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Subsurface structure identification and modeling in three-dimensional dataset is a very complex problem for the geoscientist. Manual dealing of seismic data involves different complexities and requires more time and effort in order to address the errors and mistakes associated with the quality of seismic data. It is sometimes not straightforward to classify the features of interest especially when the data are of poor quality or associated with complex micro-tectonic regime. This brings the need of automatic working engine which can handle such issues and can find the target-oriented features with the more accuracy. Therefore, advance machine learning approach has been considered as a powerful tool which extensively used in different fields of data science to enhance the quality of data and fast processing. In this study, a new technology has been introduced in the seismic processing and development to make processes in automated routine. This method is based on deep neural network (DNN) that directly translates the raw seismic data into final fault models. A test example of deep semi-supervised learning method was performed for seismic faults and reflector identification without prior information and without going into complete dataset for labeling. This example explains the challenges to the issue and gives the solution to the problem related to the structure and tectonic modeling to explain the performance gain. In this approach of semi-supervised learning, only one of any reflectors and one or two of the faults has been introduced in the learning phase to identify such features in the complete 3D cube. This is an example of deep learning neural network for machine seismic in real case study and allows exploration geoscientists to easier mark the hot spots of their interest before mining or drilling.

The focus of this study is the monitoring and analysis of the most recent crustal deformation phenomena in Southern Pakistan. For subsidence evaluation, the longer temporal monitoring is recommended; therefore, the time period chosen for this measurement is between two separate 2005 to 2008 and 2014 to 2018 time series analysis. Remote sensing, synthetic aperture radar interferometry techniques (InSAR) technique is employed for the understanding of localized phenomena of crustal deformation in spatio-temporal coverage. Thatta and Jamshoro districts of the Southern Pakistan region are selected as areas of interest (AOI). These districts are notable for the subsidence phenomena in Pakistan. ENVISAT and central sub swath of Sentinal-1A satellites and InSAR datasets are utilized for quantification of subsidence rate of the area of interest because of the accuracy of their sensors up to millimeters. These satellite missions have widespread spatial coverage, continuous temporal sampling, and free data accessibility due to open data policy of European Space Agency. These two different satellite datasets for different periods of acquisitions provided the comparison of the subsidence rate between temporal periods of these two satellite imageries for area of interest. The long-term data monitoring enables to understand temporal-spatial characteristic to the AOI and understanding of the localized trend. Computation of localized subsidence pattern from ENIVSAT and central sub swath IW2 of Sentinel-1A had been carried out from period Jan 2005 to Dec 2009 with 32 acquisitions and from April 2014 to Nov 2018 with 49 imagery acquisitions, respectively, for ascending orbit. For this Line of Sight (LOS) displacement measurement, InSAR time-series analysis is utilized with the application of the small baseline subset (SBAS) interferogram technique. SBAS time series analysis emerged as strong algorithm with capability to reduce temporal decorrelation for deformation investigation within last couple of years. A total of 53 interferograms from ENVISAT and 141 interferograms from Sentinel-1A are formed. Relative temporal baseline positions are of ± 300 m for ENVISAT and ± 150 m for Sentinel-1A with respect to master acquisition. Subsidence of average 4 mm per year is measured along the cross section line from ENVISAT in the area of interest with total subsidence of 24 mm over a period of 6 years. During the Sential-1A acquisition period, the subsidence rate is the same but the area of subsidence is smaller in size as compared with the previous period of monitoring with ENVISAT. Maximum subsidence noted for the period from May 2005 to Aug 2006. It was the same period when over pumping of water was carried out in the area of interest. The larger deformation zone is due to the intense extraction of subsurface freshwater, because the subsidence correlated with the data of tube wells provided by the country water management agency. The data showed the increase in numbers of tube wells installed in the locality over the first phase study time period with extraction of water up to 7000 ha per year (Development Statistics of Sindh 2001-2016) with depletion of groundwater table up to 122 m from the surface.

The neutron activation analysis (NAA) is a very powerful method for multi-element analysis of samples. NAA technique needs to be developed for low neutron flux in millicuries using the 241Am-Be neutron source. To optimize sample and neutron source geometry for maximum γ-ray yield for neutron activation analysis. Gamma ray neutron activation analysis (NAA) has been set up where a 50-millicurie Am-Be neutron source is available. In this experimental setup, the neutron source is placed at the center of a cylindrical water tank of 40 x 40 cm in xyz plan, and thermal neutrons are produced at a distance of 1.2 cm from the source. The system is used for the activation analysis of sodium carbonate, sodium chloride and copper with the gamma ray emissions of 1.37, 1.73, 2.25 and 2.76 MeV for 24Na and 0.511 MeV for 64Cu having half lives of 15 hrs and 12.9 hrs, respectively. The thermal neutron activation analysis has been demonstrated by placing the samples at about 1.2 cm from the neutron source. HPGe and 3x3 inch NaI(Tl) gamma ray spectrometers have been used for the gamma ray spectrometry of these activated samples with the use of a Marinelli beaker. The geometry of the sample with respect to the neutron source has been optimized for the maximum γ-ray yield in NAA. The geometry of the sample with respect to the neutron source has been optimized for the maximum γ-ray yield in NAA. The increase in gamma ray flux distribution in the medium of water, due to the neutron source inside the tank, has been observed by a GM detector which is due to the production of short-lived 16N on activation of 16O in water. This system can also be used for prompt γ-ray analysis.

Due to an unfortunate oversight the author name “Nabeel Ahamd” has been misspelt. It should be read: “Nabeel Ahmad”.