3 research outputs found
Binary Segmentation of Seismic Facies Using Encoder-Decoder Neural Networks
The interpretation of seismic data is vital for characterizing sediments'
shape in areas of geological study. In seismic interpretation, deep learning
becomes useful for reducing the dependence on handcrafted facies segmentation
geometry and the time required to study geological areas. This work presents a
Deep Neural Network for Facies Segmentation (DNFS) to obtain state-of-the-art
results for seismic facies segmentation. DNFS is trained using a combination of
cross-entropy and Jaccard loss functions. Our results show that DNFS obtains
highly detailed predictions for seismic facies segmentation using fewer
parameters than StNet and U-Net
Seismic Shot Gather Noise Localization Using a Multi-Scale Feature-Fusion-Based Neural Network
Deep learning-based models, such as convolutional neural networks, have
advanced various segments of computer vision. However, this technology is
rarely applied to seismic shot gather noise localization problem. This letter
presents an investigation on the effectiveness of a multi-scale
feature-fusion-based network for seismic shot-gather noise localization.
Herein, we describe the following: (1) the construction of a real-world dataset
of seismic noise localization based on 6,500 seismograms; (2) a multi-scale
feature-fusion-based detector that uses the MobileNet combined with the Feature
Pyramid Net as the backbone; and (3) the Single Shot multi-box detector for box
classification/regression. Additionally, we propose the use of the Focal Loss
function that improves the detector's prediction accuracy. The proposed
detector achieves an [email protected] of 78.67\% in our empirical evaluation
Deep learning for lithological classification of carbonate rock micro-CT images
In addition to the ongoing development, pre-salt carbonate reservoir
characterization remains a challenge, primarily due to inherent geological
particularities. These challenges stimulate the use of well-established
technologies, such as artificial intelligence algorithms, for image
classification tasks. Therefore, this work intends to present an application of
deep learning techniques to identify patterns in Brazilian pre-salt carbonate
rock microtomographic images, thus making possible lithological classification.
Four convolutional neural network models were proposed. The first model
includes three convolutional layers followed by fully connected layers and is
used as a base model for the following proposals. In the next two models, we
replace the max pooling layer with a spatial pyramid pooling and a global
average pooling layer. The last model uses a combination of spatial pyramid
pooling followed by global average pooling in place of the last pooling layer.
All models are compared using original images, when possible, as well as
resized images. The dataset consists of 6,000 images from three different
classes. The model performances were evaluated by each image individually, as
well as by the most frequently predicted class for each sample. According to
accuracy, Model 2 trained on resized images achieved the best results, reaching
an average of 75.54% for the first evaluation approach and an average of 81.33%
for the second. We developed a workflow to automate and accelerate the
lithology classification of Brazilian pre-salt carbonate samples by
categorizing microtomographic images using deep learning algorithms in a
non-destructive way.Comment: 13 pages, 8 figure