Technology Engineering 2,500 'high-risk' U.S. dams are sinking into the ground Radar shows that the damage may be out of sight for inspectors. Breakthroughs, discoveries, and DIY tips sent every weekday. Dams across the United States are in dire need of repairs, and the critical infrastructure may be even worse off than engineers anticipated. According to geoscientists, over 2,500 of the more than 16,700 structures in the country are classified as high hazard potentials and in "poor condition." This classification means that the dams that would cause significant death and destruction if compromised.

Geologists have raised concerns about potential Chinese censorship and bias in a chatbot being developed with the backing of the International Union of Geological Sciences (IUGS), one of the world's largest scientific organisations and a Unesco partner. The GeoGPT chatbot is aimed at geoscientists and researchers, particularly in the global south, to help them develop their understanding of earth sciences by drawing on swaths of data and research on billions of years of the planet's history. It is an initiative from Deep-time Digital Earth (DDE), a largely Chinese-funded programme founded in 2019 to enhance international scientific cooperation and help countries to realise the UN's sustainable development goals. Part of the underlying AI for GeoGPT is Qwen, a large language model built by the Chinese tech company Alibaba. Responding to the article, DDE representatives Michael Stephenson, Hans Thybo, Chengshan Wang and Ishwaran Natarajan said the chatbot also used Meta's Llama, another large language model, and that during testing they had not noticed any state censorship, which they said was "unlikely" given that the system was "based entirely in geoscience information".

SensOre Ltd (ASX:S3N) is pleased to announce that SensOre and Gold Road (ASX: GOR) have reached agreement to restructure arrangements surrounding the Yilgarn Exploration Ventures (YEV) portfolio. SensOre has agreed to acquire Gold Road Resources' 40% minority interest in YEV for 800,000 SensOre shares. Yilgarn Exploration Ventures holds a portfolio of prospective gold assets in the Eastern Goldfields of Western Australia. SensOre aims to become the top performing minerals targeting company in the world through the deployment of AI and machine learning (ML) technologies, specifically its Discriminant Predictive Targeting (DPT) workflow. SensOre collects all available geological information in a terrane and places it in a multidimensional hypercube or data cube.

The past three months alone has seen the UK announce three major milestones – covering carbon storage, offshore wind and hybrid energy projects – to propel it further down the road towards net zero. But that journey is no longer only about creating a sustainable, green future. World events have brought security of supply sharply into focus, placing new impetus on governments to accelerate alternative energy projects. While moving at pace is critical for the planet, the old proverb of more haste, less speed – warning against making errors by acting too quickly and without due diligence – should be weighing on the minds of developers. Nicola Blanshard, CEO of Geoteric, a world-leading AI-driven seismic interpretation software provider, believes the balance of speed and success can be achieved through appropriate application of technology.

"An opportunity to pursue an alternate paradigm of research that explores data science methods in earth sciences." At its inception, most geoscientists believed that the oceans and landmasses were fixed and permanent, as the basic features of the earth's crust. Subsequently, geology underwent a conceptual revolution, geoscientists came to a consensus that indeed the earth was covered by rigid plates, thin in relation to the earth's diameter. These plates' creation, displacement, and destruction were consequential of the mid-ocean ridges, the areas of mountains and earthquake activity, and the deep ocean trenches. Evidently, the conceptual revolution has opened Pandora's box. There are more questions, questions that need answers.

So maybe you can make it more tangible. But that's the understanding I have. Where do you really see digital twins driving value in terms of day-to-day decisions for executives who really need to steer the company?

Bluware Corp, the digital innovation platform that enables the oil and gas industry to accelerate digital transformation initiatives using deep learning, is pleased to announce a new agreement with BP (NYSE: BP). Bluware's technology will help BP to improve quality and speed when delivering seismic interpretation products. "BP recognizes the significant impact advances in digital technology can bring and we are pleased to implement Bluware InteractivAI, a new and innovative deep learning technology, augmenting our geoscientists' ability to accelerate subsurface data interpretation," says Ahmed Hashmi, Upstream Chief Digital and Technology Officer at BP. Large seismic data sets are difficult to move and use in workflows and time consuming to interpret. InteractivAI, powered by Bluware Volume Data Store (VDS) cloud-native data environment, enables the acceleration of detailed interpretation tasks. With this tool geoscientists can now train and correct deep learning results interactively, significantly improving structural interpretation workflows.

Jensen Sun developed Geoweaver, a system that uses machine learning for earth science data. Today's Earth scientists are spending less time standing in fields collecting soil samples, and more time behind a computer screen. Most geoscience data is automatically collected by sensors and satellites. The big challenge is making sense of all that data so that scientists can get back to what they do best: Observing the world, asking questions, conducting experiments, and finding evidence. Scientists use large, publicly available datasets from government programs such as NASA, NOAA, and USGS (that's the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and US Geological Survey, in non-acronym speak).

The modern world is becoming increasingly technology driven. Many areas, such as healthcare, have been quick to realise the possibilities. AI and machine learning in oil & gas focused sectors has been slower to establish itself. This is largely because the industry has been slow to realise the potential. However this is slowly changing. Machine learning in oil & gas can be used to enhance the capabilities of this increasingly competitive sector. Not only can it help to streamline the workforce. The technology can also be used to optimise extraction and deliver accurate models. These benefits are just some of the reasons why machine learning in oil & gas is becoming increasingly important. Here are 10 ways that the impact of machine learning in oil & gas industries is being felt. One of the most noticeable impacts of machine learning in oil & gas focused industries is how it transforms discovery processes. Applications employing machine learning in oil & gas enable computers to quickly and accurately analyse huge amounts of data. This includes being able to sift precisely through signals and noise in seismic data.

IBM and Galp, a Portuguese energy group with a global footprint, have developed an AI-based advisor to enhance seismic interpretation in the oil and gas exploration area. This tool can facilitate creation of enhanced geological models, risk assessment of new prospects, and optimization of the placement of new oil wells. As global energy consumption increases and much of the globe still relies on fossil fuels to supply its energy needs, the oil and gas industry is facing the challenge of finding new resources. More advanced analysis and computing are required to find and evaluate hidden sources of fuel. IBM and Galp are helping to solve that.