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We analyzed the radiolarian assemblages (pelagic sediments) and geochemical studies of 15 samples collected from Meluri district Nagaland, Naga Hills Ophiolite belt (NHO) and it consists of the Flysch Naga-Arakan-Yoma trough beyond the Indo-Myanmar border. It reflects one of the many components of the Himalayan Orogenic systems of Tethyan oceanic crust discovered in Nagaland at Meluri district of India and they have very well-preserved studies and can be identified to Upper Jurassic (Kimmeridgian-mid-Tithonian). As well as a continuous sequence of Kimmeridgian-mid-Tithonian, five radiolarian assemblages have been identified in this study. This is so far the first recovery of pelagic sediment assemblages (Radiolarians) from the Naga Hills Ophiolite complex. Samples of Naga Hills Ophiolite belts fell in the Sublitharenite, Litharenite, and Fe-shale Fe-sand area, and the A-CN-K diagram indicates that these sediments in the source region were exposed deep to moderate weathering conditions. In the discriminant plot, the Naga Hill-Ophiolite-Chert samples occupy the field of origin of the sediment quartzose, and the log (K2O/Na2O) versus SiO2 samples fall into the active continental marginal field, and only one sample falls into an island arch, suggesting that the tectonic framework of the Naga Hills-Chert samples was deposited in the active continental margin and the diagrams K2O/Al2O3 and MgO/Al2O3 illustrated the tectonic setting of the marine environment from Naga Hill Ophiolite Chert. Such findings have been consistent with the current geology of Naga Hills in the province of Nagaland.

We are entering the era of big data, and big data achievements have benefited ordinary people. However, big data application in geochemistry research has not been really appreciated. Big data is a kind of method and a pattern of thought. It is different from traditional scientific research methods and thinking in that it starts from the data and adopts full data mode. This paper introduces basalt, andesite and continental marginal arc basalt tectonic environment discrimination diagrams made by the author and his collaborators using all the global rock geochemical data available. We have discussed the following scientific problems of petrology and geochemistry using big data research: 1. we found many better illustrations in the study of discriminant diagrams, mainly relying on the relationship among the major elements, transition elements, and metal elements. What the points between the relationships are? Why did they work? These new questions need to be studied by the geochemists. 2. Data mining found that mid-ocean ridge is extremely lack of intermediate-acidic rocks. Does it mean that the upper mantle is an acute shortage of water? 3. This study found that the Miocene is the most developed epoch of magmatic activities. The Miocene appeared many important geological events in the world. Is there a connection between them? 4. The Miocene adakite most developed in the world, according to adakite exposed. There may be a giant Eurasian plateau from the Tibetan plateau to the Carpathians. 5. According to the spatial and temporal distribution of Cenozoic picrite, we put forward how to realize a global hotspot issues, etc. The paper also put forward some suggestions of further research and emphasized that science has entered the era of big data. In the era of big data, scientific classification standards have changed: any discipline which can be expressed in data is called science, and which cannot be expressed in data is called non-science. Whether it can be expressed by data is the watershed of science and non-science. In the era of big data, the geology encountered an unprecedented crisis. According to our forecast, in the foreseeable future, geophysics will be far ahead of geology. Space science will boom, and geochemistry will lead the way for a long time in the field of geology. Keywords: Big data, Petrology, Geochemistry, Basalt, Andesite, Discrimination diagram

To understand the sub-surface lithology variation and identify possible groundwater potential zones, multi-electrode resistivity scanning was carried out at 7 locations in the part of Chittar sub-basin, Tirunelveli District, South India. Physiographically, the selected study area is a plain and falls under the semi-arid climatic type. Archaean crystalline of hornblende biotite gneiss and charnockite are the major rock types, pediments and buried pediments are the major geomorphological units. A length of 100 m was scanned using 50 electrodes with interspacing of 2 m by SSR-MP-AT-ME model resistivity meter and analysed in IPI 2WIN software. The data obtained from resistivity showed huge resistivity variations in areas where Narikudi, Rastha, Therkkupatti and Vagaikulam locations represented ≤ 1000 Ohm m and Alagiyapandiapuram, Muthamalpuram and Nilethanallur locations represented 9000 Ohm m. To validate the scanning interpretation, constructed geoelectric layers were correlated to existing lithology of the region and it showed an appreciable match. Considering the resistivity range of 50 to 120 Ohm m and bore well lithology, Narikudi and Muthamalpuram locations have been identified as potential groundwater zones while Nilethanallur, Rastha, Therkkupatti, Alagiyapandiapuram and Vagaikulam locations were also identified as low to moderate groundwater zones.

Hydrofracturing process was developed during the 1940s for improving the yield from oil and gas wells. Later this technique was adopted for water well development. The hydrofracturing process consists of pumping test before and after fracturing a well. The pre-fracturing and post-fracturing pumping test results are analysed and correlated to estimate the yield improvement realized due to fracturing. An attempt was made to improve the groundwater yield from a borewell in hard rock formation in Thachchur Village of Cuddalore District, Tamil Nadu, India. The pre-pumping static water level was 4.25 m below measuring point (bmp), and the safe yield was 5 litres per minute (lpm)/16 m drawdown/8 h of pumping per day. Hydrofracturing was carried out using a single packer assembly system, and two fracture zones lying at the depth of 15 m and 45 m were treated. After fracturing treatment, pumping test was carried out. Post-fracturing pumping test results revealed that the safe yield was 80 lpm/16 m drawdown/8 h of pumping per day. The yield improvement from 5 to 80 lpm substantiated the success of hydrofracturing in the hard rock borewell. The groundwater yield improvement in the borewell is due to the enlargement of fissures and cracks in and around the borewell by hydrofracturing.

The distribution of elements in soil is affected by various natural processes, and therefore, it is essential that exploration geochemists are able to recognize whether an elemental concentration is related to mineralization or other processes. This research aimed to recognize the elemental distribution and its controlling factors around the Zhaojikou Pb–Zn ore deposit, SE China, via fractal and compositional data analysis. Firstly, histograms over the raw data and log-ratio transformations were used to check whether the concentration data followed a normal distribution or not. The concentration distribution maps of elements were then generated via spretrum–area fractal plot to show the spatial relationships. Afterward, the inter-relationships among different elements were studied through robust principal component analysis based on compositional data theory. The factors controlling the distribution of elements were discussed and geochemical indices were proposed for future exploration. Finally, a comprehensive anomaly map was generated to show important prospecting areas. Results showed how the interpretation of distributions of elements was enhanced due to the application of data log-ratio transformation in univariate and multivariate analysis compared to the use of raw data. The histograms based on isometric log-ratio performed best among the three log-ratio transformations. The concentration distribution maps showed three anomalous areas at the surface. It clearly allowed identifying the relationships between the elements and their possible source patterns. Area I with anomalous assemblage of Pb–Zn–Ag–Cu–Cd corresponded to the current known ore deposits, while Area II with anomalous assemblage of Pb–Zn–Ag–Cu–Cd may indicate the occurrence of new potential base metal (Pb–Zn) ore bodies, and Area III with anomalous assemblage of W–As–Sb may indicate the occurrence of new potential Au mineralization/ore occurences. The robust biplot of PCA shows that the distributions of elements of interest were generally controlled by three geological processes: Pb–Zn ore-forming process, Au mineralization and weathering. Lead, Zn, Ag, Cu, and Cd, were affected mainly by Pb–Zn ore-forming processes; W, Sb, and As were affected mainly by Au mineralization, whereas Al2O3, K2O, Rb, Ba, Sr, Na2O, CaO, MgO, and SiO2 were affected to a greater extent by weathering processes. Sulphur, a mineralizing agent, was controlled by the ore-forming processes, being affected greatly by weathering. Finally, we proposed Pb > 160 ppm, Zn > 120 ppm, Ag > 193 ppb, Cu > 50 ppm, Cd > 210 ppb, W > 5 ppm, As > 25 ppm, Sb > 3.5 ppm, and S > 120 ppm as geochemical indices based on concentration-area fractal model analysis for future surficial geochemical prospecting in the Zhaojikou District.