New Techniques in Geophysical Exploration

Mineral Potential Zone Mapping using Google Earth Engine (GEE) & Remote Sensing. I recently explored an exciting geospatial workflow for identifying mineral-rich zones using multispectral and hyperspectral satellite data and geoscientific indicators. Tutorial Link: Link is attached in the comment section 🔍 Key Highlights: ✅ Used ASTER, Sentinel-2, and Landsat data to derive spectral indices (Iron Oxide, Clay, AlOH, OH⁻). ✅ Applied band ratios and spectral transformations to highlight alteration zones. ✅ Integrated ancillary data like geology, fault lines, and elevation models. ✅ Employed both knowledge-driven (AHP) and data-driven (Machine Learning) approaches to map mineral potential zones. ✅ Classified zones into high, moderate, and low mineral prospectivity for further exploration. 📊 Whether it's for gold, ironstone, tourmaline, or lithium, this technique enables fast, scalable, and cost-effective mineral exploration—without needing extensive fieldwork initially. 🧠 Remote sensing + AI/ML = Next-gen mineral prospecting. #MineralExploration #RemoteSensing #GoogleEarthEngine #Geoinformatics #Geology #MachineLearning #ASTER #Sentinel2 #SustainableMining #Lithium #GIS #EarthObservation #MiningInnovation #HyperspectralImaging

Biological fingerprints in soil show where diamond-containing ore is buried tens of meters below the earth’s surface without having to drill.  DNA sequencing technique can help source minerals that are key to the green-energy transition. University of British Columbia (UBC). A new tool for mineral exploration. Published: October 25, 2023. Excerpt: Researchers have identified buried #kimberlite, the rocky home of #diamonds, by #testing the #DNA of #microbes in the #surface #soil. The research published in Nature Communications Earth and Environment represents a new tool for mineral exploration, where a full toolbox could save prospectors time and a lot of money, says co-author Bianca Iulianella Phillips, a doctoral candidate at UBC’s department of earth, ocean and atmospheric sciences (EOAS). The technique adds to the relatively limited number of tools that help find buried ore, including initial scans of the ground and analysis of elements in the overlying rock. “This technique was born from a necessity to see through the earth with greater sensitivity and resolution, and it has potential to be used where other techniques aren’t working,” said Phillips. #Note: When ore interacts with soil, it changes the communities of microbes in the soil. The researchers tested this in the lab, introducing kimberlite to soil microbes and watching how they changed in number and species. “We took changed communities of microbes as indicators for presence of ore materials, or biological fingerprints in the soil of buried mineral deposits,” said Phillips. Using these ‘indicator’ microbes and their DNA sequences, the team tested the surface soil at an exploration site in the Northwest Territories where kimberlite had previously been confirmed through drilling. They found 59 of the 65 indicators were present in the soil, with 19 present in high numbers directly above the buried ore. They also identified new indicator microbes to add to their set. Publication: Nature Communications | Earth and Environment 21 October 2023 DNA sequencing, microbial indicators, and the discovery of buried kimberlites https://lnkd.in/efNt6Haf

🧠 What if AI had the power to ‘see’ beneath the Earth's surface? 🤯  This question has driven me for years now. What if we could apply the power of SciML to tackle this challenge? Today, I’m beyond excited to share that we at S2 Labs, alongside my incredible partners at EmPact Artificial Intelligence, Texas Department of Transportation, Drone Geoscience, LLC, Kraken Robotics, Amazon Web Services (AWS) and Shadeform (YC S23), have successfully demonstrated a groundbreaking (pun intended!) non-invasive technology—one that could transform the way we explore the unseen world below us.  Subsurface imaging is essential for construction, energy, and environmental monitoring, yet traditional methods have limitations in resolution, depth, and cost. Our latest research demonstrates the power of deep-learning-based 3D inversion of magnetic data to enhance subsurface imaging—both onshore and offshore. 🔍 Key Highlights:  ✅ Used AI-driven inversion to map buried utilities before construction at Texas A&M’s Rellis Campus  ✅ Located oil well conductors buried under 35-45m of sediment in the Gulf of Mexico/America, post-Hurricane Ivan  ✅ Achieved unprecedented accuracy (17 cm precision) compared to excavation data 🌍 Why does this matter? AI-based geophysical techniques are scalable, cost-effective, and adaptable across diverse environments. I’m truly excited to see how this can reduce carbon footprint by slashing construction costs and delays, preventing oil and gas leaks, and minimizing environmental disruption through smarter, data-driven decision-making.    A heartfelt congratulations to my fellow authors: Souvik Mukherjee, Jacques Guigne, Gary Young, Harshit Shukla, Kevin Kennelley, Dillon Hoffman, Ron Bell, Bill Barkhouse! I would also like to thank my cloud partners Vidyasagar Ananthan, Ph.D., Xuele (Ryan) Qi, Srinivas Tadepalli, Ph.D., MBA, Ed Goode and Ronald Ding. 🚀 Read the full study here:  https://lnkd.in/ejUN6Hvj 📌 #Geophysics #ArtificialIntelligence #MachineLearning #DeepLearning #Energy #Infrastructure #AIinGeoscience   

🔌 Can We See Underground Without Digging? The Magic of Electrical Resistivity! What if we could “see” underground using nothing but electricity? Welcome to the fascinating world of Electrical Resistivity Tomography (ERT) — the method that allows geophysicists to explore beneath our feet… without lifting a shovel. --- ⚡ What’s Happening in the Image? In this animated model, two current electrodes (orange) inject electrical current into the ground. The potential difference is then measured using voltage electrodes (purple), and from that, we can infer subsurface resistivity. 🟢 The colored curves represent equipotential lines, showing how electrical energy disperses through the subsurface. Different materials (clay, rock, water, voids) resist current flow differently — and that's the secret! --- 🧠 Why Is This So Powerful? ✅ Water Exploration: Locate groundwater aquifers without drilling blindly ✅ Archaeological Mapping: Detect buried walls, tombs, and ruins ✅ Environmental Assessments: Identify contamination zones ✅ Engineering Investigations: Detect voids or unstable zones before construction ✅ Mining & Resource Exploration: Find ore bodies or cavities beneath the surface --- 🧲 How Does It Work? It all comes down to Ohm's Law in 3D. By injecting current and measuring voltage, we calculate resistivity – which varies depending on what’s underground. 🪨 Dry rocks = high resistivity 💧 Saturated zones = low resistivity 🪫 Clays = very low resistivity ⚒️ Ore bodies = distinct patterns --- So the next time you see a line of electrodes across a field, remember — we’re not just poking the earth… We’re reading its hidden secrets. --- 🛢️ Geophysicist AbdelFattah Sabry Founder of Geo Hub | Exploring the Unseen --- 📚 Sources: Telford, Geldart & Sheriff – Applied Geophysics USGS ERT Guidelines SEG Geophysics Archives --- #Geophysics #ERT #ElectricalResistivity #ExplorationGeophysics #GroundwaterExploration #EnvironmentalGeophysics #AbdelFattahSabry #GeoHub #NonInvasiveExploration #SubsurfaceMapping

Interested in diffusion models and geophysics? Or just curious how AI can tackle scientific inverse problems at scale? The PhysicsNeMo team at NVIDIA has been exploring diffusion models for physics-ML, and we’ve released a new example on Full-Waveform Inversion (FWI)—the seismic technique that reconstructs subsurface velocity models by fitting recorded waveforms. 🎯 The library provides an end-to-end training recipe for elastic FWI with variable density, including data prep, training, and both zero-shot and physics-informed sampling on an extended E-FWI pipeline. 🧠 We use a built-in U-Net diffusion backbone from the PhysicsNeMo SDK and train it with the EDM framework, pairing seismic inputs with velocity-model outputs in a configuration tailored to geophysics. 🧩 The generation pipeline supports DPS-style physics-informed posterior sampling at generation time, adding a guidance term so samples better satisfy the elastic wave equation and observed data. 🎛️ Both conditional and unconditional setups are supported, so you can explore conditioning strategies—including patterns like classifier-free guidance—without being locked into one regime. 📈 Generation scales to large ensembles for uncertainty-aware decisions, with distributed sampling for UQ, sensitivity exploration, and even guiding well-log/core sampling placement using ensemble variance. 🌍 Who is this for? Oil & Gas exploration and CO2 sequestration teams, seismology researchers, and anyone working on AI for inverse problems. 🔧 Beyond FWI: the same diffusion-plus-physics recipe extends to non-destructive testing and medical/acoustic tomography–style problems. 🔗 Keep it simple: all how-to details live in the example docs—jump in when you’re ready. Interested? Give it a try! • Example docs (Diffusion-FWI): https://lnkd.in/gGBUifzw • PhysicsNeMo docs hub: https://lnkd.in/gkxWHuGa • PhysicsNeMo GitHub repo: https://lnkd.in/gX8fmt4x #Geophysics #FWI #SeismicImaging #GenerativeAI #DiffusionModels #PhysicsAI #AI4Science #Seismology #NVIDIA #PhysicsNeMo

Unlocking Subsurface Secrets: The Power of Full Waveform Inversion (FWI) in Seismic Resolution In the world of seismic imaging, precision is everything. The ability to accurately resolve complex subsurface structures can make all the difference in exploration, development, and production decisions. This is where Full Waveform Inversion (FWI) comes into play—pushing the boundaries of seismic resolution beyond conventional methods. FWI is a powerful computational technique that utilizes the full physics of recorded seismic wavefields to refine velocity models with unparalleled detail. Unlike traditional tomography, which relies on travel times, FWI leverages the amplitude, phase, and waveform characteristics of seismic data. This leads to: ✅ High-Resolution Velocity Models – Capturing fine-scale variations critical for accurate depth imaging. ✅ Improved Reservoir Characterization – Enhancing our understanding of subsurface heterogeneities. ✅ Better Imaging in Complex Geology – Overcoming challenges in areas with salt bodies, carbonate reservoirs, and deepwater environments. ✅ Reduced Interpretation Uncertainty – Providing a more reliable foundation for drilling and field development. As computational power and data acquisition techniques continue to evolve, FWI is becoming a game-changer in seismic exploration and reservoir monitoring. The future of seismic imaging is not just about looking deeper but seeing clearer. What are your thoughts on FWI and its impact on seismic resolution? Have you seen its benefits in action? Let’s discuss! #SeismicImaging #FullWaveformInversion #FWI #Geophysics #ReservoirCharacterization #OilAndGas #egypt #seismicprocessing #westerndesert #niledelta #gulfofsuez

Researchers at the The University of Texas at Austin developed a new computational technique called "deformation imaging" that allows scientists to look inside the Earth using surface mapping technology like #GPS and #INSAR #radar. Here is a summary of the key points: 1. The method uses surface deformation data, primarily from GPS stations, but can also incorporate other geodetic data like InSAR (Interferometric Synthetic Aperture Radar). 2. The technique provides information about the rigidity of the Earth's crust and mantle, which is important for understanding #earthquakes and #geological processes. 3. The method was applied to GPS data from #Japan's 2011 Tohoku earthquake to image the subsurface down to about 100 km. 4. It revealed the boundary between Japan's continental plate and the stiffer oceanic plate, as well as a possible deep magma reservoir feeding Japan's #volcanoes. 5. The technique combines GPS data with computer modeling to create 3D images of the Earth's interior based on surface deformation. 6. It provides results comparable to #seismicimaging but offers direct information about rock rigidity which could be used to predict its behavior. 7. The method could be applied to data from satellites like NASA - National Aeronautics and Space Administration's upcoming #NISAR mission to study geologically hazardous regions. 8. It has the potential to be integrated with other geophysical techniques to provide a more comprehensive understanding of Earth's structure and dynamics. 9. The study demonstrates that lateral variations in elastic strength can be recovered using #geodetic data alone if fault geometries are reasonably well known. 10. The approach opens up new possibilities for studying fault and volcano dynamics, especially when combined with InSAR deformation time series data. Researchers The primary researchers involved in developing the deformation imaging technique, based on the information provided, are: 1. Simone Puel - Lead researcher, formerly at the University of Texas at Austin (UT Austin), now a postdoctoral scholar in Geophysics at the California Institute of Technology. 2. Thorsten W. Becker - Professor at the Jackson School of Geosciences, UT Austin. 3. Omar Ghattas - Professor at the UT Walker Department of Mechanical Engineering and UT Oden Institute for Computational Engineering and Sciences. 4. Umberto Villa - Research ScientistOptimization, Inversion, Machine Learning, and Uncertainty for Complex Systems. 5. Dunyu Liu - COMPUTATIONAL GEOSCIENTIST Associated with the Institute for Geophysics, Jackson School of Geosciences, UT Austin.

🌍 𝗧𝗵𝗲 𝗢𝗶𝗹 𝗙𝗶𝗲𝗹𝗱𝘀 𝗪𝗲 𝗠𝗶𝘀𝘀𝗲𝗱: 𝗔𝗜 𝗜𝘀 𝗔𝗹𝗿𝗲𝗮𝗱𝘆 𝗙𝗶𝗻𝗱𝗶𝗻𝗴 𝗧𝗵𝗲𝗺 Oil fields don’t always hide. Some of them bleed to the surface. For over a century, oil seeps, gas chimneys, and structural lineaments led us to major discoveries. Yet, today, surface exploration is largely forgotten, pushed aside by seismic and drilling-first strategies. Have we abandoned one of the most powerful, lowest-cost exploration tools at our disposal? 𝗪𝗵𝗮𝘁 𝗪𝗲 𝗞𝗻𝗼𝘄 𝗳𝗿𝗼𝗺 𝗗𝗲𝗰𝗮𝗱𝗲𝘀 𝗼𝗳 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵: 🔍 Williams & Lawrence (2002) revealed active oil seepages across Angola, Brazil, and the Caspian through SAR imaging, proving that deep-water HC systems were working, before a single well was drilled. The study showed that oil slicks may correlate with migration paths and leaking traps. 🔍 LeSchack & van Alstine (2002) showed how high-res ground magnetic surveys in Canada achieved an 85% success rate in predicting HC accumulations. The method identifies "magnetically enhanced zones", caused by microseeping HC altering mineral compositions. 🔍 Hitzman et al. (2002) explained how microseeps in Texas were mapped by measuring HC-consuming bacteria in soils. These microbial anomalies led to the discovery of new fields. HC are in constant motion, and modern biogeochemical methods may help locate their migration patterns. 🔍 Mohammed et al. (2010) show an example from Yemen’s Sabatayn Basin, where fractured basement traps are exploration targets. The study demonstrated that surface-mapped lineaments mirror basement structures that influence oil and gas migration.   🔍 Witte & Baby (2014) and Witte (2019) present cases from the Zagros and Sub-Andean basins, where oil seeps correlate spatially with neotectonic faults. They show that in the Zagros Foldbelt ~50% of seeps occurred outside apparent fault systems, that standard geological models may have overlooked. 𝗪𝗵𝗮𝘁 𝗜𝗳 𝗔𝗜 𝗛𝗮𝗱 𝗕𝗲𝗲𝗻 𝗔𝗿𝗼𝘂𝗻𝗱? ✅ What if GenAI had mapped these seeps and lineaments decades ago? ✅ Would we have identified these fields years earlier, saving millions in failed wells? ✅ More importantly, how many missed surface-driven discoveries are still waiting?     Maybe we didn’t run out of big finds. Maybe we just stopped looking in the right places.   Image: Topographic expression of the Hamrin Field, Zagros foreland (NASA elevation data). 𝗪𝗵𝗮𝘁’𝘀 𝗬𝗼𝘂𝗿 𝗣𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲? 👉 Share your experiences below. 👉 Understanding subsurface complexity is key to making solid exploration & development decisions. Let’s connect and explore solutions. ▶️ Whatch for my webinar on mining and AI - coming soon! #OilAndGas #ArtificialIntelligence #SurfaceExploration Luis Ayala Robert Clayton Steve Naruk, PhD Gregory A Donelson Leyla Keser Berber Joe Versfelt Hermann Lebit Mónica Sofia Polaco Vieira Jan King Elizabeth Freeman Daniel Alejandro Gargiulo Moyano Anna Dalaire Ian Moffat David Repol Bastian Roters

No one can see inside the Earth or drill deep enough to collect rock samples from the mantle—the layer between Earth’s core and its outermost rigid layer, the lithosphere. Measuring temperature and pressure at those depths is also impossible. To overcome these challenges, geophysicists rely on indirect methods to study what lies deep beneath our feet. One key method involves analyzing seismograms, which are recordings of earthquake waves. By measuring how fast these waves travel through the Earth, scientists can infer details about its internal structure. This process is similar to how doctors use ultrasound to create images of organs, muscles, or veins inside the body without surgery. Here’s how it works: when an earthquake occurs, seismic waves radiate outward from the epicenter in all directions. As they travel through the Earth, these waves can be refracted (bent), diffracted (spread around obstacles), or reflected (bounced back). The speed of these waves depends not only on the type of wave but also on the density and elasticity of the materials they pass through. Seismographic stations around the world record these waves, allowing geophysicists to analyze the data. From these recordings, scientists can infer the Earth’s internal structure, its composition, and the dynamic processes occurring deep within the planet. Using seismic recordings, Earth scientists determined the position of submerged tectonic plates throughout the Earth’s mantle. They always found them where they expected them to be: in an area known as subduction zones, where two plates meet and one subducts beneath the other into the Earth’s interior. This has helped scientists investigate the plate tectonic cycle, i.e., the emergence and destruction of plates at Earth’s surface, through our planet’s history. Now, however, a team of geophysicists from ETH Zurich and the California Institute of Technology has made a surprising discovery: using a new high-resolution model, they have discovered further areas in the Earth’s interior that look like the remains of submerged plates. Yet, these are not located where they were expected; instead, they are under large oceans or in the interior of continents – far away from plate boundaries. There is also no geological evidence of past subduction there. This study was recently published in the journal Scientific Reports. What is new about their modeling approach is that the ETH researchers are not just using one type of earthquake wave to study the structure of the Earth’s interior, but all of them. Experts call the procedure full-waveform inversion. This makes the model very computationally intensive, which is why the researchers used the Piz Daint supercomputer at the CSCS in Lugano. Is there a lost world beneath the Pacific Ocean? https://lnkd.in/gmVsRs_B #earth #mantle #submerged #plates #modelling #lostworld