Sardar Abadi Mehrdad, Zeeden Christian, Ulfers Arne, Meyer Alex Susan, Wonik Thomas
LIAG-Institute for Applied Geophysics, Hannover, Germany.
PLoS One. 2024 Dec 30;19(12):e0315331. doi: 10.1371/journal.pone.0315331. eCollection 2024.
Spectral gamma ray borehole logging data can yield insights into the physical properties of lake sediments, serving as a valuable proxy for assessing climate and environmental changes. The presence of tephra layers resulting from volcanic ash deposition is not related to climate and environmental conditions. As a result, these layers pose challenges when attempting to analyze paleoclimate and environmental time series. Gamma rays are composed of photons, which are elementary particles of electromagnetic radiation. Tephra layers emit photons at specific energy levels that create a distinct pattern in their gamma-ray energy spectrum. The gamma-ray signature of tephra layers varies depending on the stage of the volcanic eruption. Additionally, there is a significant difference between the gamma-ray signature emitted by tephra layers and that of the background lake sediments. A composite signature can be used to predict tephra layers from background sediments by combining several gamma-ray signatures of tephra layers at different depths. We propose five-step protocol for detecting tephra layers within sediments through the utilization of gamma-ray spectroscopy. This protocol is based on a combination of physical aspects of gamma-ray spectroscopy and geological information specific to the lake system being studied. A subset of the training dataset is used, consisting of known tephra and non-tephra layers. The protocol involves identifying similarities between known tephra layers, analyzing differences in gamma-ray signals between tephra and non-tephra layers, and studying the composition of energy channels at various depths within the training dataset. Multiple linear regression models are used to predict the relationship between the composition of tephra layers as a dependent variable and the constituent energy channels of the gamma-ray signal as independent variables. The proposed protocol has the potential to accurately detect and identify thick tephra layers (> 10 cm in thickness) based on the rate of spectral gamma ray measurement in sedimentary sequences. This approach could enhance stratigraphic resolution by enabling finer subdivision of layers in an interior basin.
光谱伽马射线钻孔测井数据能够提供有关湖泊沉积物物理性质的见解,作为评估气候和环境变化的有价值替代指标。火山灰沉积形成的火山灰层的存在与气候和环境条件无关。因此,在尝试分析古气候和环境时间序列时,这些层带来了挑战。伽马射线由光子组成,光子是电磁辐射的基本粒子。火山灰层以特定能量水平发射光子,在其伽马射线能谱中形成独特模式。火山灰层的伽马射线特征因火山喷发阶段而异。此外,火山灰层发射的伽马射线特征与背景湖泊沉积物的特征存在显著差异。通过组合不同深度火山灰层的几个伽马射线特征,可以使用复合特征从背景沉积物中预测火山灰层。我们提出了一个五步协议,通过利用伽马射线光谱法在沉积物中检测火山灰层。该协议基于伽马射线光谱法的物理方面与所研究湖泊系统特定的地质信息的结合。使用训练数据集的一个子集,该子集由已知的火山灰层和非火山灰层组成。该协议包括识别已知火山灰层之间的相似性,分析火山灰层与非火山灰层之间伽马射线信号的差异,以及研究训练数据集中不同深度处能量通道的组成。使用多元线性回归模型预测作为因变量的火山灰层组成与作为自变量的伽马射线信号组成能量通道之间的关系。所提出的协议有可能根据沉积序列中光谱伽马射线测量的速率准确检测和识别厚火山灰层(厚度>10厘米)。这种方法可以通过对内陆盆地的层进行更精细的细分来提高地层分辨率。