Earthquake b-value calculator (Gutenberg-Richter)

Load a CSV or TSV catalog, build the cumulative frequency-magnitude distribution, and estimate b and a values.

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Catalog input

Ready
Number of rows read
Number of valid events
Number of events after filtering
Number of cases above Mc N
detection column

Filter (optional)

Estimated settings

Estimation method

Results

b value
a value
95% confidence interval (b)

Cumulative FMD graph

FMD (Frequency-Magnitude Distribution)

M Number of cases Cumulative (more than M) log10(cumulative)

Assumptions and limits

FAQ

What does a large or small b value mean?

A larger b value means small earthquakes dominate more strongly than large earthquakes. Interpretation still depends on data quality and Mc.

How should I choose Mc?

Catalog completeness changes with region, period, and observation network. This MVP does not auto-estimate Mc, so set it manually from your analysis context.

Can I use these results for prediction?

Use the results as exploratory evidence only. They are not sufficient by themselves for forecasting or operational disaster decisions.

What should I do first on this page?

Start with the minimum required inputs or the first action shown near the primary button. Keep optional settings at defaults for a baseline run, then change one setting at a time so you can explain what caused each output change.

Why does this page differ from another tool?

Different pages often use different defaults, units, rounding rules, or assumptions. Align those settings before comparing outputs. If differences remain, compare each intermediate step rather than only the final number.

How to use Earthquake b-value calculator (Gutenberg-Richter) effectively

What this calculator does

This page is for estimating outcomes by changing inputs in one controlled workflow. The model keeps your focus on variables, not output shape. Start with stable assumptions, then test sensitivity by changing one key input at a time to observe directional impact.

Input meaning and unit policy

Each input has an expected unit and a typical range. For reliable interpretation, check whether you are using the same unit system, period, and base assumptions across all runs. Unit mismatch is the most common source of unexpected drift in numeric results.

Use-case sequence

A practical sequence is: first run with defaults, then create a baseline log, then run one alternative scenario, and finally compare only the changed output metric. This sequence reduces cognitive load and prevents false pattern recognition in early experiments.

Common mistakes to avoid

Avoid changing too many variables at once, mixing incompatible data sources, and interpreting a one-time output without checking robustness. A single contradictory input can flip conclusions, so keep each experiment minimal and document assumptions as part of your note.

Interpretation guidance

Review both magnitude and direction. Direction tells you whether a strategy moves outcomes in the desired direction, while magnitude helps you judge practicality. If both agree, you can proceed; if not, rebuild the baseline and verify constraints before deciding.