What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Tapan Mukerji mostly deals with Mineralogy, Seismic inversion, Porosity, Geostatistics and Reservoir modeling.
The various areas that he examines in his Mineralogy study include Flow, Pore water pressure and Elastic modulus.
The concepts of his Seismic inversion study are interwoven with issues in Well logging, Algorithm, Bayesian probability and Hydrology.
Tapan Mukerji has researched Geostatistics in several fields, including Spatial correlation, Work, Geophysics, Inverse problem and Consistency.
His work carried out in the field of Reservoir modeling brings together such families of science as Seismology, Information theory and Facies.
His Petrology study which covers Turbidite that intersects with Geotechnical engineering.
His most cited work include:
- The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media (1819 citations)
- The Rock Physics Handbook (1198 citations)
- Quantitative Seismic Interpretation: Applying Rock Physics Tools to Reduce Interpretation Risk (419 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Mineralogy, Seismology, Reservoir modeling, Porosity and Geophysics.
His studies in Mineralogy integrate themes in fields like Well logging, Compaction, Oil shale and Elastic modulus.
His work deals with themes such as Facies and Seismic to simulation, Seismic inversion, which intersect with Seismology.
His study in Seismic inversion is interdisciplinary in nature, drawing from both Geostatistics and Inverse problem.
He combines subjects such as Probability distribution and Algorithm with his study of Reservoir modeling.
His Porosity research includes themes of Modulus, Mechanics and Anisotropy.
He most often published in these fields:
- Mineralogy (17.61%)
- Seismology (14.49%)
- Reservoir modeling (12.50%)
What were the highlights of his more recent work (between 2018-2021)?
- Convolutional neural network (2.84%)
- Artificial intelligence (4.26%)
- Petroleum engineering (4.55%)
In recent papers he was focusing on the following fields of study:
Tapan Mukerji spends much of his time researching Convolutional neural network, Artificial intelligence, Petroleum engineering, Geophysics and Facies.
Tapan Mukerji interconnects Seismology, Prestack, Tectonophysics and Algorithm in the investigation of issues within Convolutional neural network.
He has included themes like Tomography, Seismic inversion and Anisotropy in his Seismology study.
His research in Artificial intelligence intersects with topics in Machine learning and Pattern recognition.
His work in Geophysics tackles topics such as Basin modelling which are related to areas like Pore water pressure, Bayesian inference and Uncertainty analysis.
The concepts of his Workflow study are interwoven with issues in Earth materials, Geotechnical engineering and Magma.
Between 2018 and 2021, his most popular works were:
- The Rock Physics Handbook (1198 citations)
- Convolutional neural network for seismic impedance inversion (42 citations)
- Prestack and poststack inversion using a physics-guided convolutional neural network (30 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
His primary areas of study are Convolutional neural network, Data mining, Algorithm, Prestack and Mechanics.
His studies in Convolutional neural network integrate themes in fields like Petrophysics and Petroleum engineering.
His work carried out in the field of Petrophysics brings together such families of science as Feature engineering, Deep learning and Drilling.
His Data mining study combines topics in areas such as Partial least squares regression, Bayesian probability, Statistical model, Hydrogeology and Supervised learning.
In general Mechanics study, his work on Computer simulation often relates to the realm of Scale effects, thereby connecting several areas of interest.
Tapan Mukerji integrates several fields in his works, including Test data and Artificial intelligence.
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