Integrated methodology for gas content assessment and prediction in shallow muddy lake sediments: acoustic mapping and correlation analysis

E. Uzhansky, R. Katsman, A. Lunkov, B. Katsnelson

Research output: Contribution to journalArticlepeer-review

Abstract

This paper provides a step-by-step description of integrated methodology for quantification and prediction of gas (methane, CH4) content dynamics in shallow aquatic sediments under changing spatial and temporal conditions. Presence of gas bubbles even in small concentrations significantly affects sediment compressibility, which in turn decreases sound speed in sediment. Our integrated methodology consists of two basic steps. In the first step, free gas content is evaluated by acoustic applications based on the sound speed inferred from the reflection coefficient from gassy bottom. The experimental bottom reflections are registered and compared to the simulated ones, using a geoacoustic inversion technique. The best match between the model and the experiment provides sediment sound speed estimate, which is converted into free gas content using a basic relation. In the second step, a multivariate linear regression is fitted for gas content and closed form expression of gas content dependence on the following predictors, which change spatially and temporally over the aquatic ecosystem, is obtained: 1) water depth, 2) short-leaving CH4 production rate peaks fueled by punctuated organic matter deposition; and 3) CH4 bubble dissolution rates.

Original languageEnglish
Article number102799
JournalMethodsX
Volume13
DOIs
StatePublished - Dec 2024

Bibliographical note

Publisher Copyright:
© 2024

Keywords

  • Acoustic application
  • Gassy sediment
  • Geoacoustic inversion
  • Methane bubbles
  • Regression analysis

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Medical Laboratory Technology

Fingerprint

Dive into the research topics of 'Integrated methodology for gas content assessment and prediction in shallow muddy lake sediments: acoustic mapping and correlation analysis'. Together they form a unique fingerprint.

Cite this