BCGS Technical Talk – August 12, 2019

Speaker: Professor Eun-Jung Holden, University of Western Australia

Title: Developing Data Science Applications in Geosciences: An End-User Focused Approach

Date/Time: Monday, August 12, 2019 @ 4:30pm PST

Location: 4th Floor Conference Room, Room 451, 409 Granville St. (UK Building at Granville and Hastings), Vancouver

Abstract:

Developing Data Science Applications in Geosciences: An End-User Focused Approach
Professor Eun-Jung Holden, Geodata Algorithms Team, UWA

Understanding complex subsurface geology is a challenging task where interpretations are performed using diverse types of geoscientific data. The Geodata Algorithms Team at UWA has been working closely with the minerals industry for the past 12 years and developed machine-assisted solutions to improve the efficiency and the robustness of geological interpretation. This talk will present a number of applications of automated image analysis and machine learning, which were  developed by the team in recent years in partnership with mining companies.  They include drillhole data analysis and integration methods which are currently used by industry end-users; on-going research on advanced drillhole data visualisation and interpolation methods; and on-going development a geological knowledge mining system for exploration reports using text mining.

Biography:

Professor Holden received her BSc, MSc and PhD in computer science from the University of Western Australia.  Her postgraduate and postdoctoral research focused on developing visualization, automated image analysis and machine learning techniques for hand gesture recognition.  Then in 2006, she made a transition to geoscience and now leads the Geodata Algorithms Team at UWA.  The team effectively spans the boundaries of computational science and geoscience and links academia and industry.  Three suites of their data analytics algorithms were commercialized, namely CET Grid Analysis and CET Porphyry Detection extensions for Geosoft’s Oasis Montaj; and Image Structure and Interpretation module for Advanced Logic Technology (ALT)’s WellCAD, which are marketed and licensed by Geosoft (based in Canada) and ALT (based in Luxembourg) respectively.  She is currently leading the UWA-Rio Tinto Iron Ore data fusion projects which aim to achieve machine-assisted modelling of geology/resource through transformational and interpretive data science solutions.  Her research team won the UWA Vice Chancellor Award in Impact and Innovation in 2015.

BCGS Technical Talk – June 20, 2019

Speaker: Mike McMillan, Computational Geosciences

Title: Machine Learning in the Natural Resources Industry – examples and applications for mining, oil and gas and water exploration

Date/Time: Thursday, June 20, 2019 @ 4:30pm PST

Location: 4th Floor Conference Room, Room 451, 409 Granville St. (UK Building at Granville and Hastings), Vancouver

Abstract:

Machine learning and artificial intelligence (AI) are all the rage in 2019, and we take a look at recent applications of AI across a wide spectrum of problems in the natural resource industry. These vary from mineral and water prospectivity mapping to airborne induced-polarization detection, to borehole classification and seismic horizon picking. These new developments use deep convolutional neural networks to train the computer to detect subtle patterns across many geoscience data layers in order to help (and importantly not to replace) the geoscientist. Given a data-rich region with many overlapping geoscience data layers, the key is to come up with a well-defined question with examples of training labels. In the training labels we want both positive outcomes (ie. what you’re actually looking for) and negative outcomes (ie. what you’re definitely not looking for). These labels can be anything from gold-assays, rock types, fault types to salinity values in water. The type of label doesn’t really matter as long as it represents the thing or things you’re trying to find (or not trying to find). The areas without labels are the unknown regions that the machine learning algorithm will try to predict on based on signatures it learns from the training labels. In some settings this may be predicting gold grades in un-drilled areas, it may be predicting which electromagnetic decays have induced-polarization responses, or it may be predicting the likelihood of a water aquifer occurring in a remote region of the desert. The convenient aspect of these convolutional neural networks is that the algorithm architecture can be used to answer a multitude of questions, depending on the input training labels. This means that unlike geophysical inversion where a completely different code is required for magnetics, gravity or electromagnetics, we can generally use one AI framework, with some minor tweaks, for every problem. In this manner, we can throw in all available data sets and collectively use this information to help answer relevant questions that will help drive a data-driven cost-effective exploration program.

BCGS Technical Talk – April 18, 2019

Speaker: Kevin Fan, B.Sc., UBC

Title: Humanitarian Geophysics in Myanmar: Partnering with Local Governments and Universities to Alleviate Seasonal Droughts

Date/Time: Thursday, April 18, 2019 @ 4:30pm PST

Location: 4th Floor Conference Room, Room 451, 409 Granville St. (UK Building at Granville and Hastings), Vancouver

Abstract:

BCGS Technical Talk – March 28, 2019

Speaker: Dr. James Macnae, RMIT University, Melbourne, Australia

Title: Can machine learning, AI, analytics and the IoT add to AEM processing and interpretation?

Date/Time: Thursday, March 28, 2019 @ 5:00pm PST

Location: 4th Floor Conference Room, Room 451, 409 Granville St. (UK Building at Granville and Hastings), Vancouver

Abstract:

Can machine learning, AI, analytics and the IoT add to AEM processing and interpretation?
James Macnae, RMIT University

Some recent papers in computer science related to the “Internet of Things” (IoT) have presented examples of detecting anomalies in IoT time series using deep (machine) learning.  These examples have some elements in common with AEM data processing and interpretation, and may in the future lead to automated QC and first pass physical property prediction and ultimately geological interpretation.  However, implementing and setting up such processes will still be a very significant challenge for geoscientists. I therefor suspect that the Australian mineral explorer that last year made its geologists and geophysicists redundant, and advertised for “data mining specialists” will be too far ahead of its time.

To extract “useful” physical property information from this mountain of data, and thereby infer useful geology, there are many options. The historically most useful process combines physical insight to infer conductivity from the observed response with statistical methods to improve signal/noise. For example, EM data from a controlled source survey are presented as profiles or inverted with logarithmic time spacing, sensible for EM diffusion. Reduced noise has come from e.g. binary stacking, and recognition that sferic source energy is non-stationary and can usefully be “pruned” from the data before stacking.  Subsets of the acquired data are then selected, modelled and inverted based on simple models or a-priori assumptions. Questions remain as to whether commonly applied a-priori assumptions are reasonable, whether all the useful implications of the data, such as induced polarization (AIP) and superparamagnetic (ASPM) effects have been extracted, and whether sferics, powerline signals and VLF can provide complementary conductivity information in a controlled source survey.

Electromagnetic (EM) data is being collected at ever higher streaming rates, with airborne AEM data sampling rates approaching 1 MHz in some systems, and ground penetrating radar (GPR) sampling approaching 1 GHz. Six hours of data acquisition with BIPTEM, a 24-bit, 12 channel AEM system (6 B field sensor, 3 dB/dt, 3 rotation rate), each channel sampled at 156250 Hz will deliver over 150 GB of data.

The BC Geophysical Society will be hosting Dr. James Macnae, the inaugural Len and Genice Collett Distinguished Visiting Lecturer in Geophysics, who will be presenting a half-day short course titled:

“Integrated interpretation of airborne electromagnetic surveys: Determination and use of appropriate geophysical models”

Dr. James Macnae, RMIT University, Melbourne, Australia.

This half-day (AM) course will cover:

1)      The basics of geophysical electromagnetics (EM), time and frequency domain
2)      Airborne EM systems, trade-offs and recent advances
3)      The physical limits of conductivity resolution with AEM
4)      Detectable geology: Earth conductivity variations
5)      Consideration of computed geophysical models, direct and inverse

• Stitched 1D for quasi-layered environments
• 2D and 2&1/2D
• 3D, parametric and voxellated

6)      Case histories: Using computed AEM models to guide geological interpretation: Successes, limitations and failures
7)      Where might AEM be in 10 years?

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(Deadline 5:00pm, Tuesday, March 26, 2019)

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