This extended abstract discusses a novel approach to identify geological sub-seismic scale features using angle-gather spectrograms. Traditional seismic methods face challenges in identifying fine scale geological structures, especially in sublayers thinner than about 1/10 of the wavelength. This paper introduces the use of spectrogram analysis on angle gathers, to capture local spectrum information which is not visible and distinguishable in the reflectivity image and angle gathers themselves. The authors apply this technique to simulated seismic data for a range of basic geological scenarios, extracting spectrograms for each incident angle within angle gathers. The analysis explores the impact of density, velocity variations, layer number and layer thickness on spectrogram patterns, providing insights into their effectiveness for computer vision-related research. The study aims to rejuvenate the utilization of spectrograms for revealing hidden geological structures beyond standard seismic resolution.@en

In the process of seismic subsurface imaging, there is no acceptable forward model reflecting the AVO response in a laterally inhomogeneous medium for reservoir characterization. This means that even when inversion is performed in full waveform, local heterogeneity is typically not fully incorporated while emplying a local 1.5D assumption. Thus, it is impossible to image and classify the subsurface features with these local heterogeneities. Still, the angle-dependent response encodes heterogeneity information that assists overcoming this issue if used properly. To exploit its capabilities, we present a way for identifying reservoir characteristics in the presence of local heterogeneity by linking encoded angle-dependent responses created using angle-dependent Full Wavefield Migration with their originating source - the relevant geological context. To accomplish this purpose, a pipeline technique that integrates the produced angle-dependent responses with a pattern categorization deep-learning tool is proposed. For a basic test on synthetic data, the method successfully identified the produced different stratigraphic architectures and classified them in the training stage. The method is then validated on angle gathers generated from different models with comparable geological circumstances.@en

Many stratigraphic features occur at a scale that is at the edge or below vertical seismic resolution. Thus, they cannot be directly observed in the seismic data, while still having an important effect on the fluid flow within the system. The better understanding of these sub-seismic scale features or heterogeneities can help decrease subsurface uncertainty. Here we present a novel method that integrates forward stratigraphic modelling, petrophysics, and geophysics to decipher the seismic imprint of heterogeneities in wave-dominated, shallow marine environments. The proposed three-stepped method starts with defining geology-related input parameters for BarSim, a stratigraphic forward modelling software that produces models that include stratigraphic architecture, grain size distribution, and facies distribution. Then, the geological data is translated, cell by cell, into petrophysical data (density, Vp, and Vs) using emphirical relationships. Finally, the forward seismic modelling is performed by combining a finite difference approach strategy and angle-dependent full wavefield migration to retrieve the angle gathers This method also allows the generation of large amounts of field-independent data suitable for machine learning applications.@en

Crack initiation is important in rock mechanics because it is the first stage in the process of rock failure. Understanding the mechanisms of crack initiation can help engineers predict when and where rock failure is likely to occur, which can inform decisions about design and safety. Crack initiation in rocks can be studied using image processing techniques. Image processing involves the analysis and manipulation of digital images to extract meaningful information. This can be done through techniques such as digital image correlation (DIC), which allows for the measurement of displacement and strain fields in rocks under different loading conditions. Therefore, this paper presents a study aimed at predicting tensile crack initiation in rock samples using DIC. The study used rock specimens of Marble and Travertine and performed direct tensile strength tests to determine their strength characteristics. To do that, the DIC technique was employed to measure the rock surface displacement field under direct tensile stress. Afterwards, the divergence of this field was calculated to identify the location of crack initiation. The results showed that the regions with the maximum positive divergence value reflect the pure relative expansion of the surface, thereby helping in identifying the location of crack initiation.@en