Ontario
I have a Ph.D. from the University of Tuebingen, Germany, and over 7 years of industry and research experience with positions at the University of Toronto, Canada, the University of Ibadan, Nigeria, and Fugro Germany GmbH, Germany. I currently lead the Hydro- and Environmental Geophysics Research Group at the University of Toledo, Ohio. My research focuses on characterizing variations in soil and aquifer properties and monitoring transient hydrological and biogeochemical processes. I also use geophysical methods to image the shallow subsurface with applications for forensic, archaeological, and engineering investigations.
- North America > Canada > Ontario > Toronto (0.75)
- North America > United States > Ohio > Lucas County > Toledo (0.35)
- Europe > Germany > Baden-Württemberg > Tübingen Region > Tübingen (0.35)
We are thrilled to announce the 2024 SPE Canadian Educational Foundation (SPECEF) Scholarship Award recipients! These deserving post-secondary students have impressive credentials and aspirations to enter into and contribute to our energy industry in their future careers! Our award winners represent 6 Canadian schools across 4 provinces, including the University of Calgary, University of Alberta, Southern Alberta Institute of Technology (SAIT), University of Toronto, University of New Brunswick, and Memorial University of Newfoundland. SPE is looking for members to volunteer on committees that manage various programs and activities. Your knowledge, experience, and participation is needed to help ensure the success of these SPE programs.
- North America > Canada > Ontario > Toronto (0.95)
- North America > Canada > Alberta > Census Division No. 6 > Calgary Metropolitan Region > Calgary (0.29)
- Instructional Material > Online (0.35)
- Instructional Material > Course Syllabus & Notes (0.35)
- Energy (1.00)
- Education > Educational Setting > Online (0.82)
Joint Inversion of Potential Field Data with Adaptive Unstructured Tetrahedral Mesh
Cai, Hongzhu (China University of Geosciences, Hubei Subsurface Multi-scale Imaging Key Laboratory, State Key Laboratory of Geological Processes and Mineral Resources) | Kong, Ruijin (China University of Geosciences) | He, Ziang (China University of Geosciences) | Wang, Xinyu (China University of Geosciences) | Liu, Shuang (China University of Geosciences) | Huang, Sining (China University of Geosciences) | Kass, M. Andy (Aarhus University) | Hu, Xiangyun (China University of Geosciences, Hubei Subsurface Multi-scale Imaging Key Laboratory, State Key Laboratory of Geological Processes and Mineral Resources)
Inverting potential field data presents a significant challenge due to its ill-posed nature, often leading to non-unique model solutions. Addressing this, our work focuses on developing a robust joint inversion method for potential field data, aiming to achieve more accurate density and magnetic susceptibility distributions. Unlike most previous work that utilizes regular meshes, our approach adopts an adaptive unstructured tetrahedral mesh, offering enhanced capabilities in handling the inverse problem of potential field methods. During inversion, the tetrahedral mesh is refined in response to the model update rate. We integrate a Gramian constraint into the objective function, allowing enforcement of model similarity in terms of either model parameters or their spatial gradients on an unstructured mesh. Additionally, we employ the moving least-squares method for gradient operator computation, essential for model regularization. Our model studies indicate that this method effectively inverts potential field data, yielding reliable subsurface density and magnetic susceptibility distributions. The joint inversion approach, compared to individual dataset inversion, produces coherent geophysical models with enhanced correlations. Notably, it significantly mitigates the non-uniqueness problem, with the recovered anomaly locations aligning more closely with actual ground truths. Applying our methodology and algorithm to field data from the Ring of Fire area in Canada, the joint inversion process has generated comprehensive geophysical models with robust correlations, offering potential benefits for mineral exploration in the region.
- Geophysics > Magnetic Surveying (1.00)
- Geophysics > Gravity Surveying > Gravity Acquisition (1.00)
- Geophysics > Electromagnetic Surveying (1.00)
- (2 more...)
- Materials > Metals & Mining (1.00)
- Energy > Oil & Gas > Upstream (1.00)
When using forward modeling to estimate model parameters, such as the dip, it is also important to estimate the corresponding uncertainty in the model parameters. For gravity data, these uncertainties are dependent on the uncertainty in the Bouguer corrected data. The uncertainty in the gravity meter reading and the height used in the free-air and Bouguer corrections are amongst the most important factors influencing the uncertainty in the Bouguer-corrected data. We used two methods for estimating the uncertainty in the Bouguer corrected data, which give similar answers (0.121 and 0.109 mGal). The uncertainty in the model parameters can be estimated by perturbing the corrected data multiple times by amounts consistent with the estimated uncertainty in the corrected gravity. The standard deviation of the model parameters derived from each perturbed dataset gives an estimate of their uncertainty. Using this procedure for Bouguer gravity profiles that cross the Porcupine Destor fault (a fault that is prospective for gold in the Timmins camp of Ontario, Canada), we found the uncertainty in the dip was one or two degrees, assuming a planar or linear fault. If the uncertainty in the corrected data had been 1 mGal (a value typical of regional surveys, instead of 0.1 mGal for a local survey), then the uncertainty in the dip is 41 degrees for the same model. Knowing the uncertainties in the corrected data is thus very important for estimating the uncertainty in model parameters. Conversely, if a model parameter is known to be required to a specific precision, the survey can be planned so that the corrected gravity has an uncertainty appropriate to achieve that precision.
- Geology > Rock Type (0.68)
- Geology > Structural Geology (0.67)
- Geology > Sedimentary Geology (0.46)
- Geophysics > Gravity Surveying > Gravity Processing (0.88)
- Geophysics > Gravity Surveying > Gravity Acquisition (0.67)
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Metals & Mining (0.93)
- North America > United States > California > Tisdale Field (0.89)
- Europe > Ireland > North Atlantic Ocean > Porcupine Basin > Druid Prospect (0.89)
- Europe > Ireland > North Atlantic Ocean > Porcupine Basin > Dromberg Prospect (0.89)
Canada's controversial oil-producing tar sands generate a substantial amount of unaccounted-for carbon-based emissions that can affect air quality, according to measurements taken by aircraft. The sands release more of these pollution-causing gases than megacities such as Los Angeles, California, and about the same as the rest of Canada's human-generated sources combined--including emissions from motor traffic and all other industries. "No rules have been broken, or guidelines exceeded here," said Janetta McKenzie, an oil and gas analyst for the Pembina Institute, a think tank in Calgary, Canada. "But that speaks to some issues in our rules and our guidelines." The team that conducted the study--led by environmental engineer Drew Gentner at Yale University in New Haven, Connecticut, and chemist John Liggio at the federal agency Environment and Climate Change Canada (ECCC) in Toronto--used an innovative approach to measure all the carbon-based molecules in the air over oil sands in the province of Alberta.
- North America > Canada > Alberta > Census Division No. 6 > Calgary Metropolitan Region > Calgary (0.29)
- North America > United States > Connecticut > New Haven County > New Haven (0.27)
- North America > United States > California > Los Angeles County > Los Angeles (0.27)
- North America > Canada > Ontario > Toronto (0.27)
Ian F. Jones received a joint honours BSc in Physics with Geology from the University of Manchester, UK, in 1977, an MSc in Seismology from the University of Western Ontario, Canada, and a PhD in Geophysical Signal Processing from the University of British Columbia, Canada. After working for'Inverse Theory & Applications Inc.' in Canada for two years, he joined CGG, where for 15 years he was involved in R&D in the London and Paris offices, latterly as manager of the depth imaging research group. In 2000 he joined ION GX Technology, as a Senior Geophysical Advisor in their London office, and in 2021 joined BrightSkies Geoscience as Senior Geophysical Advisor. His interests include velocity model building and migration, having written the text books: 'Velocities, Imaging, and Waveform Inversion: the evolution of characterising the Earth's subsurface' published by the EAGE in 2018; 'An Introduction to Velocity Model Building' published by the EAGE in 2010; and co-editing the SEG Geophysics Reprints series volumes'Classics of Elastic Wave Theory' and also'Pre-Stack Depth Migration and Velocity Model Building', as well as contributing the chapter on model building to the SEG online encyclopedia. He has served as an associate editor for the journals'Geophysics' and'Geophysical Prospecting', and teaches the EAGE/SEG/GESGB continuing education course on'Velocity Model Building and Migration' and was an external lecturer at the University of Leeds and Imperial College London.
- North America > Canada > Ontario (0.25)
- North America > Canada > British Columbia (0.25)
- Instructional Material (0.90)
- Personal (0.70)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.98)
- Geophysics > Seismic Surveying > Seismic Processing (0.89)
- Information Technology > Knowledge Management (0.40)
- Information Technology > Communications > Collaboration (0.40)
Three dimensional cooperative inversion of airborne magnetic and gravity gradient data using deep-learning techniques
Hu, Yanyan (University of Houston) | Wei, Xiaolong (University of Houston, Stanford University) | Wu, Xuqing (University of Houston) | Sun, Jiajia (University of Houston) | Huang, Yueqin (Cyentech Consulting LLC, University of Houston) | Chen, Jiefu (University of Houston)
ABSTRACT Using multiple geophysical methods has become a prevailing approach in numerous geophysical applications to investigate subsurface structures and parameters. These multimethod-based exploration strategies have the potential to greatly diminish uncertainties and ambiguities encountered during geophysical data analysis and interpretation. One of the applications is the cooperative inversion of airborne magnetic and gravity gradient data for the interpretation of data obtained in mineral, oil and gas, and geothermal explorations. In this paper, a unified cooperative inversion framework is designed by combining the standard separate inversions with a deep neural network (DNN), which serves as the link between different types of data. A well-trained DNN takes the separately inverted susceptibility and density models as the inputs and provides improved models that will be used as the initial models of deterministic inversions. A two-round iteration strategy is adopted to guarantee the reasonability of the recovered models and overall efficiency of the inversion. In addition, this deep-learning (DL)-based framework demonstrates excellent generalization abilities when tested on models that are entirely distinct from the training data sets. The framework can easily incorporate multiphysics without necessitating any structural changes to the network. Synthetic experiments validate that our DL-based method outperforms conventional separate inversions and cross-gradient-based joint inversion in view of the accuracy of the recovered models and inversion efficiency. Successful application to field data further verifies the effectiveness of our DL-based method.
- North America > United States > Texas (0.29)
- North America > Canada > Ontario (0.28)
- Materials > Metals & Mining (1.00)
- Energy > Oil & Gas > Upstream (1.00)
A deep learning benchmark for first break detection from hardrock seismic reflection data
St-Charles, Pierre-Luc (Mila — Québec Artificial Intelligence Institute) | Rousseau, Bruno (Mila — Québec Artificial Intelligence Institute) | Ghosn, Joumana (Mila — Québec Artificial Intelligence Institute) | Bellefleur, Gilles (Geological Survey of Canada) | Schetselaar, Ernst (Geological Survey of Canada)
ABSTRACT Deep learning techniques are used to tackle a variety of tasks related to seismic data processing and interpretation. Although many works have shown the benefits of deep learning, assessing the generalization capabilities of proposed methods for data acquired in different conditions and geologic environments remains challenging. This is especially true for applications in hardrock environments. The primary factors that impede the adoption of machine learning in geosciences include the lack of publicly available and labeled data sets and the use of inadequate evaluation methodologies. Because machine learning models are prone to overfit and underperform when the data used to train them are site specific, the applicability of these models on new survey data that could be considered “out-of-distribution” is rarely addressed. This is unfortunate, as evaluating predictive models in out-of-distribution settings can provide a good insight into their usefulness in real-world use cases. To tackle these issues, we develop a simple benchmarking methodology for first break picking to evaluate the transferability of deep learning models that are trained across different environments and acquisition conditions. For this, we consider a reflection seismic survey data set acquired at five distinct hardrock mining sites combined with annotations for first break picking. We train and evaluate a baseline deep learning solution based on a U-Net for future comparisons and discuss potential improvements to this approach.
- Research Report (0.46)
- Overview (0.46)
- Geology > Mineral (0.67)
- Geology > Geological Subdiscipline (0.46)
Three-dimensional cooperative inversion of airborne magnetic and gravity gradient data using deep-learning techniques
Hu, Yanyan (University of Houston) | Wei, Xiaolong (University of Houston, Stanford University) | Wu, Xuqing (University of Houston) | Sun, Jiajia (University of Houston) | Huang, Yueqin (Cyentech Consulting LLC, University of Houston) | Chen, Jiefu (University of Houston)
ABSTRACT Using multiple geophysical methods has become a prevailing approach in numerous geophysical applications to investigate subsurface structures and parameters. These multimethod-based exploration strategies have the potential to greatly diminish uncertainties and ambiguities encountered during geophysical data analysis and interpretation. One of the applications is the cooperative inversion of airborne magnetic and gravity gradient data for the interpretation of data obtained in mineral, oil and gas, and geothermal explorations. In this paper, a unified cooperative inversion framework is designed by combining the standard separate inversions with a deep neural network (DNN), which serves as the link between different types of data. A well-trained DNN takes the separately inverted susceptibility and density models as the inputs and provides improved models that will be used as the initial models of deterministic inversions. A two-round iteration strategy is adopted to guarantee the reasonability of the recovered models and overall efficiency of the inversion. In addition, this deep-learning (DL)-based framework demonstrates excellent generalization abilities when tested on models that are entirely distinct from the training data sets. The framework can easily incorporate multiphysics without necessitating any structural changes to the network. Synthetic experiments validate that our DL-based method outperforms conventional separate inversions and cross-gradient-based joint inversion in view of the accuracy of the recovered models and inversion efficiency. Successful application to field data further verifies the effectiveness of our DL-based method.
- North America > United States > Texas (0.29)
- North America > Canada > Ontario (0.28)
- Materials > Metals & Mining (1.00)
- Energy > Oil & Gas > Upstream (1.00)
A deep learning benchmark for first break detection from hardrock seismic reflection data
St-Charles, Pierre-Luc (Mila — Québec Artificial Intelligence Institute) | Rousseau, Bruno (Mila — Québec Artificial Intelligence Institute) | Ghosn, Joumana (Mila — Québec Artificial Intelligence Institute) | Bellefleur, Gilles (Geological Survey of Canada) | Schetselaar, Ernst (Geological Survey of Canada)
ABSTRACT Deep learning techniques are used to tackle a variety of tasks related to seismic data processing and interpretation. Although many works have shown the benefits of deep learning, assessing the generalization capabilities of proposed methods for data acquired in different conditions and geologic environments remains challenging. This is especially true for applications in hardrock environments. The primary factors that impede the adoption of machine learning in geosciences include the lack of publicly available and labeled data sets and the use of inadequate evaluation methodologies. Because machine learning models are prone to overfit and underperform when the data used to train them are site specific, the applicability of these models on new survey data that could be considered “out-of-distribution” is rarely addressed. This is unfortunate, as evaluating predictive models in out-of-distribution settings can provide a good insight into their usefulness in real-world use cases. To tackle these issues, we develop a simple benchmarking methodology for first break picking to evaluate the transferability of deep learning models that are trained across different environments and acquisition conditions. For this, we consider a reflection seismic survey data set acquired at five distinct hardrock mining sites combined with annotations for first break picking. We train and evaluate a baseline deep learning solution based on a U-Net for future comparisons and discuss potential improvements to this approach.
- Research Report (0.46)
- Overview (0.46)
- Geology > Mineral (0.67)
- Geology > Geological Subdiscipline (0.46)