Petrology and Structural Characterization of Post-Neoproterozoic Dolerites from the Kimberlite Fields in the Kéniéba Region (Western Mali)

Post-Neoproterozoic dolerites from the Kéniéba region (Western Mali) are often associated with kimberlites. The rarity of kimberlite outcrops led to the study of doleritic rocks, spatially associated with them. The petrographic and lithogeochemical study showed that the dolerites of the Kéniéba kimberlitic fields are of tholeiitic nature and of the E-MORB (Enriched-Mid Ocean Ridge Basalt) type. This reflects an enrichment over time, compared to the Birimian dolerites of the volcano-sedimentary greenstone belt of Toumodi, in central C?te d’Ivoire. Furthermore, these dolerites are enriched in SiO2, TiO2, Zr and poor in Fe2O3, MgO. These dolerites would have formed in a late to post-orogenic intracontinental context during the breakup of Gondwana. Structurally, Kéniéba dolerites are often associated with kimberlite pipes, fractures and large deep structures identified using aeromagnetic images. Taking into account the fact that kimberlites do not outcrop in the Kéniéba region, the geochemical study coupled with the interpretation of aeromagnetic data proved to be very useful for the search for pipes.

Optimal Classification of Minerals by Microscopic Image Analysis Based on Seven-State “Deep Learning” Combined with Optimizers

The development of artificial intelligence (AI), particularly deep learning, has made it possible to accelerate and improve the processing of data collected in different fields (commerce, medicine, surveillance or security, agriculture, etc.). Most related works use open source consistent image databases. This is the case for ImageNet reference data such as coco data, IP102, CIFAR-10, STL-10 and many others with variability representatives. The consistency of its images contributes to the spectacular results observed in its fields with deep learning. The application of deep learning which is making its debut in geology does not, to our knowledge, include a database of microscopic images of thin sections of open source rock minerals. In this paper, we evaluate three optimizers under the AlexNet architecture to check whether our acquired mineral images have object features or patterns that are clear and distinct to be extracted by a neural network. These are thin sections of magmatic rocks (biotite and 2-mica granite, granodiorite, simple granite, dolerite, charnokite and gabbros, etc.) which served as support. We use two hyper-parameters: the number of epochs to perform complete rounds on the entire data set and the “learning rate” to indicate how quickly the weights in the network will be modified during optimization. Using Transfer Learning, the three (3) optimizers all based on the gradient descent methods of Stochastic Momentum Gradient Descent (sgdm), Root Mean Square Propagation (RMSprop) algorithm and Adaptive Estimation of moment (Adam) achieved better performance. The recorded results indicate that the Momentum optimizer achieved the best scores respectively of 96.2% with a learning step set to 10−3 for a fixed choice of 350 epochs during this variation and 96, 7% over 300 epochs for the same value of the learning step. This performance is expected to provide excellent insight into image quality for future studies. Then they participate in the development of an intelligent system for the identification and classification of minerals, seven (7) in total (quartz, biotite, amphibole, plagioclase, feldspar, muscovite, pyroxene) and rocks.

Predictive Study of Trace Metal Pollution of Gold Panning Sites in Birimian Formations: Case of the Yaouré Furrow (Côte d’Ivoire)

The present study was carried out on small-scale gold mining sites in the Yaouré region of Côte d’Ivoire. This region is geologically representative of the Birimian formations (2.1 Ga) of West Africa. The aim is to determine the potentially toxic trace metals (TMEs) generated by these sites, with a view to preventing possible contamination and/or metal pollution of the waters that provide fish products for local populations. To this end, a sampling campaign was carried out, resulting in the collection of 20 mining waste samples analyzed by X-ray fluorescence spectrometry (XRF) and 10 by X-ray diffractometer (XRD). The XRF analysis detected 06 predominant TMEs: arsenic, chromium, copper, nickel, zinc and vanadium. Statistical analysis was carried out to determine the distributions and correlations between these ETMs. To assess contamination and/or pollution levels, the following indices were calculated on the basis of reference concentrations of upper continental crust MTEs: Enrichment Factor, Geo-accumulation Index, Concentration Factor, Degree of contamination and those related to ecological risks. The results of statistical analyses and indices have shown that arsenic and chromium are the most predominant and can be, depending on the chemical form, potentially more toxic. The results of the DRX analysis show the occurrence of several minerals carrying these two MTEs, especially that of a rare mineral, Stenhuggarite, an arsenic oxide linked to hydrothermal veins. The majority of gold mining operations in West Africa are located in the birimian zone, hence the need for environmental monitoring by the relevant authorities, to prevent potential ecological risks to water and possibly health risks via the food chain.

Aeromagnetic Imagery as a Tool to Help Identify the Structures Controlling the Emplacement of the Kenieba Kimberlite Pipes (Western Mali, West African Craton)

A kimberlite field, represented by fertile and sterile kimberlite pipes (chimneys) is located in the region of Kenieba (West Mali, Kédougou-Kenieba inlier, West African Craton). Thirty pipes and kimberlite dykes have been identified in the birimian formations, composed mainly of metasediments and granitoids, covered by sedimentary formations (sandstones and conglomerates) of Neoproterozoic age. All these formations are injected with dykes and doleritic sills of Jurassic age. The study of kimberlite pipes is still stammering in Mali, and thus no previous study has allowed to characterize the structures controlling their implementation. The reinterpretation of aeromagnetic data validated by field work indicates that the major structures of the Kenieba region are oriented NNE-SSW, NE-SW, E-W and NW-SE. These structures (faults and kimberlite pipes) are often associated with dolerite dykes, which would imply an injection of dolerite magma into the other formations. The location of the known kimberlite pipes makes it possible to say that the direction NW-SE is the most favorable for the exploration of kimberlites in the region of Kenieba.

Analysis of the Spatio-Temporal Evolution of Artisanal and Small-Scale Gold Mining in Central Ivory Coast, West Africa

Ivory Coast is a country rich in base metals and precious minerals: gold, manganese, diamond, iron, bauxite, cobalt and nickel. These natural resources are exposed to destruction and fragmentation by mining activities. The artisanal and small-scale exploitation of gold are increasingly practiced in our rural areas. These activities escape often in the control and monitoring of the mining administration. In order to better constrain these activities on the environment, the present work used remote sensing imageries to see its spatio-temporal impacts in the rural world in central Ivory Coast. The results show that gold artisanal activities have been practiced since 2013 and are experiencing an increasingly important growth. We note a devastation of forests and savannahs, a pollution of surface water, as well as an increase in poverty in rural areas. These activities are practiced near habited areas (villages). This creates a reduction of cultivatable soil. Remote sensing imageries make it possible to quickly map areas at large-scale gold mining in time and space.