World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
42
Citations
5938
World Ranking
6683
National Ranking
274

Stan E. Dosso publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Stan E. Dosso sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 407 publications — 90th percentile

90% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Stan E. Dosso D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Stan E. Dosso sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 42 D-Index — 35th percentile

35% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Stan E. Dosso is affiliated with the University of Victoria in Canada and has made contributions primarily in the field of Earth and Planetary Sciences. Their work spans several subfields, including Oceanography, Geophysics, Ocean Engineering, Ecology, and Archeology.

Their research covers a range of topics, notably:

  • Underwater Acoustics Research
  • Geophysical Methods and Applications
  • Marine animal studies overview
  • Seismic Waves and Analysis
  • Maritime and Coastal Archaeology
  • Earthquake and tectonic studies
  • High-pressure geophysics and materials

Dosso has published extensively, with key papers including:

  • "A review of inverse methods in seismic site characterization," 2022, Journal of Seismology
  • "Identification of fish sounds in the wild using a set of portable audio-video arrays," 2023, Methods in Ecology and Evolution
  • "Depth and frequency dependence of geoacoustic properties on the New England Mud Patch from reflection coefficient inversion," 2023, The Journal of the Acoustical Society of America
  • "On compressional wave attenuation in muddy marine sediments," 2021, The Journal of the Acoustical Society of America
  • "Controls on the evolution of Cenozoic seawater chemistry," 2022, Geochimica et Cosmochimica Acta

The scientist frequently collaborates with several co-authors, such as:

  • Julien Bonnel
  • Charles W. Holland
  • Jan Dettmer
  • Preston S. Wilson
  • Yong-Min Jiang

Dosso's publications often appear in these venues:

  • The Journal of the Acoustical Society of America
  • IEEE Journal of Oceanic Engineering
  • JASA Express Letters
  • Tectonophysics
  • Geophysical Research Letters

Best Publications

  • Quantifying uncertainty in geoacoustic inversion. I. A fast Gibbs sampler approach.

    Stan E. Dosso

  • An adaptive-hybrid algorithm for geoacoustic inversion

    S.E. Dosso;M.J. Wilmut;A.-L.S. Lapinski

  • Trans-dimensional geoacoustic inversion

    Jan Dettmer;Stan E. Dosso;Charles W. Holland

  • Alteration of ocean crust provides a strong temperature dependent feedback on the geological carbon cycle and is a primary driver of the Sr-isotopic composition of seawater

    Laurence A. Coogan;Stan E. Dosso

  • Estimation of ocean-bottom properties by matched-field inversion of acoustic field data

    S.E. Dosso;M.L. Yeremy;J.M. Ozard;N.R. Chapman

  • GEOACOUSTIC INVERSION VIA LOCAL, GLOBAL, AND HYBRID ALGORITHMS

    Mark R. Fallat;Stan E. Dosso

  • Quantifying uncertainty in geoacoustic inversion. II. Application to broadband, shallow-water data

    Stan E. Dosso;Peter L. Nielsen

  • Efficient trans-dimensional Bayesian inversion for geoacoustic profile estimation

    Stan E. Dosso;Jan Dettmer;Gavin Steininger;Charles W. Holland

  • Efficient trans-dimensional Bayesian inversion for geoacoustic profile estimation

    Stan E Dosso;Jan Dettmer;Gavin Steininger;Charles W Holland

  • Parallel tempering for strongly nonlinear geoacoustic inversion

    Stan E. Dosso;Charles W. Holland;Malcolm Sambridge

  • Trans-dimensional matched-field geoacoustic inversion with hierarchical error models and interacting Markov chains

    Jan Dettmer;Stan E. Dosso

  • Bayesian geoacoustic inversion of single hydrophone light bulb data using warping dispersion analysis

    Julien Bonnel;Stan E. Dosso;N. Ross Chapman

  • Trans-dimensional inversion of microtremor array dispersion data with hierarchical autoregressive error models

    Jan Dettmer;Sheri Molnar;Sheri Molnar;Gavin Steininger;Stan E. Dosso

  • Array element localization for horizontal arrays via Occam’s inversion

    Stan E. Dosso;Mark R. Fallat;Barbara J. Sotirin;John L. Newton

  • Uncertainty estimation in seismo-acoustic reflection travel time inversion.

    Jan Dettmer;Stan E. Dosso;Charles W. Holland

  • Uncertainty estimation in simultaneous Bayesian tracking and environmental inversion.

    Stan E. Dosso;Michael J. Wilmut

  • Data error covariance in matched-field geoacoustic inversion.

    Stan E. Dosso;Peter L. Nielsen;Michael J. Wilmut

  • Geodetically Inferred Locking State of the Cascadia Megathrust Based on a Viscoelastic Earth Model

    Shaoyang Li;Shaoyang Li;Kelin Wang;Kelin Wang;Yanzhao Wang;Yan Jiang

  • Model selection and Bayesian inference for high-resolution seabed reflection inversion

    Jan Dettmer;Stan E. Dosso;Charles W. Holland

  • Magnetotelluric appraisal using simulated annealing

    S. E. Dosso;D. W. Oldenburg

  • Bayesian matched-field geoacoustic inversion

    Stan E Dosso;Jan Dettmer

  • Data Uncertainty Estimation in Matched-Field Geoacoustic Inversion

    S.E. Dosso;M.J. Wilmut

Frequent Co-Authors

John F. Cassidy
John F. Cassidy Geological Survey of Canada
Peter V. Nielsen
Peter V. Nielsen Aalborg University
N. Ross Chapman
N. Ross Chapman University of Victoria
George D. Spence
George D. Spence University of Victoria
Michael Riedel
Michael Riedel GEOMAR Helmholtz Centre for Ocean Research Kiel
Roy D. Hyndman
Roy D. Hyndman University of Victoria
Michael Morley
Michael Morley University of Pennsylvania
Francis Juanes
Francis Juanes University of Victoria
John A. Goff
John A. Goff The University of Texas at Austin
Douglas W. Oldenburg
Douglas W. Oldenburg University of British Columbia

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