Subduction Deformation Under Frictional and Structural Controls: A DEM-Based Analysis

The deformation of accretionary prisms, which is governed by different boundary geometries and critically influenced by megathrust properties, plays a central role in seismic hazard assessment [1] because the associated fault evolution and displacement are closely linked to subduction dynamics and seismic activity [2,3]. In this study, we use the discrete element method (DEM) [4] to investigate how variations in basal frictional properties and surface roughness (especially Horst-graben structures) affect the formation and evolution of accretionary prisms.

Our numerical sandbox approach [5] based on DEM simulates the collective behaviors of brittle rocks under tectonic loading [3,5,6] by representing the crust as a collection of rigid grains interacting according to grain-scale laws. This method allows for large grain displacements without prescribing fault locations or geometries, allowing fault systems to emerge automatically in response to tectonic forces. Through such numerical sandbox modelings, we explore how frictional properties and Horst-graben structures drive thrust vergence and wedge deformation in these different tectonic settings. We apply our model to the Sumatra and Japan trenches, both highly active subduction zones, and compare simulation results with observations to understand the subduction dynamics.

Although we focus on the Sumatra and Japan Trench, the lessons learned regarding fault geometry, thrust vergence, and wedge deformation have broader implications for subduction zones worldwide, including those that host both slow and fast earthquakes. By integrating observed geophysical data with our simulation results, we aim to advance the understanding of how frictional properties and upper plate structures modulate seismic and aseismic processes in tectonic environments.

Reference:

[1]. Cubas, N., Souloumiac, P., & Singh, S. C. (2016). Relationship link between landward vergence in accretionary prisms and tsunami generation. Geology, 44(10), 787–790. https://doi.org/10.1130/g38019.1

[2]. Qin, Y., Chen, J., Singh, S. C., Hananto, N., Carton, H., & Tapponnier, P. (2024). Assessing the risk of potential tsunamigenic earthquakes in the Mentawai region by seismic imaging, Central Sumatra. Geochemistry, Geophysics, Geosystems, 25, e2023GC011149. https:// doi.org/10.1029/2023GC011149

[3]. Furuichi, M., Chen, J., Nishiura, D., Arai, R., Yamamoto, Y., & Ide, S. (2024). Virtual earthquakes in a numerical granular rock box experiment, Tectonophysics, 874 (230230), https://doi.org/10.1016/j.tecto.2024.230230.

[4]. Matuttis, H.–G., & Chen, J. (2014). Understanding the discrete element method: Simulation of non‐spherical particles for granular and multi-body systems. John Wiley & Sons.

[5]. Furuichi, M., Nishiura, D., Kuwano, O., Bauville, A., Hori, T., & Sakaguchi, H. (2018). Arcuate stress state in accretionary prisms from real‐scale numerical sandbox experiments. Scientific Reports, 8(1), 8685. https://doi.org/10.1038/s41598–018–26534–x

[6]. Scholtès, L., & Donzé, F.–V. (2013). A DEM model for soft and hard rocks: Role of grain interlocking on strength. Journal of the Mechanics and Physics of Solids, 61(2), 352–369. https://doi.org/10.1016/j.jmps.2012.10.005