Machine Learning in Seismology: Turning Data into Insights

This article provides an overview of current applications of machine learning (ML) in seismology. ML techniques are becoming increasingly widespread in seismology, with applications ranging from identifying unseen signals and patterns to extracting features that might improve our physical understanding. The survey of the applications in seismology presented here serves as a catalyst for further use of ML. Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning (EEW), ground‐motion prediction, seismic tomography, and earthquake geodesy. We conclude by discussing the need for a hybrid approach combining data‐driven ML with traditional physical modeling.

• Publication year

  2018

• Venue

  Seismological Research Letters

• Publication date

  2018-11-14

• Fields of study

  Computer Science, Geology

• Identifiers
  DOI 10.1785/0220180259
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Automatic microseismic event picking via unsupervised machine learning

  2020cited by this paper
• Deep learning of aftershock patterns following large earthquakes

  2018cited by this paper
• Prioritizing Ground‐Motion Validation Metrics Using Semisupervised and Supervised Learning

  2018cited by this paper
• A Fast Earthquake Early Warning Algorithm Based on the First 3 s of the P‐Wave Coda

  2018cited by this paper
• Preface to the Focus Section on Geophone Array Seismology

  2018cited by this paper
• EQcorrscan: Repeating and Near‐Repeating Earthquake Detection and Analysis in Python

  2018cited by this paper
• High-resolution seismic event detection using local similarity for Large-N arrays

  2018cited by this paper
• Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning

  2018cited by this paper
• Generalized Seismic Phase Detection with Deep Learning

  2018cited by this paper
• Rapid Earthquake Discrimination for Earthquake Early Warning: A Bayesian Probabilistic Approach Using Three‐Component Single‐Station Waveforms and Seismicity Forecast

  2018cited by this paper
• Oklahoma's induced seismicity strongly linked to wastewater injection depth

  2018cited by this paper
• P Wave Arrival Picking and First‐Motion Polarity Determination With Deep Learning

  2018cited by this paper
• Deep-learning tomography

  2018cited by this paper
• Strong Correlation between Stress Drop and Peak Ground Acceleration for Recent M 1-4 Earthquakes in the San Francisco Bay Area

  2018cited by this paper
• Full waveform inversion with nonlocal similarity and model-derivative domain adaptive sparsity-promoting regularization

  2018cited by this paper
• Deep Mesh Projectors for Inverse Problems

  2018cited by this paper
• Fast Matched Filter (FMF): An Efficient Seismic Matched‐Filter Search for Both CPU and GPU Architectures

  2018cited by this paper
• Detecting earthquakes over a seismic network using single-station similarity measures

  2018cited by this paper
• Visibility Graph Analysis of Seismicity around Enguri High Arch Dam, Caucasus

  2018cited by this paper
• The Predictive Power of Ground‐Motion Prediction Equations

  2017cited by this paper
• Automated fault detection without seismic processing

  2017cited by this paper
• Hidden physics models: Machine learning of nonlinear partial differential equations

  2017cited by this paper
• Convolutional neural network for earthquake detection and location

  2017cited by this paper
• PDE-Net: Learning PDEs from Data

  2017cited by this paper
• Using graph clustering to locate sources within a dense sensor array

  2017cited by this paper
• Deep learning prior models from seismic images for full-waveform inversion

  2017cited by this paper
• Fast magnitude determination using a single seismological station record implementing machine learning techniques

  2017cited by this paper
• Generating Seismograms with Deep Neural Networks

  2017cited by this paper
• Enabling large‐scale viscoelastic calculations via neural network acceleration

  2017cited by this paper
• DeepDetect: A Cascaded Region-Based Densely Connected Network for Seismic Event Detection

  2017cited by this paper
• What Is Better Than Coulomb Failure Stress? A Ranking of Scalar Static Stress Triggering Mechanisms from 105 Mainshock‐Aftershock Pairs

  2017cited by this paper
• Machine Learning Predicts Laboratory Earthquakes

  2017cited by this paper
• Aftershocks driven by afterslip and fluid pressure sweeping through a fault‐fracture mesh

  2017cited by this paper
• Travel Time Tomography With Adaptive Dictionaries

  2017cited by this paper
• GrowClust: A Hierarchical Clustering Algorithm for Relative Earthquake Relocation, with Application to the Spanish Springs and Sheldon, Nevada, Earthquake Sequences

  2017cited by this paper
• MyShake: Initial observations from a global smartphone seismic network

  2016cited by this paper
• Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

  2016cited by this paper
• Prediction of ground motion parameters using randomized ANFIS (RANFIS)

  2016cited by this paper
• Site-Condition Proxies, Ground Motion Variability, and Data-Driven GMPEs: Insights from the NGA-West2 and RESORCE Data Sets

  2016cited by this paper
• Recent and future developments in earthquake ground motion estimation

  2016cited by this paper
• Data-driven discovery of partial differential equations

  2016cited by this paper
• Deep Learning

  2016cited by this paper
• MyShake: A smartphone seismic network for earthquake early warning and beyond

  2016cited by this paper
• Earthquake detection through computationally efficient similarity search

  2015cited by this paper
• Machine learning: Trends, perspectives, and prospects

  2015cited by this paper
• Preface to the Focus Section

  2015cited by this paper
• Sparse-promoting Full Waveform Inversion based on Online Orthonormal Dictionary Learning

  2015cited by this paper
• The Gutenberg Algorithm: Evolutionary Bayesian Magnitude Estimates for Earthquake Early Warning with a Filter Bank

  2015cited by this paper
• Regionally-Adjustable Generic Ground-Motion Prediction Equation

  2015cited by this paper
• Machine-Learning Methods for Earthquake Ground Motion Analysis and Simulation

  2015cited by this paper
• Regionally Adjustable Generic Ground‐Motion Prediction Equation Based on Equivalent Point‐Source Simulations: Application to Central and Eastern North America

  2015cited by this paper
• Sparse Modeling for Image and Vision Processing

  2014influential reference
• Understanding the Magnitude Dependence of PGA and PGV in NGA‐West 2 Data

  2014cited by this paper
• Data Mining Practical Machine Learning Tools and Techniques

  2014cited by this paper
• NGA-West2 Research Project

  2014cited by this paper
• Application of real time recurrent neural network for detection of small natural earthquakes in Poland

  2014cited by this paper
• Towards fully data driven ground-motion prediction models for Europe

  2014cited by this paper
• Real-time earthquake monitoring using a search engine method

  2014cited by this paper
• PageRank for Earthquakes

  2014cited by this paper
• Big data: A review

  2013cited by this paper
• Multisensor data fusion: A review of the state-of-the-art

  2013cited by this paper
• Machine learning - a probabilistic perspective

  2012influential reference
• Constructing a Hidden Markov Model based earthquake detector: application to induced seismicity

  2012cited by this paper
• Propagation of Slow Slip Leading Up to the 2011 Mw 9.0 Tohoku-Oki Earthquake

  2012cited by this paper
• Adapting the Neural Network Approach to PGA Prediction: An Example Based on the KiK-net Data

  2012cited by this paper
• Real-time Finite Fault Rupture Detector (FinDer) for large earthquakes

  2012cited by this paper
• Slow Earthquakes and Nonvolcanic Tremor

  2011cited by this paper
• Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing

  2011cited by this paper
• MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning

  2011cited by this paper
• From Exact to Approximate Solutions

  2010cited by this paper
• An integrated perspective of the continuum between earthquakes and slow-slip phenomena

  2010cited by this paper
• Inversion of a velocity model using artificial neural networks

  2010cited by this paper
• Seismic tomography: a window into deep Earth

  2010cited by this paper
• The Status of Earthquake Early Warning around the World: An Introductory Overview

  2009cited by this paper
• An overview of full-waveform inversion in exploration geophysics

  2009cited by this paper
• Migration of early aftershocks following the 2004 Parkfield earthquake

  2009cited by this paper
• Collective behavior of earthquakes and faults: Continuum‐discrete transitions, progressive evolutionary changes, and different dynamic regimes

  2008cited by this paper
• PreSEIS: A Neural Network-Based Approach to Earthquake Early Warning for Finite Faults

  2008cited by this paper
• NGA Ground Motion Model for the Geometric Mean Horizontal Component of PGA, PGV, PGD and 5% Damped Linear Elastic Response Spectra for Periods Ranging from 0.01 to 10 s

  2008cited by this paper
• Non-volcanic tremor and low-frequency earthquake swarms

  2007cited by this paper
• The Virtual Seismologist (VS) Method: a Bayesian Approach to Earthquake Early Warning

  2007cited by this paper
• Pattern Recognition and Machine Learning

  2006cited by this paper
• Tomographic inversion using L1-norm regularization of wavelet coefficients

  2006cited by this paper
• The detection of low magnitude seismic events using array-based waveform correlation

  2006cited by this paper
• Parameter estimation and inverse problems

  2005influential reference
• Random Forests

  2001cited by this paper
• Discrimination of quarry blasts from tectonic microearthquakes in the Hyblean Plateau (Southeastern Sicily)

  2001cited by this paper
• An artificial neural network approach for broadband seismic phase picking

  1999cited by this paper
• Probabilistic Seismic Hazard Analysis without the Ergodic Assumption

  1999cited by this paper
• Detecting teleseismic events using artificial neural networks

  1999cited by this paper
• Explosion site recognition; neural net discriminator using single three-component stations

  1999cited by this paper
• A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise

  1996cited by this paper
• Discrimination between local microearthquakes and quarry blasts by multi-layer perceptrons and Kohonen maps

  1996cited by this paper
• Automatic picking of seismic arrivals in local earthquake data using an artificial neural network

  1995cited by this paper
• Neural networks and inversion of seismic data

  1994cited by this paper
• Regional seismic event classification at the NORESS array: Seismological measurements and the use of trained neural networks

  1990cited by this paper
• Stochastic simulation of high-frequency ground motions based on seismological models of the radiated spectra

  1983cited by this paper
• Automatic earthquake recognition and timing from single traces

  1978cited by this paper

• Comparison of deep learning models for 1D magnetotelluric inversion

  2026cites this paper
• Temporal and spatial evolution patterns of earthquake clusters in the southern Sichuan industrial mining zone—Advanced analysis based on multisource data fusion and machine learning

  2026cites this paper
• Designing a model for earthquake timing and magnitude prediction based on neural networks and particle swarm optimization (PSO) algorithm

  2026cites this paper
• Detection of mining-induced microseismicity through a deep convolutional neural network

  2026cites this paper
• A Hybrid Ensemble Framework for Probabilistic Earthquake Forecasting in Northern California in Support of SDG 11: Sustainable and Resilient Cities

  2026influential citation
• Machine learning approaches for earthquake prediction: a comparative analysis of seismotectonic provinces in the Iranian plateau

  2026cites this paper
• Impact of Machine-Learning Phase Picking on Seismic Tomography at Popocatépetl Volcano, Mexico

  2026cites this paper
• Leveraging Multisource Data with Machine Learning for Earthquake Loss Prediction: A Case Study from Yunnan, China

  2026cites this paper
• Use of Conditional Generative Adversarial Nets to Generate Power Spectral Density Function and Its Application for Sampling Seismic Ground Motions

  2026cites this paper
• Statistical and machine learning methods for multi-step earthquake frequency forecasting in indonesian regions

  2026cites this paper
• A machine learning framework with parameter optimization and regional clustering for strong earthquake prediction

  2026cites this paper
• Demand forecasting with artificial intelligence: a novel hybrid approach for enhanced predictive accuracy

  2026cites this paper
• High-quality control of receiver functions using a capsule neural network

  2025cites this paper
• Exploration of Machine Learning Methods to Seismic Event Discrimination in the Pacific Northwest

  2025cites this paper
• The Evolution of Seismic Tomography in Earth Sciences—Advancements, Limitations, and Its AI-Enabled Future (A Critical Review)

  2025cites this paper
• Global Distribution of Low Frequency Family Marsquakes From Deep‐Learning‐Based Polarization Estimation

  2025cites this paper
• Recurrent neural network-based seismic slope displacement hazard assessment considering the effects of slope aspects

  2025cites this paper
• An ANN, CGAN, and transform pair based framework to simulate seismic ground motions

  2025cites this paper
• Earthquake Size Distributions of Strong Worldwide Seismicity Are Similar for Background and Triggered Events

  2025cites this paper
• A Geospatial Survey of Earthquake Distribution Along Global Fault Lines

  2025cites this paper
• Detection of Significant Seismic Quiescence Patterns in the Mexican Subduction Zone Using Extended Schreider Algorithms

  2025cites this paper
• APPLICATION OF GAUSSIAN PROCESS REGRESSION TO GROUND MOTION MODELING OF ACCELERATION RESPONSE SPECTRA

  2025cites this paper
• PyQuake3D: A Python Tool for 3‐D Earthquake Sequence Simulations of Seismic and Aseismic Slip

  2025cites this paper
• Anomaly Detection in Seismic Data with Deep Learning: Application for Instrument Failure Detection and Forecasting

  2025cites this paper
• A complex Morlet- convolutional attention network framework for robust direction-of-arrival estimation

  2025cites this paper
• A Deep-Learning-Based Real-Time Microearthquake Monitoring System (RT-MEMS) for Taiwan

  2025influential citation
• Deciphering Influential Features in the Seismic Catalog for Large Earthquake Occurrence from a Machine Learning Perspective

  2025cites this paper
• Parallel Seismic Data Processing Performance with Cloud-based Storage

  2025cites this paper
• Machine Learning-based Analysis of the 2015 M5.8 Alxa Left Banner Earthquake Sequence

  2025cites this paper
• Deep neural network–powered ground-motion models for acceleration spectrum intensity, Housner’s spectrum intensity, and displacement spectrum intensity

  2025cites this paper
• An Approach to Creating Spatial Predictive Prospecting Models of Deposits Based on Convolutional Neural Networks (A Case Study of the Territory of Southeastern Transbaikalia)

  2025cites this paper
• Modeling and simulation of three-component ground motion intensity envelope function based on conditional generative adversarial network

  2025cites this paper
• Machine Learning Innovations in Revolutionizing Earthquake Engineering: A Review

  2025cites this paper
• HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity

  2025cites this paper
• A transformer-based real-time earthquake detection framework in heterogeneous environments

  2025cites this paper
• Hybrid Time Series Methods and Machine Learning for Seismic Analysis and Volcano Eruption Predict

  2025cites this paper
• Development of ann-based data-driven ground motion model for Azerbaijan using temporal earthquake records of 2022–2024

  2025cites this paper
• Conditional generative adversarial networks for the generation of strong ground motion parameters using KiK-net ground motion records

  2025cites this paper
• Real-Time Reconstruction of Ground Motion During Small Magnitude Earthquakes: A Pilot Study

  2025cites this paper
• State Estimation of Lithium-Ion Batteries via Physics-Machine Learning Combined Methods: A Methodological Review and Future Perspectives

  2025cites this paper
• Seismic risk assessment of typical bridges in Northeastern Brazil through the application of Machine Learning methods

  2025cites this paper
• Discrimination of natural and nonnatural earthquakes using a vision transformer

  2025cites this paper
• Enhanced deep learning approach for detecting and locating tectonic tremors in the Nankai subduction zone

  2025cites this paper
• Fuzzy C-Means clustering and LSTM-based magnitude prediction of earthquakes in the Aegean region of Türkiye

  2025cites this paper
• Ground motion prediction equations for the Himalayan region based on earthquake data using conventional and random forest approaches

  2025cites this paper
• Applying EQTransformer to laboratory earthquakes: detecting and picking acoustic emissions with machine learning

  2025cites this paper
• An AIoT System for Earthquake Early Warning on Resource Constrained Devices

  2025cites this paper
• Solving Inverse Problems with Deep Learning

  2025cites this paper
• Unifying the Gutenberg-Richter Law with Probabilistic Catalog Completeness

  2025cites this paper
• Classifying Thermal Moonquakes Recorded in Apollo 17 Lunar Seismic Data Using Cross‐Correlation and Diurnal Temperature Variations

  2025cites this paper
• ENGINet: End-to-end deep learning of the cumulative absolute velocity, Arias intensity, and spectrum intensity prediction for on-site earthquake early warning

  2025cites this paper
• A study on the seismic transfer functions of deeply embedded caisson foundations considering soil–foundation kinematic interaction: Experimental validation, numerical simulation, and deep learning prediction

  2025cites this paper
• MACHINE LEARNING METHODS TO SOLVE THE PROBLEM OF DETECTING EARTHQUAKE PRECURSORS BASED ON SEISMIC NOISE MONITORING DATA FOR THE BAIKAL RIFT SYSTEM

  2025cites this paper
• Electrofacies classification of a mixed carbonate-siliciclastic reservoir using machine learning techniques

  2025cites this paper
• An Attention-Based Framework With Multistation Information for Earthquake Early Warnings

  2025cites this paper
• RSNet: A Neural Network Model for Picking of Seismic Wave Arrival Times Based on Mesofluctuations

  2025cites this paper
• Physics-guided symbolic neural network reveals optimal functional forms describing ground motions

  2025cites this paper
• Simulating multi-component near-fault ground motion record containing pulse-like component

  2025cites this paper
• Integrating multi-modal data fusion approaches for analysis of dairy cattle vocalizations

  2025cites this paper
• Data Quality Aware Deep Learning for Reliable Seismic Event Detection

  2025cites this paper
• Impact of several site-condition proxies and ground-motion intensity measures on the spectral amplification factor using neuro-fuzzy approach: an example on the KiK-Net dataset

  2025cites this paper
• Coherence‐Based Characterization of a Long‐Period Monochromatic Seismic Signal

  2025cites this paper
• A computer vision-based approach to enhance seismic catalogues

  2025cites this paper
• ML-aided induced seismicity processing and interpretation for enhanced geothermal systems

  2025cites this paper
• Physics-informed neural networks for dispersion studies of SH-waves in an initially stressed sandy half-space

  2025cites this paper
• Forecasting Fluid-Induced Seismicity Using Machine Learning Time-Series Prediction

  2025cites this paper
• Development of three machine learning models for predicting the horizontal peak ground acceleration for Iran

  2025influential citation
• Improved monitoring of the 2022 Goesan earthquake sequence using a temporary seismic array

  2025cites this paper
• Systematic Review of Machine Learning Applications in Geophysics: Integrating Seismic, Potential Field, Geological Modelling, and Natural Hazard Domains

  2025influential citation
• A novel physics-constrained neural network: An illustration of ground motion models

  2025cites this paper
• Effects on a Deep-Learning, Seismic Arrival-Time Picker of Domain-Knowledge Based Preprocessing of Input Seismograms

  2024cites this paper
• Monitoring urban construction and quarry blasts with low-cost seismic sensors and deep learning tools in the city of Oslo, Norway

  2024cites this paper
• On the use of VMD-LSTM neural network for approximate earthquake prediction

  2024cites this paper
• Predicting the seismic ground-motion parameters: 3D physics-based numerical simulations combined with artificial neural networks

  2024cites this paper
• Threshold-based earthquake early warning for high-speed railways using deep learning

  2024cites this paper
• Deep reinforcement learning for inverting earthquake focal mechanism and its potential application to marine earthquakes

  2024cites this paper
• Leveraging internet of things and emerging technologies for earthquake disaster management: Challenges and future directions

  2024cites this paper
• Multi‐input integrative neural network for soil seismic response modeling at KiK‐net downhole array sites

  2024cites this paper
• Applying Machine Learning to Earthquake Engineering: A Scientometric Analysis of World Research

  2024cites this paper
• Testing pre-trained models for earthquake detection at Raoul Island volcano, Aotearoa New Zealand

  2024cites this paper
• Exploiting Signal Coherence to Simultaneously Detect and Locate Earthquakes

  2024cites this paper
• STUDY ON THE CONSTRUCTION OF A GROUND MOTION MODEL USING GAUSSIAN PROCESS REGRESSION

  2024cites this paper
• SB-ETAS: using simulation based inference for scalable, likelihood-free inference for the ETAS model of earthquake occurrences

  2024cites this paper
• Very-long-period signal reveals lava lake sloshing and its interaction with a deep reservoir in Nyiragongo volcano

  2024cites this paper
• Environmental risk assessment and management of nuclear power plants based on big data analysis

  2024cites this paper
• On the anatomy of acoustic emission.

  2024cites this paper
• Discrimination of doubled Acoustic Emission events using Neural Networks.

  2024cites this paper
• The Role of Machine Learning in Earthquake Seismology: A Review

  2024cites this paper
• The assessment of probabilistic seismic risk using ground-motion simulations via a Monte Carlo approach

  2024cites this paper
• A supervised machine learning approach for estimating plate interface locking: Application to Central Chile

  2024cites this paper
• Seismic vulnerability assessment model of civil structure using machine learning algorithms: a case study of the 2014 Ms6.5 Ludian earthquake

  2024cites this paper
• A Multi-LASSO model to forecast induced seismicity at enhanced geothermal systems

  2024cites this paper
• GPR-TransUNet: An improved TransUNet based on self-attention mechanism for ground penetrating radar inversion

  2024cites this paper
• Recent advances in earthquake seismology using machine learning

  2024cites this paper
• Earthquake prediction from seismic indicators using tree-based ensemble learning

  2024cites this paper
• Machine Learning-Based Rapid Epicentral Distance Estimation from a Single Station

  2024cites this paper
• Deep learning-based earthquake catalog reveals the seismogenic structures of the 2022 MW 6.9 Chihshang earthquake sequence

  2024cites this paper
• Mean period prediction models for Mexican interplate and intermediate-depth intraslab earthquake ground motions

  2024cites this paper
• Ground-motion simulations using two-dimensional convolution condition adversarial neural network(2D-cGAN)

  2024cites this paper
• Propose Design Of Merapi Volcano Eruption Prediction Using Hybrid Time Series And Machine Learning

  2024cites this paper