3D Carbonate Digital Rock Reconstruction by Self-Attention Network and GAN Structure

Abstract

Amidst the rapid advancements in digital technology, the pursuit of simulating geologic and mineralogic samples in a digital domain has garnered considerable attention, becoming a linchpin in modern earth science and petrological research. This manuscript intricately explores the deployment of state-of-the-art generative models for the meticulous reconstruction of digital rock core samples. Central to this investigation was the innovative incorporation of the self-attention mechanism—a pioneering endeavor in the domain of digital rock core studies. By harnessing the prowess of this sophisticated model, we endeavored to produce samples that echo the nuanced geological and mineralogical attributes emblematic of authentic rock specimens. Distinguishing our approach, the generative architecture, bolstered by the self-attention mechanism, demonstrated unparalleled proficiency in replicating quintessential rock features, ranging from porosity and granular texture to contiguous core sequences. Additionally, the idiosyncrasies of carbonate rocks were meticulously captured, highlighting phenomena like dissolution. Empirical evaluations, rooted in stringent statistical analyses, attested to the model’s capability to generate outputs that resonate closely with genuine samples. This exploration not only amplifies the potential applications of our proposed model in geoscientific endeavors but also signals a transformative stride in digital rock physics, emphasizing the harmonious amalgamation of innovative computational models with profound geological insights.