The mining and natural resources industry operates in some of the world's most challenging environments, generating vast amounts of unstructured data: geological reports, drill logs, safety incident narratives, regulatory filings, equipment sensor readings, and environmental impact assessments. Chinese LLMs like DeepSeek V4, GLM-4, and Qwen3 can process this complex, technical documentation at scale, extracting insights that drive operational efficiency and compliance. TokenEase's unified API provides access to these models for mining operations worldwide.
Transform raw drill logs, assay results, and geological surveys into actionable exploration insights and resource estimates.
import requests
def analyze_geological_data(drill_logs, assay_results, geological_context):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "You are a senior geologist. Analyze drill logs and assay data to identify mineralization zones, estimate grade continuity, and recommend next drilling targets."},
{"role": "user", "content": f"Geological context: {geological_context}\n\nDrill logs:\n{drill_logs}\n\nAssay results:\n{assay_results}\n\nProvide interpretation and recommendations."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
logs = """DDH-001: 0-45m overburden, 45-120m granite, 120-185m quartz vein with pyrite, 185-220m alteration zone
DDH-002: 0-50m overburden, 50-95m granite, 95-200m quartz-carbonate vein, visible gold at 145-160m"""
assays = """DDH-001: 120-140m: 2.3 g/t Au, 140-160m: 0.8 g/t Au
DDH-002: 130-150m: 8.7 g/t Au, 150-170m: 4.2 g/t Au"""
analysis = analyze_geological_data(logs, assays, "Greenstone belt, structurally controlled gold system")
Analyze safety incident reports to identify patterns, root causes, and preventive measures before accidents recur.
def analyze_safety_incidents(incident_reports, historical_data):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "Analyze mining safety incidents to identify patterns, root causes, and preventive recommendations. Focus on systemic issues."},
{"role": "user", "content": f"Historical context: {historical_data}\n\nRecent incidents:\n{incident_reports}\n\nIdentify trends and recommend preventive actions."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
incidents = """
1. Aug 15: Rock fall in Level 3 stope, minor injury, inadequate ground support
2. Aug 12: Vehicle collision in haul road, near miss, poor visibility
3. Aug 8: Equipment fire in processing plant, equipment damage, electrical fault
4. Aug 3: Chemical spill in leach pad area, environmental impact, containment failure
"""
history = "Previous 6 months: 12 ground control incidents, 8 vehicle incidents, 3 equipment fires"
report = analyze_safety_incidents(incidents, history)
Automate the generation of environmental compliance reports from monitoring data, survey results, and regulatory requirements.
def generate_environmental_report(site_data, monitoring_results, regulatory_framework):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "glm-4-plus",
"messages": [
{"role": "system", "content": "Generate professional environmental impact reports for mining operations. Include all required sections per the regulatory framework."},
{"role": "user", "content": f"Regulatory framework: {regulatory_framework}\nSite data: {site_data}\nMonitoring results: {monitoring_results}\n\nGenerate full EIA report."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
site = "Open pit copper mine, 2,500 ha, elevation 1,200m, annual rainfall 800mm"
monitoring = "Water quality: pH 6.8-7.2, Cu 0.02mg/L (below 0.1 limit). Air: PM10 45 ug/m3 (below 50 limit). Biodiversity: 3 species of concern identified."
eia = generate_environmental_report(site, monitoring, "ISO 14001, local EPA requirements")
Extract maintenance insights from unstructured equipment logs, operator reports, and sensor data narratives.
def predict_maintenance_needs(equipment_logs, maintenance_history):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "kimi-k2",
"messages": [
{"role": "system", "content": "Analyze equipment maintenance logs to predict failure risks and recommend maintenance schedules. Prioritize by severity and cost impact."},
{"role": "user", "content": f"Maintenance history: {maintenance_history}\nRecent logs:\n{equipment_logs}\n\nPredict maintenance needs for next 30 days."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
logs = """
Haul Truck HT-03: Aug 18: Vibration in transmission, oil temp elevated
Excavator EX-07: Aug 17: Hydraulic leak detected, pressure drop 15%
Crusher CR-01: Aug 16: Bearing temperature spike to 85C, reduced throughput
Conveyor CV-12: Aug 15: Belt slippage reported, tensioner adjustment needed
"""
history = "HT-03: Transmission serviced 4 months ago. EX-07: Hydraulic system replaced 8 months ago."
prediction = predict_maintenance_needs(logs, history)
Extract compliance requirements from complex mining regulations and map them against current operational practices.
def check_compliance_gap(regulation_text, current_practices):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "Compare mining regulations against current practices. Identify compliance gaps and provide remediation recommendations."},
{"role": "user", "content": f"Current practices: {current_practices}\nRegulation: {regulation_text}\n\nIdentify gaps and suggest remediation."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
regulation = "All underground workings must have secondary egress within 200m of primary access. Ventilation systems must provide minimum 0.5 m/s air velocity."
practices = "Primary egress via main decline. Secondary escapeways exist but maximum distance is 350m in Level 5. Ventilation measured at 0.3 m/s in Level 3 development."
gaps = check_compliance_gap(regulation, practices)
Generate JORC/NI 43-101 compliant resource estimation reports from geological models and statistical analysis.
def generate_resource_estimate(geological_model, statistical_data, confidence_level):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "Generate mineral resource estimation reports following JORC/NI 43-101 standards. Include classification, cut-off grades, and confidence statements."},
{"role": "user", "content": f"Geological model: {geological_model}\nStatistical data: {statistical_data}\nConfidence: {confidence_level}\n\nGenerate resource estimate report."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
model = "Porphyry copper system, 3.5km strike length, 800m depth, hypogene enrichment"
stats = "Indicated: 450Mt at 0.62% Cu, 0.25 g/t Au. Inferred: 200Mt at 0.45% Cu. Cut-off: 0.3% Cu."
estimate = generate_resource_estimate(model, stats, "Indicated at 50m spacing, Inferred at 100m spacing")
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