Rock magnetism and geomagnetic field strength of the rare Iron Age (300–500 BC) artifacts from Tamilnadu: The first Virtual Axial Dipole Moment determination from India

GeoResJ Pub Date : 2017-12-01 DOI:10.1016/j.grj.2017.11.002
R. Mohamed Asanulla , T. Radhakrishna , R. Venkatachalapathy , C. Manoharan , G.S. Soumya , P. Sutharsan
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引用次数: 2

Abstract

Archaeological artifacts are abundant in India to undertake archaeointensity (AI) research. High quality AI data from this region are essential to improve global geomagnetic field (GGF) model secular variation curve for the past few millennia for its applicability to the Indian region. Rock magnetic and AI investigations are carried out on 15 rare Megalithic/Iron Age (300–500 BC) pottery samples from the Sengalur site, Tamilnadu, India. Rock magnetic results indicate that either SD/PSD type of ferrimagnetic mineral (magnetite/titanomagnetite) is responsible for magnetic remanence. Temperature versus susceptibility experiments for most of the samples yield reversible heating and cooling curves with Curie temperatures of 565–585 °C. AI values are determined by the Thellier–Thellier method as modified by Coe 1967 (Zero-field/In-field method). The AI data of the present study meets the reliability and quality criteria adopted for the AI determinations worldwide. The mean AI of 47.48 ± 1.72 µT and a mean Virtual Axial Dipole Moment of 11.7 ± 0.4 × 1022 Am2 are estimated. This new AI data are in good agreement with the predictions of ARCH3K.1 GGF model for the period of 300–500 BC for India derived from the GEOMAGIA. V3 updated database. Other models incorporating sediment data are not consistent with the actual values of direct determination.

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来自泰米尔纳德邦的罕见铁器时代(公元前300-500年)文物的岩石磁性和地磁场强度:来自印度的第一个虚拟轴向偶极矩测定
印度有丰富的考古文物,可以进行考古强度(AI)研究。该地区的高质量人工智能数据对于改善过去几千年全球地磁场(GGF)模型的长期变化曲线,使其适用于印度地区至关重要。对印度泰米尔纳德邦Sengalur遗址的15个罕见的巨石/铁器时代(公元前300-500年)陶器样本进行了岩石磁性和人工智能调查。岩石磁学结果表明,SD/PSD型铁磁性矿物(磁铁矿/钛磁铁矿)中的任何一种都是产生剩磁的原因。在565-585℃的居里温度范围内,大多数样品的温度-磁化率实验得到可逆的加热和冷却曲线。AI值由由Coe 1967(零场/场内法)修改的Thellier-Thellier方法确定。本研究的人工智能数据符合全球人工智能确定所采用的可靠性和质量标准。平均AI为47.48±1.72µT,平均虚轴偶极矩为11.7±0.4 × 1022 Am2。这一新的人工智能数据与ARCH3K.1的预测非常吻合印度公元前300-500年的GGF模型来源于GEOMAGIA。V3更新的数据库。其他纳入泥沙数据的模型与直接测定的实际值不一致。
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