Adjoint‐Based Marker‐In‐Cell Data Assimilation for Constraining Thermal and Flow Processes From Lagrangian Particle Records
Year: 2025
Type: article
Abstract: Geophysical problems often involve Lagrangian particles that follow surrounding flows and record information about the system, such as the pressure and temperature path recorded in metamorphic rocks. These Lagrangian particles can be useful for constraining unknown parameters, such as their sources and the thermal and flow processes of the surrounding fluid. To use information about Lagrangian particles to constrain unknown parameters about the surrounding fluid in an inverse manner, we have developed a 4D‐Var (four‐dimensional variational) data assimilation for thermal convection in a particle‐grid coupled system. Here we consider particles advected in a thermally convecting, highly viscous fluid that mimics geochemical tracers in the Earth's mantle, and estimate time series of thermal and velocity fields only from the particle records, focusing on their high traceability in the laminar flow. We present preliminary 4D‐Var results using a sufficient amount of synthetic particle position and velocity data. The 4D‐Var run achieves a 60‐Myr time reversal of thermal convection at a horizontal wavelength of 6,000 km, without using any temperature data. For complex convection patterns, the cost function tends not to decrease well, likely indicating that the method is successful when the time reversal is much smaller than the mixing time scale, over which information about the initial particle arrangement is lost. Our framework has the potential to constrain thermal, flow, and mixing processes not only in the Earth's mantle but also in any other laminar flow containing Lagrangian particles that record useful information.
DOI: 10.1029/2024jh000288
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,500
Awards: Quantitative imaging of Earth's interior materials based on Bayesian sensing, G1241563046, G2521286739, G2861967088, Development of mesoscale experimental geophysical exploration technique and its application to learning for dynamics in deeper subsurface environment through controling it, G3355192005, G3658334539, G3729995708, Modeling of mantle evolution and geochemical tracers, G4543225726, 破壊過程と流体輸送-データ同化・可視化によるアプローチ, International Joint Research on Seismological Big Data Analysis Techniques Using Artificial Intelligence, G5277313638, G6314936711, G6648827385, G6718509927, 次世代地震計測と最先端ベイズ統計学との融合によるインテリジェント地震波動解析, Mathematical Foundation for Seismic Modeling Pioneered by Information Statistics, G7879866177, G8888343401, Development of a sequential/non-sequential hybrid-type data assimilation method, G8989651440, ベイズ推論とスパースモデリングによる計測と情報の融合
OpenAlex ID: W4409155209
