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Lower Permian limestone associated with ocean floor rocks in the Inthanon Zone of northern Thailand: new evidence for mélange 泰国北部 Inthanon 区与洋底岩石有关的下二叠统石灰岩：混杂的新证据

IF 1.2 4区地球科学 Q4 GEOSCIENCES, MULTIDISCIPLINARY

Geosciences Journal

Pub Date : 2024-01-16 DOI: 10.1007/s12303-023-0036-3

Kritsada Moonpa, Weerapan Srichan, Thasinee Charoentitirat, Thanpimol Intawong

Scattered outcrops of various lithologies such as limestone, chert, sandstone, mudstone or shale, and microgabbro have been recently exposed in the Hariphunchai Education Center of Chiang Mai University (HECCMU), the northern Lamphun area of the northern Thailand. The outcrops are located in the Inthanon Zone where Palaeo-Tethyan ocean plate stratigraphy is well known. Field and microfacies analysis of limestone blocks recognizes four microfacies interpreted as low-energy lagoon or platform interior and reef flank or shoal of fore-reef. Foraminiferas from the limestone are the Sakmarian (lower Cisuralian) to Kungurian (upper Cisuralian) of the early Permian in age. Field and petrographic analysis of co-occurring radiolarian chert, microgabbro, pelagic mudstone, and turbidites suggest that the limestone blocks were mid-oceanic carbonate build-ups on seamount which were chaotically mixed with oceanic rocks such as chert and gabbro and then embedded in shale matrix. It is concluded that the outcrops are part of mélange formed during the Palaeo-Tethys closure and are significant in representing the Cisuralian (lower Permian) carbonate remnant in the mélange of the Inthanon Zone.

最近在泰国北部南奔地区的清迈大学哈里蓬猜教育中心（Hariphunchai Education Center of Chiang Mai University，HECCMU）发现了石灰岩、燧石、砂岩、泥岩或页岩以及微辉长岩等各种岩性的零星露头。这些露头位于 Inthanon 区，该区的古泰西洋板块地层非常著名。对石灰岩块进行的实地和微地层分析确认了四种微地层，分别解释为低能泻湖或平台内部、礁侧或前礁浅滩。石灰岩中的有孔虫属于二叠纪早期的萨克玛期（下西苏拉期）至昆古里亚期（上西苏拉期）。对同时存在的放射虫白垩岩、微辉长岩、浮游泥岩和浊积岩进行的现场和岩石学分析表明，石灰岩块是海山上的中洋碳酸盐堆积物，与白垩岩和辉长岩等大洋岩石混杂在一起，然后嵌入页岩基质中。结论是，这些露头石灰岩是古泰西闭合时期形成的混杂岩的一部分，对代表 Inthanon 区混杂岩中的 Cisuralian（下二叠统）碳酸盐残余具有重要意义。

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引用次数: 0

Source investigation of Ikanje artesian spring in north-central Nigeria, using VLF-EM and VES geophysical techniques 利用 VLF-EM 和 VES 地球物理技术对尼日利亚中北部 Ikanje 自流泉进行泉源调查

IF 1.2 4区地球科学 Q4 GEOSCIENCES, MULTIDISCIPLINARY

Geosciences Journal

Pub Date : 2024-01-04 DOI: 10.1007/s12303-023-0035-4

Abstract

The study is aimed to examine the subsurface fracture system surrounding the Ikanje Artesian spring as regards the water-bearing zones to determine the source of the spring. The area is in a part of the basement complex of southwestern Nigeria. Using the ABEM WADI Very Low Frequency-Electromagnetic (VLF-EM) equipment, 40 profiles were established, 10 on each geographical coordinate at a 5 m inter-station gap, giving a total of 800 station positions. Four resistivity VES points were deployed for correlation purposes. Excel, Fraser Filter, and Karous-Hjelt Filter software were employed for the analysis. The Fraser Filter and Karous-Hjelt plots revealed underlying water-bearing fractures in all directions with prominence toward the south. The trend of the water-bearing fractures that links all the southern profiles indicates that the spring’s source is in the southern area. Inferred 3D conductive maps equally confirmed the highest conductivity in the southern region of the area, indicating wet fractures. The 1D pseudo sections from VES points indicated that the main aquifer linked to Ikanje artesian spring is hosted by a 64.5 m thick fractured basement, overlain by a weathered basement in the SW flank; thus the location could be most suitable for groundwater exploitation.

摘要该研究旨在考察 Ikanje 自流水泉周围地下含水带的断裂系统，以确定泉水的来源。该地区位于尼日利亚西南部基底复合体的一部分。利用 ABEM WADI 甚低频电磁（VLF-EM）设备绘制了 40 条剖面图，每个地理坐标 10 条，站间间隔 5 米，共 800 个站位。部署了四个电阻率 VES 点，用于相关目的。采用 Excel、Fraser 滤波和 Karous-Hjelt 滤波软件进行分析。Fraser 滤波图和 Karous-Hjelt 滤波图显示了各个方向的地下含水断裂，其中南侧的含水断裂最为突出。连接所有南部剖面的含水断裂趋势表明，泉源位于南部地区。推断的三维电导图同样证实了该地区南部的电导率最高，表明该地区为湿断裂。从 VES 点得到的一维伪断面图表明，与 Ikanje 自流泉相连的主要含水层由 64.5 米厚的断裂基底承载，西南侧被风化基底覆盖；因此，该地点最适合开采地下水。

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引用次数: 0

High-resolution imaging coupled with deep learning model for classifying water body of Soyang Lake, South Korea 高分辨率成像结合深度学习模型对韩国索阳湖水体进行分类