Spatial distribution analysis and susceptibility mapping of landslides triggered before and after Mw7.8 Gorkha earthquake along Upper Bhote Koshi,Nepal

The 2015 Mw7.8 Gorkha earthquake triggered thousands of landslides of various types scattered over a large area. In the current study, we utilized pre- and post-earthquake high-resolution satellite imagery to compile two landslide inventories before and after earthquake and prepared three landslide susceptibility maps within 404 km² area using frequency ratio (FR) model. From the study, we could map about 519 landslides including 178 pre-earthquake slides and 341 coseismic slides were identified. This study investigated the relationship between landslide occurrence and landslide causative factors, i.e., slope, aspect, altitude, plan curvature, lithology, land use, distance from streams, distance from road, distance from faults, and peak ground acceleration. The analysis showed that the majority of landslides both pre-earthquake and coseismic occurred at slope >30°, preferably in S, SE, and SW directions and within altitude ranging from 1000 to 1500 m and 1500 to 3500 m. Scatter plots between number of landslides per km^?2 (LN) and percentage of landslide area (LA) and causative factors indicate that slope is the most influencing factor followed by lithology and PGA for the landslide formation. Higher landslide susceptibility before earthquake is observed along the road and rivers, whereas landslides after earthquake are triggered at steeper slopes and at higher altitudes. Combined susceptibility map indicates the effect of topography, geology, and land cover in the triggering of landslides in the entire basin. The resultant landslide susceptibility maps are verified through AUC showing success rates of 78, 81, and 77%, respectively. These susceptibility maps are helpful for engineers and planners for future development work in the landslide prone area.     

Continuous wavelet transforms of spectral decomposition analyses for fluvial reservoir characterization of Miano Gas Field,Indus Platform,Pakistan

Fluvial depositional systems constitute diverse host for the hydrocarbons all over the globe. Nevertheless, predicting the hydrocarbon sands within these assorted sand systems is a challenge for the prospect generation. We apply the continuous wavelet transform of spectral decomposition technique to a 3D seismic data of the Miano Gas Field in the Indus Platform, Pakistan. Full spectrum seismic attributes like the amplitude and root mean square (RMS) detect the reservoir sands down to a restricted reservoir compartment. Nonetheless, the continuous wavelet transforms can be advantageous in imaging and enactment of these clastic reservoirs within the whole reservoir zone. Rock physics reveal high-quality reservoir with an average porosity ～25% together with the discrimination of lithology/fluids, signifying the bright chances for ascertaining the potential stratigraphic reservoirs.     

Influence of water stress and exogenous glycinebetaine on sunflower achene weight and oil percentage

The study was carried out to assess whether exogenously applied glycinebtaine has any role in reducing the adverse effects of water stress on sunflower achene yield and oil contents. Two sunflower lines, Gulshan-98 and Suncross were subjected to water stress at the vegetative and reproductive stages of plant growth. Three levels of glycinebtaine (0, 50 and 100 mM) were applied before sowing (seed treatment) and at the time of initiation of stress at the vegetative and reproductive stages. A marked adverse effect of water stress on 100-achene weight and achene oil contents were observed in both sunflower lines. Exogenous supply of glycinebtaine was not effective in alleviating the adverse effects of water stress on achene oil percentage. Foliar spray of glycinebtaine, however, significantly reduced the negative effects of water stress on achene weight. Seed treatment with either level of glycinebtaine was not effective in increasing the 100-achene weight and achene oil percentage under both normally irrigated and water stress conditions. The sunflower line, Suncross produced higher oil yield than that of Gulshan-98.     

Vegetation and soil characteristics of the wasteland of Valika Chemical Industries near Manghopir, Karachi

The vegetation of the wasteland of Valika chemical industries near Manghopir road, Karachi was studied. Nine plant communities were recognized based on dominant species. In these plant communities the vegetation was disturbed, mostly halophytic and dominated bySuaeda fruticosa,Tamarix indica,Salsola baryosma,Cressa cretica,Atriplex griffithii,Haloxylon recurvum,Indigofera hochstetteri,Prosopis julifloraandChenopodium album.The physico-chemical properties of the soils were also analysed. Soil texture was mostly sandy loam, which exhibited slight variations in the water-holding capacity. The soils contained a sufficient amount of CaCO₃⁻and exhibited mostly alkaline soil pH. The soils of the different plant communities had scarcely any organic matter or inorganic phosphorus. The exchangeable sodium in the soils of many halophytic plant communities was high, with appreciable concentrations of potassium.     

Porosity prediction of lower cretaceous unconventional resource play,south Indus Basin,Pakistan, using the seismic spectral decomposition technique

Incised-valley shale systems are renowned as the fruitful exploration domains. However, the stratigraphic heterogeneity is significant, and thus, identifying the porous compartments within the thin-bedded and fractured shale system is one of the unconventional stratigraphic challenges. We present an innovative scheme for the exploration of the porous unconventional resources within the south Indus basin, Pakistan. The continuous wavelet transforms (CWT) exclusively detects the porous shales within the complete fractured system as compared to the full spectrum seismic data interpretation tool. The seismic amplitude, instantaneous frequency, and reflection strength tools show some tuning effects due to lithology and fluids. However, the tuning block of 28 Hz envelope sub-bands in conjunction with instantaneous phase tool accurately mark out the shale-plugged pinch out reservoirs. The synthetic wedge model based on the conventional seismic wavelet resolves the shale bed (incised-valley shale-filled lens) of about 23 m thickness, but shows limited imaging capability due to frequency band limitation for the porous shale locale. The acoustic impedance based on instantaneous spectral porosity modeling using the tuning volume of 28 Hz CWT has spectacularly resolved the thin-beds (gas shows) within the porosity range of 20 to 30%, which may have implications for future exploration of unconventional stratigraphic plays.     

Comparison of DSHA-based response spectrum with design response spectrum of building code of Pakistan (BCP-SP-2007) for a site in Muzaffargarh area,Pakistan

The building code of any country is considered to be a basic technical guidance document for the seismic design of structures. However, building codes are typically developed for the whole country, without considering site specific models that incorporate detailed site-specific data. Therefore, the adequacy of the design spectrum for building codes may sometimes be questionable. To study the sufficiency of the building codes of Pakistan (BCP-SP-2007), a deterministic seismic hazard analysis (DSHA) based spectrum was developed for a site in the Muzaffargarh area, Pakistan, using an updated earthquake catalogue, seismic source model, and a next generation attenuation model (NGA-WEST-2). Further, an International Building Code (IBC-2000) spectrum was developed for the study area to compare the results. The DSHA-based response spectrum resulted in a peak ground acceleration (PGA) value of 0.21 g for the Chaudwan fault. The evaluation of BCP-SP-2007 and IBC-2000 spectra provided a critical assessment for analyzing the associated margins. A comparison with the DSHA-based response spectrum showed that the BCP-SP-2007 design spectrum mostly overlapped with the DSHA spectrum unlike IBC-2000. However, special attention is needed for designing buildings in the study area when considering earthquake periods longer than 1 s, and the BCP-SP-2007 spectrum can be enhanced when considering a period range of 0.12–0.64 s. Finally, BCP-SP-2007 is based on a probabilistic approach and its comparison with deterministic results showed the significance of both methods in terms of design.     

一种水污染的综合控制模型

本文将水质预测及水污染控制措施有机地结合,选取水环境容量和污染指数作为水污染控制的参数,这样,一给对流－扩散水质方程的求解除就是实现本研究目的的关键,本研究彩和的模型在现有的水质模型基础上有所改进,因为其采用四点隐格式对水质进行预测,推求可接给污染物的环境容量值有为保证水质而陷定的污染物浓度值,从而制定相应的水污染控制措施,为整体考虑各种污染物的情况,建议彩和河流的污染指数进而推求综合污染指数,总之,本研究为水质保护提供了科学的计算方法,该法对水污染及污水对河道水质的影响是实用有效的。     

Macrophytes’ Performance in Floating Treatment Wetlands for the Remediation of Ciprofloxacin-Contaminated Water

The presence of trace antibiotics in domestic and industrial effluents poses a risk of toxicity to fauna and flora. The application of floating treatment wetlands (FTWs) is a low-cost and sustainable approach to remediate wastewater. In this study, the performance of different macrophytes vegetated in FTWs for the remediation of ciprofloxacin (CIP)-contaminated water is investigated. Six macrophytes, Brachia mutica, Typha domingensis, Phragmites australis, Canna indica, Cyperus laevigatus, and Leptochloa fusca, are vegetated in FTWs for the removal of CIP (50 mg L⁻¹) from water. The FTWs show the potential to remove 30–43.58 mg L⁻¹ CIP from water in 28 days. They also reduce the chemical oxygen demand (118–138 mg L⁻¹) and biochemical oxygen demand (35–45 mg L⁻¹) of water. Among the macrophytes, C. indica removes maximum (43.58 mg L⁻¹) and T. domingensis minimum (30 mg L⁻¹) CIP. Canna indica and T. domingensis exhibit the maximum and minimum increase (30% and 12% of dry biomass) in growth, respectively. This study reveals that the FTWs vegetated with different plant species exhibit varying performances in removing CIP from water. This investigation is a step forward toward sustainable bioremediation of water contaminated with antibiotics.     

Classification of rocks radionuclide data using machine learning techniques

The aim of this study is to assess the performance of linear discriminate analysis, support vector machines (SVMs) with linear and radial basis, classification and regression trees and random forest (RF) in the classification of radionuclide data obtained from three different types of rocks. Radionuclide data were obtained for metamorphic, sedimentary and igneous rocks using gamma spectroscopic method. A P-type high-purity germanium detector was used for the radiometric study. For analysis purpose, we have determined activity concentrations of ²³²Th, ²²⁶Ra and ⁴⁰K radionuclides, published elsewhere (Rafique et al. in Russ Geol Geophys 55:1073–1082, 2014), in different rock samples and built the classification model after pre-processing the data using three times tenfold cross-validation. Using this model, we have classified the new samples into known categories of sedimentary, igneous and metamorphic. The statistics depicts that RF and SVM with radial kernel outperform as compared to other classification methods in terms of error rate, area under the curve and with respect to other performance measures.     

Seismic activity prediction using computational intelligence techniques in northern Pakistan

Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar’s statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.