Deep Learning on Real Geophysical Data: A Case Study for Distributed
Acoustic Sensing Research
- URL: http://arxiv.org/abs/2010.07842v1
- Date: Thu, 15 Oct 2020 15:59:52 GMT
- Title: Deep Learning on Real Geophysical Data: A Case Study for Distributed
Acoustic Sensing Research
- Authors: Vincent Dumont, Ver\'onica Rodr\'iguez Tribaldos, Jonathan
Ajo-Franklin, Kesheng Wu
- Abstract summary: We present a search for a finely-tuned and efficiently scaled deep learning classifier to identify usable energy from seismic data.
We show that by using 16 times more GPUs, we can increase the training speed by more than two orders of magnitude on a 50,000-image data set.
- Score: 1.7237878022600697
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep Learning approaches for real, large, and complex scientific data sets
can be very challenging to design. In this work, we present a complete search
for a finely-tuned and efficiently scaled deep learning classifier to identify
usable energy from seismic data acquired using Distributed Acoustic Sensing
(DAS). While using only a subset of labeled images during training, we were
able to identify suitable models that can be accurately generalized to unknown
signal patterns. We show that by using 16 times more GPUs, we can increase the
training speed by more than two orders of magnitude on a 50,000-image data set.
Related papers
- Scaling Wearable Foundation Models [54.93979158708164]
We investigate the scaling properties of sensor foundation models across compute, data, and model size.
Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, electrodermal activity, accelerometer, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM.
Our results establish the scaling laws of LSM for tasks such as imputation, extrapolation, both across time and sensor modalities.
arXiv Detail & Related papers (2024-10-17T15:08:21Z) - Rethinking Transformers Pre-training for Multi-Spectral Satellite
Imagery [78.43828998065071]
Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks.
Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data.
In this paper, we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities.
arXiv Detail & Related papers (2024-03-08T16:18:04Z) - RGB-D based Stair Detection using Deep Learning for Autonomous Stair
Climbing [6.362951673024623]
We propose a neural network architecture with inputs of both RGB map and depth map.
Specifically, we design the selective module which can make the network learn the complementary relationship between RGB map and depth map.
Experiments on our dataset show that our method can achieve better accuracy and recall compared with the previous state-of-the-art deep learning method.
arXiv Detail & Related papers (2022-12-02T11:22:52Z) - A Novel Approach For Analysis of Distributed Acoustic Sensing System
Based on Deep Transfer Learning [0.0]
Convolutional neural networks are highly capable tools for extracting spatial information.
Long-short term memory (LSTM) is an effective instrument for processing sequential data.
VGG-16 architecture in our framework manages to obtain 100% classification accuracy in 50 trainings.
arXiv Detail & Related papers (2022-06-24T19:56:01Z) - Neurosymbolic hybrid approach to driver collision warning [64.02492460600905]
There are two main algorithmic approaches to autonomous driving systems.
Deep learning alone has achieved state-of-the-art results in many areas.
But sometimes it can be very difficult to debug if the deep learning model doesn't work.
arXiv Detail & Related papers (2022-03-28T20:29:50Z) - Weakly Supervised Change Detection Using Guided Anisotropic Difusion [97.43170678509478]
We propose original ideas that help us to leverage such datasets in the context of change detection.
First, we propose the guided anisotropic diffusion (GAD) algorithm, which improves semantic segmentation results.
We then show its potential in two weakly-supervised learning strategies tailored for change detection.
arXiv Detail & Related papers (2021-12-31T10:03:47Z) - Generation of microbial colonies dataset with deep learning style
transfer [0.0]
We introduce a strategy to generate a synthetic dataset of microbiological images of Petri dishes that can be used to train deep learning models.
We show that the method is able to synthesize a dataset of realistic looking images that can be used to train a neural network model capable of localising, segmenting, and classifying five different microbial species.
arXiv Detail & Related papers (2021-11-06T03:11:01Z) - Learning Co-segmentation by Segment Swapping for Retrieval and Discovery [67.6609943904996]
The goal of this work is to efficiently identify visually similar patterns from a pair of images.
We generate synthetic training pairs by selecting object segments in an image and copy-pasting them into another image.
We show our approach provides clear improvements for artwork details retrieval on the Brueghel dataset.
arXiv Detail & Related papers (2021-10-29T16:51:16Z) - Homography augumented momentum constrastive learning for SAR image
retrieval [3.9743795764085545]
We propose a deep learning-based image retrieval approach using homography transformation augmented contrastive learning.
We also propose a training method for the DNNs induced by contrastive learning that does not require any labeling procedure.
arXiv Detail & Related papers (2021-09-21T17:27:07Z) - Self-supervised Audiovisual Representation Learning for Remote Sensing Data [96.23611272637943]
We propose a self-supervised approach for pre-training deep neural networks in remote sensing.
By exploiting the correspondence between geo-tagged audio recordings and remote sensing, this is done in a completely label-free manner.
We show that our approach outperforms existing pre-training strategies for remote sensing imagery.
arXiv Detail & Related papers (2021-08-02T07:50:50Z) - Deep Learning for Surface Wave Identification in Distributed Acoustic
Sensing Data [1.7237878022600697]
We present a highly scalable and efficient approach to process real, complex DAS data.
Deep supervised learning is used to identify "useful" coherent surface waves generated by anthropogenic activity.
Our method provides interpretable patterns describing the interaction of ground-based human activities with the buried sensors.
arXiv Detail & Related papers (2020-10-15T15:53:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.