thirdExec/seisground-weights

SeisGround — trained weights

Vision–language weights for seismic fault interpretation: a frozen seismic encoder measures fault geometry (count, location, dip, throw, per-fault mask); a language model then copies those measured numbers into tagged narration through a non-differentiable digit seam, so the text can only state values the vision reader actually measured.

Pipeline: frozen SFM-Base-512 encoder → DETR reader (48 queries, Hungarian, ∅) → digit-copy seam → Qwen2.5-1.5B LM narrator (LoRA).

⚠️ The encoder is NOT in this repo. These weights run on top of the third-party Seismic Foundation Model (ViT-B/16 @512), which we use frozen and unmodified. Download it from the authors: shenghanlin/SeismicFoundationModel (Sheng et al., arXiv:2309.02791) and place it at hybrid/checkpoints/SFM-Base-512.pth.

Code, scripts, and setup: github.com/Thirdbot/ModelV2 (see SETUP.md).

What each file is — and which result it backs

The repo mirrors the code's hybrid/checkpoints/ tree, so hf download … --local-dir hybrid/checkpoints reconstructs a runnable layout with no path surgery.

Main pipeline (synthetic training → deployable narration)

File	What it is	Backs
reader.pt	Synthetic base reader — DETR set-prediction that measures fault count/location/dip/throw + per-fault mask.	Synthetic vision table (pooled IoU 0.230, det F1 0.433, class 0.93, dip 28.06° vs const 32.19°, throw 53.60 vs 56.23 ms)
stage2_grounding.pt	Grounding-stage LM adapter — teaches the narrator to read the digit seam.	(pipeline component)
stage3_narrator.pt	Deployed narrator (grounding+fuse LoRA) — free-generates tagged narration that copies the measured facts.	Faithfulness table (copy 0.77 GT-injected / 0.89 reader-piped; CHAIR$_I$ 0.185; dip-swap 16/16)
stage3_answer.pt	Answer-fold narrator variant (the </think>→<answer> fold).	(ablation / alternate narration)
stage1_e12dcce6ed/	Geology LoRA adapter — Qwen2.5-1.5B-Instruct, r16/α16, lr 2e-5, 4-bit, trained on GeoGPT-CoT-QA. Frozen thereafter; supplies the <think>/<answer> reasoning scaffold.	Geology stage-1

Real-field A/B + ratio-selection track (ab_experiment/)

Real adapters (r32, base frozen, zero-init residual) trained on top of a fresh synthetic base. See ab_experiment/PROVENANCE.md for the full identity config.

File	What it is	Backs
ab_experiment/reader_synth.pt	Synthetic reader base for this track (the frozen substrate the real adapters sit on).	—
ab_experiment/B_joint.pt	DEPLOYED model. 1:1:1 joint round-robin real adapter with real dip/throw supervision (TRAIN_MEASURE=1, data-gated to Smeaheia).	A/B table (B row) + all deployed inference
ab_experiment/A_joint.pt	Control — same 1:1:1, no real attribute supervision (TRAIN_MEASURE=0). Identical to B except the measurement heads never see real dip/throw.	A/B table (A row)
ab_experiment/ratio1.pt, ratio2.pt	The two non-selected mixing ratios (4:3:3 and 8:1:1) from ratio selection. 1:1:1 won on held-out val (mean det F1 0.363 vs 0.333 vs 0.166) and became A/B.	Ratio-selection table
ab_experiment/alone_cracks.pt, alone_smeaheia.pt	Single-survey "alone" baselines (no joint mixing).	Zero-shot / alone / joint table

If you release only a subset, keep the rows for the files you actually upload. A_joint is the paper's control; deployment needs only B_joint (+ reader, stage3_narrator, the geology adapter, and the SFM link).

How to use

# 1) get the weights (mirrors hybrid/checkpoints/)
hf download thirdExec/seisground-weights --local-dir hybrid/checkpoints
# 2) add the frozen SFM encoder (third-party — see the note above) → hybrid/checkpoints/SFM-Base-512.pth
# 3) run inference (from the ModelV2 repo)
DATASET=synthetic python -m hybrid.eval.inference                                        # in-distribution
DATASET=thebe READER=hybrid/checkpoints/ab_experiment/B_joint.pt python -m hybrid.eval.inference   # a real survey
IMAGE=path/to/section.png READER=hybrid/checkpoints/ab_experiment/B_joint.pt python -m hybrid.infer  # your own image

The narrator defaults to stage3_narrator.pt; override with CKPT= / NARRATOR=.

Training data

Dataset	Role	Source
Synthetic seismic VQA (1,261 scenes / 1,320 regions)	full supervision — masks, attributes, narration	thirdExec/synthetic-seismic-vlm
GeoGPT-CoT-QA	geology reasoning scaffold (stage 1)	GeoGPT-Research-Project/GeoGPT-CoT-QA
Thebe (37,796 patches)	real fault masks	Kaggle mycarta/thebe-fault-patches-256 / Harvard Dataverse DOI 10.7910/DVN/YBYGBK
CRACKS (397)	real fault masks	gOLIVES/CRACKS
Smeaheia (430, GN1101 3-D cube)	real masks + independent dip/throw GT	co2datashare.org

Identity config (defines the numbers)

Encoder SFM-Base-512 frozen (d768/depth12/patch16/img512/tile512/grid32) · loss Focal-Tversky α0.4/β0.6/γ1.0, POS_WEIGHT_MAX 15, clDice 1.0 · N_QUERIES 48 · DET_TAU 0.1 · DET_THRESH 0.9 · DILATE_R 0 (pure masks, 16px floor) · geology LoRA r16/α16 lr2e-5 4-bit · reader lr1e-4 (encoder frozen) · grounding lr1e-4 · fold lr2e-5 · real adapter r32 lr1e-4 · ACTIVE_CLASSES=fault · TOTAL_STEPS 100000 (1 round-robin epoch) · single seed 42.

Trained on RTX 3090 Ti 24 GB / 62 GB RAM; Python 3.13, CUDA 12.8, torch 2.10, transformers 5.5, peft 0.19, trl 0.21. The method is VRAM-agnostic — only compute-layout knobs (batch, grad-ckpt, cache cap) change to fit smaller GPUs; the identity config, and therefore the result, does not move.

Provenance & honest limits

• Single run, seed pinned (SEED=42 in the training entry points) — not multi-seed averaged. Small-margin metrics (CRACKS det F1, dip parity, low-count Smeaheia) carry run variance; CUDA kernels are not bit-deterministic.
• Masks are pure (DILATE_R 0). Segmentation numbers are at zero dilation; they are not comparable to any dilated (DILATE_R > 0) evaluation.
• Two Thebe builds are not comparable. These A/B weights use the 37,796-patch Kaggle build (pure masks). Do not compare their mask numbers to any older dilated-mask Thebe result.
• The narrator copies measured facts (digit seam); it does not regress numbers and cannot state a value the reader did not measure. Out-of-domain prompts are answered against the on-screen seismic scene, not as a general chatbot.

License

Weights released under CC-BY-4.0 (confirm/adjust before publishing). Derived from Qwen2.5-1.5B-Instruct (Apache-2.0) via LoRA; respect the upstream licenses of the SFM encoder and each training dataset. The SFM encoder itself is not redistributed here — obtain it from its original release.

Citation

If you use these weights, cite the paper (see the GitHub repo for the current reference) and the underlying assets: the SFM encoder (Sheng et al., 2023), GeoGPT-CoT-QA, Thebe, CRACKS, and Smeaheia.