Subtopic Deep Dive

Surface Wave Tomography from Microseisms
Research Guide

What is Surface Wave Tomography from Microseisms?

Surface Wave Tomography from Microseisms uses cross-correlations of ambient seismic noise, primarily ocean-generated microseisms, to extract Rayleigh and Love wave dispersion curves for inverting shear-wave velocity structures from local to regional scales.

This technique relies on empirical Green's functions derived from long-term noise cross-correlations to measure phase and group velocities. Key advancements include broad-band dispersion measurements (Bensen et al., 2007, 2345 citations) and high-resolution tomographic imaging in regions like California (Shapiro et al., 2005, 2222 citations). Over 10 foundational papers from 1998-2008 established the method, with thousands of citations.

Curated Papers

Key Challenges

Why It Matters

Microseism-based tomography provides high-resolution crustal and near-surface shear velocity models essential for earthquake hazard assessment and ground motion prediction. Bensen et al. (2007) enabled reliable dispersion data across broad bands, supporting applications in seismic risk mapping. Shapiro et al. (2005) demonstrated imaging of southern California basins, improving models for urban seismic hazards. Lin et al. (2008) mapped western US phase velocities, aiding tectonic and volcanic studies.

Key Research Challenges

Noise Source Directionality

Ocean microseisms exhibit directional bias, complicating isotropic Green's function retrieval. Shapiro et al. (2005) noted path coverage limitations from USArray data. Stehly et al. (2006) analyzed long-range correlations to address noise origin variability.

Short-Period Dispersion Accuracy

Extracting reliable short-period surface waves requires extended recording times. Yao et al. (2006) used two-station analysis for SE Tibet phase velocities at short periods. Bensen et al. (2007) developed processing to stabilize broad-band measurements.

Crustal Inversion Non-Uniqueness

Shear velocity inversions from dispersion curves suffer from trade-offs between layers. Yao et al. (2008) inverted Rayleigh phases for 3D crustal structure in Tibet. Lin et al. (2008) mapped Rayleigh and Love waves, highlighting resolution limits in upper mantle.

Essential Papers

Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements

G. D. Bensen, M. H. Ritzwoller, M. P. Barmin et al. · 2007 · Geophysical Journal International · 2.3K citations

Ambient noise tomography is a rapidly emerging field of seismological research. This paper presents the current status of ambient noise data processing as it has developed over the past several yea...

High-Resolution Surface-Wave Tomography from Ambient Seismic Noise

Н. М. Шапиро, Michel Campillo, Laurent Stehly et al. · 2005 · Science · 2.2K citations

Cross-correlation of 1 month of ambient seismic noise recorded at USArray stations in California yields hundreds of short-period surface-wave group-speed measurements on interstation paths. We used...

Surface-wave array tomography in SE Tibet from ambient seismic noise and two-station analysis - I. Phase velocity maps

Huajian Yao, Robert D. van der Hilst, Maarten V. de Hoop · 2006 · Geophysical Journal International · 1.0K citations

Empirical Green's functions (EGFs) between pairs of seismographs can be estimated from the time derivative of the long-time cross-correlation of ambient seismic noise. These EGFs reveal velocity di...

Surface wave tomography of the western United States from ambient seismic noise: Rayleigh and Love wave phase velocity maps

Fan‐Chi Lin, Morgan P. Moschetti, M. H. Ritzwoller · 2008 · Geophysical Journal International · 799 citations

We present the results of Rayleigh wave and Love wave phase velocity tomography in the western United States using ambient seismic noise observed at over 250 broad-band stations from the EarthScope...

Imaging dispersion curves of surface waves on multi‐channel record

Choon Byong Park, Richard D. Miller, Jianghai Xia · 1998 · 762 citations

PreviousNext No AccessSEG Technical Program Expanded Abstracts 1998Imaging dispersion curves of surface waves on multi‐channel recordAuthors: Choon Byong ParkRichard D. MillerJianghai XiaChoon Byon...

Passive image interferometry and seasonal variations of seismic velocities at Merapi Volcano, Indonesia

Christoph Sens‐Schönfelder, Ulrich Wegler · 2006 · Geophysical Research Letters · 720 citations

We propose passive image interferometry as a technique for seismology that allows to continuously monitor small temporal changes of seismic velocities in the subsurface. The technique is independen...

Dynamic strain determination using fibre-optic cables allows imaging of seismological and structural features

Philippe Jousset, Thomas Reinsch, T. Ryberg et al. · 2018 · Nature Communications · 577 citations

Reading Guide

Foundational Papers

Start with Shapiro et al. (2005) for core ambient noise tomography proof in California; Bensen et al. (2007) for standardized processing; Yao et al. (2006) for phase velocity mapping techniques.

Recent Advances

Study Lin et al. (2008) for Rayleigh/Love US tomography; Sens-Schönfelder (2006) for temporal velocity monitoring; Ajo-Franklin (2019) for DAS extensions to dark fiber.

Core Methods

Cross-correlation of vertical components yields EGFs (Shapiro et al., 2005); automated dispersion picking (Bensen et al., 2007); two-station phase analysis (Yao et al., 2006); eikonal inversion for 3D structure (Lin et al., 2008).

How PapersFlow Helps You Research Surface Wave Tomography from Microseisms

Discover & Search

Research Agent uses searchPapers with query 'surface wave tomography microseisms' to retrieve Bensen et al. (2007), then citationGraph reveals 2345 citing works including Lin et al. (2008), and findSimilarPapers expands to Yao et al. (2006) for SE Tibet applications.

Analyze & Verify

Analysis Agent applies readPaperContent on Shapiro et al. (2005) to extract group-speed tomography details, verifyResponse with CoVe checks inversion claims against raw dispersion data, and runPythonAnalysis fits NumPy dispersion curves with GRADE scoring for statistical reliability.

Synthesize & Write

Synthesis Agent detects gaps in short-period coverage across Bensen (2007) and Yao (2006), flags contradictions in noise assumptions; Writing Agent uses latexEditText for model sections, latexSyncCitations integrates 10 foundational papers, and latexCompile generates tomography reports with exportMermaid for velocity inversion flowcharts.

Use Cases

"Plot dispersion curves from Bensen 2007 microseism data and compare to Shapiro 2005 California paths"

Research Agent → searchPapers → Analysis Agent → readPaperContent + runPythonAnalysis (NumPy curve fitting, matplotlib plots) → output: overlaid dispersion plots with RMSE statistics.

"Write LaTeX section on Rayleigh-Love tomography workflow citing Lin 2008 and Yao 2006"

Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations + latexCompile → output: compiled LaTeX with synced citations and phase velocity maps.

"Find GitHub repos implementing ambient noise cross-correlation from Park 1998 or Sens-Schönfelder 2006"

Research Agent → paperExtractUrls on Park (1998) → Code Discovery → paperFindGithubRepo + githubRepoInspect → output: verified MSNoise or ObsPy fork with dispersion imaging code.

Automated Workflows

Deep Research workflow scans 50+ microseism papers starting with citationGraph on Shapiro (2005), producing structured reports on phase velocity evolution. DeepScan applies 7-step CoVe to verify Bensen (2007) processing against Yao (2006) Tibet data. Theorizer generates hypotheses on microseism directionality biases from Stehly (2006) correlations.

Try Doxa for Surface Wave Tomography from Microseisms Research

Frequently Asked Questions

What defines surface wave tomography from microseisms?

It extracts Rayleigh and Love dispersion from ambient noise cross-correlations, primarily ocean microseisms, for velocity inversion (Shapiro et al., 2005; Bensen et al., 2007).

What are core methods?

Methods include 1-bit normalized cross-correlation for empirical Green's functions, phase/group velocity measurement, and eikonal tomography (Bensen et al., 2007; Yao et al., 2006).

What are key papers?

Bensen et al. (2007, 2345 citations) for processing; Shapiro et al. (2005, 2222 citations) for high-res California imaging; Lin et al. (2008, 799 citations) for US Rayleigh/Love maps.