Porosity Prediction With Uncertainty Quantification From Multiple Seismic Attributes Using Random Forest

Zou, Caifeng; Zhao, Luanxiao; Xu, Minghui; Chen, Yuanyuan; Geng, Jianhua

doi:10.1029/2021jb021826

Cited by 44 publications

(17 citation statements)

References 71 publications

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“…In terms of model uncertainty, it could be inferred from Figure that the MLM has the lowest uncertainty. Herein, we evaluate the uncertainty as described elsewhere, , in terms of R 2 and RMSE. A high R 2 and a low RMSE generally indicate greater confidence in model predictions.…”

Section: Resultsmentioning

confidence: 99%

Predicting Atmospheric Water-Soluble Organic Mass Reversibly Partitioned to Aerosol Liquid Water in the Eastern United States

El-Sayed,

Parida,

Shekhar

et al. 2023

Environ. Sci. Technol.

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Water-soluble organic matter (WSOM) formed through aqueous processes contributes substantially to total atmospheric aerosol, however, the impact of water evaporation on particle concentrations is highly uncertain. Herein, we present a novel approach to predict the amount of evaporated organic mass induced by sample drying using multivariate polynomial regression and random forest (RF) machine learning models. The impact of particle drying on fine WSOM was monitored during three consecutive summers in Baltimore, MD (2015, 2016, and 2017). The amount of evaporated organic mass was dependent on relative humidity (RH), WSOM concentrations, isoprene concentrations, and NO x /isoprene ratios. Different models corresponding to each class were fitted (trained and tested) to data from the summers of 2015 and 2016 while model validation was performed using summer 2017 data. Using the coefficient of determination (R 2) and the root-mean-square error (RMSE), it was concluded that an RF model with 100 decision trees had the best performance (R 2 of 0.81) and the lowest normalized mean error (NME < 1%) leading to low model uncertainties. The relative feature importance for the RF model was calculated to be 0.55, 0.2, 0.15, and 0.1 for WSOM concentrations, RH levels, isoprene concentrations, and NO x /isoprene ratios, respectively. The machine learning model was thus used to predict summertime concentrations of evaporated organics in Yorkville, Georgia, and Centerville, Alabama in 2016 and 2013, respectively. Results presented herein have implications for measurements that rely on sample drying using a machine learning approach for the analysis and interpretation of atmospheric data sets to elucidate their complex behavior.

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Section: Resultsmentioning

confidence: 99%

Predicting Atmospheric Water-Soluble Organic Mass Reversibly Partitioned to Aerosol Liquid Water in the Eastern United States

El-Sayed,

Parida,

Shekhar

et al. 2023

Environ. Sci. Technol.

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“…The results showed that the approach could help highlight interesting geological and hydrocarbon characteristics and improve traditional seismic interpretation techniques. Likewise, [19] used the Random Forest algorithm to deduce porosity from a number of seismic attributes, and the results proved the effectiveness of ML to characterize spatially varying porosity in a reservoir rock with quantification of the uncertainty. Reference [20] proposed applying DL to predict lithofacies from seismic data and found that the approach improved resolution in the presence of complex geological environments.…”

Section: Introductionmentioning

confidence: 88%

Ensemble Deep Learning-Based Porosity Inversion From Seismic Attributes

et al. 2023

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Underground porosity is important in many earth sciences and engineering fields, including hydrocarbon reservoir characterization and geothermal energy production. Popular methods largely rely on the analysis of lithological core data, well logs, and seismic inversion methods. While these methods are reliable, they are also time-consuming, expensive, and difficult to implement. In addition, seismic inversion has nonlinearity, data dimensionality, and non-uniqueness issues. However, deep learning (DL) can provide a more flexible, efficient, and accurate capability by mapping directly from seismic attributes to underground porosity. Therefore, we trained several DL models with different optimization functions. In the training steps, we labelled every seismic attribute data point with its corresponding porosity derived from the welllogs. In contrast to popular ensemble techniques, we proposed a weighted prediction approach based on the strengths of each model. Testing results showed a coefficient of determination (R 2 ) of 0.94345 and a Pearson's correlation coefficient of 0.9725 between the actual model and the model of the proposed approach, versus 0.9681 and 0.9716 for the best single and popular ensemble models, respectively. Further, we tested the effectiveness of our method using real seismic data from the North Sea. With a Pearson's correlation value of 0.9743, the inverted model ranges from 27 to 35%, compared to the reference model, which has an overall range of 20 to 33%. These results provide insights into the potential of the proposed method and its applicability to any other seismic volume to determine spatially varying underground porosity from seismic attributes directly.INDEX TERMS Deep neural networks, ensemble methods, seismic inversion, reservoir properties.

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“…Most studies in this category design deep neural networks that are capable to capture the complex transformation from the measured data space to the desired model parameter space, train these machines using paired models and their corresponding synthetic data, and apply the trained machines to field datasets. Applications of such a framework range across the whole spectrum of geophysical inverse problems, including surface wave dispersion inversion and tomography (Cai et al., 2022; X. Zhang & Curtis, 2021), seismic‐to‐petrophysics inversion (Xiong et al., 2021; C. Zou et al., 2021), crustal thickness and Vp / Vs estimation from receiver functions (F. Wang et al., 2022), earthquake and microseismicity moment tensor inversion (Chen et al., 2022; Steinberg et al., 2021), magnetic, gravity, and ground‐penetrating radar (GPR) data inversion (R. Huang et al., 2021; Leong & Zhu, 2021; Nurindrawati & Sun, 2020), and thermal evolution estimation for Mars (Agarwal et al., 2021). Y. Wu et al.…”

Section: Highlightsmentioning

confidence: 99%

“…Applications of such a framework range across the whole spectrum of geophysical inverse problems, including surface wave dispersion inversion and tomography (Cai et al, 2022;X. Zhang & Curtis, 2021), seismic-to-petrophysics inversion (Xiong et al, 2021;C. Zou et al, 2021), crustal thickness and Vp/Vs estimation from receiver functions (F. Wang et al, 2022), earthquake and microseismicity moment tensor inversion (Chen et al, 2022;Steinberg et al, 2021), magnetic, gravity, and ground-penetrating radar (GPR) data inversion (R. Leong & Zhu, 2021;Nurindrawati & Sun, 2020), and thermal evolution estimation for Mars (Agarwal et al, 2021).…”

Section: Geophysical Inversionmentioning

confidence: 99%

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

O’Malley

Beroza

et al. 2023

JGR Solid Earth

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After decades of low but continuing activity, applications of machine learning (ML) in solid Earth geoscience have exploded in popularity. This special collection provides a snapshot of those applications, which range from data processing to inversion and interpretation, for which ML appears particularly well suited. Inevitably, there are variations in the degree to which these methods have been developed. We hope that the progress seen in some areas will inspire efforts in others. Challenges remain, including the formidable task of how geoscience can keep pace with developments in ML while ensuring the scientific rigor that our field depends on, but with improvements in sensor technology and accelerating rates of data accumulation, the methods of ML seem poised to play an important role for the foreseeable future.

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Porosity Prediction With Uncertainty Quantification From Multiple Seismic Attributes Using Random Forest

Cited by 44 publications

References 71 publications

Predicting Atmospheric Water-Soluble Organic Mass Reversibly Partitioned to Aerosol Liquid Water in the Eastern United States

Predicting Atmospheric Water-Soluble Organic Mass Reversibly Partitioned to Aerosol Liquid Water in the Eastern United States

Ensemble Deep Learning-Based Porosity Inversion From Seismic Attributes

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

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