Microseismic Fno Eval

---
name: microseismic-fno-eval
description: Evaluates a lightweight Fourier Neural Operator model's ability to classify microseismic events versus noise in seismic waveforms. It probes resolution-invariant signal processing, cross-domain generalization, and real-time detection efficiency under varying signal-to-noise ratios. Use when the user wants to benchmark on STEAD, Microseismic dataset, or asks about evaluating this task. Reports F1 score.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2512.07425
  bibtex_key: [REDACTED]
  confidence: high
---

# microseismic-fno-eval

> Microseismic event classification with a lightweight Fourier Neural Operator model — Abdullin et al. (2025) (arXiv:2512.07425, 2025)

## What this evaluates

Evaluates a lightweight Fourier Neural Operator model's ability to classify microseismic events versus noise in seismic waveforms. It probes resolution-invariant signal processing, cross-domain generalization, and real-time detection efficiency under varying signal-to-noise ratios.

## Datasets

- **STEAD** — total 2000; splits: test (2000)
- **Microseismic dataset** — total 1000; splits: test (1000)

## Metrics

- `F1 score` **(primary)** — range: [0, 1]
  - F1 = 2 * (Precision * Recall) / (Precision + Recall), where Precision = TP/(TP+FP) and Recall = TP/(TP+FN). Computed after selecting an optimal probability threshold.
- `Accuracy` — range: [0, 1]
  - Fraction of correctly classified samples (TP+TN) out of total samples.
- `Precision` — range: [0, 1]
  - Fraction of true positives among all positive predictions (TP/(TP+FP)).
- `Recall` — range: [0, 1]
  - Fraction of true positives among all actual positives (TP/(TP+FN)).

## Input / output format

**Input**: Single-station seismic waveform traces (signal or noise samples) processed as 1D time-series inputs to the FNO model.

**Output**: Binary classification label (event/signal vs. noise/false event) or a continuous probability score for event detection.

## Scoring recipe

```python
def compute_f1(tp, fp, fn):
    precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
    recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
    if (precision + recall) == 0:
        return 0.0
    return 2 * (precision * recall) / (precision + recall)

# Apply optimal probability threshold to convert model scores to binary predictions
# Then compute TP, FP, FN against gold labels
# Return compute_f1(tp, fp, fn)
```

## Common pitfalls

- Threshold selection heavily influences F1 scores; the paper emphasizes finding an optimal probability threshold rather than using a fixed 0.5 cutoff.
- Incomplete phase annotations (missing P- or S-waves) in the microseismic dataset degrade performance for models that require both phase types for detection.
- Cross-domain generalization varies significantly due to differences in SNR, data collection standards, and annotation methods across datasets, making direct comparisons sensitive to dataset composition.

## Evidence (verbatim from paper)

> The model's performance was measured by metrics such as accuracy, precision, recall, and F1 score. The achieved accuracy on the test set was 95% , precision 94% , recall 96% , and F1 score 95% .

## Citation

```bibtex
@misc{abdullin2025microseismic,
  title={Microseismic event classification with a lightweight Fourier Neural Operator model},
  author={Abdullin et al. (2025)},
  year={2025},
  note={arXiv:2512.07425}
}
```

- arXiv: 2512.07425