Abstract Long-term changes in flood activity have often been reconstructed to understand their relationships to climate changes. This requires identification of flood layers according to certain characteristics (e.g., texture, geochemical composition, grain-size) and then to count them using naked-eye observation. This method is, however, time-consuming, and intrinsically characterized by a low resolution that may lead to bias and misidentification. To overcome this limitation, high-resolution analytical approaches can be used, such as X-ray fluorescence spectroscopy (XRF), X-ray computed tomography, or hyperspectral imaging (HSI). When coupled with discriminant algorithms, HSI allows for automatic identification of event layers. Here, we propose a new method of flood layers identification and counting based on the combination of both HSI and XRF core scanner analyses, applied to a Lake Bourget (French Alps) sediment sequence. We use a hyperspectral sensor from the short wave-infrared spectral range to create a discrimination model between event layers and continuous sedimentation. This first step allows the estimation of a classification map, with a prediction accuracy of 0.96, and then the automatic reconstruction of a reliable chronicle of event layers (including their occurrence and deposit thicknesses). XRF signals are then used to discriminate flood layers among all identified event layers based on site-specific geochemical elements (in the case of Lake Bourget: Mn and Ti). This results in an automatically generated flood chronicle. Changes in flood occurrence and event thickness through time reconstructed from the automatically generated floods chronicle are in good agreement with the naked-eye-generated chronicle. In detail, differences rely on a larger number of detected flood events (i.e., increase of 9% of the number of layers detected) and a more precise layer thickness estimation, thanks to a higher resolution. Therefore, the developed methodology opens a promising avenue to increase both the efficiency (timesaving) and robustness (higher accuracy) of paleoflood reconstructions from lake sediments. Also, this methodology can be applied to identify any specific layers (e.g., varve, tephra, mass-movement turbidite, tsunami) and, thereby, it has a direct implication in paleolimnology, paleoflood hydrology and paleoseismology from sediment archives.

中文翻译：

---

XRF 和高光谱分析作为检测沉积物核心洪水事件的自动方法

摘要 洪水活动的长期变化经常被重建，以了解它们与气候变化的关系。这需要根据一定的特征（如质地、地球化学成分、粒度）识别洪水层，然后通过肉眼观察进行计数。然而，这种方法很耗时，并且本质上的特点是分辨率低，可能会导致偏差和错误识别。为了克服这一限制，可以使用高分辨率分析方法，例如 X 射线荧光光谱 (XRF)、X 射线计算机断层扫描或高光谱成像 (HSI)。当与判别算法结合使用时，HSI 允许自动识别事件层。这里，我们提出了一种基于 HSI 和 XRF 岩心扫描仪分析相结合的洪水层识别和计数的新方法，适用于布尔歇湖（法国阿尔卑斯山）沉积物序列。我们使用来自短波红外光谱范围的高光谱传感器来创建事件层和连续沉积之间的区分模型。第一步允许估计分类图，预测精度为 0.96，然后自动重建可靠的事件层编年史（包括它们的发生和沉积厚度）。然后使用 XRF 信号根据特定地点的地球化学元素（在布尔歇湖的情况下：Mn 和 Ti）区分所有已识别事件层中的洪水层。这会导致自动生成洪水编年史。从自动生成的洪水编年史重建的洪水发生和事件厚度随时间的变化与肉眼生成的编年史非常吻合。详细地说，差异取决于检测到的洪水事件的数量更多（即检测到的层数增加了 9%）和更精确的层厚度估计，这要归功于更高的分辨率。因此，所开发的方法开辟了一条有前途的途径，以提高从湖泊沉积物重建古洪水的效率（节省时间）和稳健性（更高的准确性）。此外，该方法可用于识别任何特定层（例如，斑块、火山灰、质量运动浊积岩、海啸），因此，它对沉积物档案中的古湖泊学、古洪水水文学和古地震学具有直接意义。