Tutorial on seismic signal/noise classification; from linear to deep classifiers

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The toolbox for the oil fields modelling and analysis (oil forecasting, seismic slices classification, etc)

Maximal overlap discrete wavelet transform is combined with recurrent neural network to automate the labeling of seismic facies data. The proposed framework generates more accurate results in an efficient way.

Python Interface for the Classification of Seismic Signals

Bayesian Convolutional Neural Networks for Seismic Facies Classification

CapsPhase: Capsule Neural Network for Seismic Phase Classification and Picking

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Classification of seismic events based on moment tensor solutions

An efficient few-shot segmentation diffusion model for seismic facies classification

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Texture classification of seismic data

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Seismic events such as Earthquakes are very under-researched in global scientific domains but, with advancements in Machine Learning, classification and analysis of seismic data is becoming more feasible. This project focuses on developing techniques to effectively classify Seismic data into earthquake events and seismic noise (mining blasts, man-made noise etc.). The project not only focuses on developing deep learning models but, is also focused on collecting seismic data from credible repositories with proper labelling. An extensive research in seismology ensued proper knowledge of Earthquake waves, their propagation and behavior. Main motivation behind this project is to defy already accepted triggering (STA/LTA, Z Transform etc.) as well as Picking (AR picker, baer picker etc.) algorithms and formulate new techniques to extract P and S wave features. Problem with these algorithms is that different threshold values can produce entirely different triggers and accurate thresholds are hard to generalize for large number of events.

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Dataset used in "Data-Driven Prediction of Seismic Intensity Distributions Featuring Hybrid Classification-Regression Models"

# This is a repository for the masters thesis: - Analysis of the benefits and limitations of seismic attribute classification using a 3D facies model

Seismic Phase Classification

Script for Classification of Glacial Seismic Events using Machine Learning

We apply several powerful machine learning techniques to automatically classify noise, earthquake, and blast events, including a variety of classical convolutional neural networks and unsupervised learning methods.

The Seismic Wave Classifier is a deep learning project that classifies seismic waves (P-Waves, S-Waves, Surface Waves) using synthetic data. It includes a 1D CNN model and accuracy tracking for identifying and analysing seismic waveforms.

% Ref: General regression neural network (GRNN)-based seismic site classification scheme for Chinese seismic code using HVSR curves % Authors: Ji Kun ; Ren Yefei*; Ruizhi Wen; Zhu ChuanBin; Liu Ye;

EDA and Classification models on Seismic bumps Dataset

Nepal Earthquake Trend Analysis and Seismic Risk Prediction Based on Severity Classification

Official implementation of the paper - "Ensemble and self-supervised learning for improved classification of seismic signals from the ̊Aknes rockslope"

SeismoLift is a lightweight tool designed to assess the seismic classification of elevators in compliance with the NP EN 1998-1:2009 (Eurocode 8) and EN 81-77 standards. It supports engineers, architects, and safety professionals in identifying appropriate seismic categories based on geographic location and regional parameters within Portugal.