Quality Assessment

Understand the quality scoring system and how to filter events by quality metrics for reliable seismological analysis.

Overview

The platform implements a comprehensive quality assessment system that evaluates earthquake locations based on multiple factors. Events are graded from A+ (excellent) to F (poor), based on international standards (Bondár, 2004; Bormann, 2012) and regional quantitative assessments of the GeoNet network performance in New Zealand (Warren-Smith et al., 2025). This grading helps you identify the most reliable data for your analysis.

Quick Reference

Grade	Recommended Use
A+/A	Precision studies, location-dependent analysis, publications
B+/B	General research, statistical analysis, most applications
C	Completeness studies, broad pattern analysis
D/F	Avoid for location-dependent analysis; review for errors

Key Principle: Higher grades mean more reliable locations and magnitudes. Always filter by quality appropriate to your research question.

Quality Grades

Grade Definitions

Grade	Score Range	Description
A+	95-100	Excellent quality with minimal uncertainties
A	90-94	Very good quality, reliable parameters
B+	85-89	Good quality, suitable for most analyses
B	80-84	Good quality with moderate uncertainties
C	70-79	Fair quality, use with caution
D	60-69	Poor quality, significant uncertainties
F	0-59	Very poor quality, unreliable parameters

Quality Components

The overall quality score is calculated from four weighted components:

1. Location Quality (35%)

Based on horizontal and vertical uncertainties:

Horizontal Uncertainty:

Excellent: < 1 km
Good: 1-5 km
Fair: 5-10 km
Poor: > 10 km

Vertical Uncertainty (Depth):

Excellent: < 2 km
Good: 2-10 km
Fair: 10-20 km
Poor: > 20 km

2. Network Geometry (25%)

Based on azimuthal gap and station distribution:

Azimuthal Gap:

Excellent: < 90°
Good: 90-180°
Fair: 180-270°
Poor: > 270°

Station Distribution:

Well-distributed stations around event
Adequate depth control
Sufficient near-source stations

These criteria follow the network geometry recommendations of Bondár (2004).

3. Solution Quality (15%)

Based on phase counts and residuals:

Used Phase Count:

Excellent: > 30 phases
Good: 20-30 phases
Fair: 10-20 phases
Poor: < 10 phases

Used Station Count:

Excellent: > 15 stations
Good: 10-15 stations
Fair: 5-10 stations
Poor: < 5 stations

Standard Error (RMS):

Excellent: < 0.3 s
Good: 0.3-0.5 s
Fair: 0.5-1.0 s
Poor: > 1.0 s

These thresholds align with the International Seismological Centre (ISC) standards for high-quality locations (Bondár & Storchak, 2011).

4. Magnitude Quality (15%)

Based on station count and uncertainty:

Magnitude Station Count:

Excellent: > 10 stations
Good: 5-10 stations
Fair: 3-5 stations
Poor: < 3 stations

Magnitude Uncertainty:

Excellent: < 0.1
Good: 0.1-0.2
Fair: 0.2-0.3
Poor: > 0.3

Magnitude type preference (Mw > Ms > mb > ML) is based on the ISC-GEM standards for magnitude reliability (Storchak et al., 2013).

5. Evaluation Status (10%)

Based on the review status of the origin and magnitude solution:

Final / Reviewed: Full points — solution has been manually verified.
Confirmed: Partial points — solution has passed automated checks.
Preliminary: Minimal points — automatic solution, not yet reviewed.
Rejected / Unknown: No points.

Quality Filtering

Filter by Quality Grade

On the Analytics or Catalogues page:

Click Filters
Select Minimum Quality Grade
Choose grade (e.g., B or better)
Apply filter

Common filters:

A or better: High-quality events for precise analysis
B or better: Good quality for most research
C or better: Include fair quality events

Filter by Specific Metrics

Apply custom thresholds:

Location Uncertainty:

Max Horizontal Uncertainty: 5 km
Max Depth Uncertainty: 10 km

Network Geometry:

Max Azimuthal Gap: 180°
Min Station Count: 10

Solution Quality:

Min Phase Count: 15
Max RMS: 0.5 s

Quality Visualization

Quality Score Distribution

View distribution of quality grades:

Bar chart showing event counts per grade
Percentage of high-quality events
Identify quality issues in catalogue

Quality on Map

Events color-coded by quality:

Green: A+, A (excellent/very good)
Yellow: B+, B (good)
Orange: C (fair)
Red: D, F (poor/very poor)

Quality Trends

Analyze quality over time:

Time series of average quality score
Identify periods of poor network coverage
Track improvements in monitoring

Improving Quality

For Data Uploads

To improve quality of uploaded data:

Include uncertainty values
Provide phase counts and station counts
Add azimuthal gap information
Include RMS residuals
Specify evaluation status

For GeoNet Imports

Import high-quality events:

Minimum Magnitude: 3.0
(Larger events generally better located)

Enable “Update Existing Events” to get revised parameters.

For Merged Catalogues

Use quality-aware merging:

Prioritize high-quality catalogues
Use “Most Complete” strategy
Filter low-quality events before merging

Quality Metrics Reference

Azimuthal Gap

Definition: Largest angle between adjacent stations as seen from the epicenter.

Interpretation:

Small gap (< 90°): Good azimuthal coverage
Large gap (> 180°): Poor coverage, elongated uncertainty
Gap > 270°: Very poor coverage, unreliable location

Impact: Affects horizontal location uncertainty and reliability.

Phase Count

Definition: Number of seismic phases (P-waves, S-waves) used in location.

Interpretation:

More phases = better constrained location
Minimum 8 phases for reliable location
20+ phases for high-quality location

Impact: Affects all uncertainty estimates.

Station Count

Definition: Number of seismic stations contributing to location.

Interpretation:

More stations = better coverage
Minimum 8 stations for reliable location in sparse regions (Warren-Smith et al., 2025)
10+ stations for good quality globally
30+ stations for high-precision studies

Impact: Affects network geometry and solution stability.

RMS Residual

Definition: Root mean square of travel time residuals.

Interpretation:

Low RMS (< 0.3 s): Good fit to data
High RMS (> 1.0 s): Poor fit, possible errors

Impact: Indicates solution quality and data consistency.

Best Practices

For Analysis

Use A or B grade events for precise studies
Include C grade for completeness analysis
Exclude D and F grades for location-dependent studies

For Visualization

Color-code by quality on maps
Show uncertainty ellipses for context
Filter low-quality events for clarity

For Export

Include quality grades in exports
Document quality thresholds used
Preserve all quality metrics

For Reporting

Report percentage of high-quality events
Document quality filtering applied
Include quality distribution statistics

Next Steps

Visualization - Visualize quality metrics
Exporting Data - Export with quality filters
Data Validation Guide - Data validation guide

References

The quality assessment metrics and scoring system in this platform are based on established seismological literature:

Warren-Smith, E., et al. (2025). A quantitative assessment of GeoNet earthquake location quality in Aotearoa New Zealand. New Zealand Journal of Geology and Geophysics. (Specific assessment of regional network performance and station thresholds).
Bondár, I. (2004). Epicentre Accuracy Based on Seismic Network Criteria. Geophysical Journal International. (Network geometry and station count requirements).
Bondár, I., & Storchak, D. A. (2011). Improved Location Procedures at the International Seismological Centre. Geophysical Journal International. (Quality scoring and azimuthal gap/RMS thresholds).
Bormann, P., Ed. (2012). IASPEI New Manual of Seismological Observatory Practice (NMSOP-2). Deutsches GeoForschungsZentrum GFZ. (Standardized quality metrics and reporting).
Storchak, D. A., et al. (2013). Public Release of the ISC-GEM Global Instrumental Earthquake Catalogue (1900-2009). Seismological Research Letters. (Parameter selection and magnitude hierarchy).
Woessner, J., et al. (2016). Seismicity Catalogs. Community Online Resource for Statistical Seismicity Analysis (CORSSA). (Best practices for catalog quality assessment).