A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels

Di, Haibin; AlRegib, Ghassan

doi:10.1111/1365-2478.12865

Cited by 29 publications

(5 citation statements)

References 58 publications

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“…Machine learning (ML) is gaining a lot of traction as a tool to help us solve outstanding problems in image processing, classification, segmentation, among many other tasks. Most of the applications in many fields have relied on supervised training of neural network (NN) models, where the labels (answers) are available (Ronneberger et al, 2015 ; He et al, 2016 ; Osisanwo et al, 2017 ; Di and AlRegib, 2020 ). These answers are often available for synthetically generated data as we numerically control the experiment, or they are determined using human interpretation or human crafted algorithms applied to real data.…”

Section: Introductionmentioning

confidence: 99%

Direct domain adaptation through reciprocal linear transformations

Alkhalifah

Ovcharenko

2022

Front. Artif. Intell.

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We propose a direct domain adaptation (DDA) approach to enrich the training of supervised neural networks on synthetic data by features from real-world data. The process involves a series of linear operations on the input features to the NN model, whether they are from the source or target distributions, as follows: (1) A cross-correlation of the input data (i.e., images) with a randomly picked sample pixel (or pixels) of all images from the input or the mean of all randomly picked sample pixel (or pixels) of all input images. (2) The convolution of the resulting data with the mean of the autocorrelated input images from the other domain. In the training stage, as expected, the input images are from the source distribution, and the mean of auto-correlated images are evaluated from the target distribution. In the inference/application stage, the input images are from the target distribution, and the mean of auto-correlated images are evaluated from the source distribution. The proposed method only manipulates the data from the source and target domains and does not explicitly interfere with the training workflow and network architecture. An application that includes training a convolutional neural network on the MNIST dataset and testing the network on the MNIST-M dataset achieves a 70% accuracy on the test data. A principal component analysis (PCA), as well as t-SNE, shows that the input features from the source and target domains, after the proposed direct transformations, share similar properties along the principal components as compared to the original MNIST and MNIST-M input features.

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Section: Introductionmentioning

confidence: 99%

Direct domain adaptation through reciprocal linear transformations

Alkhalifah

Ovcharenko

2022

Front. Artif. Intell.

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“…Machine learning (ML) is gaining a lot of traction as a tool to help us solve outstanding problems in seismic waveform data processing and interpretation. Most of the applications in our field have relied on supervised training of neural network (NN) models, where the labels (answers) are available (Wrona et al, 2018;Araya-Polo et al, 2018;Ovcharenko et al, 2019;Di and AlRegib, 2020). These answers are often available for synthetic data as we numerically control the experiment, or they are determined using human interpretation or human crafted algorithms applied to real data.…”

Section: Introductionmentioning

confidence: 99%

MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning

Alkhalifah¹,

Wang²,

Ovcharenko³

2021

Preprint

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Among the biggest challenges we face in utilizing neural networks trained on waveform data (i.e., seismic, electromagnetic, or ultrasound) is its application to real data. The requirement for accurate labels forces us to develop solutions using synthetic data, where labels are readily available. However, synthetic data often do not capture the reality of the field/real experiment, and we end up with poor performance of the trained neural network (NN) at the inference stage. We describe a novel approach to enhance supervised training on synthetic data with real data features (domain adaptation). Specifically, for tasks in which the absolute values of the vertical axis (time or depth) of the input data are not crucial, like classification, or can be corrected afterward, like velocity model building using a well-log, we suggest a series of linear operations on the input so the training and application data have similar distributions. This is accomplished by applying two operations on the input data to the NN model: 1) The crosscorrelation of the input data (i.e., shot gather, seismic image, etc.) with a fixed reference trace from the same dataset. 2) The convolution of the resulting data with the mean (or a random sample) of the autocorrelated data from another domain. In the training stage, the input data are from the synthetic domain and the auto-correlated data are from the real domain, and random samples from real data are drawn at every training epoch. In the inference/application stage, the input data are from the real subset domain and the mean of the autocorrelated sections are from the synthetic data subset domain. Example applications on passive seismic data for microseismic event source location determination and active seismic data for predicting low frequencies are used to demonstrate the power of this approach in improving the applicability of trained models to real data.

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“…Machine learning (ML) is gaining a lot of traction as a tool to help us solve outstanding problems in image processing, classification, segmentation, among many other tasks. Most of the applications in many fields have relied on supervised training of neural network (NN) models, where the labels (answers) are available (Osisanwo et al, 2017;He et al, 2016;Ronneberger et al, 2015;Di and AlRegib, 2020). These answers are often available for synthetically generated data as we numerically control the experiment, or they are determined using human interpretation or human crafted algorithms applied to real data.…”

Section: Introductionmentioning

confidence: 99%

Direct domain adaptation through reciprocal linear transformations

Alkhalifah¹,

Ovcharenko²

2021

Preprint

View full text Add to dashboard Cite

We propose a direct domain adaptation (DDA) approach to enrich the training of supervised neural networks on synthetic data by features from real-world data. The process involves a series of linear operations on the input features to the NN model, whether they are from the source or target domains, as follows: 1) A cross-correlation of the input data (i.e. images) with a randomly picked sample pixel (or pixels) of all images from that domain or the mean of all randomly picked sample pixel (or pixels) of all images. 2) The convolution of the resulting data with the mean of the autocorrelated input images from the other domain. In the training stage, as expected, the input images are from the source domain, and the mean of auto-correlated images are evaluated from the target domain. In the inference/application stage, the input images are from the target domain, and the mean of auto-correlated images are evaluated from the source domain. The proposed method only manipulates the data from the source and target domains and does not explicitly interfere with the training workflow and network architecture. An application that includes training a convolutional neural network on the MNIST dataset and testing the network on the MNIST-M dataset achieves a 70% accuracy on the test data. A principal component analysis (PCA), as well as t-SNE, show that the input features from the source and target domains, after the proposed direct transformations, share similar properties along with the principal components as compared to the original MNIST and MNIST-M input features.

show abstract

A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels

Cited by 29 publications

References 58 publications

Direct domain adaptation through reciprocal linear transformations

Direct domain adaptation through reciprocal linear transformations

MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning

Direct domain adaptation through reciprocal linear transformations

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