arXiv subjects

Sanjay Srinivasan

Publications and source records attributed to Sanjay Srinivasan.

4 recordsLinked to original sources

arXiv2026

A Novel Approach for Testing Water Safety Using Deep Learning Inference of Microscopic Images of Unincubated Water Samples

Fecal-contaminated water causes diseases and even death. Current microbial water safety tests require pathogen incubation, taking 24-72 hours and costing \$20-\$50 per test. This paper presents a solution (DeepScope) exceeding UNICEF's ideal Target Product Profile requirements for presence/absence testing, with an estimated per-test cost of \$0.44. By eliminating the need for pathogen incubation, DeepScope reduces testing time by over 98\%. In DeepScope, a dataset of microscope images of bacteria and water samples was assembled. An innovative augmentation technique, generating up to 21 trillion images from a single microscope image, was developed. Four convolutional neural network models were developed using transfer learning and regularization techniques, then evaluated on a field-test dataset comprising 100,000 microscope images of unseen, real-world water samples collected from fourteen different water sources across Sammamish, WA. Precision-recall analysis showed the DeepScope model achieves 93\% accuracy, with precision of 90\% and recall exceeding 94\%. The DeepScope model was deployed on a web server, and mobile applications for Android and iOS were developed, enabling Internet-based or smartphone-based water safety testing, with results obtained in seconds.

cs.OH↗

arXiv2024

Constrained Transformer-Based Porous Media Generation to Spatial Distribution of Rock Properties

Pore-scale modeling of rock images based on information in 3D micro-computed tomography data is crucial for studying complex subsurface processes such as CO2 and brine multiphase flow during Geologic Carbon Storage (GCS). While deep learning models can generate 3D rock microstructures that match static rock properties, they have two key limitations: they don't account for the spatial distribution of rock properties that can have an important influence on the flow and transport characteristics (such as permeability and relative permeability) of the rock and they generate structures below the representative elementary volume (REV) scale for those transport properties. Addressing these issues is crucial for building a consistent workflow between pore-scale analysis and field-scale modeling. To address these challenges, we propose a two-stage modeling framework that combines a Vector Quantized Variational Autoencoder (VQVAE) and a transformer model for spatial upscaling and arbitrary-size 3D porous media reconstruction in an autoregressive manner. The VQVAE first compresses and quantizes sub-volume training images into low-dimensional tokens, while we train a transformer to spatially assemble these tokens into larger images following specific spatial order. By employing a multi-token generation strategy, our approach preserves both sub-volume integrity and spatial relationships among these sub-image patches. We demonstrate the effectiveness of our multi-token transformer generation approach and validate it using real data from a test well, showcasing its potential to generate models for the porous media at the well scale using only a spatial porosity model. The interpolated representative porous media that reflect field-scale geological properties accurately model transport properties, including permeability and multiphase flow relative permeability of CO2 and brine.

cs.CV↗

arXiv2024

Using Physics Informed Generative Adversarial Networks to Model 3D porous media

Micro-CT scanning of rocks significantly enhances our understanding of pore-scale physics in porous media. With advancements in pore-scale simulation methods, such as pore network models, it is now possible to accurately simulate multiphase flow properties, including relative permeability, from CT-scanned rock samples. However, the limited number of CT-scanned samples and the challenge of connecting pore-scale networks to field-scale rock properties often make it difficult to use pore-scale simulated properties in realistic field-scale reservoir simulations. Deep learning approaches to create synthetic 3D rock structures allow us to simulate variations in CT rock structures, which can then be used to compute representative rock properties and flow functions. However, most current deep learning methods for 3D rock structure synthesis don't consider rock properties derived from well observations, lacking a direct link between pore-scale structures and field-scale data. We present a method to construct 3D rock structures constrained to observed rock properties using generative adversarial networks (GANs) with conditioning accomplished through a gradual Gaussian deformation process. We begin by pre-training a Wasserstein GAN to reconstruct 3D rock structures. Subsequently, we use a pore network model simulator to compute rock properties. The latent vectors for image generation in GAN are progressively altered using the Gaussian deformation approach to produce 3D rock structures constrained by well-derived conditioning data. This GAN and Gaussian deformation approach enables high-resolution synthetic image generation and reproduces user-defined rock properties such as porosity, permeability, and pore size distribution. Our research provides a novel way to link GAN-generated models to field-derived quantities.

eess.IV↗

arXiv2020

Framework of Fracture Network Modeling using Conditioned Data with Sequential Gaussian Simulation

The fracture characterization using a geostatistical tool with conditioning data is a computationally efficient tool for subsurface flow and transport applications. The main objective of the paper is to propose a framework of geostatistical method to model the fracture network. In the method, we have chosen neighborhood area to apply the Gaussian Sequential Simulation in order to generate the fracture network in the unknown region. The angle was propagated from the seed where direction is guided by the neighborhood data in simulation regime. Initial seeds can be distributed by Poisson procedure. The method is applied for geological faults from the Central Kazakhstan and for field data from Scotland, UK. The simulation results are compatible with the original fracture network in the flow and transport modeling setting. From the research that has been carried out, it is possible to conclude that the numerical simulation of fracture network is a valuable tool in the subsurface flow and transport applications.

cs.CE↗