Magma Buoyancy Couples Lunar Mantle Convection and Magmatism: Long-Lasting Mare Volcanism and Radius Change in a 3-D Regional Model

Ken'yo U; Masanori Kameyama; Takatoshi Yanagisawa; Takehiro Miyagoshi; Masaki Ogawa

Home/Archive/arXiv

arXiv · 2610.39376

Magma Buoyancy Couples Lunar Mantle Convection and Magmatism: Long-Lasting Mare Volcanism and Radius Change in a 3-D Regional Model

Ken'yo U·Masanori Kameyama·Takatoshi Yanagisawa·Takehiro Miyagoshi·Masaki Ogawa

View original source ↗Read source document ↗Export metadata ↓

Abstract

To understand the mantle evolution that has caused the observed histories of mare volcanism and radius change of the Moon, we developed a 3-D regional spherical shell mantle convection model with magmatism. Magma generated by decompression melting segregates from the convecting matrix and percolates upward through it. In our model, internal heating forms a basal partially molten layer during the first several hundred million years. Finger-like magma ascents then grow from the top of this layer and evolve into buoyant, partially molten plumes. The plumes ascend by the buoyancy of magma, while decompression melting in the ascending plumes generates more magma that further drives the ascent. Magma supply to the top of the mantle peaks between 4 and 3.5 Gyr ago, coincident with a maximum in radial expansion driven by volume change of the mantle associated with melting, and both decline as the mantle cools and solidifies. Magma supply, however, persists for billions of years because earlier plumes leave thermally softened conduits, through which later plumes repeatedly rise at fixed locations. These stable conduits are a consequence of the 3-D geometry. Recycling of magmatic products formed in the uppermost mantle also triggers renewed magma ascent through the conduits. Our results provide a mechanism for the observed long-lasting localized volcanism that peaked at 4-3.5 Gyr ago and quantitatively account for early expansion followed by contraction. The conditions required for consistency with the observed history imply an initially hot but mostly solid mantle with a dense basal layer.

Explore related subjects

physics.geo-ph astro-ph.EP

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Ken'yo U, Masanori Kameyama, Takatoshi Yanagisawa, Takehiro Miyagoshi, Masaki Ogawa. 2026-10-29. Magma Buoyancy Couples Lunar Mantle Convection and Magmatism: Long-Lasting Mare Volcanism and Radius Change in a 3-D Regional Model. https://arxiv.org/abs/2610.39376

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Advance identification of where the 2026 Kumamoto earthquake would begin

Long-term forecasts say how likely a fault segment is to rupture, not where the rupture will begin. The b-value, the slope of the earthquake size distribution, falls as stress rises. After the 2016 Kumamoto sequence in Japan, b-values rose along the ruptured faults but fell in the unruptured central part of the Hinagu fault zone, which we identified in 2019 as where the next rupture was most likely to begin. The 2026 M7.1 Kumamoto earthquake began at the edge of this area, inside the low-b-value volume at 16 km depth. To our knowledge, this is the first starting point of a large earthquake named in print beforehand. The rupture stopped at a fault bend where b-values have stayed low since 2000 and raised Coulomb stress on faults beyond it; the low-b-value zone at this bend is where the next rupture is most likely to begin.

physics.geo-ph↗

arXiv2026

Adaptive Seismic Inversion in Piecewise Smooth Media Using Whiteness-based Alternating Directions

Post-stack acoustic impedance inversion is ill-posed because seismic data are band-limited and contaminated by noise. Regularization improves the stability of the inversion, but Tikhonov regularization tends to blur formation interfaces, whereas total variation (TV) regularization may introduce staircase artifacts. To simultaneously characterize abrupt interlayer discontinuities and gradual intralayer variations, we propose an inversion method based on anisotropic Tikhonov-TV regularization and automatic parameter selection. The method decomposes the logarithmic impedance model into piecewise-constant and smooth components. TV and Tikhonov constraints are imposed on these components, respectively, in both the vertical and horizontal directions. The performance of the inversion depends critically, among other factors, on the relative weight of the total regularization term with respect to the data misfit, the relative weight of the two regularization components (the balancing parameter), and the efficiency of computing a candidate solution for a given set of parameters. In this paper, we propose a new adaptive algorithm based on alternating directions, in which (i) a fast candidate-solution computation scheme is developed in the spectral domain by exploiting the Sylvester structure and fast Fourier transforms, thereby reducing the computational cost of parameter searching; (ii) the regularization parameter is selected adaptively using the residual whiteness principle; and (iii) the balancing parameter is adaptively updated according to robust statistical characteristics of the impedance-model gradients. Synthetic and field-data tests demonstrate that, compared with conventional Tikhonov and TV methods, the proposed method better preserves impedance interfaces while reducing staircase artifacts and inter-trace discontinuities, thereby improving the reconstruction of impedance structures.

physics.geo-ph↗

arXiv2026

Expert lithological labels do not improve learning when used to predict marine sediment properties

Scientific drilling has produced large archives of physical, chemical and magnetic measurements linked to descriptions of the rock recovered. These archives are mostly incomplete: instruments sample at different intervals, recovery is partial, and destructive analyses preclude later measurement of the same material. We train an self-supervised embedding model on the LILY ocean drilling database using six routinely logged properties (bulk density, magnetic susceptibility, natural gamma radiation, and red, green and blue reflectance) across 236,963 observations from 296 boreholes, with a masked variational autoencoder that has a heteroscedastic Gaussian decoder and learnable mask tokens. The embeddings separate geologist-assigned lithologies as well as a variant trained with those labels, and increasing the weight on the labels degrades reconstruction while leaving classification unchanged. The labels are recoverable from the measurements geologists use to assign them, so training on them adds nothing. Lithological categories compress continuous measurements into classes, and compressing them discards what the measurements record about the processes that produced the rock. The embeddings also predict physical properties that were absent from training, on boreholes the model has never seen. While depth should account for this since sediments compact with burial, it does not. On held-out boreholes depth alone reaches a median $R^2$ of -0.02, against 0.20 for the embedding.

physics.geo-ph↗