Rajesh R. Nair

Bio: Rajesh R. Nair is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Continental margin & Heavy mineral. The author has an hindex of 9, co-authored 47 publications receiving 328 citations. Previous affiliations of Rajesh R. Nair include GlobalFoundries & Indian Institute of Technology Kharagpur.

Granularity and textural analysis as a proxy for extreme wave events in southeast coast of India

Abstract: Extreme wave events of 1000 and 1500 years (radiocarbon ages) have been recently reported in Mahabalipuram region, southeast coast of India. Subsequently, we carried out extensive sedimentological analysis in regions covering a total lateral coverage of 12 km with a new archeological site as the central portion of the study area. Twelve trenches in shore normal profiles exhibit landward thinning sequences as well as upward fining sequences confirming with the global signatures of extreme wave events. The sediment size ranges from fine-to-medium and moderately well sorted-to-well sorted, and exhibit positive skewness with platykurtic-to-leptokurtic nature. We now propose the abrupt winnowing or back and forth motion including unidirectional transport of these deposited sediments, which results in positive skewness. Textural analyses derived from scanning electron microscope studies (SEM) demonstrate the alteration produced, in the ilmenite mineral with vivid presence of pits and crescents with deformation observed on the surface due to extreme wave activities. This is further confirmed with the predominance of high-density mineral such as magnetite (5.2) and other heavy minerals in these deposits inferred the high-intensity of the reworking process of the beach shelf sediments.

The efficacy of support vector machines (SVM) in robust determination of earthquake early warning magnitudes in central Japan

Abstract: This work deals with a methodology applied to seismic early warning systems which are designed to provide real-time estimation of the magnitude of an event. We will reappraise the work of Simons et al. (2006), who on the basis of wavelet approach predicted a magnitude error of ±1. We will verify and improve upon the methodology of Simons et al. (2006) by applying an SVM statistical learning machine on the time-scale wavelet decomposition methods. We used the data of 108 events in central Japan with magnitude ranging from 3 to 7.4 recorded at KiK-net network stations, for a source–receiver distance of up to 150 km during the period 1998–2011. We applied a wavelet transform on the seismogram data and calculating scale-dependent threshold wavelet coefficients. These coefficients were then classified into low magnitude and high magnitude events by constructing a maximum margin hyperplane between the two classes, which forms the essence of SVMs. Further, the classified events from both the classes were picked up and linear regressions were plotted to determine the relationship between wavelet coefficient magnitude and earthquake magnitude, which in turn helped us to estimate the earthquake magnitude of an event given its threshold wavelet coefficient. At wavelet scale number 7, we predicted the earthquake magnitude of an event within 2.7 seconds. This means that a magnitude determination is available within 2.7 s after the initial onset of the P-wave. These results shed light on the application of SVM as a way to choose the optimal regression function to estimate the magnitude from a few seconds of an incoming seismogram. This would improve the approaches from Simons et al. (2006) which use an average of the two regression functions to estimate the magnitude.

Advanced 300mm 130nm BCD technology from 5V to 85V with Deep-Trench Isolation

Abstract: This paper demonstrates an advanced 300mm 130nm BCD (Bipolar-CMOS-DMOS) automotive grade platform with high modularity. The platform offers logic-devices, flash-devices and high performance power devices with rated voltages up to 85V as well as complimentary analog devices such as a BJT, MIM and Poly Resistor. The power devices in the process have best-in-class specific on-resistance and wide safe operating region. In addition, Deep-Trench Isolation (DTI) and highly doped n-buried layers are introduced to ensure much better immunity to parasitic coupling effects and isolation between sensitive devices and power stages.

Assessment of coastal dune characteristics using georadar imaging and sedimentological analysis: Odisha and Visakhapatnam, India

Abstract: The East coast of India is subject to continuous changes by high energy events. We sought to assess the depositional conditions along the coast from the geophysical and sedimentological character of the dune sands of the Gopalpur and Paradeep coast of Odisha, and the Sagarnagar coast of North Visakhapatnam. Quartz layers of the heavy mineral-rich zone collected at a depth of ~2 m from the landward foot of the dunes in the Visakhapatnam and Odisha coast, gave the OSL age estimates as 1,050 ± 50 and 260 ± 10 years respectively, revealing that the age of the dunes in Visakhapatnam are older than those on the Odisha coast. Episodic high energy events have affected the coast. Evidence from ground penetrating radar data consists of three stratigraphic units. The upper unit consists of vague reflections, parallel to the ground in continuous manner, most probably formed by wind action. On the other hand, the middle layer shows high amplitude reflections of heavy mineral-rich massive layers, possibly the result of tsunami activity. The lower massive layer parallel to the ground surface shows a low reflection pattern. The GPR studies showed that the thickness of the heavy mineral layers is greater on the landward foot of the dune as compared to that on the seaward side. According to the grain size analysis, the dune is composed of both wind generated and tsunamigenic sediments. The scanning electron microscope studies revealed that the heavy minerals present in the dunes are mainly sillimanite, ilmenite, garnet, pyroxene, rutile, sphene, biotite, hornblende, zircon, monazite and magnetite. The study demonstrates the origin of sand dunes in different ages along the East Coast of India by the effect of various natural phenomena.

Subsurface Images Shed Light on Past Tsunamis in India

Abstract: The 2004 Indian Ocean tsunami caused massive devastation and left a lasting impact along many of the major coastal regions in South Asia, including the coast of Tamil Nadu, a state in the southeastern tip of India. Following the event, sand deposits draped the low-lying areas and buried the muddy sediments of the coastal plain [Babu et al., 2007; Srinivasalu et al., 2007]. In addition, erosional features related to the tsunami, such as channels and scarps, have been observed along many parts of the coast (Figure 1a). This tsunami, along with a recorded history of intense monsoons, has highlighted the need for focused research on the role of extreme events in shaping the geological character of India's coastal plains.

Digital isochrons of the world's ocean floor

Abstract: We have created a digital age grid of the ocean floor with a grid node interval of 6 arc min using a self-consistent set of global isochrons and associated plate reconstruction poles. The age at each grid node was determined by linear interpolation between adjacent isochrons in the direction of spreading. Ages for ocean floor between the oldest identified magnetic anomalies and continental crust were interpolated by estimating the ages of passive continental margin segments from geological data and published plate models. We have constructed an age grid with error estimates for each grid cell as a function of (1) the error of ocean floor ages identified from magnetic anomalies along ship tracks and the age of the corresponding grid cells in our age grid, (2) the distance of a given grid cell to the nearest magnetic anomaly identification, and (3) the gradient of the age grid: i.e., larger errors are associated with high age gradients at fracture zones or other age discontinuities. Future applications of this digital grid include studies of the thermal and elastic structure of the lithosphere, the heat loss of the Earth, ridge-push forces through time, asymmetry of spreading, and providing constraints for seismic tomography and mantle convection models.

Ministry of Statistics and Programme Implementation

Abstract: Meeting: International Seminar on Information and Communication Technology Statistics, Seoul, Republic of Korea, 19-21 July 2010

Machine learning for data-driven discovery in solid Earth geoscience

Abstract: BACKGROUND The solid Earth, oceans, and atmosphere together form a complex interacting geosystem. Processes relevant to understanding Earth’s geosystem behavior range in spatial scale from the atomic to the planetary, and in temporal scale from milliseconds to billions of years. Physical, chemical, and biological processes interact and have substantial influence on this complex geosystem, and humans interact with it in ways that are increasingly consequential to the future of both the natural world and civilization as the finiteness of Earth becomes increasingly apparent and limits on available energy, mineral resources, and fresh water increasingly affect the human condition. Earth is subject to a variety of geohazards that are poorly understood, yet increasingly impactful as our exposure grows through increasing urbanization, particularly in hazard-prone areas. We have a fundamental need to develop the best possible predictive understanding of how the geosystem works, and that understanding must be informed by both the present and the deep past. This understanding will come through the analysis of increasingly large geo-datasets and from computationally intensive simulations, often connected through inverse problems. Geoscientists are faced with the challenge of extracting as much useful information as possible and gaining new insights from these data, simulations, and the interplay between the two. Techniques from the rapidly evolving field of machine learning (ML) will play a key role in this effort. ADVANCES The confluence of ultrafast computers with large memory, rapid progress in ML algorithms, and the ready availability of large datasets place geoscience at the threshold of dramatic progress. We anticipate that this progress will come from the application of ML across three categories of research effort: (i) automation to perform a complex prediction task that cannot easily be described by a set of explicit commands; (ii) modeling and inverse problems to create a representation that approximates numerical simulations or captures relationships; and (iii) discovery to reveal new and often unanticipated patterns, structures, or relationships. Examples of automation include geologic mapping using remote-sensing data, characterizing the topology of fracture systems to model subsurface transport, and classifying volcanic ash particles to infer eruptive mechanism. Examples of modeling include approximating the viscoelastic response for complex rheology, determining wave speed models directly from tomographic data, and classifying diverse seismic events. Examples of discovery include predicting laboratory slip events using observations of acoustic emissions, detecting weak earthquake signals using similarity search, and determining the connectivity of subsurface reservoirs using groundwater tracer observations. OUTLOOK The use of ML in solid Earth geosciences is growing rapidly, but is still in its early stages and making uneven progress. Much remains to be done with existing datasets from long-standing data sources, which in many cases are largely unexplored. Newer, unconventional data sources such as light detection and ranging (LiDAR), fiber-optic sensing, and crowd-sourced measurements may demand new approaches through both the volume and the character of information that they present. Practical steps could accelerate and broaden the use of ML in the geosciences. Wider adoption of open-science principles such as open source code, open data, and open access will better position the solid Earth community to take advantage of rapid developments in ML and artificial intelligence. Benchmark datasets and challenge problems have played an important role in driving progress in artificial intelligence research by enabling rigorous performance comparison and could play a similar role in the geosciences. Testing on high-quality datasets produces better models, and benchmark datasets make these data widely available to the research community. They also help recruit expertise from allied disciplines. Close collaboration between geoscientists and ML researchers will aid in making quick progress in ML geoscience applications. Extracting maximum value from geoscientific data will require new approaches for combining data-driven methods, physical modeling, and algorithms capable of learning with limited, weak, or biased labels. Funding opportunities that target the intersection of these disciplines, as well as a greater component of data science and ML education in the geosciences, could help bring this effort to fruition. The list of author affiliations is available in the full article online.

The breakup of East Gondwana: Assimilating constraints from Cretaceous ocean basins around India into a best‐fit tectonic model

Abstract: Published models for the Cretaceous sea!oor-spreading history of East Gondwana result in unlikely tectonic scenarios for at least one of the plate boundaries involved and/or violate particular constraints from at least one of the associated ocean basins. We link East Gondwana spreading corridors by integrating magnetic and gravity anomaly data from the Enderby Basin off East Antarctica within a regional plate kinematic framework to identify a conjugate series of east-west-trending magnetic anomalies, M4 to M0 (~126.7–120.4 Ma). The mid-ocean ridge that separated Greater India from Australia-Antarctica propagated from north to south, starting at ~136Ma northwest of Australia, and reached the southern tip of India at ~126 Ma. Sea!oor spreading in the Enderby Basin was abandoned at ~115 Ma, when a ridge jump transferred the Elan Bank and South Kerguelen Plateau to the Antarctic plate. Our revised plate kinematic model helps resolve the problem of successive two-way strike-slip motion between Madagascar and India seen in many previously published reconstructions and also suggests that sea!oor spreading between them progressed from south to north from 94 to 84 Ma. This timing is essential for tectonic !ow lines to match the curved fracture zones of the Wharton and Enderby basins, as Greater India gradually began to unzip from Madagascar from ~100 Ma. In our model, the 85-East Ridge and Kerguelen Fracture Zone formed as conjugate !anks of a "leaky" transform fault following the ~100Ma spreading reorganization. Our model also identi"es the Afanasy Nikitin Seamounts as products of the Conrad Rise hotspot.