SEG DISC 2019: Physics and Mechanics of Rocks: A Practical Approach

Friday, September 6, 2019

 See here for more details adn registration: https://seg.org/Education/Courses/DISC/2019-DISC-Manika-Prasad

Intended Audience

  • Seismic imagers and interpreters who want to learn how fluids, stress, and other environmental effects change seismic signatures
  • Geophysicists who wish to derive rock properties and constrain well-to-seismic ties
  • Geologists and sedimentologists looking to develop predictive models of sedimentary environments and stratigraphic events
  • Reservoir engineers to build porosity, permeability, and fluid coverage models for reservoir simulations using 3D and 4D seismic data
  • Basin modelers and completions engineers to evaluate stresses from well log and seismic data
  • Geoscientists doing formation evaluation and well logging interpretations
  • Basin managers and team leaders who wish to evaluate the accuracy of predictions and understand risk and errors in models

Prerequisites (Knowledge/Experience/Education Required)

Attendees should have an understanding of basic rock properties such as porosity, permeability, sediment compositions and depositions, and structural geology. It will be helpful to have familiarity, but not necessarily expertise, in seismic properties. The accompanying textbook will include mathematical details, data and problem solutions for mineral modulus calculations, rock stiffness calculations for textural symmetries, velocity binning in flow zones, pore stiffness, and Gassmann fluid substitution. The lecture will focus on fundamental rock physics principles, applications, and analysis of results.

Course Outline

The course is organized into two main sections: Section I. Rock Physics Fundamentals (introductory section) and II. Advanced Topics in Rock Physics (application section):

Rock physics fundamentals

In this section, I will:

  • Review fundamental principles underlying rock physics, and rock properties
  • Investigate the effects of fluids on rock properties
  • Derive basic rock physics correlations and explain why and how they work
  • Review rock properties that can be mapped with remote sensing

Advanced Topics in Rock Physics

In this section, the student is introduced to:

  • Poroelasticity
  • Attenuation and dispersion
  • Geomechanics
  • Complex electrical conductivity and permeability
  • Investigate the causes for complications and deviations from basic correlations
  • Examine existing empirical and theoretical models
  • Discuss selected case studies in rock physics

Learner Outcomes

On completion of the course, the learner should be able to

  • Describe and explain the applications of rock physics for reservoir characterization, formation evaluation, and field monitoring
  • Identify and evaluate existing and potential technologies applicable to rocks physics and rock mechanics for reservoir/formation studies
  • Identify, list, and describe the physical properties of rock, and relate these properties to the mechanical behavior of rocks
  • interpret and predict the effect of mineral properties (e.g. clay minerals) on the load-bearing capacity and strength of rocks
  • Integrate and model elastic wave propagation, electrical conductivity, and fluid flow in rocks
  • Evaluate and assess errors in experimental data, uncertainty, and the value of theoretical models
  • Develop expertise in rock physics interpretations of seismic and electrical conductivity to identify fluids and quantify saturations
  • Gather key strengths in rock physics interpretations by developing a broad understanding of existing or potential technology transfers between engineering and earth science fields that relate rock physics to reservoir geophysics and reservoir engineering
  • Gain knowledge and expertise to understand physical and mechanical behavior of rocks through examples of stress-dependent changes in strains, seismic velocity, electrical conductivity, and pore structure
  • Interpret rock physics and rock mechanics data and model elastic wave propagation, electrical conductivity, and fluid flow in rocks
  • Assess errors in experimental data, assess the uncertainty and the value of rock physics models
  • These learning objectives will allow geoscientists and engineers to:
  • Distinguish major trends in and control factors for velocity and impedance changes in the subsurface
  • Describe and evaluate velocity and impedance data for changes in fluids and stresses
  • Apply basic rock physics techniques to evaluate reservoirs
  • Identify and select the best practice workflows when using rock physics for seismic interpretations
  • Analyze complex conductivity data to interpret reservoir properties



Rock physics is an interdisciplinary branch of geophysics that explains geophysical remote sensing data, such as seismic wave velocities and electrical conductivity, in the context of mineralogy, fluid content, and environmental conditions. Thus, rock physics interpretations often require inputs from physics, geology, chemistry, chemical engineering, and other fields. For example, seismic waves travel faster in cemented rocks than in loose sediments. Since the physical behavior of rocks controls their seismic response, rock physics brings key knowledge that helps with the interpretation of rock properties such as porosity, permeability, texture, and pressure. Rock physics combines indirect geophysical data (such as seismic impedance, sonic log velocities, and laboratory measurements) with petrophysical information about porosity, fluid type, and saturation for use in reservoir characterization, evaluation, and monitoring. Typically, rock physics is used by petroleum engineers doing reservoir simulations, geologists evaluating over-pressures and making basin models, and anyone doing a monitoring survey to map fluids from 4D seismic. For all such purposes, an understanding of wave propagation is required to relate seismic properties (e.g. velocity and attenuation) to the physical properties of rocks and to evaluate seismic data in terms of subsurface petrophysical parameters.  For example, an application of rock physics is seen in 4D seismic data (i.e. repeated seismic data acquired from the same field), where fluid saturation changes are evaluated from changes in velocity using fluid substitution models. Another rock physics application is to understand and predict the effect of clay minerals on the load-bearing capacity and strength of rocks using fundamental knowledge about the properties of clay minerals (e.g. CEC, surface area, dispersability, charge, sorption, plasticity, etc.), the clay water content, as well as the effects of their distribution within the rock. Thus, an effective prediction of rock properties from indirect measurements requires a solid understanding of the physical behavior of rocks under in situ conditions of pore and confining pressures and fluid saturations.

During this one-day short course, I will provide the earth scientist and engineer with a foundation in rock physics to describe the physical processes that govern the response of rocks to the external stresses essential for reservoir characterization. The course will also offer practical guidance to help better analyze existing data. A major goal of this course is to offer practical instruction and provide working knowledge in the areas of rock physics and rock mechanics for rock characterization.

New Applications of Machine Learning to Oil & Gas Exploration and Production

Monday, July 8, 2019

2 Day EAGE course in Perth. For more information and registration, see here:


Course description

The course introduction will attempt to answer the question: How will A.I. change the way we work in the Oil and Gas industry in the coming years? Looking at what is underway in other industries and guessing what type of projects are under development in R&D departments in our industry will help answer that question. Oil and Gas examples will be presented corresponding to each of the terms A.I., Machine Learning, and Deep Learning, allowing participants to reach a clear understanding on how they differ.


The course will then focus on Deep Learning (DL) and address all key aspects of developing and applying the technology to Oil and Gas projects. 

 - What is DL and how different is it from traditional neural networks?

 - A peek at the mathematics behind Deep Neural Networks (DNN)

 - Typical workflow to design and develop a deep learning application in an E&P project

 - Common challenges, difficulties, and pitfalls in deep learning projects

 - Software tools and hardware required + Cloud computing vs in-house solutions.


This will be followed by live demonstrations of two DNN-based applications specific to Oil and Gas upstream domains. 


First, we'll run software performing automatic fault identification will be run on released seismic data from New Zealand basins to demonstrate how a DNN recognizes faults and how it differs from other algorithms such as ant tracking. Starting from default training, the DNN can gradually learn to recognize faults like the Geophysicist or Structural Geologist. The training set constantly evolves incorporating feedback from human experts. 


Second, the identification of resource opportunities in very large repositories of text and image documents will be demonstrated. This will be done with a deep learning application that performs contextual search and linguistic analysis. Unlike keyword search, contextual search extracts information based on its context, just like humans do. And then linguistic analysis is run on the extracted information to identify actionable opportunities. This list of opportunities can then be further evaluated by human experts.


Finally, the course conclusion will summarize key learnings and answer any additional questions/queries from participants.

Course objectives

Upon completion of the course, participants will have acquired detailed knowledge of what deep learning is exactly, how it works, and in which way it differs from traditional neural networks that have been used in the industry during the last 30 years. They will understand which domains this can be applied to and for what type of applications. And they will also understand what are the main challenges, difficulties, and pitfalls when developing new applications. Finally they will have seen demonstrations of deep neural networks applied to Exploration and Production disciplines and will be able to evaluate how useful the technology could be for their own domain.


Participants' profile

The course is designed for geoscientists, petroleum engineers, and petrophysicists from new ventures/basin, exploration, and development & production disciplines- from early career to senior, working in oil & gas companies or service companies. 



Participants should be familiar with at least one discipline in the Oil and Gas Exploration and Production (G&G, Petrophysics, Reservoir and Production Engineering, Petroleum Engineering)


About the instructor

Dr. Bernard Montaron is CEO of Fraimwork SAS, Paris, France, and CTO of Cenozai Sdn Bhd, Kuala Lumpur, Malaysia. Two start-ups, created in mid-2017, that are specialized in the application of Artificial Intelligence to various domains, and provide services to oil and gas companies for exploration and production. In 2015-2017 he was Chief Geoscientist of BeicipTecsol in Kuala Lumpur. Prior to this, Bernard Montaron worked 30 years for Schlumberger where he held a number of positions in R&D and Marketing. He has worked for the oil and gas industry in Europe, in the United States, in the Middle East, in China, and Malaysia. Bernard was General Manager of the Schlumberger Riboud Product Center in Paris - Clamart, France (2002-2003) and he was VP Marketing of Schlumberger Middle East and Schlumberger Europe-Africa-Russia regions (2000-2001). Bernard holds a MSc degree in Physics from ESPCI, Paris, France, and a PhD in Mathematics from University Pierre et Marie Curie, Paris, France. He also has a Machine Learning certificate from Andrew Ng's course (Stanford Univ./Coursera). Bernard Montaron received the best oral presentation award at the APGCE 2017 conference for his paper on "Deep Learning Technology for Pattern Recognition in Seismic Data – A Practical Approach".

Seismic Diffraction-Modelling, Imaging and Applications

Thursday, July 4, 2019

2 Day EAGE course. For more details and registration, see here:


Course description

Diffraction phenomena have been identified as the key seismic manifestation of fractures and other small-scale reservoir heterogeneities. This two-day course will present the current state-of-the-art of diffraction technology and put this in context by a review of its past developments. The course will cover both forward diffraction modeling and diffraction imaging. Case studies of diffraction imaging will be presented covering applications in seismic exploration and other areas of geoscientific interest.

Course objectives

The course will be clearly structured in topics and subtopics. At the end of each topic, a number of bullet points will summarize the items meant to be memorized and taken home by the learner. Interaction between the teacher and learner will be encouraged. The course material will be enlightened by out-of-the box examples demonstrating diffraction phenomena that support the techniques. 

By the end of this course, the learner will:

Have a detailed and up-to-date understanding of the physics of diffraction, diffraction modelling and imaging;

Be able to effectively communicate the key aspects of diffraction technology with other professionals;

Have a good understanding of the added value that seismic diffraction brings to current exploration and production projects. 


Course outline

1 Introduction 

Motivation, basic ideas and concepts 

Reflection versus diffraction

Applications of diffraction analysis and imaging

Interpretation value

2 History

Discovery and founding years (1650-1820): Grimaldi, Huygens, Newton, Young, Fresnel, Poisson, Arago

Scalar diffraction: mathematical foundation- 19th century: Green, Helmholtz, Kirchhoff, Sommerfeld

Towards Geometrical Theory of Diffraction- early 20th century: Maggi, Rubinowicz, Keller

Towards Modern Theory: Trorey, Klem-Musatov

3 Diffraction Modeling

Motivation, definitions, objectives

Physical modeling

Numerical modeling: integral methods, boundary layer methods, surface and caustic diffraction, finite differences, time-lapse, scattering methods

Case study: Diffraction analysis on Ground Penetrating Radar Data

Case study: Diffraction Response of Salt Diapirs

4 Diffraction Imaging in the Time Domain

Motivation, definitions, objectives

Anatomy of diffraction

Diffraction and standard processing

Detection of diffracted waves

Separation of diffracted waves

Inversion of diffracted waves


Common Reflection Surface/Multifocusing

Focusing and velocity estimation

Fracture detection

5 Diffraction Imaging in the Depth Domain


Velocity model considerations

Illumination: edge and tip diffraction imaging

Depth imaging: general principles

Resolution and super-resolution

Image processing and diffraction imaging

Diffraction imaging by specularity suppression

Applications: sandstone reservoirs, time-lapse, stratigraphic terminations against salt, Carbonate reservoirs, shale resource plays, unconventional reservoirs

Case studies 

Participants' profile

The target audience of the course consists in geoscientists from industry and academia with a basic knowledge of seismic processing and an interest in innovative interpretation technologies. 



Prerequisites are a basic knowledge of seismic processing and imaging and a very elementary mathematical background. 


About the instructors

Evgeny Landa obtained his MSc degree in geophysics at Novosibirsk University (1972) and PhD degree in geophysics at Tel Aviv University (1986). He started his carrier in the former Soviet Union, Novosibirsk as a researcher, and senior geophysicist at the Siberian Geophysical Expedition. After immigrating to Israel, he worked at the Geophysical Institute of Israel as a researcher, Head of the R&D group and Head of the Seismic Department (1981—2002), and Director of OPERA (Applied Geophysical Research Group) in Pau (France) (2002-2014) where he was involved in different aspects of seismic data processing, velocity model building and time and depth imaging. His work on velocity model building by coherency inversion has had a strong impact on today’s seismic depth imaging workflows and forms an important part of the GeoDepth (Paradigm) software package. Recently, his research interest involves using non-reflecting energy for increasing seismic resolution and imaging without precise velocity information. He has published more than 60 papers in international journals and his book ‘Beyond Conventional Seismic Imaging’. He is a member of EAGE and SEG, from which he received the Awards of Best Paper (SEG, Honorary Mentioned, 2005) and the EAGE Eotvos Award (2007 and 2009).


Tijmen Jan Moser has a PhD from Utrecht University and has worked as a geophysical consultant for a number of companies and institutes (Amoco, Institut Français du Pétrole, Karlsruhe University, Bergen University, Statoil/Hydro, Geophysical Institute of Israel, Fugro-Jason, Horizon Energy Partners). For the last few years he has been working independently with associations with ZTerra, SGS-Horizon and others. He is based in The Hague, The Netherlands. His main interests include seismic imaging, asymptotic methods, seismic reservoir characterization, diffraction and geothermal exploration. He has authored many influential papers on ray theory and ray methods, Born inversion and modeling, macro-model independent imaging, and diffraction imaging, several of which have received Best Paper awards (SEG, 2005 Honorary mention, EAGE 2007 and 2009, Eotvos Award). He is Editor-in-Chief of Geophysical Prospecting and is serving on SEG's Publication Committee and EAGE's Oil Gas & Geoscience Division Committee. He is a member of SEG and MAA and honorary member of EAGE.

Rock Physics and Computational Geophysics, Dr José Carcione (OGS)

Thursday, June 27, 2019

2 Day EAGE course. For more details and registration, see here:



Course description

This course presents the fundamentals of the physical principles and computational techniques for wave propagation in anisotropic, anelastic and porous media, including the analogy between acoustic waves (in the general sense) and electromagnetic (EM) waves.  The emphasis is on geophysical applications for hydrocarbon exploration, but researchers in the fields of earthquake seismology, rock physics, and material science, -- including many branches of acoustics of fluids and solids (acoustics of materials, non-destructive testing, etc.) -- may also find the material useful. The course illustrates the use of seismic and EM modeling, with an account of the numerical algorithms for computing synthetic seismograms, diffusion fields and radargrams, with applications in the field of geophysical prospecting, seismology and rock physics, such as evaluation of methane hydrate content, upscaling techniques, detection of overpressure, Antarctic and permafrost exploration, exploration of the Earth's deep crust, time-lapse for monitoring of CO2 injection, seismic modeling in geothermal fields, seismic inversion, etc.


Course objectives

On completion of the course, participants will be able to:

• Understand the physics of seismic (and EM) wave propagation and diffusion fields in real media, such as rocks and geological formations.

• Solve complex problems using numerical methods, as finite-differences, Fourier techniques, and machine learning methods. 

• Apply these concepts to seismic and EM applications, such as hydrocarbon prospection, earthquakes, surface radar applications, EM low-frequency methods for environmental problems, rock physics, etc. 


Course outline

Mechanical viscoelastics models.

The wave equation with attenuation.

Seismic anisotropy.

Seismic attenuation.


Seismic rock physics.

Hooke’s law and wave equation.

Forward modeling. Computation of synthetic seismograms.

Reflection coefficients. AVO.

EM rock physics

Maxwell’s equation.

The seismic-EM analogy.

Geo-radar equations.

The diffusion equation in EM prospecting.

Machine learning methods. Neural networks, genetic algorithms, etc.


Fluid flow in porous rocks.

Unconventional resources. Oil and gas shales.

Cross-well seismic and EM methods.

Upscaling methods.

AVO cases

Rock-physics templates.

Q and velocity anisotropy in fractured media.

Geophone-soil coupling models.

Physics and simulation of waves at the ocean bottom.

Recent advances to model waves in reservoir and source rocks

Theory, simulation and case histories for detection and quantification of gas hydrates.

Theories for pore-pressure prediction and mud-weight design, with case histories.

Seismic-modeling case histories.

Seismic inversion.


Borehole waves.

Injection of fluids and seismic and EM monitoring. Time-lapse cases.

Tools for GPR applications.

Participants' profile

The course is useful for geologists, geophysicists, petrophysicists and reservoir engineers. The emphasis is on geophysical applications for hydrocarbon exploration, but researchers in the fields of earthquake seismology, rock acoustics and material science – including many branches of acoustics of fluids and solids (acoustics of materials, nondestructive testing, etc.) – may also find this course useful.



Participants should have knowledge of the basic concepts of wave theory.

About the instructor

José M. Carcione has the degrees ``Licenciado in Ciencias Físicas" (Buenos Aires University), ``Dottore in Fisica" (Milan University) and Ph.D. in Geophysics (Tel-Aviv University). From 1978 to 1980 he worked at the ``Comisión Nacional de Energía Atómica" at Buenos Aires. From 1981 to 1987 he was employed as a research geophysicist at YPF (national oil company of Argentina). Presently, he is Director of Research at OGS. He was awarded the Alexander von Humboldt scholarship for a post-doc at Hamburg University (1987-1989). In 2007, he received the Anstey award at the EAGE in London and the 2017 EAGE Conrad Schlumberger award in Paris. Carcione published more than 280 journal articles on acoustic and electromagnetic numerical modeling, with applications to oil exploration and environmental geophysics. He is the author of the books "Wave fields in Real Media – Theory and numerical simulation of wave propagation in anisotropic, anelastic, porous and electromagnetic media" (see (Elsevier, 2015, 3rd edition), and "Seismic Exploration of Hydrocarbons in Heterogeneous Reservoirs" (Elsevier, 2015) He has been editor of "Geophysics" since 1999. He has coordinated many projects funded by the EU and private companies. Carcione has been a member of the commission (GEV04) for evaluation of Italian research in the _eld of Earth Sciences (ANVUR) in the periods 2004-2010 and 2011-2014. Carcione has an H-index: 53, according to Google Scholar. For more detail see his website: http://www.lucabaradello.it/carcione/ 

WA Technical Night - Teena Zn Prospect - New Insights for Geophysical Discovery of Shale-hosted Zinc Deposits

Wednesday, May 8, 2019

Teena Zn Prospect - New Insights for Geophysical Discovery of Shale-hosted Zinc Deposits
Darren Hunt 
(Senior Project Geophysicist - Teck)

Teena is a Paleoproterozoic stratiform Zn-Pb deposit located 8 kilometres west of the McArthur River Zn-Pb Mine, NT.  Higher grade mineralization was drilled by Teck in 2013, and represents the most significant SHMS-style discovery in Australia since the early 1990’s.  While the style of mineralization is similar to McArthur river (HYC) Mine, Teena lies beneath 600 to 1000m of siliciclastic and calcareous sediments and is completely blind.  Thus, the system gives the explorationist  valuable insights into the challenge of exploring for blind SHMS deposits, which often present as challenging targets for geophysical detection.  This talk will present physical properties data in the context of the mineralogy and stratigraphic host and discuss the geophysical methodologies applied to at Teena and the subsequent toolkit developed for the detection of deeply buried systems using Teena datasets as examples.

ASEG-WA - Young Professional Speaker Night

Tuesday, April 16, 2019


Exploring the Triassic Oil Potential on the North West Shelf, Australia

Claudia Valenti (Carnarvon Petroleum)


The exploration history of the North West Shelf suggests that the Australian petroleum systems are predominantly gas prone, typically found beneath the mid-Cretaceous regional seal.

Carnarvon Petroleum regional and local seismic mapping, in conjunction with exploration drilling results, indicates that the Triassic has three oil prone petroleum systems, the Early, Middle and Late Triassic.

Fundamental to understanding the areas where these three petroleum systems will work is a thorough understanding of palaeogeographies, palaeoenvironments and stratigraphy. Carnarvon believes the petroleum systems have a distinct palaeogeographic distribution and are not ubiquitous source rock/reservoir horizons across the North West Shelf.

Various laboratory analysis, sedimentology, seismic mapping, seismic facies work have contributed to the identification of the most encouraging areas to explore.

Additionally, the proximity of the Triassic geology of the North West Shelf to SE Asia Triassic geology, with their similar depositional setting, prior to Gondwana break-up, adds credence to the proposed theories. Examples of the early, middle and late Triassic oil petroleum systems will be illustrated and identified in some of the major basins on the NW Shelf.


Groundwater Throughflow and Seawater Intrusion in High Quality Coastal Aquifers
Alex Costall (PhD Student - Curtin University)

This last year has taken me into the wonderful world of karstic aquifers, numerical groundwater flow - and solute transport - modelling. A ‘karst’ refers to terrain with distinctive structures formed from highly soluble rocks, such as limestone, dolomites, gypsums, etc. Exposure to flowing groundwater causes the rock matrix to dissolve, which can greatly affect the flow of groundwater. Caves, conduits, and fractures all present high-permeability pathways for groundwater to travel. Some of the highest quality coastal groundwater reserves around the world exist as extensive karstic aquifer systems. At coastal margins, the invisible and ever-looming seawater interface threatens fresh groundwater resources.  As the changing climate affects groundwater recharge and growing world population continues to rely on these aquifers for drinkable groundwater, the risk of contamination has implications for millions of peoples worldwide. 

Geophysical exploration methods offer unparalleled access to subsurface information, but it is not without flaws. Resistivity imaging is commonly used, but rarely with time-lapse investigation nor optimal acquisition/inversion strategy in mind. Numerical solute transport modelling has potential to aid our understanding, and define the limitations, of the both geophysical and conventional sampling methodology. Outcomes from this research aim to improve groundwater monitoring practices and numerical modelling outcomes with regard to the seawater interface. These outcomes will ultimately aid groundwater management decisions and help to preserve our fresh water resources for future generations.

Alex is a PhD student at Curtin University, Department of Exploration Geophysics, specializing in near-surface hydrogeophysical problems. He completed a Bachelor of Science (Geophysics) with first-class honours in 2014, and works part-time within the geophysics department as a research assistant and laboratory demonstrator. Alex’s research interests include groundwater and solute transport modelling, cooperative geophysical modelling, and exploration with ground penetrating radar and electrical resistivity.

Register your attendance by emailing wapresident@aseg.org.au.

WA SEG DL - Sometimes it pays to be cheap – Compressive time-lapse seismic data acquisition. SEG Distinguished Lecturer, Felix Herrmann

Monday, April 15, 2019

Sometimes it pays to be cheap – Compressive time-lapse seismic data acquisition

Felix Herrmann (SEG Distinguished Lecturer)

During these times of sustained low oil prices, it is essential to look for new innovative ways to collect (time-lapse) seismic data at reduced costs and preferably also at reduced environmental impact. By now, there is an increasing body of corroborating evidence — whether these are simulated case studies or actual acquisitions on land and marine — that seismic acquisition based on the principles of compressive sensing delivers on this premise by removing the need to acquire replicated dense surveys. Up to ten-fold increases in acquisition efficiency have been reported by industry while there are indications that this breakthrough is only the beginning of a paradigm shift where full-azimuth time-lapse processing will become a reality. To familiarize the audience with this new technology, I will first describe the basics of compressive sensing, how it relates to missing-trace interpolation and simultaneous source acquisition, followed by how this technology is driving innovations in full-azimuth (time-lapse) acquisition, yielding high-fidelity data with a high degree of repeatability and at a fraction of the costs.
Felix J. Herrmann graduated from Delft University of Technology in 1992 and received in 1997 a Ph.D. in engineering physics (DELPHI Consortium) from that same institution. After research positions at Stanford University and the Massachusetts Institute of Technology (Earth Resources Laboratory), he joined the faculty of the University of British Columbia in 2002 where he is now affiliate professor. Since 2017, he is cross-appointed at the Schools of Earth & Atmospheric Sciences, Computational Science & Engineering, and Electrical & Computer Engineering of the Georgia Institute of Technology. His research program spans several areas of computational exploration seismology including economic and low-environmental impact (time-lapse) acquisition with compressive sensing, data processing, and wave-equation-based imaging and inversion. He was among the first to recognize the importance of curvelet transforms, compressive sensing, and large-scale (convex) optimization addressing problems involving simultaneously acquired/blended (time-lapse) data with surface-related multiples. He developed curvelet-based denoising and matched filtering methods that are now widely used by industry. He also made several contributions to full-waveform inversion and (least-squares) reverse-time migration by introducing concepts from stochastic and constrained optimization designed to produce high-fidelity results at lower costs. More recently, he has been involved in developing rank minimization techniques for seismic data acquisition, in the development of a domain-specific language for finite differences called Devito, and in the application of deep convolutional neural nets to seismic data processing and inversion. To drive innovations within industry, he started in 2004 SINBAD, a research consortium responsible for several major breakthroughs resulting in tangible efficiency improvements in industrial data acquisition and full-waveform inversion. At Georgia Tech, he vows to continue these activities by setting up a new research consortium with a focus on machine learning. He serves as deputy editor for Geophysical Prospecting and is a Georgia Research Alliance eminent scholar.

Register your attendance by emailing wapresident@aseg.org.au.

Proudly supported by: PGS and Equinor