AI for Seismic Analysis and Monitoring

Can AI help us predict the unpredictable? Earthquake forecasting is one of science’s grand challenges. Despite decades of research, traditional models that built on seismic and geomechanical data still fall short of delivering reliable, actionable forecasts. In our new cross-disciplinary review, “Integrating Artificial Intelligence and Geophysical Insights for Earthquake Forecasting” (Ying Zhang, Congcong Wen, Chengxiang Zhan & Didier Sornette, Earth-Science Reviews 270, 105232, pp. 1-37, 2025), we argue that the fusion of AI and geophysics may finally offer a breakthrough. AI, and especially deep learning, excels at uncovering hidden patterns in massive, complex datasets. This is precisely what is needed to integrate seismic signals with geophysical, geochemical, and even atmospheric precursors. But AI alone is not enough: without physics-informed constraints, models risk oversimplifying reality. The real promise lies in AI + domain knowledge working together. The review examines today’s most promising AI approaches, from deep neural networks to spatio-temporal clustering models, and shows how they can be combined with geophysical insights, specialized loss functions, and multi-source data fusion. It also highlights key pitfalls, like data imbalance and false positives, that need careful handling to make forecasting trustworthy. This matters beyond earthquakes, because the same challenge (detecting faint precursors to sudden, catastrophic events) arises in financial markets. Just as tectonic stresses build silently before a quake, speculative bubbles accumulate hidden tension before a crash. In both domains, the key is combining data-driven AI with deep structural understanding. Bridging AI with the sciences of Earth and markets opens the door to more accurate, reliable, and impactful forecasting tools. https://lnkd.in/d5QSxAz7 (https://lnkd.in/dtBRTZ3R) #AI #EarthquakeForecasting #FinancialCrises #InterdisciplinaryResearch #DragonKings

A powerful 7.1 magnitude earthquake struck the border region between China and Nepal today, resulting in at least 95 fatalities. The seismic event occurred in a highly active geological zone where the Indian and Eurasian tectonic plates collide, causing the ongoing uplift of the Himalayan mountains and making the area prone to intense earthquakes. Earthquakes remain a significant global concern, with an average of 15,600 earthquakes detected worldwide annually. In 2024, there were 1,374 earthquakes with a magnitude of 5 or higher recorded globally. The frequency of major earthquakes (magnitude 7.0 or greater) averages about 15 per year. Recent advancements in AI and machine learning have revolutionized earthquake prediction and early warning systems. Several startups are at the forefront of this technology: 1. SeismicAI This Israeli startup, founded in 2014, uses AI-powered technology to detect and alert about earthquakes anywhere in the world, including offshore locations. Their system can provide warnings seconds to tens of seconds before destructive waves hit, potentially reducing non-structural damage by up to 50%. 2. Astroteq.ai Founded in 2019 in Poland, this company is developing an AI-based system that monitors and predicts earthquakes using space technologies. They utilize cosmic radiation, radar data, and thermal data from satellites to create a comprehensive earthquake prediction solution. 3. Terramoto: This small Israeli startup has developed a three-stage method using existing equipment to predict and potentially prevent major earthquakes along known fault lines. Their system aims to predict the center of an upcoming earthquake within one meter, half a Richter scale in magnitude, and within a timeframe of between a week to a month. As we confront escalating natural disasters, AI technology emerges as a crucial tool for predicting earthquakes and safeguarding lives, exemplifying how AI can be used to protect our future and prosperity.

🧠 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