Joint inversion of multi-height gravity and vertical gradient via physics-informed neural network

Yinshuo Li, Wenkai Lu, Cao Song

International Meeting for Applied Geoscience and Energy (IMAGE)

...]. The inversion model is based on convolutional layers. Since the 3D convolution neural network is computationally heavy, this abstract proposed to reduce...

Using deep learning for automatic detection and segmentation of carbonate microtextures

Claire Birnie, Viswasanthi Chandra

International Meeting for Applied Geoscience and Energy (IMAGE)

... on Microsoft’s Common Objects in COntext (COCO) dataset. The resulting model accurately detects and separates a number of crystals observed within...

Applying Machine Learning Technologies in the Niobrara Formation, DJ Basin, to Quickly Produce an Integrated Structural and Stratigraphic Seismic Classification Volume Calibrated to Wells

Carolan Laudon, Jie Qi, Yin-Kai Wang

Unconventional Resources Technology Conference (URTEC)

... Detection Methodology Seismic amplitude is the basis for machine learning fault detection which uses deep learning Convolutional Neural Networks (CNNs...

Use of Machine Learning to Estimate Sonic Data for Seismic Well Ties; #42471 (2019)

Thanapong Ketmalee

Search and Discovery.com

... Computed Convolutional Model Filter DT Casing Bad hole condition Spike RC * Wavelet Synthetic Seismogram AI Comparison Scenarios Actual DT ML...

High-resolution seismic detection of shallow natural gas beneath Hutchinson, Kansas

Susan E. Nissen, W. Lynn Watney, Jianghai Xia

Environmental Geosciences (DEG)

...-frequency reflections are artifacts of the overlying gas, which cannot be accurately reproduced with the simple convolutional model used to create...

The Geophysical Case History of Rengasdengklok Area, North West Java

Basuki Puspoputro, Emir Lubis

Indonesian Petroleum Association

... No. 506.78.03. Robinson, E.A., 1983. Seismic velocity analysis and the convolutional model. IHRDC, Boston. Schlumberger, 1987. Well Seismic Service Processing...

Deep learning based microearthquake location prediction at Newberry EGS using physics-informed synthetic dataset

Zi Xian Leong, Tieyuan Zhu

International Meeting for Applied Geoscience and Energy (IMAGE)

...-velocity model to simulate physicsinformed synthetic MEQ events and corresponding acoustic waveforms. We introduce a deep learning-based method namely...

An integrated workflow of improving the accuracy of first arrivals picking via deep learning

Yitao Pu, Bo Zhang, Chenglin Wei, Yingyu Xu, Hongfei Liu

International Meeting for Applied Geoscience and Energy (IMAGE)

... learning. Firstly, we compute a probability image by applying a model, which is trained using the Historically nested U-Net (HUnet), to the seismic shot...

S-wave velocity prediction using a deep learning scheme and attention mechanism

Gang Feng, Wen-Qin Liu, Zhe Yang, Wei Yang, Jian-Hua Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... several limitations, such as poor model generalization, inadequate exploration of logging curve patterns. In this study, a novel approach based on one...

Applying deep learning for identifying bioturbation from core photographs

Eric Timmer, Calla Knudson, and Murray Gingras

AAPG Bulletin

... to the convolutional layers to reduce model overfitting (Srivastava et al., 2014). Overfitting occurs when the neural network memorizes the data set...

A deep learning workflow for petro-mechanical facies predictions in unconventionals

Noah R. Vento, Enru Liu, Mary Johns

International Meeting for Applied Geoscience and Energy (IMAGE)

... extracted from 23 well locations across the survey and used as inputs to the proposed DL model. Due to poor S/N ratio and the presence of residual...

Stability criteria for seismic data interpolation artificial neural networks

Yiying Xing, Wenlong Wang, Florian Bossmann, Xuexin An

International Meeting for Applied Geoscience and Energy (IMAGE)

... select three artificial neural networks that have been successfully applied in seismic data interpolation Deep Convolutional Neural Network (DnCNN...

Sedimentology Of Prealpine Flysch Sequences, Switzerland

John F. Hubert

Journal of Sedimentary Research (SEPM)

.... Some of the flysch basins can be differentiated by the proportions of various rock fragments. The model for internal divisions in graded sandstones...

Solving seismic inverse problems by an unsupervised hybrid machine-learning approach

Mrinal K. Sen, Arnab Dhara

International Meeting for Applied Geoscience and Energy (IMAGE)

... carried out by iterative data fitting in which the model updates are evaluated by solving the corresponding physics-based forward modeling. Local...

Seismic facies segmentation via mask-assisted transformer

Jinlong Huo, Naihao Liu, Zhiguo Wang, Yang Yang, Yijie Zhang, Jinghuai Gao

International Meeting for Applied Geoscience and Energy (IMAGE)

... on the characteristics of seismic reflectors. The application of using convolutional neural networks (CNNs) in seismic facies segmentation is growing rapidly. However, CNN...

GeoStreamer X Delivers Near-Field Multi-Azimuth Dataset for Accurate Lead Characterisation, South Viking Graben, Norway

Cyrille Reiser, Eric Mueller, PGS

GEO ExPro Magazine

... summarised below: • Comprehensive demultiple sequence addressing the short and long period multiples integrating 3D convolutional and wave equation...

Spatial statistical analysis and geomodelling of banana holes using point patterns and generative adversarial networks

Rayan Kanfar, Charles Breithaupt, Tapan Mukerji

International Meeting for Applied Geoscience and Energy (IMAGE)

... to successfully model complex geologic spatial patterns (Zhang et al. 2019; Song et al. 2021; 2022). GANs is an unsupervised learning algorithm...

AAPG ACE 2018

Search and Discovery.com

Automated active learning for seismic facies classification

Haibin Di, Leigh Truelove, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... convolutional neural networks have been popularly implemented for seismic image interpretation including facies classification, the performance...

AAPG Annual Convention and Exhibition 2020; - Abstracts, #91200 (2022).

Search and Discovery.com

Deep Learning Applied to Fault Interpretation and Attribute Computation

Search and Discovery.com

Abstract: Unsupervised Segmentation of Rock MicroCT Scans Using Deep Learning;

Fernando Bordignon, Giovanni Formighieri, Eduardo Burgel, Bruno Rodrigues

Search and Discovery.com

... of DNNs when working with images are the Convolutional Neural Networks (CNN), usually employed in conjunction with supervised training, which needs...

Using Machine Learning Methods to Identify Coals from Drilling and Logging-While-Drilling LWD Data

Ruizhi Zhong, Raymond L. Johnson Jr., Zhongwei Chen

Unconventional Resources Technology Conference (URTEC)

... (Schmidhuber 2015). Supervised learning is learning a predictive model that maps certain inputs to a desired output. To build the supervised learning...

Well Performance and Completion Efficiency Assessment in the Delaware Basin using the Diffusive Time of Flight

Jaeyoung Park, Yuxing Ben, Vivek Muralidharan

Unconventional Resources Technology Conference (URTEC)

... et al. 2019, Park et al. 2020, Santos et al. 2020, Zalavadia and Gildin 2021), yet the numerical simulation still requires a static geologic model...

Amplitude enhancement of far-offset refractions via machine learning

Lurun Su, Han Wang, Jie Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

... refractions using a physical model. Such methods are time-consuming, especially for 3D applications. In this study, we apply a machine learning method...