Abstract Estimating porosity distribution is essential for hydrocarbon exploration, carbon capture and storage, and geothermal energy. Traditional workflows, which rely on seismic inversion and rock physics equations, are time-intensive, parameter-dependent, and sensitive to variations in mineralogy and fluid composition. On the other hand, workflows based on machine learning algorithms in the state of the art are also limited by their reliance on specific rock physics equations that may oversimplify geologic complexity. In addition, there are assumptions of data availability in the time domain and requirements for multifrequency seismic inputs, which are not commonly accessible. This study proposes a workflow that uses 1D convolutional neural networks to directly estimate porosity from poststack seismic data, addressing the aforementioned traditional limitations. The workflow implements a custom loss function during training that incorporates porosity derived from well logs. Our proposed workflow outperformed alternative machine learning approaches for porosity estimation from seismic data in both the time and depth domains, using synthetic and field data. Experiments on synthetic data demonstrated its superior predictive capability compared to conventional and state-of-the-art methods. Moreover, evaluations on field data achieved a determination coefficient (R2) of 0.860 on the test set, an outcome reinforced by five-fold cross-validation. This performance shows the network's robust generalization, resilience to data variability, and stability under moderate noise, yielding interpretable results. The network was trained efficiently in less time by requiring fewer trainable parameters, significantly reducing the time required compared to conventional and state-of-the-art workflows.

Abstract Recognizing seismic phase as a primary attribute in seismic processing workflows, we apply circular statistics, a robust data-driven approach for correcting phase distortions in prestack seismic data. Unlike traditional linear methods that struggle with wrapped phase and often defer phase diagnostics to the final processing stages, the proposed approach treats phase as a circular variable. We compute the circular mean, variance, and von Mises concentration parameter directly from phase ensembles in the frequency domain. These parameters provide insights into phase stability and coherence without needing phase unwrapping or wavelet assumptions. Synthetic tests using additive and multiplicative noise models confirm that phase distributions follow the von Mises distribution, an analog of the normal distribution for circular variables, with circular statistics reliably tracking the true phase even in low-signal-quality scenarios. Field examples demonstrate how this framework can map phase behavior across frequency and offset, enabling the detection of coherence bands and assessing the impact of each processing step on phase fidelity. The proposed approach can be particularly valuable in land acquisition, where prestack data often exhibit a low signal-to-noise ratio. Circular statistics allow us to evaluate phase integrity at each frequency, facilitating novel data conditioning and acquisition design strategies.

Abstract Urban environments require high-resolution, continuous subsurface imaging and monitoring to address challenges in infrastructure resilience, groundwater management, and seismic hazard assessment. However, traditional seismic networks are often limited by sparse spatial coverage, high deployment costs, and logistical constraints in urban areas. Distributed acoustic sensing (DAS) has emerged as a promising sensing technology, repurposing preexisting telecommunication fiber networks into dense, large-scale seismic arrays. This study demonstrates the potential of urban DAS for near-surface characterization and time-lapse monitoring using data from the Stanford DAS-2 experiment. A targeted interferometry workflow extracts high-quality virtual source gathers from vehicle-induced signals, enabling daily time-lapse elastic full-waveform inversion. Ambient noise interferometry extends both the offset range and low-frequency content beyond what targeted interferometry can achieve, while the nonrepeatability of ambient noise suggests the need for careful data selection to ensure robust subsurface monitoring. Additionally, earthquake recordings provide constraints on urban fault structures through the analysis of scattered wavefields. As DAS seismic arrays continue to expand in urban settings, they are poised to play a critical role in high-resolution, continuous near-surface characterization and monitoring.

Urban and infrastructure geophysics is emerging as a critical field at the intersection of geoscience, engineering, and urban planning. As cities grow denser and infrastructure systems age, there is an increasing demand for noninvasive, high-resolution subsurface characterization and continuous environmental monitoring. Geophysical methods — traditionally applied in natural resource exploration and earthquake studies — are now being adapted to meet the unique challenges of the built environment, where access constraints, anthropogenic noise, and complex near-surface conditions require innovative sensing and processing strategies. The goal is not only to map subsurface structures but to develop dynamic, real-time insights that inform infrastructure resilience, hazard mitigation, and sustainable urban development.

The Editorial Calendar details upcoming publication plans for The Leading Edge.

Applications for Active membership have been received from the candidates listed herein.

This year marks a significant milestone for the geoscience community. The International Meeting for Applied Geoscience and Energy (IMAGE) returns to Houston, Texas, from 25–28 August 2025, celebrating its fifth anniversary as the premier global event in geoscience.

Abstract The concept of critical porosity was introduced to the rock physics community in the 1990s by Amos Nur and colleagues at Stanford University. Critical porosity has proved useful in reservoir characterization using seismic, borehole acoustic waves, and sonic logs, as well as in geomechanical applications such as prediction of rock strength and in-situ stress. This article reviews the concept and its relation to the phenomenon of mechanical percolation in statistical physics. Estimates of the critical porosity using laboratory measurements, computer simulations, and effective medium and effective field theories are discussed. Effective medium and effective field theories are not able to describe accurately the behavior of rocks in the vicinity of the critical porosity due to assumptions of idealized pore shapes and failure to explicitly account for pore connectivity. However, they do prove useful for understanding the effect of porosity, pore shape, and pore orientation distribution on elastic wave velocities, and suggest relations between porosity and elastic moduli useful for modeling of elastic waves in porous rocks.

Abstract Shear-wave splitting, also known as shear-wave birefringence, has been widely used to evaluate subsurface anisotropy. Yet, the effects of stress and fluid saturation on S-wave anisotropy are still not well understood. A laboratory study was conducted on a Bentheim sandstone sample subjected to different stress and saturation conditions. The S-wave stress-induced anisotropy of the sample was measured using a laser Doppler interferometer (LDI), which allows estimating the particle displacement vector at the central point on the surface of the sample. Based on the derived hodograms, the polarization of the fast S-wave and the degree of the S-wave anisotropy were inferred. In the dry sample, a shift in the polarization direction toward the direction of the applied stress and an increase in the anisotropy degree in some cases were observed, most likely due to the preferential opening of cracks in the direction along the applied stress. For the water-saturated case, no significant change in the S-wave anisotropy was observed.

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