Magnetotelluric inversion using supervised learning trained with random smooth geoelectric models

Lian Liu, Bo Yang, Yixian Xu, Dikun Yang

International Meeting for Applied Geoscience and Energy (IMAGE)

... frequencies and observation stations, noise, and the model equivalence regarding its resolution (Backus & Gilbert 1967; Parker 1983). Geophysicists...

Strike-slip fault skeletonization based on deep learning cascade ant tracking method

Zhipeng Gui, Junhua Zhang, Rujun Wang, Yintao Zhang, Chong Sun, Mei Yang

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Then, utilizing advantages of ant tracking, fault skeletonization also called as fault thinning is operated. Model test and real data application show: (1...

Label-defect tolerance ability of deep learning inversion networks and its applications

Yang Ping, Xu Hunqun, Liu Di, Tao Chunfeng, Wang Chengxiang, Yue Changqing

International Meeting for Applied Geoscience and Energy (IMAGE)

... inversion accuracy. Through model testing, we confirm that Deep Learning (DL) networks have the ability to tolerate labels’ defects, which we name...

Improving Depth Prediction Accuracy of Quantified Drilling Hazards

W. Scott Leaney, William H. Borland

Geological Society of Malaysia (GSM)

... problem without a forward problem, and the forward problem underlying seismic trace inversion is the convolutional model. A processed seismic trace...

Seismic random noise attenuation via enhanced similarity self-supervised learning

Jiale Wang, Naihao Liu, Yihuai Lou, Jinghuai Gao

International Meeting for Applied Geoscience and Energy (IMAGE)

... in the frequency domain, which seriously affects the subsequent seismic data processing and geological interpretation (Dong et al., 2020). Therefore, many...

Evaluation of AI-enhanced processing for automated passive seismic detection and location

Aaron Booterbaugh, Evgeny Naumov

International Meeting for Applied Geoscience and Energy (IMAGE)

... model and provide access to the rich datasets collected by both public arrays and Nanometrics’s private installations. CONVOLUTIONAL NEURAL NETWORK...

ABSTRACT: Deep forest cover classification of consecutive landsat imageries over Borneo

Azalea Kamellia Abdullah, Mohd Nadzri Md Reba, Nur Efarina Jali, Sikula Magupin, Diana Anthony

Geological Society of Malaysia (GSM)

... learning image classification algorithms such as Convolutional Neural Networks (CNN) attains higher accuracy mapping with low human interruption. Deep...

Statistical Analysis of Estimated Ultimate Recovery: Comparing Machine Learning and Traditional DCA Methods in the Eagle Ford and Bakken

Palash Panja, Carlos Vega-Ortiz, Milind Deo, Brian McPherson, Rasoul Sorkhabi

Unconventional Resources Technology Conference (URTEC)

...) (Yan et al., 2021), the Temporal Convolutional Network (TCN) (Lea et al., 2016), and the transformer model (Vaswani et al., 2017). The efficacy of LSTMs...

Generating geophysical models from text for constructing the dataset of learning-based MT inversion

Yutong Li, Hongyu Zhou, Rui Guo, Maokun Li, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

..., Rapid surrogate modeling of electromagnetic data in frequency domain using neural operator: IEEE Transactions on Geoscience and Remote Sensing, 60...

Machine-learning based arrival-picking in continuous DAS recordings „ Application to the Utah FORGE EGS project

Nepomuk Boitz, William Tegtow, Serge Shapiro

International Meeting for Applied Geoscience and Energy (IMAGE)

... a Convolutional Neural Network (CNN). In contrast to geophone-based picking methods, we suggest to train CNNs only on data from a specific dataset to best...

Deep Learning for Quantitative Hydraulic Fracture Profiling from Fiber Optic Measurements

Weichang Li, Han Lu, Yuchen Jin, Frode Hveding

Unconventional Resources Technology Conference (URTEC)

... to the synced pump data independently; and II) a convolutional LSTM (long short-term memory) sequence learning model maps time segments...

Seismic imaging uncertainty using deep learning predicted Greens functions

Han Liu, Anar Yusifov, Muhong Zhou, Linda Hodgson

International Meeting for Applied Geoscience and Energy (IMAGE)

... velocity model. We used U-net architecture (Ronneberger et al., 2015) with 5 layers, and each layer has 2 convolutional layers. Ensemble models were...

Prediction of porosity in CO2 sequestration tight reservoirs based on multi-source and multi-scale data fusion

Ping Lu, Peixue Jiang, Ruina Xu, Huaqing Xue, Haojie Li, Lei Yu, Jiashu Han

International Meeting for Applied Geoscience and Energy (IMAGE)

... it difficult for the reservoir porosity model to simultaneously consider prediction accuracy and generalization performance. Therefore, this study focuses...

A two-stage deep learning workflow for automated seismic inversion

Haibin Di, Wenyi Hu, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... of four major steps, (i) large-scale structural model construction, (ii) initial property model estimation via a multi-task convolutional neural...

Deep Convolutional Neural Networks for Seismic Salt-Body Delineation

Search and Discovery.com

Unlocking the Potential of Unlabeled Data in Building Deep Learning Model for Dynamometer Cards Classification

Ramdhan Wibawa, Rosyadi Rosyadi, Maulirany Nancy, Muhammad Awqi Gibran, Supriono Hariyadi

Indonesian Petroleum Association

... for building a Convolutional Neural Network (CNN) model using state of the art Self-Supervised Learning (SSL). The need to identify moretypes of failure has been...

3D Seismic Facies Classification on CPU and GPU HPC Clusters

Sergio Botelho, Vishal Das, Davide Vanzo, Pandu Devarakota, Vinay Rao, Santi Adavani

Unconventional Resources Technology Conference (URTEC)

...; second, neural network design becomes increasingly challenging due to the higher number of parameters in the model and its larger training time. We...

Comparison of Machine Learning and Statistical Predictive Models for Production Time Series Forecasting in Tight Oil Reservoirs

Hamid Rahmanifard, Ian Gates, Abdolmohsen Shabib-Asl

Unconventional Resources Technology Conference (URTEC)

... shale as the model was physically more complex than the one in ES–SAGD. However, the ANN models showed successful prediction performance after applying...

Relative geologic time generation based on 3D-CNNs and domain adaptation

Xin He, Bangli Zou, Yifeng Fei, Gang Yu, Dajun Li

International Meeting for Applied Geoscience and Energy (IMAGE)

... of the domain adaptation layer, the trained model exhibits good generalization ability to field seismic data. Furthermore, fine-tuning allows...

Seismic super resolution method for enhancing stratigraphic interpretation

Chengbo Li, Qingrong Zhu, Baishali Roy

International Meeting for Applied Geoscience and Energy (IMAGE)

... 𝒎 and employ the convolutional model to illustrate the method. Assuming the wavelet is stationary within a given interval, the seismic can...

Reservoir prediction using graph-regularized deep learning

Kaiheng Sang, Nanying Lan, Fanchang Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

... of these explicit formulas are based on strong approximation to the underground media properties, such as convolution model, Aki-Richard approximate...

Abstract: A Convolutional Neural Network for Vuggy Facies Classification from Borehole Images;

Jiajun Jiang, Dawn McAlpin, Chicheng Xu, Rui Xu, Scott James, Weichang Li

Search and Discovery.com

...Abstract: A Convolutional Neural Network for Vuggy Facies Classification from Borehole Images; Jiajun Jiang, Dawn McAlpin, Chicheng Xu, Rui Xu, Scott...

Abstract: Horizontal Stress Prediction Using Seismic Velocities Based on Convolutional Neural Network; #91206 (2023)

Fatemeh Saberi, Esmael Makarian, Ayub Elyasi, Tomomewo Stanley Olusegun

Search and Discovery.com

...Abstract: Horizontal Stress Prediction Using Seismic Velocities Based on Convolutional Neural Network; #91206 (2023) Fatemeh Saberi, Esmael Makarian...

Seismic data reconstruction using denoising convolutional neural network combined with regularization by denoising

Nanying Lan, Kaiheng Sang, Fanchang Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

...Seismic data reconstruction using denoising convolutional neural network combined with regularization by denoising Nanying Lan, Kaiheng Sang...

Bayesian RockAVO: Direct petrophysical inversion with hierarchical conditional GANs

Miguel Corrales, Muhammad Izzatullah, Matteo Ravasi, Hussein Hoteit

International Meeting for Applied Geoscience and Energy (IMAGE)

... originating from inaccuracies in the measurements, modeling errors, and complex geological processes. Moreover, the non-linearity of the rock-physics model...