• Corpus ID: 235457954

Active learning for seismic processing parameterisation, with an application to first break picking

  title={Active learning for seismic processing parameterisation, with an application to first break picking},
  author={Alan Richardson},
Parameter values for seismic processing steps are often chosen on a regular grid of samples and interpolated. Active learning instead attempts to optimally select the samples on which parameter values are chosen. For parameters that do not vary smoothly, this often reduces the number of samples that need to be labelled in order to achieve a desired accuracy on the whole dataset. In regression tasks this is typically achieved using a query by committee strategy that selects the samples on which… 

Figures from this paper

MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network

—Picking the first arrival times of prestack gathers is called First Arrival Time (FAT) picking, which is an indispensable step in seismic data processing, and is mainly solved manually in the past.

An Active Learning Approach for Classifying Explosion Quakes

The results demonstrate that the proposed active learning approach improves the probability distribution of the events after the human intervention, in particular, after a cut-out phase of the signals with low probabilities.



Automatic QC of denoise processing using a machine learning classification

Abstract Noise attenuation is an important part of a typical seismic data processing sequence. The general purpose of noise attenuation is to improve the resolution of seismic images. It can also be

Comparison of seismic inversion methods on a single real data set

Papers in this volume explore the potential of a variety of seismic inversion methods applied to the same data set. They cover a wide range of topics, including effects of rock properties on seismic

Faster Rates in Regression via Active Learning

A practical algorithm capable of exploiting the extra flexibility of the active setting and provably improving upon the classical passive techniques is described.

Automatic first-breaks picking: New strategies and algorithms

Three methods for the automatic picking of first-break picks are developed, including Modified Coppens’s method, an entropy-based method, and a variogram fractal-dimension method, which show that accurate and consistent picks can be obtained in an automated manner even under the presence of correlated noise, bad traces, pulsechanges, andindistinct firstbreaks.

Model-Free and Model-Based Active Learning for Regression

Model-free approaches, in addition to being less computationally intensive to implement, are more effective in improving the performance of linear regressions than model-based alternatives.

Active Learning

  • David Cohn
  • Education
    Encyclopedia of Machine Learning
  • 2010
In practice, active learning refers to activities that are introduced into the classroom, which could include traditional activities such as homework, in practice.

Automatic first-breaks picking

  • New strategies and algorithms. Geophysics,
  • 2010

Active learning. Synthesis lectures on artificial intelligence and machine learning

  • 2012