EGU24-13794, updated on 09 Mar 2024
https://doi.org/10.5194/egusphere-egu24-13794
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Estimating Biomass in the Lithosphere: A Machine Learning Approach

Wenyu Zhao and Johnny Zhangzhou
Wenyu Zhao and Johnny Zhangzhou
  • Zhejiang University, School of Earth Science, Hangzhou, China (zhangzhou333@zju.edu.cn)

Research on sampling and analysis of fluids and rocks within both continental and oceanic crusts has revealed a significant portion of Earth's prokaryotic biomass residing in the rock-hosted biosphere, extending to depths of several kilometers. This deep subsurface life within the lithosphere may host a substantial amount of Earth's biomass. However, more constrained estimates of this biomass are still lacking. In this study, we first determine the habitable volume of the lithospheric biosphere and then estimate its potential biomass. Temperature, a critical factor influencing the limits of life, is considered the primary focus in this context. We employ a machine learning approach to estimate the habitable volume of the lithosphere above the 122°C isotherm, which is considered the upper limit for prokaryotic life.

 

Our methodology involves selecting a range of geophysical and geological features that are thought to impact geothermal gradients. These include Moho depth, lithosphere-asthenosphere boundary (LAB) depth, topography, susceptibility, tectonic units, gravity mean curvature, vertical magnetic field, and distances to ridges, trenches, transform faults, young rifts, and volcanoes, as well as P-wave and S-wave velocities in the crust. We used a Gradient Boosted Regression Tree algorithm to develop two models correlating geothermal gradients in continental and oceanic settings with these geophysical and geological features. The models were applied to a binned grid with a 0.5° x 0.5° resolution, enabling the estimation of the depth to the 122°C isotherm for each grid based on the machine learning model's predictions of geothermal gradients.

 

Our results reveal substantial habitable volumes within the lithosphere. The habitable volume in the continental crust may account for about 7% of the total crustal volume, while in the oceanic crust, it could be around 5%. Given the cell density range of 102 to 106 cells per gram in oceanic rocks and 102 to 104 cells per gram in continental rocks, along with an estimated carbon mass of 2x10-14 grams per cell, the lithospheric biomass is estimated to be between 0.008 and 33.2 Gt Carbon. For context, the biomass found in plants and marine microbes is estimated to be around 450 Gt Carbon and 2.9 Gt Carbon, respectively.

How to cite: Zhao, W. and Zhangzhou, J.: Estimating Biomass in the Lithosphere: A Machine Learning Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13794, https://doi.org/10.5194/egusphere-egu24-13794, 2024.