Lithostratigraphy, Structure, Geological History, Economic Geology and Paleontology of Mari Bugti Hills and Surrounding Areas of Balochistan, South Punjab and North Sindh (Pakistan)

Mari Bugti hills and surroundings belong to Sulaiman basin (fragment of Gondwana) lies under the territory of Balochistan, South Punjab and North Sindh (Pakistan) but luckily have diverse marine and terrestrial sediments varying in age from Triassic to Recent, petroleum and a large number of mineral resources especially huge gypsum and cement resources, numerous suitable anticlinal structures and world-famous paleontology. The large-sized poripuchian titanosaurs, theropods, mesoeucrocodiles, pterosaur, bird and snake were reported from the latest Cretaceous Vitakri Formation, and largest terrestrial mammals and eucrocodiles were reported from the Oligocene Chitarwata Formation and other mammals were also reported from Late Paleogene and Neogene terrestrial deposits. Previously part of this area was not mapped due to remoteness and security problems but recently the mapping of these areas was started. Further, the structural and geological maps of previously omitted parts are added here. After performing the multidisciplinary field investigations by senior author, the corresponding results were obtained. The main objective of this work is to focus on the lithostratigraphic deposits, structure, geological history, economic geology and paleontology of the Mari Bugti Hills and surrounding areas.

ECONOMIC GEOLOGY

20162439 Chen Yuchuan(Chinese Academy of Geological Sciences,Beijing 100037,China);Pei Rongfu Natural Classification of Mineral Deposits:Discussion on Minerogenetic Series of Mineral Deposits(Ⅳ)(Mineral Deposits,ISSN0258-7106,CN11-1965/P,34(6),2015,p.1092-1106,6illus.,1table,63refs.)Key words:genetic types of mineral deposit The concept of minerogenetic series

ECONOMIC GEOLOGY

1. METALS DEPOSITS20180072 Cao Jianjin （School of Earth Science and Geological Engineering, Sun Yat-Sen University, Guangzhou 510275, China）; Li Yingkui Research on Geogas Particles from Bingba Copper Deposit in Guanling County of Guizhou Province （Journal of Jilin University, ISSN1671-5888, CN22-1343/P, 47（1）, 2017, p. 95-105, 4 illus. , 3 tables, 40 refs. ） Key words, copper ores, geogas methods, blind deposit, Guizhou Province To find out the relationship between the characteristic of geogas particles and concealed deposit, geogas particles have been sampled from Bingba copper deposit in Guanling County of Guizhou Province,and analyzed by transmission electron microscope. The results revealed that geogas particles are in the form of particle aggregations and individual particles, of which the former is dominant. The shapes of individual particles are mainly spherical, platy, cuboidal, ellipsoidal, strip shaped, andirregular, with a size generally ranging from several nanometers to 300 nm. The shapes of particle aggregations are mainly chain-shaped, rounded, and irregular. The elemental composition of geogas particles shows a good correlation with concealed metal ore bodies.

ECONOMIC GEOLOGY

20161650Huang Chuanguan(Geological Survey of Jiangxi Province,Nanchang 330030,China);Liu Chungen Prediction Types and Characteristics of Main Mineral Resources in Jiangxi Province(Resources Survey&Environment,ISSN1671-4814,CN32-1640/N,36(3),2015,p.196-202,1illus.,1table,20refs.)Key words:metallogenic prediction,Jiangxi

Assessing the impact of conceptual mineral systems uncertainty on prospectivity predictions

The past two decades have seen a rapid adoption of artificial intelligence methods applied to mineral exploration. More recently, the easier acquisition of some types of data has inspired a broad literature that has examined many machine learning and modelling techniques that combine exploration criteria,or ’features’, to generate predictions for mineral prospectivity. Central to the design of prospectivity models is a ’mineral system’, a conceptual model describing the key geological elements that control the timing and location of economic mineralisation. The mineral systems model defines what constitutes a training set, which features represent geological evidence of mineralisation, how features are engineered and what modelling methods are used. Mineral systems are knowledge-driven conceptual models, thus all parameter choices are subject to human biases and opinion so alternative models are possible.However, the effect of alternative mineral systems models on prospectivity is rarely compared despite the potential to heavily influence final predictions. In this study, we focus on the effect of conceptual uncertainty on Fe ore prospectivity models in the Hamersley region, Western Australia. Four important considerations are tested.(1) Five different supergene and hypogene conceptual mineral systems models guide the inputs for five forest-based classification prospectivity models model.(2) To represent conceptual uncertainty, the predictions are then combined for prospectivity model comparison.(3)Representation of three-dimensional objects as two-dimensional features are tested to address commonly ignored thickness of geological units.(4) The training dataset is composed of known economic mineralisation sites(deposits) as ’positive’ examples, and exploration drilling data providing ’negative’sampling locations. Each of the spatial predictions are assessed using independent performance metrics common to AI-based classification methods and subjected to geological plausibility testing. We find that different conceptual mineral systems produce significantly different spatial predictions, thus conceptual uncertainty must be recognised. A benefit to recognising and modelling different conceptual models is that robust and geologically plausible predictions can be made that may guide mineral discovery.

Mineral Resources Classification of the State Subsoil Fund

General principles and description of Mineral Resources the United Nations Framework Classification of Mineral Resources. Classification of the State Subsoil Fund of Ukraine are adapted to The system that is used to classify the resources and reserves of all minerals and fuels in Ukraine has been developed and described. The classification system is part of an official procedure determined by the Ukrainian State Commission on Reserves. Following preparation of resource estimates the results that are registered with the State, which maintains an official inventory of all mineral resources.