Glacier mass variations have a direct impact on some of the key components of the global water cycle, including sea level rise and freshwater availability. Apart from being one of the largest Himalayan glaciers, Gangotri is one of the sources of water for the Ganges river, which has a considerable influence on the socioeconomic structure of a largely over-populated catchment area accounting for ∼26% of India's landmass. In this study, we present the most recent assessment of the Gangotri glacier dynamics, combining the use of interferometric techniques on synthetic aperture radar data and sub-pixel offset tracking on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. Results show that on average, the Gangotri glacier snout has receded at a rate of 21.3 ± 3 m year −1 over a period of 6 years (2004-2010). While glacier surface velocity near the snout is estimated to be between 24.8 ± 2.3 and 28.9 ± 2.3 m year −1 , interior portions of the glacier recorded velocities in the range of 13.9 ± 2.3 to 70.2 ± 2.3 m year −1 . Further, the average glacier surface velocity in the northern (lower) portions (28.1 ± 2.3 m year −1 ) is observed to be significantly lower than in the southern (higher) portions (48.1 ± 2.3 m year −1 ) of the Gangotri glacier. These values are calculated with an uncertainty of less than 5 m year −1 . Results also highlight a consistent retreat and non-uniform dynamics of the Gangotri glacier.

Seismic facies, combined with well-log data and other seismic attributes such as coherency, curvature, and AVO, play an important role in subsurface geological studies, especially for identification of depositional structures. The effectiveness of any seismic facies analysis algorithm depends on whether or not it is driven by local geologic factors, the absence of which may lead to unrealistic information about subsurface geology, depositional environment, and lithology. This includes proper identification of number of classes or facies existing in the data set. We developed a hybrid waveform classification algorithm based on an artificial immune system and self-organizing maps (AI-SOM), that forms the class of unsupervised classification or automatic facies identification followed by facies map generation. The advantage of AI-SOM is that, unlike, a stand-alone SOM, it is more robust in the presence of noise in seismic data. Artificial immune system (AIS) is an excellent data reduction technique providing a compact representation of the training data; this is followed by clustering and identification of number of clusters in the data set. The reduced data set from AIS processing serves as an excellent input to SOM processing. Thus, facies maps generated from AI-SOM are less affected by noise and redundancy in the data set. We tested the effectiveness of our algorithm with application to an offshore 3D seismic volume from F3 block in the Netherlands. The results confirmed that we can better interpret an appropriate number of facies in the seismic data using the AI-SOM approach than with a conventional SOM. We also examined the powerful data-reduction capabilities of AIS and advantages the of AI-SOM over SOM when data under consideration were noisy and redundant.

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The 3D post-stack seismic attributes provide an intuitive and effective way of using seismic volumes for reservoir characterization and development, and further identification of exploration targets. Some of the seismic attributes can aid in the precise prediction of the geometry and heterogeneity of subsurface geological settings. These also can provide useful information on petrophysical and lithological properties when combined with well-log information. There exist numerous seismic attributes that provide a unique interpretation on some aspects of subsurface geology. Of these, the proper demarcation of structural featuressuch as location and edges of faults and salt domes, and their throw and extent-always has been of primary concern. In this paper, we propose new multiattribute seismic algorithms by using fractal dimension and 2D/3D continuous wavelet transform (CWT). The use of higher-dimensional wavelets incorporates information from the ensemble of traces and can correlate information between neighboring traces in seismic data. The spectral decomposition that is based on the CWT aids in resolving various features of geological interest at a particular scale or frequency, which, when rendered with fractal attribute, demarcates the boundaries between those. We apply these two algorithms separately to a seismic amplitude volume and co-render output volumes together with some weights to yield a final attribute volume incorporating information from the aforementioned algorithms. We demonstrate the efficacy of these two algorithms in terms of the resolution and proper demarcation of various geological structures on real seismic data. The application of these algorithms results in better illumination and proper demarcation of various geological features such as salt domes, channels, and faults, and it illustrates how these simple tools can help to extract detailed information from seismic data.