.... Embracing deep learning capabilities, we introduce an innovative strategy that incorporates neural network-based regularizers to boost the accuracy of Dix inversion with synthetic and field RMS velocity data. This approach confines the model possibilities to only plausible geological scenarios and helps...

... noise can cause significant issues with subsequent processing steps. There have been several works on attenuating Vz noise with different levels of success. Here we propose an effective deep learning (DL) approach for this task. neural network machine learning geology reservoir characterization...

... Seismic interpolation is an effective technology of reconstructing missing traces to improve the quality of seismic data. Over the past years, the deep learning methods show their powerful performance on seismic interpolation using a convolutional neural network. Recently, an unsupervised deep...

.... geologist deep learning machine learning upstream oil & gas neural network compression reservoir characterization geology artificial intelligence autoencoder representation american association dataset international energy 10 applied geoscience post-migration dataset transformation...

... to optimize the drilling process and reduce costs in geothermal drilling operations. united states government prediction geologist deep learning rop geology artificial intelligence geological subdiscipline upstream oil & gas neural network machine learning forecaster bit selection...

... a convolutional neural network (CNN), which is trained on a vast database of autocorrelations obtained from synthetic GPR images for a comprehensive range of stochastic subsurface models. An important aspect of the training process is that it the synthetic GPR data are generated using a computationally efficient...

... Seismic interpretation of geological units is a tedious task difficult to solve with deep learning. Current methods often require considerable labelled data to provide satisfactory results. We introduce an innovative approach based on Physics-Informed Neural Network (PINN) to constrain...

... a neural network with synthetic data and testing it on field data. We demonstrate the efficacy of this approach in a field case study involving CO 2 injection microseismic data from the Decatur area. The successful relocation of passive seismic events and identification of faults not only improved...

... is necessary to improve generalization capabilities of deep learning-based FWI models. upstream oil & gas inversion waveform inversion geologist neural network machine learning geology artificial intelligence dataset deep learning reservoir characterization exploration geophysicist energy...

... introduce a deep learning workflow that uses Fourier neural operators (FNOs) to estimate corrections to velocity models from migrated images. The workflow is akin to traditional migration velocity analysis (MVA), but it uses a neural network in place of a back projection operator. It can iteratively make...

... alignment, facilitating more precise interpretation and analysis in seismic processing workflows. upstream oil & gas europe government geologist deep learning geology norway government exploration geophysicist neural network reservoir characterization american association energy 10...

... We propose a joint data- and physics-model-driven fullwaveform inversion (FWI) method based on semisupervised learning framework, which uses well-logging data, pseudo labels produced from conventional FWI and common mid-point (CMP) gathers to train neural network. Neural network builds mapping...

... This paper discusses the automatic interpretation of seismic data by using a CNN (Convolutional Neural Network) to predict RGT (Relative Geologic Time). RGT is used as a high-resolution geometric framework and is defined on a regular 3D grid, with the same dimensions as the seismic data...

... learning (DL) methods for reconstructing missing traces in observed seismic data. While many DL-based reconstruction methods employ convolutional neural networks (CNNs) as their core components in supervised or unsupervised settings, the accuracy of CNN-based DL reconstruction methods can still be enhanced...

... superior prediction accuracy compared to the conventional FNO-based method. equation neural network operator upstream oil & gas artificial intelligence u-fno geologist deep learning machine learning geology reservoir characterization fourier neural operator exploration geophysicist...

... includes applications to 2012 and 2014 field waveforms. geologist united states government artificial intelligence structural geology north america government upstream oil & gas neural network dataset newberry eg american association machine learning energy 10 multilayer perceptron...

... obtained with a Convolutional Neural Network (CNN) on a simple synthetic test and a more complex synthetic scenario. In both cases, we achieved an accurate modelling of the source which is supported also by a low data misfit. upstream oil & gas gravity machine learning geologist neural...

... In this paper, we design a deep learning (DL) enhanced joint inversion method using airborne geophysical data collected over the Decorah area. We combine the traditional separate inversions with deep neural network (DNN), which performs as the link between magnetic and gravity gradient data...

... computed in the Fourier domain. We show the advantages of using global FNOs over conventional convolutional neural networks (CNN), to achieve a better non-linear mapping between the recorded data and the subsurface velocity. We show that FNOs can be used to automate velocity model building from field data...

... Accurate monitoring of injected CO 2 volumes and CO 2 migration is critical for the success of carbon sequestration projects. In this work, we first use unsupervised deep learning to denoise seismic data. Then, we propose the use of Invertible Neural Networks (INNs) to estimate porosity and CO...