Solve Geosolutions is a data science consultancy based in Melbourne. Founded in 2016, we specialise in statistical analysis and machine learning-based solutions for exploration and mining.
As an industry we are seeing a rapid increase in the volume and diversity of geoscientific data. We need a new generation of tools to harness the complexity of this data and extract everything we can from our dataset.
At Solve, our aim is to develop and implement data science-based solutions that improve how we mine and explore large and complex multivariate datasets.
In addition to designing end-to-end solutions, we also want to empower our clients to think about their data in new and innovative ways through collaborative engagement and training.
Solve uses a wide variety of mathematical/statistical tools, workflows and algorithms to obtain geological insights. This process generally begins with exploratory data analysis to understand the structure in the data. Once the problem is clearly understood, there are a number of solutions that Solve can provide, which are centred around the classification and prediction of geoscientific datasets.
By using our toolkit of mathematics and statistics workflow, we can optimise our extraction of information from complex geoscience datasets.
To understand and visualise high dimensional data, and analyse outliers and domains within multivariate datasets, we frequently use the following methods:
Significant value can be added to new and existing datasets by using regression analysis to understand the relationships between different datasets. This can allow prediction between datasets.
Using sophisticated algorithms including Neural Networks and Ensemble learners, we are able to search for complex geological signatures across many layers of geoscience data.
In exploration and mining, we constantly need to categorise rocks into groups and domains that share properties.
In the mine environment, these methods are commonly applied to classification models for:
Gone are the days of geologists relying on a small set of expensive and task-specific commercial software packages to carry out their day-to-day jobs. Enter Solve: we prefer using open source software in our projects, and believe in the power and flexibility of collaborative and transparent solutions. Some of the software that we commonly use and provide training for includes:
R (and the associated R Studio) is a programming language primarily focused on statistical techniques, which can be used to automate complex processes and create high quality visualisations for any geological problem. Similar to QGIS, R has an extensive online community meaning that answers to problems are readily available.
Orange is an open source, widget-based software package for data visualisation, machine learning, data mining and data analysis. Orange takes away the requirement of learning code, which makes it a powerful tool for teaching machine learning concepts and workflows.
QGIS is an open source, cross platform GIS package that can read, edit and process a vast array of data types and allow for easy integration between different software packages. Its large development community mean that QGIS improves almost on a weekly basis and specific software problems can be addressed and integrated rapidly.
With the decreasing price of cloud computing services, the need to have substantial personal computing power is rapidly diminishing. The AWS EC2 platform provides an easily scalable solution for even the largest computational problem.
Inkscape is an extremely flexible graphics package that allows for the creation of unique, publication-quality graphic solutions for any situation, and features a vast library of add-ons and extensions.
Our consultants are from diverse backgrounds and specialise in both geoscience and mathematics, which means our data science workflows are grounded in a strong understanding of geology.
After studying structural geology and geophysics at Monash University, Brenton has consulted as a structural geophysicist for PGN Geoscience as well as working in a variety of geological and geophysical roles—predominantly in exploration. Most recently, Brenton has worked as a geophysicist and data scientist for MMG Exploration on a range of commodities. Brenton has a passion for the integration of regional geophysical and remote sensing datasets using machine learning and prospectivity analysis.
Liam is a geoscientist based in Melbourne and specialises in geophysics, 3D geological modelling (Leapfrog) and the integration of multivariate datasets in 3D. Originally from Tasmania, Liam studied at the University of Tasmania where he majored and completed his honours in geophysics, focusing on machine learning methods in blast hole ore categorisation. From greenfields exploration through to project mine geology, Liam has worked across a variety of commodities and terranes. Outside of geology, Liam is an avid sports fan and enjoys playing, watching and using machine learning to win fantasy sports.
The technical lead on most of Solve’s projects, Tom is an experienced mathematical geologist with a strong background in statistics, computer science and geology. Tom develops, integrates and implements fit-for-purpose machine learning solutions that enable clients to expand their knowledge base. Outside work hours, Tom can be found at sports grounds around Melbourne either playing (badly) or supporting (far too enthusiastically) any sport that involves people chasing a ball.
Mark is an established geoscientist with significant experience driving and developing innovation within global mineral exploration programs. His background in geophysics and mathematics has seen him work in a wide range of settings, from developing algorithms in physics laboratories to exploring the Andean copper belt.
He hopes to promote the acceptance of machine learning and computer vision in the mining and exploration industries by building practical, effective solutions to geoscientific problems.
Yasin joined Solve after a career in mine and blast engineering.
He transitioned into geostats and data science via PhD and post-doc positions, both locally and abroad.
At Solve, he provides key insight into the geotechnical aspects of how data science techniques can increase geological and geotechnical understanding.
Harvey came to Solve from a computer science and full-stack development background. He transitioned into data science and more specifically computer-vision through a Master’s degree.
At Solve, he implements cutting-edge computer vision technologies to geoscientific imagery, including core photos, remote sensing data, and other high-resolution datasets.
Tom is a geophysicist with a keen interest in applied geophysics. He graduated with first class honours from the University of Tasmania in 2012 and spent several years working as a field geophysicist specialising in potential field survey methods.
After gaining extensive experience in the field, he returned to Tasmania to commence work on a PhD project investigating the 3D electrical structure of the Tasmanian lithosphere using magnetotelluric methods.
Tom is now in the final stages of his PhD project and is applying his new-found programming and computing skills to traditional geophysical consulting as well as data science projects.