arXiv subjects

Sherif Hanafy

Publications and source records attributed to Sherif Hanafy.

3 recordsLinked to original sources

arXiv2026

Robustness and Transferability of Pix2Geomodel for Bidirectional Facies Property Translation in a Complex Reservoir

Reservoir geomodeling is central to subsurface characterization, but it remains challenging because conditioning data are sparse, geological heterogeneity is strong, and conventional geostatistical workflows often struggle to capture nonlinear relationships between facies and petrophysical properties. This study evaluates the robustness and transferability of Pix2Geomodel on a different and more complex reservoir dataset with reduced vertical support. The new case includes a heterogeneous reservoir-quality classification and only 54 retained layers, providing a stricter test of whether Pix2Pix-based image-to-image translation can preserve facies-property relationships under constrained data conditions. Facies, porosity, permeability, and clay volume (VCL) were extracted from a reference reservoir model, exported as aligned two-dimensional slices, augmented using consistent geometric transformations, and assembled into paired image datasets. Six bidirectional tasks were evaluated: facies to porosity, facies to permeability, facies to VCL, porosity to facies, permeability to facies, and VCL to facies. The Pix2Pix model, consisting of a U-Net generator and PatchGAN discriminator, was evaluated using image-based metrics, visual comparison, and variogram-based spatial-continuity validation. Results show that the model preserves the dominant geological architecture and main spatial-continuity trends. Facies to porosity achieved the highest pixel accuracy and frequency-weighted intersection over union of 0.9326 and 0.8807, while VCL to facies achieved the highest mean pixel accuracy and mean intersection over union of 0.8506 and 0.7049. These findings show that Pix2Geomodel can transfer beyond its original case study as a practical framework for rapid bidirectional facies-property translation in complex reservoir modeling.

physics.geo-ph↗

arXiv2025

Rocketquake Seismology with a Falcon 9 Rocket Source

We investigate the feasibility of using rocket launches, specifically rocketquakes, as a seismic source to image subsurface velocity and geology of planetary bodies. Toward this goal, we record the seismic vibrations excited by a Falcon 9 rocket launch from Vandenberg Space Force Base (SFB) near Lompoc, California. Nine passive three-component (3C) seismometers were deployed every 18.75 meters along a 45-degree line from the launch site starting at the offset of about 7 km kilometers from the launch pad. Results show that coherent body waves can be recorded with a P-velocity of more than 2.0 km/s and a penetration depth of 1 km or deeper. Stronger Rayleigh waves were also recorded and inverted to give an S-velocity profile to a depth of 60 m. Notably, a 3C recorder placed approximately 15 km from the launch site did not capture any discernible body wave arrivals. The imaging techniques employed for rocketquake seismology integrate inversion methods from earthquake and exploration seismology, yielding the P- and S-velocity profiles of the subsurface. These results suggest that rocket launches as seismic sources will provide unprecedented opportunities for identifying the subsurface hazards, faults, tunnels, water ice, and mineral deposits of planetary bodies and their moons.

physics.geo-ph↗

arXiv2021

Deep learning for low-magnitude earthquake detection on a multi-level sensor network

Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe. The detection of low-magnitude seismic events is essential for microseismic monitoring of hydraulic fracturing, carbon capture and storage, and geothermal operations for hazard detection and mitigation. Moreover, the detection of micro-earthquakes is crucial to understand the underlying mechanisms of larger earthquakes. Various algorithms, including deep learning methods, have been proposed over the years to detect such low-magnitude events. However, there is still a need for improving the robustness of these methods in discriminating between local sources of noise and weak seismic events. In this study, we propose a convolutional neural network (CNN) to detect seismic events from shallow borehole stations in Groningen, the Netherlands. We train a CNN model to detect low-magnitude earthquakes, harnessing the multi-level sensor configuration of the G-network in Groningen. Each G-network station contains four geophones at depths of 50, 100, 150, and 200 meters. Unlike prior deep learning approaches that use 3-component seismic records only at a single sensor level, we use records from the entire borehole as one training example. This allows us to train the CNN model using moveout patterns of the energy traveling across the borehole sensors to discriminate between events originating in the subsurface and local noise arriving from the surface. We compare the prediction accuracy of our trained CNN model to that of the STA/LTA and template matching algorithms on a two-month continuous record. We demonstrate that the CNN model shows significantly better performance than STA/LTA and template matching in detecting new events missing from the catalog and minimizing false detections.

physics.geo-ph↗