Preface

Recently, the Ministry of Industry and Information Technology and the National Data Administration jointly launched the Modulus-Data Synergy Initiative. Centering on the coordinated development of data resources and AI models, the initiative targets major industrial pain points including low data utilization, difficult AI model deployment and insufficient scenario empowerment. Focused on enabling in-depth linkage and iterative evolution among data, models and application scenarios, it fosters a new landscape of industrial intelligence featuringdata as the foundation, models as the driver, scenario implementation and win-win ecosystem. The initiative provides strong policy support for the intelligent and high-quality upgrading of the real economy, and serves as a key measure for advancing new industrialization in the new era.

The core logic of the Modulus-Data Synergy Initiative lies in the synchronous iteration and mutual empowerment of industrial datasets and industry-specific AI models. High-quality and standardized industrial data are used to train practical dedicated models tailored to industrial mechanisms. These models in turn empower diverse business scenarios and generate new operational data to further drive model optimization, forming a virtuous closed loop ofdata-model-scenario. This effectively addresses the long-standing dilemmas of idle data, impractical models and disconnection between technologies and businesses in traditional industries.

The geology industry is a key sector empowered by the Modulus-Data Synergy Initiative. For a long time, the industry has been plagued by fragmented geological data and massive untapped exploration and monitoring data. Traditional 3D modeling is time-consuming and subject to human bias, while business decisions rely heavily on expert experience, resulting in low operational efficiency and unmet demands in niche markets, creating major bottlenecks for intelligent transformation.

Backed by 30 years of expertise in 3D geological modeling, GridWorld has developedtEgg, a generative pre-trained large AI model for geology. Fully compliant with the national requirements of the Modulus-Data Synergy Initiative, tEgg has become a benchmark practice for intelligent transformation in the geology sector.

Built on massive calibrated geological datasets, the tEgg Geology Large Model undergoes pre-training and domain-specific fine-tuning via dedicated neural network architectures, fully implementing the closed-loop system of the Modulus-Data Synergy Initiative. Compared with manual modeling, it can automatically update geological models using exploration and monitoring data, greatly shortening modeling cycles and eliminating subjective human errors. It transforms traditional geology work reliant on experts and experience into a new paradigm of scientific decision-making driven by data and intelligence, and efficiently supports core scenarios such as engineering scheme comparison, rapid decision-making and geological risk early warning.

Furthermore, tEgg has achieved a complete transition from technological breakthrough to commercial success. It revitalizes idle geological data elements, breaks the technical boundaries of conventional software with intelligent models, and unlocks the trillion-level niche market of the industry through real-world applications. It fully fulfills the core objectives of the Modulus-Data Synergy Initiative: building industry-specific knowledge datasets, developing domain-exclusive models and fostering an innovative industrial ecosystem.

Leveraging favorable policies and technological innovation, the tEgg Geology Large Model is building a world-leading digital infrastructure for underground spaces and realizing full-process intelligent upgrade of the PDCA cycle in the geology field. Moving forward, as the Modulus-Data Synergy Initiative continues to advance, industry-specific models represented by tEgg will further strengthen the mutual promotion and cyclic iteration of data and models. They will help the geology industry break through traditional constraints and achieve comprehensive intelligent transformation and high-quality industrial development.