Texts, whether literary or historical, exhibit structural and stylistic patterns shaped by their purpose, authorship, and cultural context. Formulaic texts, characterized by repetition and constrained expression, tend to have lower variability in self-information compared to more dynamic compositions. Identifying such patterns in historical documents, particularly multi-author texts like the Hebrew Bible provides insights into their origins, purpose, and transmission. This study aims to identify formulaic clusters -- sections exhibiting systematic repetition and structural constraints -- by analyzing recurring phrases, syntactic structures, and stylistic markers. However, distinguishing formulaic from non-formulaic elements in an unsupervised manner presents a computational challenge, especially in high-dimensional textual spaces where patterns must be inferred without predefined labels. To address this, we develop an information-theoretic algorithm leveraging weighted self-information distributions to detect structured patterns in text, unlike covariance-based methods, which become unstable in small-sample, high-dimensional settings, our approach directly models variations in self-information to identify formulaicity. By extending classical discrete self-information measures with a continuous formulation based on differential self-information, our method remains applicable across different types of textual representations, including neural embeddings under Gaussian priors. Applied to hypothesized authorial divisions in the Hebrew Bible, our approach successfully isolates stylistic layers, providing a quantitative framework for textual stratification. This method enhances our ability to analyze compositional patterns, offering deeper insights into the literary and cultural evolution of texts shaped by complex authorship and editorial processes.