The identification of small scale faults (SSFs) and fractures provides an improved understanding of geologic structural features and can be exploited for future drilling prospects. Conventional SSF and fracture characterization are challenging and time-consuming. Thus, the current study was conducted with the following aims: (a) to provide an effective way of utilizing the seismic data in the absence of image logs and cores for characterizing SSFs and fractures; (b) to present an unconventional way of data conditioning using geostatistical and structural filtering; (c) to provide an advanced workflow through multi-attributes, neural networks, and ant-colony optimization (ACO) for the recognition of fracture networks; and (d) to identify the fault and fracture orientation parameters within the study area. Initially, a steering cube was generated, and a dip-steered median filter (DSMF), a dip-steered diffusion filter (DSDF), and a fault enhancement filter (FEF) were applied to sharpen the discontinuities. Multiple structural attributes were applied and shortlisted, including dip and curvature attributes, filtered and unfiltered similarity attributes, thinned fault likelihood (TFL), fracture density, and fracture proximity. These shortlisted attributes were computed through unsupervised vector quantization (UVQ) neural networks. The results of the UVQ revealed the orientations, locations, and extensions of fractures in the study area. The ACO proved helpful in identifying the fracture parameters such as fracture length, dip angle, azimuth, and surface area. The adopted workflow also revealed a small scale fault which had an NNW–SSE orientation with minor heave and throw. The implemented workflow of structural interpretation is helpful for the field development of the study area and can be applied worldwide in carbonate, sand, coal, and shale gas fields.

Zaheen Ullah, Muhammad Tahir Shah, Rehanul Haq Siddiqui, Dong-Yang Lian, and Asad Khan. Episodes 1999;0:. https://doi.org/10.18814/epiiugs/2020/020045

The Tongshankou skarn deposit in the Edong ore district is a typical metasomatic deposit associated with adjacent granodiorite porphyry and carbonate rocks. Using comprehensive microscopic observations, mineralogical and geochemical analysis, scheelite grains in the skarns can be classified into three major types, showing multi-stage mineralization characteristics. In the redox fluid environment, scheelites that occur with garnets usually have affinity to garnets, while in later skarn phases others exist with oxides and sulfides. They can be subdivided by trace elements, such as Nb and Eu, to discuss the detailed ore-forming process. Scheelites have three typical substitution mechanisms including: 2Ca^{2 +} ⇌ REE^{3 +} +Na⁺ (1); Ca^{2 +} + W^{6 +} ⇌ REE^{3 +} +Nb⁵⁺(2); and 3Ca^{2 +} ⇌ 2REE³⁺+ □Ca (□Ca = Ca site vacancy) (3). Plagioclase or various hydrothermal stages can cause Eu anomalies to fluctuate from positive to negative, and these processes can cause particular zonation in W and Mo contents in scheelites. This study highlights the use of texture and geochemistry of scheelites in skarn deposits, depicting the W mineralization processes.