以X光電腦斷層掃描進行岩相辨識

Abstract

岩相是指具有特定沉積特徵的沉積單元，其沉積構造或是粒徑分佈等特徵對於了解其沉積過程與重建沉積環境至關重要。然而，傳統方法如視覺岩心描述和粒徑分析雖廣泛應用，但往往受限於對內部結構的有限觀察、離散的採樣間隔，以及可能的主觀判斷等因素的影響。X光電腦斷層掃描為一廣泛應用、非破壞性且高解析度的造影技術，可提供三維視覺化和影像定量分析。本研究以台灣西南部大鵬灣地區的兩根岩心為研究對象，旨在評估X光電腦斷層掃描技術在量化沉積特徵和輔助岩相辨識中的應用潛力，同時探討其作為一種標準化分析工具的可行性。方法除了利用影像觀察岩心中肉眼不易辨識如泥幔層等的沉積特徵，並透過影像衍生參數包括關注物質 (ROI) 的比例、型態資訊、平均衰減強度及衰減強度變異係數，以將定性資訊轉化為量化資料。研究結果將上述參數組合且分類出14種以電腦斷層為基礎的岩相，並進一步歸納為三大沉積相—潟湖相、河道相與潮坪相，反映了研究區域沉積環境的整體變化，並對岩相特別是粗顆粒類別的形成機制中提供了額外見解。本研究透過X光電腦斷層影像的定性觀察與量化分析，將微觀結構與宏觀沉積變化結合，提出了一種基於電腦斷層的輔助岩相辨識方法。此外，該方法具備系統性檢視多岩心細節沉積特徵的潛力，能有效應用於事件層的辨識與對比。同時，其在揭示沉積系統演化的能力，也可望為儲集層評估與資源管理策略提供了可靠的參考基礎。

Lithofacies, characterized by specific lithological features like grain size and sedimentary structures, are crucial for a detailed understanding of their hydrodynamic processes and reconstructing paleo-environments. However, conventional approaches like visual core description and grain size analysis could face some limitations, including restricted observation of internal structures, discrete sampling intervals, and the susceptibility to observer bias, which may impede the accurate identification of lithofacies. X-ray Computed Tomography (CT), a non-destructive, high-resolution imaging technique, offers a widely applicable approach for three-dimensional visualization and quantitative analysis. This study explores the feasibility of using CT as a supplementary and standardized tool for the characterization of sedimentary features to support lithofacies identification based on two sediment cores from the Dapeng Bay region in southwestern Taiwan. In addition to qualitative ability to reveal imperceptible features such as mud drapes, CT-derived parameters—including ratios of regions of interest (ROI), morphological parameters (Th), mean CT intensity, and the coefficient of variation (CV) of CT intensity—were systematically employed to transform qualitative observations into quantitative data. By integrating these parameters, fourteen CT-based lithofacies were identified, which were further grouped into three CT-derived sedimentary facies—lagoon, channel, and tidal flat. This CT-based classification reflects the general environmental changes in the study area, with additional perspectives on the formation mechanisms of the CT-based lithofacies, especially for the coarse-grained category. By integrating qualitative CT imaging with quantitative parameterization, this study demonstrates CT serves as a complementary tool in lithofacies identification that bridges micro-scale observations and macro-scale variation. The ability of CT to apply consistent standards across spatially extensive cores and detailed sedimentary structures suggests its potential to support the identification and correlation of event deposits characterized by distinct sedimentary features over broad regions. Furthermore, the capability of CT in contributing to elucidating the sedimentary system evolution across spatial and temporal scales, also showing promises in reservoir evaluation and resource management strategies.