pycsamt.agents.inv3d_agent#

pycsamt.agents.inv3d_agent#

Inv3DAgent — Graph-convolutional 3-D MT spatial inversion.

Wraps GCNInverter3D:

  • Represents the survey network as a spatial graph whose edges connect stations within a configurable radius. Spectral GCN message-passing propagates information between neighbouring stations so the resulting 3-D resistivity volume is spatially coherent — artefacts from station-by-station 1-D inversion are suppressed.

  • Trains on synthetic 3-D profiles assembled by tiling independent 1-D forward models across a virtual station grid, then predicts on the observed Sites dataset.

  • Outputs log₁₀ρ per depth layer and log₁₀h per interface for every station, giving a full layered earth model that can be gridded into a 3-D resistivity volume.

  • Optionally runs MC-dropout uncertainty (n_mc stochastic passes) to produce depth-resolved confidence maps alongside the main prediction.

Requires PyTorch or TensorFlow.

Architecture#

GCNNet — spectral graph convolutional network (Kipf & Welling 2017). No external graph library is required.

References

Classes

Inv3DAgent(*[, api_key, model, ...])

3-D MT profile inversion using a graph-convolutional network (GCN).
class pycsamt.agents.inv3d_agent.Inv3DAgent(*, api_key=None, model=None, llm_provider='claude', n_layers=5, n_freqs=32, n_train_profiles=150, epochs=30, radius=5000.0, hidden=(256, 128, 64), dropout=0.1, n_mc=20)[source]#

Bases: BaseAgent

3-D MT profile inversion using a graph-convolutional network (GCN).

Parameters:

  • api_key (str)

  • model (str)

  • llm_provider (str)

  • n_layers (int) – Number of depth layers per station (default 5).

  • n_freqs (int) – Number of frequencies used for feature extraction (default 32).

  • n_train_profiles (int) – Number of synthetic 3-D training profiles (default 150).

  • epochs (int) – Training epochs (default 30).

  • radius (float) – Maximum inter-station edge distance in metres for the adjacency graph (default 5 000 m). Stations farther apart than radius are disconnected in the graph.

  • hidden (tuple of int) – GCN hidden-layer sizes (default (256, 128, 64)).

  • dropout (float) – Dropout probability (default 0.1); also used for MC uncertainty.

  • n_mc (int) – Number of Monte-Carlo dropout passes for uncertainty estimation. Set to 0 to skip uncertainty (faster, default 20).

  • keys (Output data)

  • ----------

  • path (sites /)

  • coords (ndarray (n_stations, 2), optional — station (x, y) in metres.) – Auto-extracted from EDI lat/lon when absent.

  • adjacency (ndarray (n_stations, n_stations), optional — pre-computed) – normalised adjacency; overrides radius when supplied.

  • output_dir (str, optional)

  • period_range ([T_min, T_max], optional)

  • keys

  • ----------------

  • (n_sta (adjacency ndarray)

  • log₁₀ρ (n_layers) —)

  • (n_sta

  • (metres) (n_layers-1) — log₁₀h)

  • (n_sta

  • std (n_layers) or None — MC-dropout)

  • (km) (depths_km ndarray — depth axis at station midpoints)

  • list[str] (station_names)

  • (n_sta

  • metres (2) —)

  • (n_sta

  • n_sta)

  • float (rms_global)

  • GCNInverter3D (inverter)

  • dict (figure_paths)

  • dict

Examples

>>> agent = Inv3DAgent(n_layers=5, epochs=20, n_mc=10)
>>> result = agent.execute({
...     "path":       "/data/WILLY_EDIs",
...     "output_dir": "/out/inv3d",
... })
>>> result["rms_global"]
0.28
SYSTEM_PROMPT: str = 'You are an expert in 3-D MT inversion using graph-convolutional deep learning.\nGiven a GCN-based 3-D inversion result, write 4-5 sentences that:\n1. Describe the survey geometry (station count, spatial extent, adjacency radius).\n2. Interpret the dominant 3-D resistivity structures and their spatial continuity.\n3. Assess prediction quality (RMS, depth range) relative to station spacing.\n4. Compare the GCN spatial result to independent 1-D predictions where possible.\n5. Recommend geological follow-up and areas with highest uncertainty.\nReply in plain scientific English.\n'#

Override in subclasses to give the LLM its domain expertise.

execute(input_data)[source]#

Run this agent on input_data and return an AgentResult.

Subclasses must implement this method. The contract:

  • Reset self._last_cost = 0.0 at the top.

  • Record wall-clock time with t0 = time.time().

  • Return AgentResult(elapsed_seconds=time.time()-t0, cost_estimate_usd=self._last_cost, ...).

Parameters:

input_data (dict[str, Any])

Return type:

AgentResult