Microfacies Analysis and Depositional Environment of Middle Jurassic Samana Suk Formation, Chichali Nala Section, Surghar Range, Pakistan

The Middle Jurassic age Samana Suk Formation, exposed in Chichali Nala section of Surghar ranges has been investigated by field work, petrographic study and XRD analysis to understand the microfacies, depositional environment and fault related dolomitization of the Samana Suk Formation. This formation is widely distributed in the upper Indus basin of Pakistan and considered the most prominent stratigraphic unit of the Jurassic period. The project area lies in the Chichali Nala Section of Surghar range (Trans Indus Salt Ranges). In this section, Samana Suk Formation constitutes the lithology of carbonate having CaCo3 as a major mineral, where dolomite is present in minor amount, which is restricted to fluids along fault zone. During the study two major microfacies have been identified including the Grainstone microfacies and Mudstone-Wackestone microfacies. Samana Suk. Formation was formed under stormy influence in the environment of deposition of Formation. Its depositional environment is the inner-middle shelf which suggests the marine shelf depositional environment

Resource potential of gas reservoirs in South Pakistan and adjacent Indian subcontinent revealed by post-stack inversion techniques

Seismic post-stack inversion facilitates the interpretation, mapping and quantification of hydrocarbon-bearing zones. This study estimates reservoir properties (i.e. acoustic impedance and porosity) by applying post-stack seismic inversion techniques to a gas prone reservoir in the Sawan area, Southern Indus Basin, Pakistan. In this particular study, model-based and sparse-spike inversion algorithms are successfully applied on 3D seismic and wireline log data to predict reservoir character in the Lower Goru Formation (C-sand interval). Our results suggest that model-based post-stack seismic inversion provides more reasonable estimates (i.e. returning detailed spatial variations) for acoustic impedance and porosity when compared to sparse-spike inversion algorithms. The calibration of these estimates with petrophysical data from wireline log data indicates an appropriate agreement amongst them. Importantly, the results obtained in our case study can be applied to similar basins in Asia with 'tight' oil and 'tight' gas filling sand-shale intercalations with different thickness and areal distributions

Resource potential of gas reservoirs in South Pakistan and adjacent Indian subcontinent revealed by post-stack inversion techniques

Seismic post-stack inversion facilitates the interpretation, mapping and quantification of hydrocarbon-bearing zones. This study estimates reservoir properties (i.e. acoustic impedance and porosity) by applying post-stack seismic inversion techniques to a gas prone reservoir in the Sawan area, Southern Indus Basin, Pakistan. In this particular study, model-based and sparse-spike inversion algorithms are successfully applied on 3D seismic and wireline log data to predict reservoir character in the Lower Goru Formation (C-sand interval). Our results suggest that model-based post-stack seismic inversion provides more reasonable estimates (i.e. returning detailed spatial variations) for acoustic impedance and porosity when compared to sparse-spike inversion algorithms. The calibration of these estimates with petrophysical data from wireline log data indicates an appropriate agreement amongst them. Importantly, the results obtained in our case study can be applied to similar basins in Asia with 'tight' oil and 'tight' gas filling sand-shale intercalations with different thickness and areal distributions

DHCAE: Deep Hybrid Convolutional Autoencoder Approach for Robust Supervised Hyperspectral Unmixing

Hyperspectral unmixing (HSU) is a crucial method to determine the fractional abundance of the material (endmembers) in each pixel. Most spectral unmixing methods are affected by low signal-to-noise ratios because of noisy pixels and bands simultaneously, requiring robust HSU techniques that exploit both 3D (spectral–spatial dimension) and 2D (spatial dimension) domains. In this paper, we present a new method for robust supervised HSU based on a deep hybrid (3D and 2D) convolutional autoencoder (DHCAE) network. Most HSU methods adopt the 2D model for simplicity, whereas the performance of HSU depends on spectral and spatial information. The DHCAE network exploits spectral and spatial information of the remote sensing images for abundance map estimation. In addition, DHCAE uses dropout to regularize the network for smooth learning and to avoid overfitting. Quantitative and qualitative results confirm that our proposed DHCAE network achieved better hyperspectral unmixing performance on synthetic and three real hyperspectral images, i.e., Jasper Ridge, urban and Washington DC Mall datasets

Modelling and Mapping of Soil Erosion Susceptibility of Murree, Sub-Himalayas Using GIS and RS-Based Models

Soil erosion is one of Pakistan’s most serious environmental threats. This study used geospatial modelling to identify the distinct zones susceptible to soil erosion in Murree, Pakistan. Using a machine learning technique in the Google Earth engine (GEE) and Google Earth, we identified 1250 soil erosion events. The inventory (dependent variable) was separated into two datasets, one for training (70%) and one for testing (30%). Elevation, slope, aspect, curvature, stream, precipitation, LULC, lithology, soil, NDVI, and distance to road were prepared in ArcGIS and considered as independent variables in the current research. GIS and RS-based models such as WOE, FR, and IV were used to assess the relationship between both variables and produce soil erosion susceptibility maps. Finally, the Area Under Curve (AUC) approach was used to confirm the research results. According to the validation data, the SRC for WOE, FR, and IV were 88%, 91%, and 87%, respectively. The present study’s validation results show that the PRC for WOE, FR, and IV are 92%, 94%, and 90%, respectively. Based on the AUC validation approach, we determined that the FR model had the highest accuracy when compared to the other two techniques, the WOE and IV models. The current analysis and final susceptibility maps of soil erosion could be useful for decision-makers in the future to prevent soil erosion and its negative repercussions

Effect of heterogeneity on the diffusion of Pb in apatite for petrochronological applications: A multiscale approach to characterizing the influence of apatite chemistry and anisotropy on Pb diffusion

International audienceThe investigation of various factors effecting the Lead (Pb) diffusion in phosphate minerals such as apatite is still challenging in the interpretation of (Usingle bondTh)/Pb geochronology. For (Usingle bondTh)/Pb system, apatite minerals have closure temperatures in the range of 375 to 600 °C and therefore can be used for the investigation of mid-temperature thermochronological and/or petrochronological questions i.e., the reconstruction of thermal events in Earth's crust. There is still uncertainty whether Pb diffusion in apatite is characterized by thermally activated volume and/or anisotropic diffusion profiles or is instead impacted by novel growth processes and recrystallization (chemical substitutions). As the apatite structure support extensive compositional variability, including partial or total substitution of both the cationic and anionic sites and forms solid solutions therefore, it necessitates a thorough examination of these effects and anisotropy on Pb diffusivity and (Usingle bondTh)/Pb geochronometric system. For this, a multi-scale study is carried out to examine the effects of chemical composition, anisotropy, and growth structure on the diffusion of Pb in order to better understand the behaviour of Pb diffusion in apatite. This study employed computational techniques like Density Functional Theory (DFT) and Transition State Theory (TST) at the atomic level and integrates it with the Kinetic Monte Carlo (KMC) simulations at the macroscopic level. Models of this study shows that Pb diffusion is completely anisotropic along the preferred z-axis or [001] direction and Pb readily escapes faster from Na-substituted apatite when compared to pure F-apatite and Cl-substituted apatite. Because of this anisotropy and chemical substitutions, Pb diffusivity in apatite either increases by opening of diffusion channels or decreases by blocking the diffusion channels depending on the site and type of chemical substitution. Further, in case of blocking effect the Pb diffusion occurs through workaround pathways and approaches towards the isotropic diffusion. For Na-substituted apatite, the impact of Na occupation on anisotropic Pb diffusion is significantly greater while in case of Cl-substituted apatite the Cl occupation mostly leads towards isotropic diffusion by opening the diffusion paths along other directions (mostly along the in-plane direction). Furthermore, the high closure temperatures (Tc) (e.g., ∼1370 °C) of the modelled apatites (except the perfect Na-substituted apatite e.g., ∼500 °C) of this study when compared to the Tc of Durango apatite obtained experimentally for the effective grain size of 100 μm and cooling rates of 10 °C/Ma indicate that the effective closure temperature dominantly depends on the degree and types of chemical substitutions and play a crucial role for the closure or opening of Pb diffusion/loss in apatites

Modelling and Mapping of Soil Erosion Susceptibility of Murree, Sub-Himalayas Using GIS and RS-Based Models

Soil erosion is one of Pakistan’s most serious environmental threats. This study used geospatial modelling to identify the distinct zones susceptible to soil erosion in Murree, Pakistan. Using a machine learning technique in the Google Earth engine (GEE) and Google Earth, we identified 1250 soil erosion events. The inventory (dependent variable) was separated into two datasets, one for training (70%) and one for testing (30%). Elevation, slope, aspect, curvature, stream, precipitation, LULC, lithology, soil, NDVI, and distance to road were prepared in ArcGIS and considered as independent variables in the current research. GIS and RS-based models such as WOE, FR, and IV were used to assess the relationship between both variables and produce soil erosion susceptibility maps. Finally, the Area Under Curve (AUC) approach was used to confirm the research results. According to the validation data, the SRC for WOE, FR, and IV were 88%, 91%, and 87%, respectively. The present study’s validation results show that the PRC for WOE, FR, and IV are 92%, 94%, and 90%, respectively. Based on the AUC validation approach, we determined that the FR model had the highest accuracy when compared to the other two techniques, the WOE and IV models. The current analysis and final susceptibility maps of soil erosion could be useful for decision-makers in the future to prevent soil erosion and its negative repercussions