ERT Field Data Processing with RESIPY

This example demonstrates how to load, quality control, and export field ERT data using PyHydroGeophysX’s data_processing module with RESIPY integration.

The workflow includes: 1. Loading ERT data from commercial instruments (E4D example) 2. Automatic quality control with diagnostic plots 3. Export to pyGIMLi/BERT format for inversion

Supports 14+ commercial instruments including E4D, Syscal, ABEM-Lund, Sting, ARES, Protocol DC/IP, BERT, DAS-1, Electra, and more.

Import Required Modules

Import the ERT data processing functions and data structures.

import os
import sys

# Setup package path for development
try:
    # For regular Python scripts
    current_dir = os.path.dirname(os.path.abspath(__file__))
except NameError:
    # For Jupyter notebooks
    current_dir = os.getcwd()

# Add the parent directory to Python path
parent_dir = os.path.dirname(current_dir)
if parent_dir not in sys.path:
    sys.path.append(parent_dir)

from PyHydroGeophysX.data_processing.ert_data_agent import (
    load_ert_resipy, qc_and_visualize, export_for_inversion, LocalRef
)

Load ERT Field Data

Load ERT data from E4D instrument format. The function automatically handles: - Coordinate reference systems (local, projected, geographic) - Windows/OneDrive permission issues - Flexible column name detection - Data validation and error checking

ert = load_ert_resipy(
    project_dir="data/ERT/E4D",
    data_file="data/ERT/E4D/2021-10-08_1400.ohm",
    instrument="E4D",
    crs="local",
    local_ref=LocalRef(origin_x=0.0, origin_y=0.0, azimuth_deg=90.0)
)

print(f"Loaded {len(ert.electrodes)} electrodes")
print(f"Loaded {len(ert.observations)} measurements")
print(f"Coordinate system: {ert.crs}")

Quality Control and Visualization

Generate diagnostic plots and statistical summaries: - Apparent resistivity histogram (log-scale) - Pseudosection visualization - Summary statistics (JSON)

artifacts = qc_and_visualize(ert, outdir="results/ert_data_process")

print("\nGenerated QC artifacts:")
for artifact_type, filepath in artifacts.items():
    print(f"  {artifact_type}: {filepath}")

Export for Inversion

Export to pyGIMLi/BERT format with: - Electrode coordinates (x, y, z) - Measurement data (13 columns including geometric factors, resistance, validity) - Compatible with pyGIMLi and BERT inversion codes

bert_path = export_for_inversion(
    ert,
    outdir="results/ert_data_process",
    fmt="pgimli",
    filename="bert_data.dat"
)

print(f"\nExported to pyGIMLi/BERT format: {bert_path}")
print("\nReady for inversion workflow!")

Alternative: UTM Coordinates

For data with projected coordinates (e.g., UTM):

ert_utm = load_ert_resipy(
    project_dir="data/ERT/Syscal",
    data_file="data/ERT/Syscal/survey.txt",
    instrument="Syscal",
    crs="EPSG:32615",  # UTM Zone 15N
    epsg=32615
)

Time-Lapse Processing

For time-lapse monitoring, process each timestep separately:

from pathlib import Path
from datetime import datetime

data_files = [
    "2021-10-08_1400.ohm",
    "2021-10-09_1400.ohm",
    "2021-10-10_1400.ohm",
]

for data_file in data_files:
    ert = load_ert_resipy(
        project_dir="data/ERT/E4D",
        data_file=f"data/ERT/E4D/{data_file}",
        instrument="E4D",
        crs="local",
        local_ref=LocalRef(0, 0, 90)
    )

    timestamp = datetime.strptime(Path(data_file).stem, "%Y-%m-%d_%H%M")
    bert_path = export_for_inversion(
        ert,
        outdir="results/time_lapse",
        filename=f"survey_{timestamp.strftime('%Y%m%d_%H%M')}.dat"
    )

Supported Instruments

The following instruments are supported:

• E4D: E4D resistivity format (.ohm)

• Syscal: Iris Instruments Syscal (.txt)

• ABEM-Lund: ABEM and Lund Imaging systems

• Sting: AGI Sting systems (.stg)

• ARES: GF Instruments ARES systems (.ares)

• Protocol DC/IP: Iris Instruments Protocol (.pro)

• BERT: pyGIMLi/BERT format (.dat)

• DAS-1: DAS-1 systems

• Electra: Electra systems

• ResInv: ResInv format (.inv)

• PRIME/RESIMGR: Prime/Resimgr format

• Lippmann: Lippmann systems

• Custom: User-defined formats

• Merged: Combined datasets

For complete documentation, see: https://geohang.github.io/PyHydroGeophysX/api/data_processing.html