pycsamt.agents.inv2d_agent#

pycsamt.agents.inv2d_agent#

Inv2DAgent — U-Net based 2-D MT profile inversion.

Wraps EMInverter2D:

  • Assembles a 2-D pseudosection image from the observed Sites (station × frequency × impedance component) as the U-Net input.

  • Generates a matching synthetic 2-D training dataset by tiling 1-D forward models into profile-shaped arrays.

  • Trains the U-Net and produces a resistivity section output: (n_depth × n_stations) in log₁₀ Ω·m.

  • Visualises the result with plot_inversion_result_2d().

The U-Net treats the whole profile at once, so it naturally captures lateral continuity — a key advantage over station-by-station 1-D inversion.

Requires PyTorch or TensorFlow.

Classes

Inv2DAgent(*[, api_key, model, ...])

2-D MT profile inversion using a U-Net convolutional architecture.
class pycsamt.agents.inv2d_agent.Inv2DAgent(*, api_key=None, model=None, llm_provider='claude', n_depth=40, n_freqs=32, n_components=4, arch='unet', n_train_profiles=200, n_stations_per_profile=20, epochs=30)[source]#

Bases: BaseAgent

2-D MT profile inversion using a U-Net convolutional architecture.

Parameters:

  • api_key (str)

  • model (str)

  • llm_provider (str)

  • n_depth (int) – Number of depth cells in the output section (default 40).

  • n_freqs (int) – Number of input frequencies (default 32).

  • n_components (int) – Number of impedance components in input (default 4: Re/Im × xy/yx).

  • arch (str) – U-Net variant (default "unet").

  • n_train_profiles (int) – Number of synthetic 2-D profiles for training (default 200).

  • n_stations_per_profile (int) – Stations per synthetic profile (default 20).

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

  • keys (Output data)

  • ----------

  • path (sites /)

  • output_dir (str, optional)

  • period_range ([T_min, T_max], optional)

  • keys

  • ----------------

  • ρ (pred_section ndarray (n_depth × n_stations) — log₁₀)

  • (km) (depths_km ndarray — depth axis)

  • list[str] (station_names)

  • float (rms_global)

  • EMInverter2D (inverter)

  • dict (figure_paths)

  • dict

SYSTEM_PROMPT: str = 'You are an expert in 2-D MT inversion using deep learning (U-Net architecture).\nGiven a 2-D AI inversion result, write 4-5 sentences that:\n1. Describe the input pseudosection geometry (stations × frequencies).\n2. Interpret the dominant structural features in the resistivity section.\n3. Assess lateral continuity and compare to classical smoothness-constrained results.\n4. Identify artefacts or stations with poor convergence.\n5. Recommend follow-up (regularisation, 3-D verification, drilling targets).\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