scholarly journals Seismic inversion with deep learning

2021 ◽  
Author(s):  
Silvia L. Pintea ◽  
Siddharth Sharma ◽  
Femke C. Vossepoel ◽  
Jan C. van Gemert ◽  
Marco Loog ◽  
...  
Keyword(s):  
Deep Learning ◽  
Radon Transform ◽  
Network Optimization ◽  
Seismic Data ◽  
Seismic Inversion ◽  
Single Step ◽  
Background Model ◽  
Learning Methods ◽  
Type Classes ◽  
Type Classification

AbstractThis article investigates bypassing the inversion steps involved in a standard litho-type classification pipeline and performing the litho-type classification directly from imaged seismic data. We consider a set of deep learning methods that map the seismic data directly into litho-type classes, trained on two variants of synthetic seismic data: (i) one in which we image the seismic data using a local Radon transform to obtain angle gathers, (ii) and another in which we start from the subsurface-offset gathers, based on correlations over the seismic data. Our results indicate that this single-step approach provides a faster alternative to the established pipeline while being convincingly accurate. We observe that adding the background model as input to the deep network optimization is essential in correctly categorizing litho-types. Also, starting from the angle gathers obtained by imaging in the Radon domain is more informative than using the subsurface offset gathers as input.

scholarly journals Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

2021 ◽  
Vol 7 (3) ◽  
pp. 51
Author(s):  
Emanuela Paladini ◽  
Edoardo Vantaggiato ◽  
Fares Bougourzi ◽  
Cosimo Distante ◽  
Abdenour Hadid ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Digital Pathology ◽  
Texture Features ◽  
Tissue Type ◽  
Cancer Tissue ◽  
Learning Methods ◽  
Feature Based ◽  
Type Classification ◽  
Whole Slide Images

In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.

High-resolution acoustic impedance inversion to characterize turbidites at Marlim Field, Campos Basin, Brazil

2014 ◽  
Vol 2 (3) ◽  
pp. T143-T153 ◽  
Author(s):  
Tatiane M. Nascimento ◽  
Paulo T. L. Menezes ◽  
Igor L. Braga
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Structural Information ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Background Model ◽  
Rock Properties ◽  
High Porosity ◽  
Campos Basin

Seismic inversion is routinely used to determine rock properties, such as acoustic impedance and porosity, from seismic data. Nonuniqueness of the solutions is a major issue. A good strategy to reduce this inherent ambiguity of the inversion procedure is to introduce stratigraphic and structural information a priori to better construct the low-frequency background model. This is particularly relevant when studying heterogeneous deepwater turbidite reservoirs that form prolific, but complex, hydrocarbon plays in the Brazilian offshore basins. We evaluated a high-resolution inversion workflow applied to 3D seismic data at Marlim Field, Campos Basin, to recover acoustic impedance and porosity of the turbidites reservoirs. The Marlim sandstones consist of an Oligocene/Miocene deepwater turbidite system forming a series of amalgamated bodies. The main advantage of our workflow is to incorporate the interpreter’s knowledge about the local stratigraphy to construct an enhanced background model, and then extract a higher resolution image from the seismic data. High-porosity zones were associated to the reservoirs facies; meanwhile, the nonreservoir facies were identified as low-porosity zones.

Robust deep learning seismic inversion with a priori initial model constraint

2021 ◽  
Author(s):  
Jian Zhang ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Yuanqiang Li ◽  
Guangtan Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
A Priori ◽  
Seismic Inversion ◽  
Training Dataset ◽  
Initial Model ◽  
Data Set ◽  
The Real ◽  
Model Constraint ◽  
Trained Network

Summary Seismic inversion is one of the most commonly used methods in the oil and gas industry for reservoir characterization from observed seismic data. Deep learning (DL) is emerging as a data-driven approach that can effectively solve the inverse problem. However, existing deep learning-based methods for seismic inversion utilize only seismic data as input, which often leads to poor stability of the inversion results. Besides, it has always been challenging to train a robust network since the real survey has limited labeled data pairs. To partially overcome these issues, we develop a neural network framework with a priori initial model constraint to perform seismic inversion. Our network uses two parts as one input for training. One is the seismic data, and the other is the subsurface background model. The labels for each input are the actual model. The proposed method is performed by log-to-log strategy. The training dataset is firstly generated based on forward modeling. The network is then pre-trained using the synthetic training dataset, which is further validated using synthetic data that has not been used in the training step. After obtaining the pre-trained network, we introduce the transfer learning strategy to fine-tune the pre-trained network using labeled data pairs from a real survey to acquire better inversion results in the real survey. The validity of the proposed framework is demonstrated using synthetic 2D data including both post-stack and pre-stack examples, as well as a real 3D post-stack seismic data set from the western Canadian sedimentary basin.

scholarly journals Deep-learning inversion: A next-generation seismic velocity model building method

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. R583-R599 ◽  
Author(s):  
Fangshu Yang ◽  
Jianwei Ma
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Computational Time ◽  
Learning Methods ◽  
Velocity Models ◽  
Velocity Model Building ◽  
Training Stage

Seismic velocity is one of the most important parameters used in seismic exploration. Accurate velocity models are the key prerequisites for reverse time migration and other high-resolution seismic imaging techniques. Such velocity information has traditionally been derived by tomography or full-waveform inversion (FWI), which are time consuming and computationally expensive, and they rely heavily on human interaction and quality control. We have investigated a novel method based on the supervised deep fully convolutional neural network for velocity-model building directly from raw seismograms. Unlike the conventional inversion method based on physical models, supervised deep-learning methods are based on big-data training rather than prior-knowledge assumptions. During the training stage, the network establishes a nonlinear projection from the multishot seismic data to the corresponding velocity models. During the prediction stage, the trained network can be used to estimate the velocity models from the new input seismic data. One key characteristic of the deep-learning method is that it can automatically extract multilayer useful features without the need for human-curated activities and an initial velocity setup. The data-driven method usually requires more time during the training stage, and actual predictions take less time, with only seconds needed. Therefore, the computational time of geophysical inversions, including real-time inversions, can be dramatically reduced once a good generalized network is built. By using numerical experiments on synthetic models, the promising performance of our proposed method is shown in comparison with conventional FWI even when the input data are in more realistic scenarios. We have also evaluated deep-learning methods, the training data set, the lack of low frequencies, and the advantages and disadvantages of our method.

Seismic Inversion via Closed-Loop Fully Convolutional Residual Network and Transfer Learning

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Lingling Wang ◽  
Delin Meng ◽  
Bangyu Wu
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Seismic Data ◽  
Closed Loop ◽  
Seismic Inversion ◽  
Open Loop ◽  
Residual Network ◽  
Velocity Inversion ◽  
Good Convergence ◽  
Loop Network

Because deep learning networks can 'learn' the complex mapping function between the labeled inputs and outputs, they have shown great potential in seismic inversion. Conventional deep learning algorithms require a large amount of labeled data for sufficient training. However, in practice, the number of well logs is limited. To address this problem, we propose a closed-loop fully convolutional residual network (FCRN) combined with transfer learning strategy for seismic inversion. This closed-loop FCRN consists of an inverse network and a forward network. The inverse network predicts the inversion target from seismic data, whereas the forward network calculates seismic data from the inversion target. The inverse network is initialized by pre-training on the Marmousi2 model and fine-tuned with the limited labeled data around the wells through transfer learning, to suit the target seismic data. The forward network is initialized by training with the limited labeled data around the wells. In this way, the closed-loop network is well initialized to ensure relatively good convergence. Then, the misfit of the limited labeled data and the error between the true and the forward seismic data are used to regularize the training of the initialized closed-loop network. The inverse network of the optimized closed-loop network is used to obtain the final inversion results. The proposed work flow can be used for velocity, density, and impedance inversion from post-stack seismic data. This paper takes velocity inversion as an example to illustrate the effectiveness of the method. The experimental results show that the closed-loop FCRN with transfer learning is superior than the open-loop FCRN with better lateral continuity and velocity details. The closed-loop FCRN can effectively predict the velocity with high accuracy on the synthetic data, has good anti-noise performance, and also can be effectively used for the field data with spatial heterogeneity.

Physics-guided deep learning for seismic inversion with hybrid training and uncertainty analysis

Geophysics ◽  
2021 ◽  
pp. 1-64
Author(s):  
Jian Sun ◽  
Kristopher A. Innanen ◽  
Chao Huang
Keyword(s):  
Deep Learning ◽  
Elastic Property ◽  
Seismic Data ◽  
Degrees Of Freedom ◽  
Seismic Inversion ◽  
Training Data ◽  
Data Driven ◽  
Model Data ◽  
Data Set ◽  
Propagation Network

The determination of subsurface elastic property models is crucial in quantitative seismic data processing and interpretation. This problem is commonly solved by deterministic physical methods, such as tomography or full-waveform inversion. However, these methods are entirely local and require accurate initial models. Deep learning represents a plausible class of methods for seismic inversion, which may avoid some of the issues of purely descent-based approaches. However, any generic deep learning network capable of relating each elastic property cell value to each sample in a seismic data set would require a very large number of degrees of freedom. Two approaches might be taken to train such a network: first, by invoking a massive and exhaustive training data set and, second, by working to reduce the degrees of freedom by enforcing physical constraints on the model-data relationship. The second approach is referred to as “physics-guiding.” Based on recent progress in wave theory-designed (i.e., physics-based) networks, we have developed a hybrid network design, involving deterministic, physics-based modeling and data-driven deep learning components. From an optimization standpoint, a data-driven model misfit (i.e., standard deep learning) and now a physics-guided data residual (i.e., a wave propagation network) are simultaneously minimized during the training of the network. An experiment is carried out to analyze the trade-off between two types of losses. Synthetic velocity building is used to examine the potential of hybrid training. Comparisons demonstrate that, given the same training data set, the hybrid-trained network outperforms the traditional fully data-driven network. In addition, we performed a comprehensive error analysis to quantitatively compare the fully data-driven and hybrid physics-guided approaches. The network is applied to the SEG salt model data, and the uncertainty is analyzed, to further examine the benefits of hybrid training.

Sign in / Sign up

Export Citation Format

Share Document