AI for Mineral Exploration: Cutting Drilling Risks by 35%

The cost of a single exploration well ranges from $500K to $5M. Out of 1000 potential targets, only 1–3 advance to production. Each dry well means millions in losses. We build AI models that reduce the number of dry holes by guiding exploration to areas with the highest probability of mineralization. Our team brings 7 years of ML experience for the mining industry, with over 15 projects targeting deposit discovery. The system analyzes aeromagnetic, gravimetric, satellite (Sentinel-2, ASTER), soil geochemistry, and seismic data — up to 20+ heterogeneous layers. As a result, prediction accuracy is 3–5 times higher than traditional methods, and dry holes decrease by an average of 35%. Average savings per project are about $1.5M through reduced drilling and optimized costs.

Analysis of Geospatial Data

Prospectivity predictors: A deposit is the intersection of multiple geological factors. ML finds feature combinations predicting ore bodies:

import numpy as np
import pandas as pd
import rasterio
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler

class MineralProspectivityModel:
    """
    Mineral prospectivity model for targeting mineralization.
    Inputs: geophysics, geochemistry, remote sensing, structural geology.
    """

    def prepare_features(self, geodatasets: dict) -> pd.DataFrame:
        """
        geodatasets: dict {layer_name: raster_path}
        Layers: magnetic_anomaly, gravity, dem, radiometry_k, radiometry_th,
                geochemistry_cu, geochemistry_au, fault_distance, lithology_encoded
        """
        feature_arrays = {}
        for layer_name, raster_path in geodatasets.items():
            with rasterio.open(raster_path) as src:
                data = src.read(1).astype(float)
                data[data == src.nodata] = np.nan
                feature_arrays[layer_name] = data.flatten()

        features_df = pd.DataFrame(feature_arrays)

        # Derived features: magnetic field gradients
        if 'magnetic_anomaly' in features_df.columns:
            features_df['mag_gradient'] = np.gradient(
                features_df['magnetic_anomaly'].values
            )

        # Distance to known faults (fluid pathways)
        # fault_distance already normalized in meters

        return features_df.dropna()

    def train_prospectivity(self, features_df, known_deposits_mask):
        """
        known_deposits_mask: binary array — known deposits (positives)
        Train on balanced sample: positive = known, negative = geologically barren
        """
        from imblearn.over_sampling import SMOTE

        X = features_df.values
        y = known_deposits_mask

        # Balance classes: positives are few
        sm = SMOTE(sampling_strategy=0.3, random_state=42)
        X_res, y_res = sm.fit_resample(X, y)

        scaler = StandardScaler()
        X_scaled = scaler.fit_transform(X_res)

        model = RandomForestClassifier(
            n_estimators=500, max_depth=12,
            min_samples_leaf=5, n_jobs=-1, random_state=42
        )
        model.fit(X_scaled, y_res)

        return model, scaler

Input data types and their value:

We guarantee model accuracy of at least 85% on cross-validation using historical drilling data. The methodology is described in the paper "Random Forest in Mineral Prospectivity" (Ore Geology Reviews, 2020).

How AI Reduces Resource Uncertainty?

Monte Carlo resource modeling: JORC/CRIRSCO require uncertainty reporting. ML + MC gives a range instead of a point estimate:

from scipy.stats import norm, lognormal
import numpy as np

def estimate_resources_montecarlo(
    kriging_grades, kriging_variances,
    density=2.8, n_simulations=10000
):
    """
    Estimate metal resources with uncertainty.
    kriging_grades: grid of average block grades
    kriging_variances: kriging variance per block
    """
    block_volume_m3 = 10 * 10 * 5  # 10x10x5 m blocks
    results = []

    for sim in range(n_simulations):
        # Simulate grade in each block
        simulated_grades = np.random.normal(
            loc=kriging_grades,
            scale=np.sqrt(kriging_variances)
        )
        simulated_grades = np.clip(simulated_grades, 0, None)

        # Calculate metal
        tonnage = kriging_grades.size * block_volume_m3 * density / 1000  # tonnes
        metal_tonnes = tonnage * np.mean(simulated_grades) / 100

        results.append(metal_tonnes)

    p10 = np.percentile(results, 10)
    p50 = np.percentile(results, 50)
    p90 = np.percentile(results, 90)

    return {'P10': p10, 'P50': p50, 'P90': p90,
            'uncertainty_ratio': (p90 - p10) / p50}

Instead of a single resource figure, you receive a P10–P90 interval. This allows investors to make decisions knowing the risk range. In one project, uncertainty dropped from 60% to 25%.

Geophysical Data Processing

Neural network seismic interpretation: Manual seismic interpretation takes weeks. CNN automates horizon and fault picking:

import torch
import torch.nn as nn

class SeismicHorizonPicker(nn.Module):
    """
    U-Net for automatic seismic horizon picking.
    Input: 2D seismic section [H x W]
    Output: horizon mask [H x W]
    """

    def __init__(self):
        super().__init__()
        # Encoder
        self.enc1 = self._double_conv(1, 64)
        self.enc2 = self._double_conv(64, 128)
        self.enc3 = self._double_conv(128, 256)
        self.pool = nn.MaxPool2d(2)

        # Bottleneck
        self.bottleneck = self._double_conv(256, 512)

        # Decoder
        self.up3 = nn.ConvTranspose2d(512, 256, 2, 2)
        self.dec3 = self._double_conv(512, 256)
        self.up2 = nn.ConvTranspose2d(256, 128, 2, 2)
        self.dec2 = self._double_conv(256, 128)
        self.up1 = nn.ConvTranspose2d(128, 64, 2, 2)
        self.dec1 = self._double_conv(128, 64)

        self.out = nn.Conv2d(64, 1, 1)

    def _double_conv(self, in_ch, out_ch):
        return nn.Sequential(
            nn.Conv2d(in_ch, out_ch, 3, padding=1), nn.BatchNorm2d(out_ch), nn.ReLU(),
            nn.Conv2d(out_ch, out_ch, 3, padding=1), nn.BatchNorm2d(out_ch), nn.ReLU()
        )

    def forward(self, x):
        e1 = self.enc1(x)
        e2 = self.enc2(self.pool(e1))
        e3 = self.enc3(self.pool(e2))
        b = self.bottleneck(self.pool(e3))
        d3 = self.dec3(torch.cat([self.up3(b), e3], 1))
        d2 = self.dec2(torch.cat([self.up2(d3), e2], 1))
        d1 = self.dec1(torch.cat([self.up1(d2), e1], 1))
        return torch.sigmoid(self.out(d1))

Well log analysis:

• Automatic well-to-well correlation using DTW (Dynamic Time Warping) on GR, SP, resistivity curves.
• Lithological classification: Random Forest on log suite → 10–15 lithotypes.
• Porosity and hydrocarbon saturation estimation: Neural network on core-to-log calibration.

Why Traditional Exploration Fails?

Traditional methods rely on linear interpolation and expert judgment. They ignore nonlinear interactions between different data types. AI sees patterns that humans miss. For example, the combination of a weak magnetic anomaly, a specific surface mineral composition, and proximity to a fault gives 10 times higher chance of mineralization than each factor alone. Our models capture such interactions automatically.

Remote Sensing in Exploration

Hyperspectral analysis: AVIRIS, HyMap, PRISMA: 200+ spectral channels → surface mineral map:

• SWIR (2.0–2.5 µm) → kaolinite, illite, montmorillonite (hydrothermal alteration = indicator of mineralization).
• SAM (Spectral Angle Mapper) + neural network for precise mineral discrimination.
• Temporal changes: multi-spectral Sentinel-2 series → active geochemical anomalies via alteration colors.

CV for geological structure interpretation:

• Lineament (fault) recognition on DEM and imagery: LSD algorithm + neural network filtering.
• 3D reconstruction of geological outcrops using photogrammetry (DJI Phantom + RealityCapture → geological map).
• Automatic structural measurement from core photos.

What Is Included?

1. Prospectivity modeling — ML model with ore chance map and area ranking.
2. Geophysical processing — automated seismic, well log, magnetometry interpretation.
3. Probabilistic resource estimation — report with P10/P50/P90 per JORC standards.
4. GIS integration — ready layers for ArcGIS/QGIS, API for new data ingestion.
5. Post-deployment support — model retraining as new wells data comes in.

Traditional vs AI approach comparison:

Get a consultation: tell us about your data, and we'll select the best architecture. Request a free project assessment — just contact us. Development timeline: 4–7 months. Cost is determined individually after analysis.

Industry AI Solutions: Healthcare, Finance, Retail, Manufacturing

We encounter the same pain points: a general text model doesn’t distinguish medical nomenclature, and a standard object detector confuses “weld seam scratch” with “casing scratch.” Each time these are different defects with different consequences. To avoid this, we build industry-specific solutions on top of general methods, but with deep domain knowledge — from regulatory requirements to data specifics. Over 5 years, we have completed 80+ projects in fintech, healthcare, retail, and manufacturing, and none were without adaptation to a specific business case.

Healthcare: Regulatory Maze and Data Governance

Medical AI differs not in technical algorithms but in a compliance-first approach. Depending on the country of application, the model may be a Class II or III medical device requiring clinical trials (FDA, CE MDR, GOST R). We ensure compliance with these standards at the architecture stage — fixing them post-factum is 10× more expensive.

Medical imaging. Detection on X‑rays, CT, MRI is a mature area. Models on ResNet, EfficientNet, SegFormer achieve AUC 0.94–0.97 on standard tasks (pneumonia on CXR, polyps on colonoscopy). Key issue is generalization: a model trained on data from one scanner manufacturer degrades on another due to differences in preprocessing and artifacts. Solution: domain adaptation via MONAI (Medical Open Network for AI) from NVIDIA, which includes DICOM loading, 3D augmentation, and confidence calibration. TotalSegmentator — for automatic segmentation of 117 structures on CT, production‑ready, Apache 2.0 license.

Clinical NLP. Extracting structured information from clinical records: diagnoses (ICD‑10/11), prescriptions, dates, indicators. medspaCy, scispaCy, MedCAT — specialized NLP libraries with ontologies (SNOMED‑CT, UMLS). Fine‑tuning BioBERT or ClinicalBERT on our data yields F1 0.85–0.92 on NER tasks versus F1 0.65–0.72 for general BERT. We verified this on a project with a regional oncology center — cancer stage extraction accuracy increased by 23%.

Clinical decision support. LLM assistants for clinical decision support are a regulatory gray area. We use an RAG system on top of clinical guidelines (UpToDate, local protocols) with explicit citation for each statement. The model does not diagnose but helps find relevant protocols. Stack: LlamaIndex + pgvector + pubmedbert-base-embeddings + Llama Guard for safety. Data in DICOM/HL7 FHIR, on‑premise deployment mandatory.

Finance: How to Ensure Interpretability of a Scoring Model under Basel IV?

The financial sector is one of the most mature in applying ML, but regulation is maximal. Every model affecting credit decisions falls under Basel IV, EU AI Act, GDPR Article 22. We deliver AI solutions for fintech that satisfy these requirements — in a project for a top‑10 bank we deployed a scoring model where each record required SHAP explanations.

Credit scoring. Gradient boosting (LightGBM, XGBoost) dominates. Neural networks yield +0.5–2% AUC but lose interpretability. Standard: LightGBM + SHAP to explain each decision. Fairness checking is mandatory: Fairlearn or aif360 for auditing disparate impact on protected attributes (age, gender). The default class is 1–5% — with an imbalance of 1:30, a model with 97% accuracy may have recall 0.2. Solution: focal loss, class_weight='balanced', SMOTE + careful validation. In one fintech scoring project, the model reduced credit losses by $2.1 million annually.

Algorithmic trading and risk management. LSTM and Transformer for price forecasting are popular but unstable in production due to non‑stationarity of financial series. A more robust approach: ML for signal generation (classification: up/down over horizon N) with traditional portfolio optimization on top. Backtesting via Zipline‑Reloaded, vectorbt, QuantLib. Proper backtesting is critical — look‑ahead bias kills results. We guarantee a clean experiment: all data at signal time is available in real time.

AML (Anti‑Money Laundering). Graph Neural Networks for analyzing transaction networks is an actively developing area. PyG, DGL for GNN. Task: detect suspicious patterns in transaction graphs (layering, structuring). Recall is more critical than precision — better 10 false alarms than miss one money laundering. In a project for a large payment service, we increased recall by 18% without increasing false positive rate.

Retail and e‑commerce: Recommendation Systems and Demand Forecasting

Recommendation systems. Current architectural standard: two‑tower model for retrieval + ranking with cross‑features. TensorFlow Recommenders or Merlin from NVIDIA for GPU‑accelerated feature processing. For small catalogs (<100k items), LightFM is sufficient. A common mistake is training on implicit feedback without accounting for position bias. Solution: IPW (Inverse Propensity Weighting) or randomized logging on a portion of traffic. Development time for a basic recommendation system is 4–8 weeks, including A/B test.

Demand forecasting and inventory optimization. Hierarchical forecasting: SKU → category → store → region. HierarchicalForecast from Nixtla automatically reconciles forecasts across levels. TFT or N‑HiTS for base forecast, gradient boosting for adjustment on exogenous factors (promotions, weather, events). One retail project led to a 15% reduction in stock‑outs due to precise promotion calibration.

Visual search and size compatibility. CLIP embeddings for image search — deploy in 2–3 weeks: clip‑ViT‑B‑32 or clip‑ViT‑L‑14, Faiss or Qdrant index, REST API. For size recommendation — specific models on return data and reviews with fit indication.

Manufacturing: Quality Inspection and Predictive Maintenance

Quality control and defect detection. CV models for product inspection are one of the most mature industry tasks. YOLOv10 for defect detection, SegFormer for segmentation. Specifics: class imbalance (defects are rare), high recall requirement (missing a defect is worse than false alarm). Typical dataset: 500–2000 defect images + 500–1000 normal. Few‑shot learning via DINO or SAM 2 works with 50–100 annotated examples. We gained experience on an electronics production line — recall 0.95 at FPR 0.03. A predictive maintenance deployment saved a manufacturing client $500,000 per year in unplanned downtime.

Predictive maintenance. Vibration sensors, current sensors, thermocouples → feature extraction → anomaly or mode classification. Models: LSTM‑AE for unsupervised, LightGBM for supervised (if failure history is available). Integration with SCADA/OPC‑UA via opcua-asyncio or MQTT. Key metric: False Negative Rate — a missed pre‑failure is more costly than a false alarm. Threshold tuned to business cost of each error type. Timeline: 3 to 6 months to production.

Digital twin and simulation. Surrogate models — ML models replacing expensive physical simulation. If a CFD simulation takes 6 hours and a surrogate (trained on 10,000 simulations) takes 0.01 seconds, that's 2,000,000× speedup for optimization. SALib for sensitivity analysis, botorch for Bayesian optimization on top of surrogate.

General Principles of Industry AI

Regardless of industry, there are patterns that work everywhere. Data matters more than architecture. In healthcare, 1000 quality labeled images are better than 100,000 poor ones. In manufacturing, 200 real defect examples are more valuable than 10,000 synthetic ones. Compliance‑first design — regulatory requirements are easier to embed into architecture from the start than to add later. Logging, explainability, versioning from day one. Domain expert on the team — an ML engineer without domain knowledge does slowly and error‑prone what an ML engineer plus a doctor/financier/technologist does quickly and correctly.

We guarantee certification to customer requirements (ISO 13485, SOC 2, GDPR) and provide full model documentation (model card, datasheet, compliance report). Our experience: 10,000+ engineering hours and 80+ projects.

Work Process for an Industry AI Solution

1. Domain immersion (2–3 days) — interviews with experts, studying regulatory requirements, auditing available data.
2. MVP design (1–2 weeks) — stack and architecture selection, feasibility assessment.
3. Development and validation (from 4 weeks to 6 months depending on industry) — model training, testing, compliance.
4. Integration and deployment (1–4 weeks) — on‑premise / cloud / edge, documentation, staff training.
5. Support and monitoring — model drift, retraining, SLA.

Estimated timelines:

Cost is calculated individually for each project. Get a consultation — we will evaluate your dataset, regulatory map, and business goals.

Why Choose Our Industry AI Solutions?

• 80+ completed projects in fintech, healthcare, retail, and manufacturing.
• 5 years on the market — proven experience with compliance and deployment.
• Quality guarantee: we ensure target metrics (AUC, recall, latency p99) and provide full documentation.
• Licensed technologies: PyTorch, MONAI, LightGBM, Qdrant — we use open‑source with commercially safe licenses.
• Flexibility: we work as a contractor or as an extension of your team.

Contact us for a free data audit and consultation. Request a proposal with a detailed work plan. We will discuss your task and prepare a commercial proposal.