A Workflow to Integrate Numerical Simulation, Machine Learning Regression and Bayesian Inversion for Induced Seismicity Study: Principles and a Case Study

Szafranski, Dawid; Duan, Benchun

doi:10.1007/s00024-022-03140-7

Cited by 3 publications

(3 citation statements)

References 44 publications

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“…Larson et al (2021) used classification/ regression trees and neural networks for a similar problem. Szafranski and Duan (2022) trained ML models of random forest, bagging, and k-neighbors regression algorithms using numerical simulation results and used the resultant model for Bayesian inversion analysis to estimate subsurface conditions.…”

Section: Risk Evaluation Of Induced Earthquakesmentioning

confidence: 99%

Recent advances in earthquake seismology using machine learning

Kubo,

Naoi,

Kano

2024

Earth Planets Space

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Given the recent developments in machine-learning technology, its application has rapidly progressed in various fields of earthquake seismology, achieving great success. Here, we review the recent advances, focusing on catalog development, seismicity analysis, ground-motion prediction, and crustal deformation analysis. First, we explore studies on the development of earthquake catalogs, including their elemental processes such as event detection/classification, arrival time picking, similar waveform searching, focal mechanism analysis, and paleoseismic record analysis. We then introduce studies related to earthquake risk evaluation and seismicity analysis. Additionally, we review studies on ground-motion prediction, which are categorized into four groups depending on whether the output is ground-motion intensity or ground-motion time series and the input is features (individual measurable properties) or time series. We discuss the effect of imbalanced ground-motion data on machine-learning models and the approaches taken to address the problem. Finally, we summarize the analysis of geodetic data related to crustal deformation, focusing on clustering analysis and detection of geodetic signals caused by seismic/aseismic phenomena. Graphical Abstract

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Section: Risk Evaluation Of Induced Earthquakesmentioning

confidence: 99%

Recent advances in earthquake seismology using machine learning

Kubo,

Naoi,

Kano

2024

Earth Planets Space

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“…With the rapid development of artificial intelligence, intelligent machine learning algorithms have caused a research upsurge in the field of geophysics (Dramsch, 2020a(Dramsch, , 2020b. Szafranski and Duan (2022) applied the RFR method to numerical simulation of seismic data and discussed and prospectively compared various machine learning algorithms (Szafranski & Duan, 2022). Kuhn et al (2018) applied the random forest (RF) algorithm to lithology classification, providing an effective tool for generating and improving initial lithology maps (Kuhn et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…With the rapid development of artificial intelligence, intelligent machine learning algorithms have caused a research upsurge in the field of geophysics (Dramsch, 2020a, 2020b). Szafranski and Duan (2022) applied the RFR method to numerical simulation of seismic data and discussed and prospectively compared various machine learning algorithms (Szafranski & Duan, 2022). Kuhn et al.…”

Section: Introductionmentioning

confidence: 99%

Recovering 3D Basin Basement Relief Using High‐Precision Magnetic Data Through Random Forest Regression Algorithm: A Case Study of Tianzhen‐Yanggao Sag in Datong Basin

Yao,

Zhang,

Wang

et al. 2024

Earth and Space Science

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Inversion of magnetic basement interfaces in basins is essential for interpreting potential field data and studying geothermal resource distribution, as well as basin formation and evolution. This paper introduces a novel method for inverting magnetic basement interfaces using a random forest regression (RFR) algorithm that combines potential field processing and machine learning techniques. The method creates magnetic base interface models and corresponding magnetic anomaly data through the random midpoint displacement method and magnetic interface finite element forward simulation. These anomalies are then processed using techniques such as directional transformations, analytical continuation, spatial derivatives, and fractional transformations. Feature attributes are extracted, and Gini importance is utilized to measure the contributions of feature factors, identify effective features, and enhance algorithm efficiency. The validity and practicality of the method are demonstrated through the analysis of both idealized and noisy models. The proposed machine learning‐based approach is more intelligent, efficient, and accurately represents the relief of magnetic base interfaces. When applied to magnetic survey data in the Datong Basin, it produced a reliable basin base model that aligns with known structural information, paving the way for further research in magnetic interface inversion.

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