Techniques and methods for seafloor topography mapping: past, present, and future

Abstract

Detailed mapping of seafloor topography is essential for understanding seafloor evolution, ensuring navigational safety, and discovering mineral resources. As global environmental conditions continue to deteriorate, various international and regional initiatives have been launched to accelerate seafloor topography mapping, yielding valuable data. Currently, only about a quarter of the seafloor has been directly mapped, observed, and explored due to limitations in traditional detection techniques. However, artificial intelligence, particularly machine learning, is progressively overcoming these constraints with its advanced data processing and analysis capabilities. In recent years, machine learning has increasingly emerged as an alternative to traditional methods, particularly for mapping both open-ocean and shallow-sea topography. This paper first introduces traditional seafloor topography detection techniques and the global topography models developed using them. It then examines the application of machine learning in seafloor mapping before concluding with the challenges and future prospects of intelligent seafloor mapping, along with relevant recommendations.

Keywords

Seafloor topography / Machine learning / Oceanographic survey and mapping / Artificial intelligence / Information and Computing Sciences / Artificial Intelligence and Image Processing

References

[1] AiB, WenZ, WangZ, WangR, SuD, LiC, et al. . Convolutional neural network to retrieve water depth in marine shallow water area from remote sensing images. IEEE J Sel Top Appl Earth Observ Remote Sens, 2020, 13: 2888-2898.

[2] AlevizosE. A combined machine learning and residual analysis approach for improved retrieval of shallow bathymetry from hyperspectral imagery and sparse ground truth data. Remote Sens, 2020, 12213489.

[3] AlpersW, HenningsI. A theory of the imaging mechanism of underwater bottom topography by real and synthetic aperture radar. J Geophys Res-Oceans, 1984, 89C610529-10546.

[4] Amante C, Eakins BW (2009) ETOPO1 arc-minute global relief model: procedures, data sources and analysis. National Geophysical Data Center, Boulder, pp 1–25

[5] AnD, FengW, ChenX, SunM, ZhangZ, YangM, et al. . Predicting intermediate wavelength depth using a deep neural network combined with gravity data in the Sea of Japan. Int J Digit Earth, 2024, 1712430679.

[6] AnD, GuoJ, LiZ, JiB, LiuX, ChangX. Improved gravity-geologic method reliably removing the long-wavelength gravity effect of regional seafloor topography: a case of bathymetric prediction in the South China Sea. IEEE Trans Geosci Remote Sens, 2022, 60: 1-12.

[7] Andersen OB (2010) The DTU10 global gravity field and mean sea surface-improvements in the Arctic. In: 2nd IGFS Meeting, Fairbanks, Alaska, pp 1–17

[8] Andersen OB, Knudsen P (2008) The DNSC08BAT bathymetry developed from satellite altimetry. In: EGU-2008 Meeting, Vienna

[9] AnnanRF, WanX. Mapping seafloor topography of Gulf of Guinea using an adaptive meshed gravity-geologic method. Arab J Geosci, 2020, 137301.

[10] AnnanRF, WanX. Recovering bathymetry of the Gulf of Guinea using altimetry-derived gravity field products combined via convolutional neural network. Surv Geophys, 2022, 4351541-1561.

[11] ArabelosD, TziavosIN. Gravity-field improvement in the Mediterranean Sea by estimating the bottom topography using collocation. J Geodesy, 1998, 723136-143.

[12] BeckerJJ, SandwellDT, SmithWHF, BraudJ, BinderB, DepnerJ, et al. . Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. Mar Geod, 2009, 324355-371.

[13] Bergen KJ, Johnson PA, de Hoop MV, Beroza GC (2019) Machine learning for data-driven discovery in solid Earth geoscience. Science 363(6433):eaau0323. https://doi.org/10.1126/science.aau0323

[14] BongiovanniC, StewartHA, JamiesonAJ. High-resolution multibeam sonar bathymetry of the deepest place in each ocean. Geosci Data J, 2022, 91108-123.

[15] BourilletJF, EdyC, RambertF, SatraC, LoubrieuB. Swath mapping system processing: bathymetry and cartography. Mar Geophys Res, 1996, 182–4487-506.

[16] CahalaneC, MageeA, MonteysX, CasalG, HanafinJ, HarrisP. A comparison of Landsat 8, RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry. Remote Sens Environ, 2019, 233. 111414

[17] CalmantS, BaudryN. Modelling bathymetry by inverting satellite altimetry data: a review. Mar Geophys Res, 1996, 182123-134.

[18] CalmantS, Berge-NguyenM, CazenaveA. Global seafloor topography from a least-squares inversion of altimetry-based high-resolution mean sea surface and shipboard soundings. Geophys J Int, 2002, 1513795-808.

[19] CarronMJ, VogtPR, JungWY. A proposed international long-term project to systematically map the world’s ocean floors from beach to trench: GOMaP (Global Ocean Mapping Program). Int Hydrogr Rev, 2001, 2349-55

[20] Cerdeira-Estrada S, Heege T, Kolb M, Ohlendorf S, Uribe A, Müller A et al (2012) Benthic habitat and bathymetry mapping of shallow waters in Puerto morelos reefs using remote sensing with a physics based data processing. In: 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, pp 4383–4386. https://doi.org/10.1109/IGARSS.2012.6350402

[21] ChenM, QianZ, BoersN, CreutzigF, Camps-VallsG, HubacekK, et al. . Collaboration between artificial intelligence and Earth science communities for mutual benefit. Nat Geosci, 2024, 1710949-952.

[22] ChubbSR, ValenzuelaGR, GreenbergDA. Radar surface signatures based on the two-dimensional tidal circulation of Phelps Bank. IEEE Trans Geosci Remote Sens, 1991, 291129-134.

[23] ClaudetJ, BoppL, CheungWWL, DevillersR, Escobar-BrionesE, HauganP, et al. . A roadmap for using the UN decade of ocean science for sustainable development in support of science, policy, and action. One Earth, 2020, 2134-42.

[24] CooperAL, ChubbSR, AskariF, ValenzuelaGR, BennettJR, KellerWC. Radar surface signatures for the two-dimensional tidal circulation over Phelps Bank, Nantucket shoals: a comparison between theory and experiment. J Geophys Res- Oceans, 1994, 99C47865-7883.

[25] CostaBM, BattistaTA, PittmanSJ. Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems. Remote Sens Environ, 2009, 11351082-1100.

[26] CostelloMJ, CheungA, De HauwereN. Surface area and the seabed area, volume, depth, slope, and topographic variation for the world’s seas, oceans, and countries. Environ Sci Technol, 2010, 44238821-8828.

[27] CracknellAP, IbrahimM. Bathymetry studies on the coastal waters (Red Sea) of Jeddah, Saudi Arabia, using Shuttle MOMS-01 data. Int J Remote Sens, 1988, 961161-1165.

[28] DierssenHM, ThebergeA, WangY. Bathymetry: history of seafloor mapping. Encyclopedia Nat Resour, 2014, 2: 564.

[29] DixonTH, ParkeME. Bathymetry estimates in the southern oceans from Seasat altimetry. Nature, 1983, 3045925406-411.

[30] Doneus M, Miholjek I, Mandlburger G, Doneus N, Verhoeven G, Briese C et al (2015) Airborne laser bathymetry for documentation of submerged archaeological sites in shallow water. In: The International Archives Photogrammetr, Remote Sensing and Spatial Information Science, Volume XL-5/W5, Piano di Sorrento, pp 99–107. https://doi.org/10.5194/isprsarchives-XL-5-W5-99-2015

[31] DurapA, BalasCE. Risk assessment of submarine pipelines: a case study in Turkey. Ocean Eng, 2022, 261. 112079

[32] EakinsB, SharmanG. Volumes of the world’s oceans from ETOPO1. NOAA National Geophysical Data Center, Boulder, 2010, 7: 1

[33] EllisJI, ClarkMR, RouseHL, LamarcheG. Environmental management frameworks for offshore mining: the New Zealand approach. Mar Pol, 2017, 84: 178-192.

[34] EugenioF, MarcelloJ, MartinJ. High-resolution maps of bathymetry and benthic habitats in shallow-water environments using multispectral remote sensing imagery. IEEE Trans Geosci Remote Sens, 2015, 5373539-3549.

[35] FangS, WuZ, WuS, ChenZ, ShenW, MaoZ. Enhancing water depth inversion accuracy in turbid coastal environments using random forest and coordinate attention mechanisms. Front Mar Sci, 2024, 11: 1471695.

[36] FarrHK. Multibeam bathymetric sonar: sea beam and hydro chart. Mar Geod, 1980, 4277-93.

[37] FlemingSW, WatsonJR, EllensonA, CannonAJ, VesselinovVC. Machine learning in Earth and environmental science requires education and research policy reforms. Nat Geosci, 2021, 1412878-880.

[38] GeB, GuoJ, KongQ, ZhuC, HuangL, SunH, et al. . Seafloor topography inversion from multi-source marine gravity data using multi-channel convolutional neural network. Eng Appl Artif Intell, 2025, 139. 109567

[39] Glenn MF (2015) Introducing an operational multi-beam array sonar. Int Hydrogr Rev 47(1):35–39. https://journals.lib.unb.ca/index.php/ihr/article/view/23921

[40] GuentherGC. Airborne laser hydrography to chart shallow coastal waters. Sea Technol, 1989, 30355-59

[41] Harper H, Sandwell DT (2024) Global predicted bathymetry using neural networks. Earth Space Sci 11(3):e2023EA003199. https://doi.org/10.1029/2023EA003199

[42] HedleyJ, RoelfsemaC, KoetzB, PhinnS. Capability of the Sentinel 2 mission for tropical coral reef mapping and coral bleaching detection. Remote Sens Environ, 2012, 120: 145-155.

[43] HedleyJ, RoelfsemaC, PhinnSR. Efficient radiative transfer model inversion for remote sensing applications. Remote Sens Environ, 2009, 113112527-2532.

[44] HendersonFM. Environmental factors and the detection of open surface water areas with X-band radar imagery. Int J Remote Sens, 1995, 16132423-2437.

[45] HendersonFM, XiaZG. SAR applications in human settlement detection, population estimation and urban land use pattern analysis: a status report. IEEE Trans Geosci Remote Sens, 1997, 35179-85.

[46] HickmanGD, HoggJE. Application of an airborne pulsed laser for near shore bathymetric measurements. Remote Sens Environ, 1969, 1147-58.

[47] HilldaleRC, RaffD. Assessing the ability of airborne LiDAR to map river bathymetry. Earth Surf Proc Landf, 2008, 335773-783.

[48] HollidayD, St-CyrG, WoodsNE. A radar ocean imaging model for small to moderate incidence angles. Int J Remote Sens, 1986, 7121809-1834.

[49] HowellSM, ItoG, BehnMD, MartinezF, OliveJA, EscartínJ. Magmatic and tectonic extension at the Chile Ridge: evidence for mantle controls on ridge segmentation. Geochem Geophys Geosyst, 2016, 1762354-2373.

[50] HsiaoYS, KimJW, KimKB, LeeBY, HwangC. Bathymetry estimation using the gravity-geologic method: an investigation of density contrast predicted by the downward continuation method. Terr Atmos Ocean Sci, 2011, 223347-358.

[51] HsiaoYS, HwangC, ChengYS, ChenLC, HsuHJ, TsaiJH, et al. . High-resolution depth and coastline over major atolls of South China Sea from satellite altimetry and imagery. Remote Sens Environ, 2016, 176: 69-83.

[52] IrishJL, LillycropWJ. Scanning laser mapping of the coastal zone: the SHOALS system. ISPRS J Photogramm Remote Sens, 1999, 542123-129.

[53] IrishJL, WhiteTE. Coastal engineering applications of high-resolution lidar bathymetry. Coast Eng, 1998, 35147-71.

[54] Jakobsson M (2017) What will science gain from mapping the world ocean floor? AGU Fall Meeting Abstract, New Orleans, #OS14B-01

[55] JakobssonM, MayerLA, ArmstrongA. Analysis of data relevant to establishing outer limits of a continental shelf under law of the sea article 76. Int Hydrogr Rev, 2003, 412-18

[56] Jakobsson M, Mayer LA, Bringensparr C, Castro CF, Mohammad R, Johnson P et al (2020) The International Bathymetric Chart of the Arctic Ocean Version 4.0. Sci Data 7(1):176. https://doi.org/10.1038/s41597-020-0520-9

[57] JenaB, KurianPJ, SwainD, TyagiA, RavindraR. Prediction of bathymetry from satellite altimeter based gravity in the Arabian Sea: mapping of two unnamed deep seamounts. Int J Appl Earth Obs Geoinf, 2012, 16: 1-4.

[58] JiW, CivcoDL, KennardWC. Satellite remote bathymetry: a new mechanism for modeling. Photogramm Eng Remote Sens, 1992, 58: 545-549

[59] KannoA, TanakaY. Modified Lyzenga’s method for estimating generalized coefficients of satellite-based predictor of shallow water depth. IEEE Geosci Remote Sens Lett, 2012, 94715-719.

[60] KenyonN. Tidal current bedforms investigated by SEASAT. Satellite microwave remote sensing, 1983ChichesterEllis Horwood Limited261-270

[61] KimKB, HsiaoYS, KimJW, LeeBY, KwonYK, KimCH. Bathymetry enhancement by altimetry-derived gravity anomalies in the East Sea (Sea of Japan). Mar Geophys Res, 2010, 314285-298.

[62] Kim KB, Kim JS, Yun HS (2023) Bathymetry estimation using machine learning in the Ulleung Basin in the East Sea. Sens Mater 35(9):3351–3362. https://doi.org/10.18494/SAM4415

[63] KimKB, KimJS, YunHS. Improved bathymetry estimation using satellite altimetry-derived gravity anomalies and machine learning in the East Sea. J Mar Sci Eng, 2024, 1291520.

[64] KotilainenAT, KaskelaAM. Comparison of airborne LiDAR and shipboard acoustic data in complex shallow water environments: filling in the white ribbon zone. Mar Geol, 2017, 385: 250-259.

[65] LafonV, FroidefondJM, LahetF, CastaingP. SPOT shallow water bathymetry of a moderately turbid tidal inlet based on field measurements. Remote Sens Environ, 2002, 811136-148.

[66] LeCunY, BengioY, HintonG. Deep learning. Nature, 2015, 5217553436-444.

[67] Lee Z, Carder KL, Mobley CD, Steward RG, Patch JS (1999) Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization. Appl Optics 38(18):3831–3843. https://doi.org/10.1364/AO.38.003831

[68] LiQ, ZhaiZ, LiQ, WuL, BaoL, SunH. Improved bathymetry in the South China Sea from multisource gravity field elements using fully connected neural network. J Mar Sci Eng, 2023, 1171345.

[69] LiangY, ChengZ, DuY, SongD, YouZ. An improved method for water depth mapping in turbid waters based on a machine learning model. Estuar Coast Shelf Scie, 2024, 296. 108577

[70] Liceaga-CorreaMA, Euan-AvilaJI. Assessment of coral reef bathymetric mapping using visible Landsat Thematic Mapper data. Int J Remote Sens, 2002, 2313-14.

[71] LurtonX. An introduction to underwater acoustics. J Acoust Soc Am, 2010, 115: 443.

[72] LyzengaDR, MalinasNP, TanisFJ. Multispectral bathymetry using a simple physically based algorithm. IEEE Trans Geosci Remote Sens, 2006, 4482251-2259.

[73] MacFerrin M, Amante C, Carignan K, Love M, Lim E (2024) The Earth Topography 2022 (ETOPO 2022) Global DEM dataset. Earth Syst Sci Data Discuss, pp 1–24. https://doi.org/10.5194/essd-2024-250

[74] MacFerrin M, Amante C, Carignan K, Love MR, Lim E, Arcos NP et al (2022) ETOPO 2022: NOAA’s new seamless topography-and-bathymetry bare earth surface elevation dataset. In: AGU Fall Meeting Abstracts, Chicago, NH22C-0433

[75] MacFerrin M, Amante C, Stroker K, Carignan K, Love M, Lim E (2021) ETOPO 2022: an updated NOAA global relief model. In: AGU Fall Meeting Abstracts, New Orleans, NH25B-0560

[76] MandlburgerG, HauerC, WieserM, PfeiferN. Topo-bathymetric LiDAR for monitoring river morphodynamics and instream habitats–a case study at the Pielach River. Remote Sens, 2015, 756160-6195

[77] Manessa MDM, Kanno A, Sekine M, Haidar M, Yamamoto K, Imai T et al (2016) Satellite-derived bathymetry using random forest algorithm and worldview-2 imagery. Geoplan J Geomat Plan 3:117–126. https://doi.org/10.14710/geoplanning.3.2.117-126

[78] MarksKM, SmithWHF. An uncertainty model for deep ocean single beam and multibeam echo sounder data. Mar Geophys Res, 2008, 294239-250.

[79] Mateo-PérezV, Corral-BobadillaM, Ortega-FernándezF, Rodríguez-MontequínV. Determination of water depth in ports using satellite data based on machine learning algorithms. Energies, 2021, 1492486.

[80] MavraeidopoulosAK, PallikarisA, OikonomouEK. Satellite derived bathymetry (SDB) and safety of navigation. Int Hydrogr Rev, 2017, 17: 7-19

[81] MayerLA. Frontiers in seafloor mapping and visualization. Mar Geophys Res, 2006, 2717-17.

[82] MayerL, JakobssonM, AllenG, DorschelB, FalconerR, FerriniV, et al. . The Nippon Foundation–GEBCO Seabed 2030 Project: the quest to see the world’s oceans completely mapped by 2030. Geosciences, 2018, 8263.

[83] McNuttM. Compensation of oceanic topography: an application of the response function technique to the surveyor area. J Geophys Res-Solid Earth, 1979, 84B137589-7598.

[84] MeadowsGA, ShuchmanRA, TsengYC, KasischkeES. SEASAT synthetic aperture radar observations of wave-current and wave-topographic interactions. J Geophys Res-Oceans, 1983, 88C74393-4406.

[85] MicallefA, KrastelS, SaviniA, et al. MicallefA, et al. . Introduction. Submarine geomorphology, 2018ChamSpringer International Publishing1-9

[86] Moustier CD (2001) Field evaluation of sounding accuracy in deep water multibeam swath bathymetry. In: MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295), Honolulu, pp 1761–1765

[87] NicolasKM, DrumetzL, LefèvreS, TiedeD, BajjoukT, BurnelJCNiculescuS. Deep learning-based bathymetry mapping from multispectral satellite data around Europa Island. European spatial data for coastal and marine remote sensing, 2023ChamSpringer International Publishing97-111

[88] OliveJA, BehnMD, ItoG, BuckWR, EscartínJ, HowellS. Sensitivity of seafloor bathymetry to climate-driven fluctuations in mid-ocean ridge magma supply. Science, 2015, 3506258310-313.

[89] Olson CJ, Becker JJ, Sandwell DT (2014) A new global bathymetry map at 15 arcsecond resolution for resolving seafloor fabric: SRTM15_PLUS. In: American Geophysical Union, Fall Meeting 2014, San Francisco, OS34A-03

[90] ParedesJM, SperoRE. Water depth mapping from passive remote sensing data under a generalized ratio assumption. Appl Optics, 1983, 2281134-1135.

[91] ParkerRL. The rapid calculation of potential anomalies. Geophys J Int, 1973, 314447-455.

[92] PastolY. Use of airborne LIDAR bathymetry for coastal hydrographic surveying: the French experience. J Coast Res, 2011, 62: 6-18.

[93] ReichsteinM, Camps-VallsG, StevensB, JungM, DenzlerJ, CarvalhaisN, et al. . Deep learning and process understanding for data-driven Earth system science. Nature, 2019, 5667743195-204.

[94] Renard V, Allenou JP (1979) Sea beam, multi-beam echo-sounding in ‘Jean Charcot’–Description, evaluation and first results. Int Hydrogr Rev 56(1):35–67. https://journals.lib.unb.ca/index.php/ihr/article/view/23653

[95] SandidgeJC, HolyerRJ. Coastal bathymetry from hyperspectral observations of water radiance. Remote Sens Environ, 1998, 653341-352.

[96] Sandwell DT, Goff JA, Gevorgian J, Harper H, Kim SS, Yu Y et al (2022) Improved bathymetric prediction using geological information: SYNBATH. Earth Space Sci 9(2):e2021EA002069. https://doi.org/10.1029/2021EA002069

[97] SandwellDT, MüllerRD, SmithWHF, GarciaE, FrancisR. New global marine gravity model from CryoSat-2 and Jason-1 reveals buried tectonic structure. Science, 2014, 346620565-67.

[98] SchulerDL, LeeJS, GrandiGD. Measurement of topography using polarimetric SAR images. IEEE Trans Geosci Remote Sens, 1996, 3451266-1277.

[99] ShawJ, CourtneyRC. Multibeam bathymetry of glaciated terrain off southwest Newfoundland. Mar Geol, 1997, 1431125-135.

[100] ShenC, ApplingAP, GentineP, BandaiT, GuptaH, TartakovskyA, et al. . Differentiable modelling to unify machine learning and physical models for geosciences. Nat Rev Earth Environ, 2023, 48552-567.

[101] ShuchmanRA, LyzengaDR, MeadowsGA. Synthetic aperture radar imaging of ocean-bottom topography via tidal-current interactions: theory and observations. Int J Remote Sens, 1985, 671179-1200.

[102] SmithWHF. On the accuracy of digital bathymetric data. J Geophys Rese-Solid Earth, 1993, 98B69591-9603.

[103] SmithWHF. Introduction to this special issue on bathymetry from space. Oceanography, 2004, 1716-7.

[104] SmithWHF, MarksK, SchmittT. Airline flight paths over the unmapped ocean. Available at, 2017.

[105] SmithWHF, SandwellDT. Bathymetric prediction from dense satellite altimetry and sparse shipboard bathymetry. J Geophys Res-Solid Earth, 1994, 99B1121803-21824.

[106] SmithWHF, SandwellDT. Global sea floor topography from satellite altimetry and ship depth soundings. Science, 1997, 27753341956-1962.

[107] Stumpf RP, Holderied K, Sinclair M (2003) Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnol Oceanogr 48(1, part2):547–556. https://doi.org/10.4319/lo.2003.48.1_part_2.0547

[108] Sun H, Feng Y, Fu Y, Sun W, Peng C, Zhou X et al (2022) Bathymetric prediction using multisource gravity data derived from a parallel linked BP neural network. J Geophys Res-Solid Earth 127(11):e2022JB024428. https://doi.org/10.1029/2022JB024428

[109] SunS, ChenY, MuL, LeY, ZhaoH. Improving shallow water bathymetry inversion through nonlinear transformation and deep convolutional neural networks. Remote Sens, 2023, 15174247.

[110] SunY, ZhengW, LiZ, ZhouZ. Improved the accuracy of seafloor topography from altimetry-derived gravity by the topography constraint factor weight optimization method. Remote Sens, 2021, 13122277.

[111] ThebergeAE. Sounding pole to sea beam. Surv Cartogr, 1989, 5: 334-346

[112] TozerB, SandwellDT, SmithWHF, OlsonC, BealeJR, WesselP. Global bathymetry and topography at 15 arc sec: SRTM15+. Earth Space Sci, 2019, 6101847-1864.

[113] ValenzuelaGR, ChenDT, GarrettWD, KaiserJAC. Shallow water bottom topography from radar imagery. Nature, 1983, 3035919687-689.

[114] Vance TC, Huang T, Butler KA (2024) Big data in Earth science: emerging practice and promise. Science 383(6688):eadh9607. https://doi.org/10.1126/science.adh9607

[115] WeatherallP, MarksKM, JakobssonM, SchmittT, TaniS, ArndtJE, et al. . A new digital bathymetric model of the world’s oceans. Earth Space Sci, 2015, 28331-345.

[116] WestfeldP, MaasHG, RichterK, WeißR. Analysis and correction of ocean wave pattern induced systematic coordinate errors in airborne LiDAR bathymetry. ISPRS J Photogramm Remote Sens, 2017, 128: 314-325.

[117] WozencraftJ, MillarD. Airborne lidar and integrated technologies for coastal mapping and nautical charting. Mar Technol Soc J, 2005, 39: 27-35.

[118] WrightCW, KranenburgC, BattistaTA, ParrishC. Depth calibration and validation of the experimental advanced airborne research lidar, EAARL-B. J Coast Res, 2016, 76100764-17.

[119] YangF, QiC, SuD, DingS, HeY, MaY. An airborne LiDAR bathymetric waveform decomposition method in very shallow water: a case study around Yuanzhi Island in the South China Sea. Int J Appl Earth Obs Geoinf, 2022, 109. 102788

[120] YangF, SuD, MaY, FengC, YangA, WangM. Refraction correction of airborne LiDAR bathymetry based on sea surface profile and ray tracing. IEEE Trans Geosci Remote Sens, 2017, 55116141-6149.

[121] YangJ, JekeliC, LiuL. Seafloor topography estimation from gravity gradients using simulated annealing. J Geophys Res-Solid Earth, 2018, 12386958-6975.

[122] YangL, LiuM, LiuN, GuoJ, LinL, ZhangY, et al. . Recovering bathymetry from satellite altimetry-derived gravity by fully connected deep neural network. IEEE Geosci Remote Sens Lett, 2023, 20: 1-5.

[123] ZhaoT, WangS, OuyangC, ChenM, LiuC, ZhangJ, et al. . Artificial intelligence for geoscience: progress, challenges, and perspectives. The Innovation, 2024, 55. 100691

[124] ZhiH, SiwabessyJ, NicholSL, BrookeBP. Predictive mapping of seabed substrata using high-resolution multibeam sonar data: a case study from a shelf with complex geomorphology. Mar Geol, 2014, 357: 37-52.

[125] ZhouS, GuoJ, ZhangH, JiaY, SunH, LiuX, et al. . SDUST2023BCO: a global seafloor model determined from multi-layer perceptron neural network using multi-source differential marine geodetic data. Earth Syst Sci Data Discuss, 2024, 2024: 1-21.

[126] ZhouS, LiuX, GuoJ, JinX, YangL, SunY, et al. . Bathymetry of the Gulf of Mexico predicted with multilayer perceptron from multisource marine geodetic data. IEEE Trans Geosci Remote Sens, 2023, 61: 1-11.

[127] ZhouS, LiuX, SunY, ChangX, JiaY, GuoJ, et al. . Predicting bathymetry using multisource differential marine geodetic data with multilayer perceptron neural network. Int J Digit Earth, 2024, 1712393255.

[128] ZhuJ, YinF, QinJ, QiJ, RenZ, HuP, et al. . Shallow water bathymetry retrieval by optical remote sensing based on depth-invariant index and location features. Can J Remote Sens, 2022, 484534-550.

RIGHTS & PERMISSIONS

The Author(s)