Constraining a high-slip offshore fault area during the 2024 Noto Peninsula earthquake using tsunami inundation data and numerical simulations

The 2024 Noto Peninsula earthquake (Mw 7.5) caused significant tsunami inundation along the northeastern coast of the Noto Peninsula in Ishikawa Prefecture, Japan. To investigate the offshore slip distribution, we conducted field surveys and constructed a dataset of tsunami inundation depths. Using this dataset, we evaluated the performance of six previously proposed source fault models – developed based on crustal deformation and tsunami waveform data – in reproducing observed onshore tsunami inundation. Most of the models underestimated the tsunami inundation depth, likely due to insufficient constraints on the offshore slip amount resulting from limited geophysical observations. To address this issue, we introduced high-slip areas into offshore fault segments and tested multiple scenarios. This approach led to substantial improvements in model performance, with an appropriate scenario achieving a geometric mean (<i>K</i>) of 1.32 and a geometric standard deviation (<i>κ</i>) of 1.95. The substantial improvements in model performance demonstrate that tsunami inundation depth data provide a valuable complementary constraint for modeling offshore slip. This constraint is particularly important in areas where direct geophysical observations are scarce and key features of the slip distribution may otherwise go undetected.

2024年能登半岛地震（Mw 7.5）在日本石川县能登半岛东北沿岸引发了大规模海啸淹没事件。为研究近海断层滑动分布（offshore slip distribution），本研究开展了野外调查，构建了海啸淹没深度（tsunami inundation depth）数据集。利用该数据集，本研究评估了6种此前基于地壳形变与海啸波形数据提出的震源断层模型在复现观测到的陆上海啸淹没情况时的表现。多数模型均低估了海啸淹没深度，这可能是由于地球物理观测资料有限，导致近海滑动量的约束不足。为解决这一问题，本研究在近海断层段引入高滑动区，并测试了多种情景方案。该方法使模型表现得到了显著提升，其中一组适配情景的几何均值（<i>K</i>）为1.32，几何标准差（<i>κ</i>）为1.95。模型表现的显著提升表明，海啸淹没深度数据可为近海滑动模拟提供极具价值的补充约束条件。在直接地球物理观测资料匮乏、滑动分布关键特征可能无法被识别的区域，这类约束条件尤为重要。