Skip to main content

Advertisement

SpringerLink
Log in

Prediction of marine heat flow based on the random forest method and geological and geophysical features

  • Original Research Paper
  • Published:
Marine Geophysical Research Aims and scope Submit manuscript

Abstract

Geothermal heat flow, as a parameter characterizing the Earth's thermal state, records deep thermodynamic processes. However, measurements of heat flow (HF) in the oceanic crust are relatively sparse and susceptible to surface activity such as hydrothermal circulation. We propose a machine learning approach to predict marine HF (MHF). We apply the random forest (RF) regression algorithm to train predictors capable of mapping multiple geological and geophysical features to MHF, thus enabling HF prediction in the global oceanic crust. We generate three data sets with different qualities of HF measurements for training. The best predictor has an accuracy of ~ 0.13 (normalized root mean squared error) or ~ 0.07 (normalized mean absolute error), and the predicted global oceanic crust HF map reflects the basic pattern of the MHF distribution. We find by comparison that the quality of HF measurement affects the prediction results. Then, we use a cross-prediction scheme to screen out the "underestimated" measured HF cases, which are mostly located in tectonic environments such as mid-ocean ridges and back-arc basins and show high spatial correlation with hydrothermal circulation. Furthermore, we conduct experimental calculations for the extent and proportion of underestimated cases in the oceanic part of a new global heat flow (NGHF) database; for example, the proportion of records with the degree of underestimation greater than 50% is approximately 30.8%. These calculations can provide reference information for the selection and application of MHF records in the NGHF database.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Afonso JC, Salajegheh F, Szwillus W, Ebbing J, Gaina C (2019) A global reference model of the lithosphere and upper mantle from joint inversion and analysis of multiple data sets. Geophys J Int 217(3):1602–1628

    Article  Google Scholar 

  • Alekseev D, Kuvshinov A, Palshin N (2015) Compilation of 3D global conductivity model of the Earth for space weather applications. Earth Planets Space 67(1):1–11

    Article  Google Scholar 

  • Amante C, Eakins BW (2008) ETOPO1 1 Arc-minute global relief model: procedures, data sources and analysis, national geophysical data center, NESDIS, NOAA, U.S. Department of Commerce, Boulder, CO, July 2008.

  • Beaulieu SE, Szafrański KM (2020) InterRidge global database of active submarine hydrothermal vent fields version 3.4. PANGAEA. https://doi.org/10.1594/PANGAEA.917894

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Coffin MF, Gahagan LM, Lawver LA (1997) Present-day Plate Boundary Digital Data Compilation. University of Texas Institute for Geophysics Technical Report No. 174, 5p. Updated in 2014

  • Commission for the Geological Map of the World, Bouysse P, Acharyya S, Bischoff D (2000) Geological map of the world. Commission for the geological map of the world, Paris

    Google Scholar 

  • Davies JH (2013) Global map of solid Earth surface heat flow. Geochem Geophys Geosyst 14(10):4608–4622

    Article  Google Scholar 

  • Davies JH, Davies DR (2010) Earth’s surface heat flux. Solid Earth 1(1):5–24

    Article  Google Scholar 

  • Davis EE, Fisher AT (2011) Heat flow, seafloor: methods and observations. In: Gupta HK (ed) Encyclopedia of solid earth geophysics. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8702-7_65

    Chapter  Google Scholar 

  • Dong M, Zhang J, Xu X, Wu S-G (2019) The differences between the measured heat flow and BSR heat flow in the Shenhu gas hydrate drilling area, northern South China Sea. Energy Explor Exploit 37(2):756–769

    Article  Google Scholar 

  • Dong M, Zhang J, Brune S, Wu S, Fang G, Yu L (2020) Quantifying postrift lower crustal flow in the northern margin of the South China Sea. J Geophys Res Solid Earth 125(2):e2019JB018910

    Google Scholar 

  • Gao X, Wang K (2014) Strength of stick-slip and creeping subduction megathrusts from heat flow observations. Science 345(6200):1038–1041

    Article  Google Scholar 

  • Goutorbe B, Poort J, Lucazeau F, Raillard S (2011) Global heat flow trends resolved from multiple geological and geophysical proxies. Geophys J Int 187(3):1405–1419

    Article  Google Scholar 

  • Harris RN, McNutt MK (2007) Heat flow on hot spot swells: evidence for fluid flow. J Geophys Res Solid Earth. https://doi.org/10.1029/2006JB004299

    Article  Google Scholar 

  • Hasterok D (2013) A heat flow based cooling model for tectonic plates. Earth Planet Sci Lett 361:34–43

    Article  Google Scholar 

  • Hosseini K, Matthews KJ, Sigloch K, Shephard GE, Domeier M, Tsekhmistrenko M (2018) SubMachine: web-based tools for exploring seismic tomography and other models of Earth’s deep interior. Geochem Geophys Geosyst 19(5):1464–1483

    Article  Google Scholar 

  • Ince ES, Barthelmes F, Reißland S, Elger K, Förste C, Flechtner F, Schuh H (2019) ICGEM–15 years of successful collection and distribution of global gravitational models, associated services, and future plans. Earth Syst Sci Data 11(2):647–674

    Article  Google Scholar 

  • Johnson P, Hutnak M (1997) Conductive heat loss in recent eruptions at mid-ocean ridges. Geophys Res Lett 24(23):3089–3092

    Article  Google Scholar 

  • Laske G, Masters G, Ma Z, Pasyanos M (2013) Update on CRUST1.0—A 1-degree Global Model of Earth's Crust. EGU2013–2658

  • Li C-F, Lu Y, Wang J (2017) A global reference model of Curie-point depths based on EMAG2. Sci Rep 7(1):45129. https://doi.org/10.1038/srep45129

    Article  Google Scholar 

  • Lister C (1972) On the thermal balance of a mid-ocean ridge. Geophys J Int 26(5):515–535

    Article  Google Scholar 

  • Lösing M, Ebbing J (2021) Predicting geothermal heat flow in Antarctica with a machine learning approach. J Geophys Res Solid Earth. https://doi.org/10.1029/2020JB021499

    Article  Google Scholar 

  • Lucazeau F (2019) Analysis and mapping of an updated terrestrial heat flow data set. Geochem Geophys Geosyst 20(8):4001–4024

    Article  Google Scholar 

  • Mareschal JC, Jaupart C, Phaneuf C, Perry C (2012) Geoneutrinos and the energy budget of the Earth. J Geodyn 54:43–54. https://doi.org/10.1016/j.jog.2011.10.005

    Article  Google Scholar 

  • Montelli R, Nolet G, Dahlen FA, Masters G (2006) A catalogue of deep mantle plumes: new results from finite-frequency tomography. Geochem Geophys Geosyst. https://doi.org/10.1029/2006GC001248

    Article  Google Scholar 

  • Müller RD, Sdrolias M, Gaina C, Roest WR (2008) Age, spreading rates, and spreading asymmetry of the world’s ocean crust. Geochem Geophys Geosyst. https://doi.org/10.1029/2007GC001743

    Article  Google Scholar 

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  • Phrampus BJ, Harris RN, Tréhu AM (2017) Heat flow bounds over the C ascadia margin derived from bottom simulating reflectors and implications for thermal models of subduction. Geochem Geophys Geosyst 18(9):3309–3326

    Article  Google Scholar 

  • Pollack HN, Hurter SJ, Johnson JR (1993) Heat flow from the Earth’s interior: analysis of the global data set. Rev Geophys 31(3):267–280

    Article  Google Scholar 

  • Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N (2019) Deep learning and process understanding for data-driven Earth system science. Nature 566(7743):195–204

    Article  Google Scholar 

  • Rezvanbehbahani S, Stearns LA, Kadivar A, Walker JD, van der Veen CJ (2017) Predicting the geothermal heat flux in Greenland: a machine learning approach. Geophys Res Lett 44(24):12271–12279

    Article  Google Scholar 

  • Sclater JG, Hasterok D, Goutorbe B, Hillier J, Negrete R (2014) Marine heat flow. Springer, Dordrecht

    Book  Google Scholar 

  • Shapiro NM, Ritzwoller MH (2004) Inferring surface heat flux distributions guided by a global seismic model: particular application to Antarctica. Earth Planet Sci Lett 223(1–2):213–224

    Article  Google Scholar 

  • Stål T, Reading AM, Halpin JA, Whittaker JM (2020) Antarctic geothermal heat flow model: Aq1. Geochem Geophys Geosyst. https://doi.org/10.1029/2020GC009428

    Article  Google Scholar 

  • Szwillus W, Afonso JC, Ebbing J, Mooney WD (2019) Global crustal thickness and velocity structure from geostatistical analysis of seismic data. J Geophys Res Solid Earth 124(2):1626–1652

    Article  Google Scholar 

  • Turcotte DL, Schubert G (2014) Geodynamics, 3rd edn. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Zingerle P, Pail R, Gruber T, Oikonomidou X (2020) The combined global gravity field model XGM2019e. J Geodesy 94(7):1–12

    Article  Google Scholar 

Download references

Funding

The funded was provided by National natural science foundation of china (Grant Nos: 91858214 and 91858212).

Author information

Authors and Affiliations

  1. Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China

    Min Li, Song Huang, Miao Dong, Ya Xu, Tianyao Hao, Xueshan Wu & Yufeng Deng

  2. Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing, China

    Min Li, Song Huang, Miao Dong, Ya Xu, Tianyao Hao, Xueshan Wu & Yufeng Deng

  3. University of Chinese Academy of Sciences, Beijing, China

    Min Li, Song Huang, Miao Dong, Ya Xu, Tianyao Hao, Xueshan Wu & Yufeng Deng

Authors
  1. Min Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Song Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miao Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ya Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tianyao Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xueshan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yufeng Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Song Huang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 9092 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, M., Huang, S., Dong, M. et al. Prediction of marine heat flow based on the random forest method and geological and geophysical features. Mar Geophys Res 42, 30 (2021). https://doi.org/10.1007/s11001-021-09452-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11001-021-09452-y

Keywords

Search

Navigation