... study introduces a self-supervised deep-learning architecture to complete the interpolation method. The dataset comprises small-scale marine seismic survey data acquired from Youngil Bay, Pohang, near the East Sea of the Korean Peninsula. We randomly extract 2D planes from 3D seismic data to generate...

...-waveform inversion initial model fwi exploration geophysicist earth model property dataset geophysics implicit fwi model property american association applied geoscience & energy 10 innanen representation p-velocity JAX acceleration of Implicit FWI and field data application H. V...

... dispersion inversion workflow. Firstly, we use parameters with high sensitivity to create a small-scale dataset to train a neural network with a residual structure. By imposing monotonicity constraints on the model output, we achieve rapid and effective simulation of dispersion using less than 5% of the data...

.... The proposed method has potent compatibility for embracing diverse datasets and a strong ability to model complex dynamics and interactions between multiple datasets. Through an example application on field passive seismic data at the Utah FORGE site, this method demonstrates its potential to capitalize...

..., a convolutional neural network (CNN) is trained to reconstruct the near offset gap of source-over-cable common depth point gathers acquired in the North Sea, yielding low reconstruction error on unseen gathers. The trained network is then tested on a source-over-cable dataset acquired in the Barents Sea...

... subsurface, we use dense 3D node active-source and ambient noise seismic data. We outline dedicated interferometry based processing and inversion workflows for the reconstruction of P- and Swave velocity models. The potential and limitations of waveequation tomography for this kind of datasets and targets...

...-lapse processing american association co 2 geologist vertical seismic profile exploration geophysicist time-lapse vsp das data processing newell county facility geometry reverse time migration dataset baseline data Time-lapse processing and imaging of the Snowflake 3D DAS VSP CO2...

... A convolutional neural network (NN) was trained on a large dataset of over 2 million P- and S-phase pick observations across a diverse range of passive seismic monitoring arrays. We apply this model in near real-time on both single- and three-component seismometer data to generate probability...

... of the Convolutional Neural Network (CNN) trained with the proposed methodology on synthetically generated data and on field datasets. geologist artificial intelligence discrimination geology reservoir characterization deep learning machine learning seismic event multiple model neural network...

...) method is often challenged by computational burden due to the high dimensionality of the inverse problem. To mitigate this issue, we propose to train a deep convolutional autoencoder (CAE) and use it as a prior for Bayesian FWI. Given a subsurface model dataset, the CAE cast the high-dimension model...

... methods. To solve this problem, we propose a deep-learning workflow for quickly retrieving body wave events from massive ambient noise datasets. We feed relevant data to a convolutional autoencoder classifier (CAC) and directly determine whether the segment contains body wave events after training...

... datasets that contain thousands of well-logs, reducing the challenges of manual interpretation. Accurate 3D stratigraphic and log property models can be created at a basin scale using various technologies. In this article, we illustrate the concepts with an application to the Midland Basin. geologist...

... a subset of the walkaway VSP data acquired at the Containment and Monitoring Institute’s (CaMI) field research station, Alberta, to investigate a documented example of such mismatches. Our results indicated that after applying the DAS transform to particle acceleration, geophone and DAS datasets...

... elimination reflection primary reflection geophysics petroleum geology applied geoscience & energy 10 requirement data amplitude marchenko multiple elimination scheme economic geology marchenko multiple elimination dataset t-bnmme exploration geophysicist A comparison of Marchenko...

... the fidelity of acquired dataset. We present an application of the geometric mode decomposition based on the 2D Fourier spectrum (GMD-F) for simultaneous wavefield separation and denoising in VSP datasets. The GMD-F method optimizes linear patterns within amplitude-frequency modulated modes and can efficiently...

... Detecting microseismic events from Distributed Acoustic Sensing data presents significant challenges, particularly due to the prevalent label imbalance in seismic datasets. Noise data overwhelmingly outnumbers event labels, leading to a high false negative ratio when using conventional deep...

..., unconformity uranium or sediment-hosted mineral deposits. deep learning petroleum geology artificial intelligence mineral machine learning prediction xgboost economic geology hybrid ml model dropout mineral composition well logging geological subdiscipline dataset canada geologist...

..., a geological model construction method based on logging data and geological stratigraphic constraints is proposed to simulate VSP data using geological models with different logging responses, and a rich, complete, and physically meaningful training dataset is constructed based on the synthetic VSP data...

... Dynamic Matching Full Waveform Inversion (DM FWI) has been established as a proven toolkit to update velocity models. Mao et al. (2020) demonstrated its success for datasets where long offsets, wide azimuths are available. Yong et al. (2023) showcased reliable and effective updates for offsets...

... geological subdiscipline representation regularization seismic inversion american association exploration geophysicist applied geoscience & energy 10 inverse problem dataset regularization term impedance model inversion machine learning seismic data post-stack seismic inversion geophysics...