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.

Digital isochrons of the world's ocean floor

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

Ministry of Statistics and Programme Implementation

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.

Machine learning for data-driven discovery in solid Earth geoscience

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.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50079

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

Ground Penetrating Radar : Theory and Applications

Seismic data provide evidence about hydrocarbon deposition, geological and geophysical subsurface information, including geomechanical aspects. Deriving and understanding geomechanical properties is crucial for reservoir management as it can avoid drilling and production-related problems that cause environmental impacts associated with land subsidence and uplift. The Poison's ratio (PR), Young Modulus (YM), and elastic moduli for a reservoir block were estimated using 3D seismic pre-stack data and well data. 3D Mechanical Earth Models (MEM) were also developed using the well logs, seismic horizons, and drilling data. Seismic data-derived geomechanical properties were compared with the mechanical earth models for the first time for this field. Well-tie analysis was used for inversion of 3D seismic data to extract detailed waveform and amplitude information. The brittleness index of the subsurface layers was estimated, which is a critical rock property that provides information about rock hardness and fragility phenomenon. The brittleness index has a diverse range from 5-35%, with significant contrast at shallow zones. PR and YM models generated from 3D MEM and seismic data have average values of 0.2 -0.6 and 5 - 28 GPa with significant contrast from shales and carbonates. The study recommends that the drilling through these problematic zones should be avoided to avoid wellbore problems that cause challenges in maintaining wellbore integrity and reservoir management in the North-Heera field, Mumbai Offshore Basin.

https://www.ijmems.in/cms/storage/app/public/uploads/volumes/60-IJMEMS-21-0124-6-4-1025-1043-2021.pdf

Seismic Driven Geomechanical Modeling of Uplifted and Subsided Wells in Mumbai Offshore and Its Engineering Implications

We estimate the gas-hydrate saturation in fractured reservoirs of gas hydrate-rich marine sediments from Krishna-Godavari basin along the eastern margin of India. Effective medium modeling (EMM) is a combination of self-consistent approximation (SCA), differential effective medium (DEM) modeling and smoothing and it deals with the anisotropy associated with clay platelets’ orientation. It proves to be a good choice attributable to its dependency on physical parameters alone. Previous approaches appertain to the implementation of orientation distribution function (ODF) on clay platelets alone assuming a vertical ellipsoidal shape of gas hydrates whilst in the present study, we have implemented ODF on hydrate bearing fractures assigning an ellipsoidal shape to them as well. A bias of ~2-4% in the saturation estimates apparently streamlines the betterness of our method.

/pdf/influence-of-fracture-orientation-distribution-function-on-38w9o22mjt.pdf

Rajesh R. Nair

Papers

Seismic Driven Geomechanical Modeling of Uplifted and Subsided Wells in Mumbai Offshore and Its Engineering Implications

Influence of fracture orientation distribution function on the estimates of gas-hydrate saturation of site 10 at Krishna-Godavari Basin, India