel attention module to refine feature representations, enabling end-to-end high-precision mapping learning. Experimental validation on the Marmousi and OpenFWI datasets demonstrates that the proposed model accurately solves source localization problems across various highly heterogeneous and complex velocity models, achieving an average accuracy improvement of 62.9%. This method exhibits strong accuracy and adaptability for real-time earthquake early warning and resource exploration, offering a viable technical pathway for future 3D extensions and computational efficiency optimizations.

Foundation Support

中央高校基本科研业务费专项资金资助项目(24CX02012A,25CX04008A)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.12.0517
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 8

Publish History

[2026-04-22] Accepted Paper

Cite This Article

庞善臣, 王晨宇. 基于多特征融合强化的地震震源定位深度算子网络 [J]. 计算机应用研究, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0517. (Pang Shanchen, Wang Chenyu. Enhanced deep operator network for seismic source localization [J]. Application Research of Computers, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0517. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)