miscellaneous

Geophysics

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Semi-Supervised Segmentation of Salt Bodies in Seismic Images using an Ensemble of Convolutional Neural Networks

Artsiom Sanakoyeu, Yauhen Babakhin, Hirotoshi Kitamura•Mon Apr 08 2019

A semi-supervised method for segmentation (delineation) of salt bodies in seismic images which utilizes unlabeled data for multi-round self-training and outperforms state-of-the-art on the TGS Salt Identification Challenge dataset and is ranked the first among the 3234 competing methods.

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70 years of machine learning in geoscience in review

J. Dramsch•Mon Jun 15 2020

This review gives an overview of the development of machine learning in geoscience and explores the shift from mathematical fundamentals and knowledge in software development toward skills in model validation, applied statistics, and integrated subject matter expertise.

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Direct Velocity Inversion of Ground Penetrating Radar Data Using GPRNet

Zi Xian Leong, T. Zhu•Mon May 31 2021

Ground penetrating radar (GPR) is used to image the shallow subsurface as evident in earth and planetary exploration. Electromagnetic (EM) velocity (permittivity) models are inverted from GPR data for accurate migration. While conventional velocity analysis methods are designed for multioffset GPR data, to our knowledge, the velocity analysis for zero‐offset GPR has been underexplored. Inspired by recent deep learning seismic impedance inversion, we propose a deep learning guided technique, GPRNet, that is based on convolutional neural networks to directly learn the intrinsic relationship between GPR data and EM velocity. GPRNet takes in GPR data and outputs the corresponding EM velocity. We simulate numerous GPR data from a range of pseudo‐random velocity models and feed the datasets into GPRNet for training. Each training data set comprises of a pair of one‐dimensional GPR data and EM velocity. During training phase, the neural network's weights are updated iteratively until convergence. This process is analogous to full‐waveform inversion in which the best model is found by iterative optimization until simulated data matches observed data. We test GPRNet on synthetic testing datasets and the predicted velocity models are accurate. A case study is presented where this method is applied on a GPR data collected at the former Wurtsmith Air Force Base in Michigan. The inversion results agree with velocity models established by previous GPR inversion studies of the similar area. We expect the GPRNet open‐source software to be useful in imaging the subsurface for earth and planetary exploration.

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Data-driven modeling of time-domain induced polarization

Charles L. B'erub'e, P. Berube•Thu Jul 29 2021

It is determined that a single real-valued scalar parameter contains sufficient information to encode IP data, suggesting that modeling time-domain IP data using mathematical models governed by more than one free parameter is ambiguous, whereas modeling only the average chargeability is justified.

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A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design

A. Choudhary, Ankit Agrawal, Zijiang Yang, Dipendra Jha, Arindam Paul, W. Liao•Mon Jan 25 2021

This work proposes a framework consisting of generative adversarial networks and mixture density networks for inverse modeling, and it is evaluated on a materials science dataset for microstructural materials design, demonstrating that the proposed framework can overcome the above-mentioned challenges and produce multiple promising solutions in an efficient manner.

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Learned multiphysics inversion with differentiable programming and machine learning

Ali Siahkoohi, F. Herrmann, G. Rizzuti, Rafael Orozco, M. Louboutin, Ziyi Yin, Thomas J. Grady, Philipp A. Witte, O. Møyner, G. Gorman•Tue Apr 11 2023

The design principles and their benefits are illustrated and demonstrated by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which, aside from coupling of wave physics and multiphase flow, involves machine learning.

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Rethinking materials simulations: Blending direct numerical simulations with neural operators

G. Karniadakis, Vivek Oommen, K. Shukla, Saaketh Desai, Rémi Dingreville•Thu Dec 07 2023

This methodology is based on the integration of a community numerical solver with a U-Net neural operator, enhanced by a temporal-conditioning mechanism to enable accurate extrapolation and efficient time-to-solution predictions of the dynamics.

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LSTM-Based Forecasting Model for GRACE Accelerometer Data

N. Darbeheshti, Elahe Moradi•Tue Aug 15 2023

The Gravity Recovery and Climate Experiment (GRACE) satellite mission, spanning from 2002 to 2017, has provided a valuable dataset for monitoring variations in Earth's gravity field, enabling diverse applications in geophysics and hydrology. The mission was followed by GRACE Follow-On in 2018, continuing data collection efforts. The monthly Earth gravity field, derived from the integration different instruments onboard satellites, has shown inconsistencies due to various factors, including gaps in observations for certain instruments since the beginning of the GRACE mission. With over two decades of GRACE and GRACE Follow-On data now available, this paper proposes an approach to fill the data gaps and forecast GRACE accelerometer data. Specifically, we focus on accelerometer data and employ Long Short-Term Memory (LSTM) networks to train a model capable of predicting accelerometer data for all three axes. In this study, we describe the methodology used to preprocess the accelerometer data, prepare it for LSTM training, and evaluate the model's performance. Through experimentation and validation, we assess the model's accuracy and its ability to predict accelerometer data for the three axes. Our results demonstrate the effectiveness of the LSTM forecasting model in filling gaps and forecasting GRACE accelerometer data.

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Viskositas: Viscosity Prediction of Multicomponent Chemical Systems

P. D. Anjos•Mon Aug 01 2022

Viscosity in the metallurgical and glass industry plays a fundamental role in its production processes, also in the area of geophysics. As its experimental measurement is financially expensive, also in terms of time, several mathematical models were built to provide viscosity results as a function of several variables, such as chemical composition and temperature, in linear and nonlinear models. A database was built in order to produce a nonlinear model by artificial neural networks by variation of hyperparameters to provide reliable predictions of viscosity in relation to chemical systems and temperatures. The model produced named Viskositas demonstrated better statistical evaluations of mean absolute error, standard deviation and coefficient of determination (R 2 ) in relation to the test database when compared to different models from literature and 1 commercial model, offering predictions with lower errors, less variability and less generation of outliers.

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