The free surface assumption for marine data-driven demultiple methods

被引:14
|
作者
Frijlink, Martijn [1 ]
van Borselen, Roald [1 ]
Sollner, Walter [1 ]
机构
[1] GE SPS, PGS, NL-2332 KG Leiden, Netherlands
关键词
INVERSE-SCATTERING SERIES; ITERATIVE INVERSION; MULTIPLE-SCATTERING;
D O I
10.1111/j.1365-2478.2010.00914.x
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
In the past, integral formulations for marine data-driven demultiple methods have been derived from reciprocity theorems. Two fundamental assumptions in these derivations were that the sea-surface is flat and has a known reflection coefficient, often taken to be minus one. In this paper, we show that for dual sensor data these assumptions can be relaxed. The sea-surface has to obey the same conditions as any other reflecting boundary in the subsurface: it must be constant in time but shape and reflection strength can vary in space. For both surface-related multiple elimination, and multiple attenuation by multi-dimensional deconvolution, we derive integral equations that depend only on the measured pressure and particle velocity fields. Finally, we show there is an intimate connection between the integral equations for the methods.
引用
收藏
页码:269 / 278
页数:10
相关论文
共 50 条
  • [41] Data-driven and equation-free methods for neurological disorders: analysis and control of the striatum network
    Spiliotis, Konstantinos
    Koehling, Ruediger
    Just, Wolfram
    Starke, Jens
    FRONTIERS IN NETWORK PHYSIOLOGY, 2024, 4
  • [42] Application of data-driven models in the analysis of marine power systems
    Swider, Anna
    Langseth, Helge
    Pedersen, Eilif
    APPLIED OCEAN RESEARCH, 2019, 92
  • [43] Data-driven Bayesian analysis of marine accidents in the English Channel
    Gao, Xinjia
    Wu, Yutong
    Yu, Qifeng
    Dai, Wei
    JOURNAL OF TRANSPORTATION SAFETY & SECURITY, 2024, 16 (12) : 1487 - 1516
  • [44] A Review of Data-Driven Methods for Power Flow Analysis
    Akter, Mahmuda
    Nazaripouya, Hamidreza
    2023 NORTH AMERICAN POWER SYMPOSIUM, NAPS, 2023,
  • [45] Contraction scour estimation using data-driven methods
    Minh Duc Bui
    Kaveh, Keivan
    Penz, Petr
    Rutschmann, Peter
    JOURNAL OF APPLIED WATER ENGINEERING AND RESEARCH, 2015, 3 (02): : 143 - 156
  • [46] Combining Symbolic and Data-Driven Methods for Goal Recognition
    Wilken, Nils
    Stuckenschmidt, Heiner
    Bartelt, Christian
    2021 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS AND OTHER AFFILIATED EVENTS (PERCOM WORKSHOPS), 2021, : 428 - 429
  • [47] A Study on Data-Driven Novel Cancer Staging Methods
    Gao, Yuan
    Tian, Yu
    Chi, Shengqiang
    Lu, Yao
    Li, Xinhang
    Zhou, Tianshu
    Li, Jing-song
    MEDINFO 2017: PRECISION HEALTHCARE THROUGH INFORMATICS, 2017, 245 : 1263 - 1263
  • [48] Calculating material properties with purely data-driven methods
    Konstantinos, Papastamatiou
    Filippos, Sofos
    Theodoros, E. Karakasidis
    PROCEEDINGS OF THE 12TH HELLENIC CONFERENCE ON ARTIFICIAL INTELLIGENCE, SETN 2022, 2022,
  • [49] On identification methods for direct data-driven controller tuning
    van Heusden, Klaske
    Karimi, Alireza
    Soderstrom, Torsten
    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2011, 25 (05) : 448 - 465
  • [50] Data-driven Methods for Travel Time Estimation: A Survey
    Zheng, Zhipeng
    Ye, Yongchao
    Zhu, Yuanshao
    Zhang, Shiyao
    Yu, James J. Q.
    2023 IEEE 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, ITSC, 2023, : 1292 - 1299